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{
"id": "item_00402",
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"score": 920,
"rationale": "Recursive self-improvement framing: landmark-level discussion of AI building itself.",
"title": "Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models",
"url": "https://huggingface.co/papers/2606.16281",
"authors": "",
"description": "Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important question is how to combine their knowledge. Toward this, we first investigate the unique decoding dynamics of MDLMs. We find that successful generations exhibit stable confidence dynamics over answer-relevant positions, while unreliable trajectories can often be corrected by injecting promising intermediate states from other models. Guided by this observation, we propose TIE (Trajectory-based Iterative Ensembling), a knowledge fusion framework in which MDLMs iteratively identify reliable decoding trajectories and relay them across models. TIE tracks confidence dynamics over answer-relevant positions to determine which model currently follows a more reliable trajectory and selectively transfers partially denoised sequences across models. As the model on the more promising trajectory often changes across denoising steps, TIE allows different models to contribute complementary strengths at different stages of generation. Strong performance across diverse reasoning tasks, along with our analyses, suggests that TIE offers a practical approach to the underexplored problem of MDLM ensembling.",
"summary": "Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation. As MDLMs become diverse in capabilities and knowledge coverage, an important…",
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"▲ 23",
"HuggingFace Papers"
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"id": "item_01037",
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"rationale": "OpenAI model disproving 80-year unit distance conjecture: landmark AI math milestone.",
"title": "Echo-Memory: A Controlled Study of Memory in Action World Models",
"url": "https://arxiv.org/abs/2606.09803",
"authors": "Wayne King, Zeyue Xue, Yuxuan Bian, Jie Huang, Haoran Li, Yaowei Li, Yaofeng Su, Yuming Li, Haoyu Wang, Shiyi Zhang, Songchun Zhang, Yuwei Niu, Sihan Xu, Junhao Zhuang, Haoyang Huang, Nan Duan",
"description": "We present \\textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: \\emph{capacity}, \\emph{compression}, \\emph{read-out}, and \\emph{recurrence}. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the sal",
"summary": "We present \\textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and…",
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"cs.GR",
"cs.LG"
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"group": "🧠 Machine Learning",
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"rationale": "LLM found 23-year-old Linux vuln: landmark AI-for-security milestone.",
"title": "AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation",
"url": "https://huggingface.co/papers/2604.18240",
"authors": "",
"description": "As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule-based verifiers or LLM-as-a-Judge models, which struggle to generalize beyond narrow domains. Agent-as-a-Judge addresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored.\n We introduce a benchmark AJ-Bench to systematically evaluate Agent-as-a-Judge across three domains-search, data systems, and graphical user interfaces-comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents' abilities in information acquisition, state verification, and process verification. Experiments demonstrate consistent performance gains over LLM-as-a-Judge baselines, while also revealing substantial open challenges in agent-based verification. Our data and code are available at https://aj-bench.github.io/.",
"summary": "As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on…",
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],
"source": "HuggingFace",
"type": "paper",
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"id": "item_04268",
"rank": 4,
"score": 920,
"rationale": "Landmark: LLM agent found hidden 23-year Linux kernel CVE. Major AI-for-security milestone.",
"title": "Claude Code Found a Linux Vulnerability Hidden for 23 Years",
"url": "https://mtlynch.io/claude-code-found-linux-vulnerability/",
"authors": "",
"description": "Article about: Claude Code Found a Linux Vulnerability Hidden for 23 Years",
"summary": "Article about: Claude Code Found a Linux Vulnerability Hidden for 23 Years",
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],
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"score": 900,
"rationale": "Landmark: CVSS 10 in gemini-cli via npm supply chain; major agentic coding tool risk.",
"title": "VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis",
"url": "https://huggingface.co/papers/2605.22570",
"authors": "Jinho Park, Youbin Kim, Hogun Park, Eunbyung Park",
"description": "Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio-temporal reasoning benchmark datasets primarily rely on static image sets or passively curated video data, which limits the evaluation of fine-grained reasoning capabilities. In this paper, we introduce VGenST-Bench, a video benchmark that employs generative models to actively synthesize highly controlled and diverse evaluation scenarios. To construct VGenST-Bench, we propose a multi-agent pipeline incorporating a human quality control stage, ensuring the quality of all generated videos and QA pairs. We establish a comprehensive 3x2x2 video taxonomy, encompassing Spatial Scale, Perspective, and Scene Dynamics to span diverse scenarios. Furthermore, we design a hierarchical task suite that decouples low-level visual perception from high-level spatio-temporal reasoning. By shifting the paradigm from passive curation to active synthesis, VGenST-Bench enables fine-grained diagnosis of spatio-temporal understanding in MLLMs.",
"summary": "Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio-temporal reasoning benchma...",
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"type": "paper",
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"id": "item_04207",
"rank": 6,
"score": 892,
"rationale": "Mamba paper is foundational for selective state-space models; landmark architecture displacing transformers for long sequences.",
"title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"url": "https://arxiv.org/pdf/2312.00752",
"authors": "",
"description": "Article about: Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
"summary": "Article about: Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
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"lobsters"
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{
"id": "item_01690",
"rank": 7,
"score": 890,
"rationale": "Landmark: 500,000-fold cosmic ray error reduction is a major quantum hardware unlock.",
"title": "See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding",
"url": "https://huggingface.co/papers/2605.18018",
"authors": "Boyuan Sun, Bowen Yin, Yuanming Li, Xihan Wei, Qibin Hou",
"description": "We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervision only during training to guide cross-modal attention, allowing the model to automatically attend to the user-specified object at inference. Our cross-attention analysis of pretrained multimodal large languagemodels (MLLMs) reveals a systematic discrepancy: Attribute words produce sharp, localized activations in the visual modality, whereas object nouns yield diffuse and scattered patterns due to semantic reference bias and distributed high-level representations. To address this misalignment, we construct NL-Refer, an enriched dataset, in which each object mask is paired with a precise natural language referring expression. SWIM extracts multi-layer cross-attention maps from object nouns and enforces spatial consistency with ground-truth masks. Experimental results demonstrate that SWIM substantially improves text-visual alignment and achieves superior performance over visual-prompt-based methods on fine-grained object understanding benchmarks. The code and data are available at https://github.com/HumanMLLM/SWIM{https://github.com/HumanMLLM/SWIM}.",
"summary": "We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual pr...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
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{
"id": "item_00410",
"rank": 8,
"score": 880,
"rationale": "Let's Encrypt post-quantum plan via Merkle Tree Certificates; foundational PKI direction.",
"title": "UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer",
"url": "https://huggingface.co/papers/2606.16255",
"authors": "",
"description": "Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single framework. However, existing UMMs face prominent challenges: (1) the inherent learning conflicts between visual understanding and generation tasks, leading to suboptimal modeling in both tasks; (2) different understanding and generation visual spaces impeding scalability; (3) over-reliance on task-specific data that neglects the duality of text-image understanding and generation. To address these challenges, we propose UniDDT, which leverages a Noisy ViT encoder along with an LLM to unify semantic encoding for visual generation and understanding tasks, while employing a separate diffusion decoder to decouple diffusion decoding from text decoding. With this Noisy ViT encoder, UniDDT is able to leverage the latent space as a unified visual representation, enabling seamless compatibility between understanding and generation tasks. Thus, the scalability within the generation tasks and the semantic expressiveness within understanding tasks can be balanced. Also, we construct dual data structures from the same image-text pairs, fostering interdependence between the generation and understanding data to exploit their inherent duality. Extensive experiments demonstrate that UniDDT achieves effective unification of multimodal understanding and generation with enhanced semantic consistency and scalability. For visual ge",
"summary": "Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single…",
"tags": [
"▲ 7",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_01739",
"rank": 9,
"score": 880,
"rationale": "Landmark: Copy Fail CVE-2026-31431 affects every Linux distro since 2017, deterministic root exploit.",
"title": "Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?",
"url": "https://huggingface.co/papers/2605.22109",
"authors": "Caixin Kang, Tianyu Yan, Sitong Gong, Mingfang Zhang, Liangyang Ouyang",
"description": "Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability solely on numerical Big Five score prediction, leaving open whether models truly perceive personality through behavioral understanding or merely prejudge through superficial pattern matching. We address this gap with three contributions. (i) A new task: we formalize Grounded Personality Reasoning (GPR), which requires MLLMs to anchor each Big Five rating in observable evidence through a chain of rating, reasoning, and grounding. (ii) A new dataset: we release MM-OCEAN (1,104 videos, 5,320 MCQs), produced by a multi-agent pipeline with human verification, with timestamped behavioral observations, evidence-grounded trait analyses, and seven categories of cue-grounding MCQs. (iii) Benchmark and analysis: we design a three-tier evaluation (rating, reasoning, grounding) plus four sample-level failure-mode metrics: Prejudice Rate (PR), Confabulation Rate (CR), Integration-failure Rate (IR), and Holistic-grounding Rate (HR), and benchmark 27 MLLMs (13 closed, 14 open). The analysis uncovers a striking Prejudice Gap: across the field, 51% of correct ratings are not grounded in retrieved cues, and the Holistic-Grounding Rate spans only 0-33.5%. These findings expose a disconnect between getting the right score and reasoning for the right reason, charting a roadmap for grounded social cognition in MLLMs.",
"summary": "Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability solely on numerical Big Five score prediction, leaving open whether models truly perceive personality through behavioral understanding or…",
"tags": [
"arXiv:2605.22109"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
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{
"id": "item_03374",
"rank": 10,
"score": 880,
"rationale": "Mamba paper: foundational selective state-space model, landmark.",
"title": "Synthesizing Multi-Agent Harnesses for Vulnerability Discovery",
"url": "https://arxiv.org/abs/2604.20801",
"authors": "Hanzhi Liu, Chaofan Shou, Xiaonan Liu, Hongbo Wen, Yanju Chen, Ryan Jingyang Fang, Yu Feng",
"description": "LLM agents find vulnerabilities that human auditors miss, but the harness — how roles, tools, and retries are wired — dramatically affects success rates even with a fixed language model.",
"summary": "LLM agents find vulnerabilities that human auditors miss, but the harness — how roles, tools, and retries are wired — dramatically affects success rates even with a fixed language model.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-23 09:00",
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"debrief_date": "2026-04-23 09:00",
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"sort_date": "2026-04-23T09:00:00Z"
},
{
"id": "item_04372",
"rank": 11,
"score": 880,
"rationale": "SkillClaw agentic skill evolution; very high upvotes, strong agent-systems contribution.",
"title": "SkillClaw: Let Skills Evolve Collectively with Agentic Evolver",
"url": "https://huggingface.co/papers/2604.08377",
"authors": "",
"description": "arXiv:2604.08377 | Authors: Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang | Upvotes: 209",
"summary": "arXiv:2604.08377 | Authors: Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang | Upvotes: 209",
"tags": [
"Huggingface Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
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{
"id": "item_00032",
"rank": 12,
"score": 870,
"rationale": "Humanity's Last Exam is a landmark expert-level benchmark addressing LLM saturation; high-impact evaluation tool.",
"title": "Sovereign Execution Brokers: Enforcing Certificate-Bound Authority in Agentic Control Planes",
"url": "https://arxiv.org/abs/2606.20520",
"authors": "Jun He, Deying Yu",
"description": "Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not reside inside non-deterministic reasoning processes. Existing access-control mechanisms authorize identities, while assurance layers certify proposed actions; neither alone provides a mandatory enforcement point for certified authority at the moment of mutation. This paper introduces the Sovereign Execution Broker (SEB), a runtime enforcement boundary for certificate-bound agentic infrastructure. SEB consumes certificates issued by the Sovereign Assurance Boundary (SAB), verifies that the requested mutation matches the certified execution contract, checks validity windows, policy epochs, revocation epochs, and live-state drift, mints scoped execution identity, invokes infrastructure APIs, and records signed decision and outcome records. By separating proposal, admission, and execution, SEB turns certified authority into a short-lived, revocable, auditable runtime capability, provided that production mutation APIs reject non-broker identities. We present the SEB execution model, certificate and replay-verification predicates, scoped identity semantics, bypass-prevention deployment patterns, failure behavior, and a concrete prototype implementation. We evaluate the prototype on AWS and Kubernetes clusters, measuring latency overheads, revocation propagation, drift detection, and security under fault injection.",
"summary": "Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not reside inside non-deterministic reasoning processes.…",
"tags": [
"cs.CR",
"cs.AI",
"cs.DC"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Agentic / Sovereign / Execution",
"debrief_date": "2026-06-19 04:00",
"debrief_path": "debriefs/debrief-2026-06-19-04.html",
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},
{
"id": "item_00405",
"rank": 13,
"score": 870,
"rationale": "Empirical audit of Claude-assisted rsync releases; high-signal AI-generated-code study.",
"title": "CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?",
"url": "https://huggingface.co/papers/2606.15300",
"authors": "",
"description": "Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.e., file system). However, existing benchmarks usually evaluate code-centric or data-centric capabilities in isolation, leaving a clear gap with real development scenarios. In this paper, we bridge this gap by introducing CODA-BENCH, the first benchmark to jointly evaluate code and data intelligence in a data-intensive environment. We construct a data-intensive Linux sandbox based on the Kaggle ecosystem (containing hundreds of datasets), where agents must actively explore complex file hierarchies to identify relevant resources and generate code for data-driven analytical tasks. CODA-BENCH comprises 1,009 tasks spanning 31 communities, with each task environment containing an average of 980 files, simulating realistic data scale and noise. Evaluations of advanced agents reveal that even top-performing systems struggle to effectively integrate data discovery with code execution, achieving a success rate of only 61.1%. These results highlight a substantial gap in current agentic capabilities for data-intensive tasks and point to promising directions for future research.",
"summary": "Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of…",
"tags": [
"▲ 11",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
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{
"id": "item_00461",
"rank": 14,
"score": 870,
"rationale": "HTTP/2 hidden bomb attack from original spec reviewer; landmark protocol-security disclosure.",
"title": "CrankGPT — Local Human-powered AI",
"url": "https://crankgpt.com",
"authors": "",
"description": "A human-powered, fully local, fully private AI solution.",
"summary": "A human-powered, fully local, fully private AI solution.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 11:00",
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"debrief_md": ""
}
],
"sort_date": "2026-06-15T11:00:00Z"
},
{
"id": "item_03056",
"rank": 15,
"score": 870,
"rationale": "Minimax 'verification tax' for calibration is a landmark, counterintuitive result with broad impact.",
"title": "Towards Understanding the Robustness of Sparse Autoencoders",
"url": "https://huggingface.co/papers/2604.18756",
"authors": "Ahson Saiyed, Sabrina Sadiekh, Chirag Agarwal",
"description": "Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse Autoencoders (SAEs) are widely used for interpretability, their robustness implications remain underexplored. We present a study of integrating pretrained SAEs into transformer residual streams at inference time, without modifying model weights or blocking gradients. Across four model families (Gemma, LLaMA, Mistral, Qwen) and two strong white-box attacks (GCG, BEAST) plus three black-box benchmarks, SAE-augmented models achieve up to a 5x reduction in jailbreak success rate relative to the undefended baseline and reduce cross-model attack transferability. Parametric ablations reveal (i) a monotonic dose-response relationship between L0 sparsity and attack success rate, and (ii) a layer-dependent defense-utility tradeoff, where intermediate layers balance robustness and clean performance. These findings are consistent with a representational bottleneck hypothesis: sparse projection reshapes the optimization geometry exploited by jailbreak attacks.",
"summary": "Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse Autoencoders (SAEs) are widely used for interpretability,…",
"tags": [
"huggingface"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
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"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_03893",
"rank": 16,
"score": 870,
"rationale": "NIST PQC round-4 status report; landmark, authoritative PQ standardization update.",
"title": "Status report on the fourth round of the nist post-quantum cryptography standardization process",
"url": "https://nvlpubs.nist.gov/nistpubs/ir/2025/NIST.IR.8545.pdf",
"authors": "Google Scholar • cryptography post-quantum",
"description": "… NIST is also grateful for the efforts of those in the cryptographic … NIST would not be able to select post-quantum digital … that calls for NIST-approved post-quantum keyestablishment. …",
"summary": "… NIST is also grateful for the efforts of those in the cryptographic … NIST would not be able to select post-quantum digital … that calls for NIST-approved post-quantum keyestablishment. …",
"tags": [
"Google Scholar",
"cryptography post-quantum"
],
"source": "Google Scholar",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🎓 Google Scholar",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_04210",
"rank": 17,
"score": 865,
"rationale": "LLM-driven large-scale deanonymization is high-impact for AI safety, privacy, and security research; demonstrates concrete capability.",
"title": "Large-scale online deanonymization with LLMs",
"url": "https://arxiv.org/abs/2602.16800",
"authors": "",
"description": "We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.",
"summary": "Article about: Large-scale online deanonymization with LLMs",
"tags": [
"Lobsters",
"lobsters"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
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"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04358",
"rank": 18,
"score": 860,
"rationale": "Web agent distillation surpasses closed models on WebArena; landmark-ish.",
"title": "Structured Distillation of Web Agent Capabilities Enables Generalization",
"url": "https://huggingface.co/papers/2604.07776",
"authors": "",
"description": "Frontier LLMs can navigate complex websites, but their cost and reliance on third-party APIs make local deployment impractical. We introduce Agent-as-Annotators, a framework that structures synthetic trajectory generation for web agents by analogy to human annotation roles, replacing the Task Designer, Annotator, and Supervisor with modular LLM components. Using Gemini 3 Pro as teacher, we generate 3,000 trajectories across six web environments and fine-tune a 9B-parameter student with pure supervised learning on the 2,322 that pass quality filtering. The resulting model achieves 41.5% on WebArena, surpassing closed-source models such as Claude 3.5 Sonnet (36.0%) and GPT-4o (31.5%) under the same evaluation protocol, and nearly doubling the previous best open-weight result (Go-Browse, 21.7%). Capabilities transfer to unseen environments, with an 18.2 percentage point gain on WorkArena L1 (an enterprise platform never seen during training) and consistent improvements across three additional benchmarks. Ablations confirm that each pipeline component contributes meaningfully, with Judge filtering, evaluation hints, and reasoning traces each accounting for measurable gains. These results demonstrate that structured trajectory synthesis from a single frontier teacher is sufficient to produce competitive, locally deployable web agents. Project page: https://agent-as-annotators.github.io",
"summary": "arXiv:2604.07776 | Authors: Xing Han Lù, Siva Reddy | Upvotes: 15",
"tags": [
"Huggingface Papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00371",
"rank": 19,
"score": 860,
"rationale": "Temporally controlled forward-research judgment benchmark; strong methodology, high impact.",
"title": "Exact Posterior Score Estimation for Solving Linear Inverse Problems",
"url": "https://arxiv.org/abs/2606.17048",
"authors": "Abbas Mammadov, Ozgur Kara, Kaan Oktay, Iskander Azangulov, Adil Kaan Akan, Hyungjin Chung, James Matthew Rehg, Yee Whye Teh",
"description": "Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption. To use this prior to solve a linear inverse problem, one needs to sample from the posterior, but the score that the prior provides is the unconditional score, not the posterior score. Existing methods either steer a fixed pretrained denoiser with approximate measurement-matching corrections, or train a conditional restoration model that abandons the denoising structure of the prior. We derive the exact posterior score in closed form for linear Gaussian inverse problems under general Gaussian interpolants, and show that posterior sampling reduces to a denoising problem at an operator-dependent shifted pivot under an anisotropic noise covariance. We turn this identity into Exact Posterior Score (EPS), a denoising training objective that preserves the input/output structure of standard pretraining and can therefore be trained from scratch or fine-tuned from a pretrained denoiser. At inference, EPS uses the same sampler as the underlying backbone, with no likelihood gradients or projections. We evaluate EPS on five linear inverse problems across FFHQ and ImageNet, where it outperforms training-free and training-based baselines on fidelity, perceptual, and distributional metrics, while using roughly an order of magnitude fewer denoiser evaluations than gradient-based posterior samplers.",
"summary": "Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption. To use this prior to solve a linear inverse problem, one needs to sample from the…",
"tags": [
"cs.LG",
"cs.CV",
"stat.ML"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Exact / Posterior / Score",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_01210",
"rank": 20,
"score": 860,
"rationale": "First AI-developed 2FA-bypassing zero-day with self-morphing malware; landmark AI-security event.",
"title": "LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs",
"url": "https://huggingface.co/papers/2606.06286",
"authors": "daily curated papers",
"description": "Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware framework for memorization evaluation that contrasts prefix-based capability attacks with non-adversarial evaluations. We propose a metric transformation that, applied to existing functions, allows to create propensity metrics. We further introduce SimpleTrace, a lightweight tracing pipeline built on infini-gram that deterministically attributes model generations to large-scale training corpora and computes verbatim, near-verbatim, and propensity-transformed memorization metrics. Evaluating two fully-open models: Comma and DFM Decoder on two datasets: Common Pile and Dynaword in two languages, we find a consistent gap between capability and propensity: prefix attacks elicit substantially stronger memorization signals than generic or dataset-specific prompts, while propensity scores remain low overall. Thus, the models can reveal training data when directly elicited, but rarely do so in more common non-adversarial settings. We also find that DFM Decoder, which is continually pre-trained from Comma, exhibits reduced memorization and memorization propensity for Common Pile, confirming that memorization capability can decrease when later training emphasizes partially different data. Our results suggest, and we encourage, that memorization audits shou",
"summary": "Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_02089",
"rank": 21,
"score": 860,
"rationale": "Nemotron 3 Nano Omni: open efficient multimodal model w/ audio, multiple quantizations, SOTA claims.",
"title": "Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing",
"url": "https://arxiv.org/abs/2605.15179",
"authors": "Ellwil Sharma, Arastu Sharma",
"description": "Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce gradient conflict, unstable optimization, and plasticity loss in dense neural operators. In particular, broadband open-channel fluid dynamics and boundary-dominated porous media flows impose incompatible spectral and geometric demands on a single dense parameter path. We introduce Shod",
"summary": "Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce…",
"tags": [
"cs.LG",
"cs.AI",
"physics.comp-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Machine Learning",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_03249",
"rank": 22,
"score": 860,
"rationale": "Unified mechanism for LLM harmfulness via pruning is a landmark mechanistic interpretability/safety finding.",
"title": "Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models",
"url": "https://arxiv.org/abs/2604.21896",
"authors": "Chee Wei Tan, Yuchen Wang, Shangxin Guo",
"description": "This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create, customize, and deploy LLM-powered game agents while actively engaging with AI-driven strategies. The LLM-based chatbot, integrated within Nemobot, demonstrates its capabilities across four distinct class",
"summary": "This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this p…",
"tags": [
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03377",
"rank": 23,
"score": 860,
"rationale": "LLM-based large-scale deanonymization; strong security impact.",
"title": "CVEs With a CVSS Score Greater Than or Equal to 9",
"url": "https://arxiv.org/abs/2604.20765",
"authors": "Lena Sinterhauf, Andreas Aßmuth, Roland Kaltefleiter",
"description": "Analysis of 245,456 CVEs with CVSS scores >= 9.0 reveals patterns in identification and resolution timelines, providing empirical guidance for prioritizing remediation of the most critical vulnerabilities.",
"summary": "Analysis of 245,456 CVEs with CVSS scores >= 9.0 reveals patterns in identification and resolution timelines, providing empirical guidance for prioritizing remediation of the most critical vulnerabilities.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T09:00:00Z"
},
{
"id": "item_03905",
"rank": 24,
"score": 860,
"rationale": "Nemotron 3 Super: open 120B MoE hybrid, NVFP4 pretrain, LatentMoE, 1M context, major release.",
"title": "Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning",
"url": "https://huggingface.co/papers/2604.12374",
"authors": "HuggingFace • daily curated papers • 2026-04-14",
"description": "We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, and 3) include MTP layers for inference acceleration through native speculative decoding. We pre-trained Nemotron 3 Super on 25 trillion tokens followed by post-training using supervised fine tuning (SFT) and reinforcement learning (RL). The final model supports up to 1M context length and achieves comparable accuracy on common benchmarks, while also achieving up to 2.2x and 7.5x higher inference throughput compared to GPT-OSS-120B and Qwen3.5-122B, respectively. Nemotron 3 Super datasets, along with the base, post-trained, and quantized checkpoints, are open-sourced on HuggingFace.",
"summary": "We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. It matters because frontier agent research is shifting from demos toward measurable control…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_04082",
"rank": 25,
"score": 860,
"rationale": "Strong mechanistic insight into LLM harm brittleness via pruning; foundational alignment/safety result.",
"title": "Large Language Models Generate Harmful Content Using a Distinct, Unified Mechanism",
"url": "https://arxiv.org/abs/2604.09544",
"authors": "Hadas Orgad, Boyi Wei, Kaden Zheng, et al.",
"description": "Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely bypass them, and fine-tuning on narrow domains can induce ``emergent misalignment'' that generalizes broadly. Whether this brittleness reflects a fundamental lack of coherent internal organization for harmfulness remains unclear. Here we use targeted weight pruning as a causal intervention to probe the internal organization of harmfulness in LLMs.",
"summary": "Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely bypass them, and fine-tuning on narrow domains can induce ``emergent…",
"tags": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_03430",
"rank": 26,
"score": 850,
"rationale": "Cryptographer's quantum timeline analysis: high-impact, recurring, strategically vital.",
"title": "Micro Language Models Enable Instant Responses",
"url": "https://huggingface.co/papers/2604.19642",
"authors": "",
"description": "Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies that break the illusion of a responsive assistant. We introduce micro language models (μLMs): ultra-compact models (8M-30M parameters) that instantly generate the first 4-8 words of a contextually grounded response on-device, while a cloud model completes it; thus, masking the cloud latency. We show that useful language generation survives at this extreme scale with our models matching several 70M-256M-class existing models. We design a collaborative generation framework that reframes the cloud model as a continuator rather than a respondent, achieving seamless mid-sentence handoffs and structured graceful recovery via three error correction methods when the local opener goes wrong. Empirical results show that μLMs can initiate responses that larger models complete seamlessly, demonstrating that orders-of-magnitude asymmetric collaboration is achievable and unlocking responsive AI for extremely resource-constrained devices. The model checkpoint and demo are available at https://github.com/Sensente/micro_language_model_swen_project.",
"summary": "Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "📄 HuggingFace Papers",
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T11:00:00Z"
},
{
"id": "item_03072",
"rank": 27,
"score": 847,
"rationale": "Nemotron 3 Super 120B/12B hybrid Mamba-Attn MoE, NVFP4 pretrain, 1M context, beats GPT-OSS/Qwen3.5 throughput; landmark open release.",
"title": "GitHub RCE Vulnerability: CVE-2026-3854 Breakdown",
"url": "https://www.wiz.io/blog/github-rce-vulnerability-cve-2026-3854",
"authors": "",
"description": "A CVSS 8.7 vulnerability in GitHub Enterprise Server allows remote code execution. Read the threat brief and find vulnerable GHES instances from Wiz.",
"summary": "A CVSS 8.7 vulnerability in GitHub Enterprise Server allows remote code execution. Read the threat brief and find vulnerable GHES instances from Wiz.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🔐 Security & Privacy",
"debrief_date": "2026-04-29 00:00",
"debrief_path": "debriefs/debrief-2026-04-29-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 00:00",
"debrief_path": "debriefs/debrief-2026-04-29-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T00:00:00Z"
},
{
"id": "item_00183",
"rank": 28,
"score": 840,
"rationale": "SCOUT reframes prompt-injection defense as detector allocation; benchmark + operator-controllable threshold is practically significant for AI safety.",
"title": "Floquet framework for driven polar quantum systems",
"url": "https://arxiv.org/abs/2606.19330",
"authors": "Viktor Novičenko, Piotr Gładysz, Karolina Słowik, Egidijus Anisimovas",
"description": "We present an analytical and numerical Floquet treatment of a driven polar two-level quantum system characterized by both longitudinal and transverse coupling to a periodic field. Analytically, we derive a dressed-frame effective Hamiltonian up to first order in the inverse driving frequency, incorporating the longitudinal coupling nonperturbatively. This yields closed expressions for the effective transverse coupling strength and the effective detuning, both of which are modified by the presence of the longitudinal interaction. In the nonpolar limit, these expressions recover the usual near-resonant Rabi coupling and the Bloch-Siegert shift. As a second main result, we develop a numerical flow-equation framework that yields a time-independent effective Hamiltonian across a broad range of transverse and longitudinal coupling strengths. This dual framework is relevant for a variety of platforms, including driven polar quantum systems, optical lattices, superconducting circuits, and solids subject to surface acoustic waves.",
"summary": "We present an analytical and numerical Floquet treatment of a driven polar two-level quantum system characterized by both longitudinal and transverse coupling to a periodic field. Analytically, we…",
"tags": [
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Floquet / Framework / Driven",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
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"debrief_date": "2026-06-18 04:00",
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"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_03267",
"rank": 29,
"score": 840,
"rationale": "AI Index 2025 is the canonical annual reference for the field: high signal, broad impact, well-sourced.",
"title": "Long-Horizon Manipulation via Trace-Conditioned VLA Planning",
"url": "https://arxiv.org/abs/2604.21924",
"authors": "Isabella Liu, An-Chieh Cheng, Rui Yan, Geng Chen, Ri-Zhao Qiu, Xueyan Zou, Sha Yi, Hongxu Yin, Xiaolong Wang, Sifei Liu",
"description": "Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Manip, a modular framework that scales short-horizon VLA execution to long-horizon instruction following via a dedicated task-management VLM. The manager is decoupled from the executor and is invoked in a receding-horizon manner: given the current observation, it predicts a progress-aware remaining pla",
"summary": "Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Man…",
"tags": [
"cs.RO"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_path": "debriefs/debrief-2026-04-25-04.html",
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"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_00666",
"rank": 30,
"score": 831,
"rationale": "Major safety methodology paper identifying a novel confound; affects how safety evaluations should be interpreted.",
"title": "Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning",
"url": "https://huggingface.co/papers/2606.13106",
"authors": "",
"description": "Latent chain-of-thought compresses reasoning by replacing visible reasoning traces with continuous hidden-state recurrence, but existing formulations are difficult to optimize with standard on-policy reinforcement learning (RL) and hard to interpret causally. Our key insight is that a single pair of explicit boundary tokens can address both issues at once: discrete entry and exit anchors make the latent block compatible with standard on-policy RL, and the same anchors offer a natural foothold for mechanistic analysis. Motivated by this, we propose SWITCH, a switchable latent reasoning framework. The model emits to enter latent mode and to exit. Because the boundaries are ordinary discrete tokens, the GRPO policy ratio is well-defined at every decision point. The same anchors also expose the latent steps to direct probing and causal intervention. We train the model with a visible-to-latent curriculum and a Switch-GRPO objective that propagates gradients through recurrent latent computation. SWITCH consistently outperforms prior hidden-state-recurrence latent reasoning approaches at similar scale. Mechanistic analysis through the boundary tokens further reveals three findings: (i) is a sharply localised, learned switching policy rather than a stylistic artefact; (ii) the latent step it opens performs problem-specific, causally important computation rather than acting as an inert placeholder; and (iii) that computation is concentrated at a single hidden-state",
"summary": "Latent chain-of-thought compresses reasoning by replacing visible reasoning traces with continuous hidden-state recurrence, but existing formulations are difficult to optimize…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-12 04:00",
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"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_02197",
"rank": 31,
"score": 830,
"rationale": "Same Copy Fail root exploit; duplicate high-severity disclosure, high impact.",
"title": "Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection",
"url": "https://huggingface.co/papers/2605.08583",
"authors": "",
"description": "Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibliographic verification. Existing detectors often reduce verification to binary found/not-found decisions and rely on brittle parsing or incomplete retrieval, offering little field-level signal to auditors. We reframe citation hallucination detection as taxonomy-aligned field-level adjudication and introduce a 12-code taxonomy spanning Real, Potential, and Hallucinated citations. Based on this taxonomy, we build CiteTracer, a cascading multi-agent detector that extracts structured citations from PDF and BibTeX, retrieves evidence through cache lookup, URL fetch, scholar connectors, and web search, applies deterministic field matching, and routes ambiguous cases to class-specialist judgers. We release a benchmark of 2,450 synthetic citations built from real seeds with controlled LLM mutations, paired with 957 real-world fabricated citations drawn from ICLR 2026 and an anonymous conference desk-rejected submissions. CiteTracer reaches 97.1% accuracy on the synthetic benchmark, with class-level F1 scores of 97.0, 95.8, and 98.5 for Real, Potential, and Hallucinated, respectively, and detects 97.1% of fabrications on the real-world set without abstaining. Code: https://github.com/aaFrostnova/CiteTracer.",
"summary": "Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 4,
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"debrief_date": "2026-05-14 19:00",
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"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
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"debrief_date": "2026-05-14 19:00",
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"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_02196",
"rank": 32,
"score": 830,
"rationale": "CVE-2026-31431 reliable Linux LPE; major cross-distro root, high impact.",
"title": "An Empirical Study of Automating Agent Evaluation",
"url": "https://huggingface.co/papers/2605.11378",
"authors": "",
"description": "Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably automate this evaluation process? Our study shows that simply prompting coding assistants is insufficient for this task. Without domain-specific evaluation knowledge, frontier coding assistants achieve only a 30% execution success rate and produce over-engineered evaluations averaging 12+ metrics per agent, indicating that strong coding ability does not automatically translate to reliable agent evaluation. We introduce EvalAgent, an AI assistant that automates the end-to-end agent evaluation pipeline. EvalAgent encodes evaluation domain expertise as evaluation skills (procedural instructions, reusable code and templates, and dynamically retrieved API documentation) that compose into a trace-based pipeline producing complete evaluation artifacts including metrics, executable code, and reports. To systematically assess generated evaluations, we introduce a meta-evaluation framework alongside AgentEvalBench, a benchmark comprising 20 agents, each paired with evaluation requirements and test scenarios. We further propose the Eval@1 metric to measure whether generated evaluation code both executes and yields meaningful results on the first run. Our experiments show that EvalAgent produces focused evaluations, improving Eval@1 from 17.5% to 65%, and achieving 79.5%",
"summary": "Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive.…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
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"debrief_date": "2026-05-14 19:00",
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"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
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"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_00022",
"rank": 33,
"score": 830,
"rationale": "Proves a defense trilemma for prompt injection wrappers; strong theoretical impossibility result with broad impact.",
"title": "Duration Aware Scheduling for ASR Serving Under Workload Drift",
"url": "https://huggingface.co/papers/2603.11273",
"authors": "",
"description": "Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling. We integrate two classical algorithms, Shortest Job First (SJF) and Highest Response Ratio Next (HRRN), into vLLM and evaluate them under realistic and drifted workloads. On LibriSpeech test-clean, compared to baseline, SJF reduces median E2E latency by up to 73% at high load, but increases 90th-percentile tail latency by up to 97% due to starvation of long requests. HRRN addresses this trade-off: it reduces median E2E latency by up to 28% while bounding tail-latency degradation to at most 24%. These gains persist under workload drift, with no throughput penalty and <0.1\\,ms scheduling overhead per request.",
"summary": "Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": ""
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"sort_date": "2026-06-19T11:00:00Z"
},
{
"id": "item_00298",
"rank": 34,
"score": 830,
"rationale": "Subliminal learning via semantically unrelated data; landmark AI safety finding.",
"title": "GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?",
"url": "https://huggingface.co/papers/2606.17861",
"authors": "",
"description": "Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.",
"summary": "Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional…",
"tags": [
"▲ 34",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00368",
"rank": 35,
"score": 830,
"rationale": "Geometric interpretability of LLM arithmetic; mechanistic insight into errors and probing.",
"title": "ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation",
"url": "https://arxiv.org/abs/2606.16996",
"authors": "Tran Dinh Tien, Zhiqiang Shen",
"description": "Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabulary semantic segmentation (OVSS) is inefficient: full-resolution decoding is typically run over the entire dataset vocabulary, whereas each image contains only a small active subset of classes. We introduce ActiveSAM, a training-free, zero-shot inference framework that turns SAM 3 into an active-vocabulary segmenter. ActiveSAM first canonicalizes and expands class prompts, then estimates an image-conditioned active set from a low-resolution presence preview. Only the retained classes are decoded at full resolution, using bucketed prompt multiplexing with the frozen SAM 3 decoder. The preview stage uses only class-presence evidence and skips unnecessary segmentation-head computation, while the final stage applies margin-aware background calibration to suppress low-confidence pixels. ActiveSAM requires no target-dataset training, no weight updates, and no oracle class-presence labels. Across eight OVSS benchmarks, ActiveSAM improves the speed-accuracy tradeoff of training-free open-vocabulary semantic segmentation, outperforming the current state-of-the-art SegEarth-OV3 by approximately +1.4 mIoU on average while running up to 5.5x faster on large-vocabulary datasets. ActiveSAM also demonstrates the strongest robustness under image corruption that simulates real-world distribution shift, making it well-suited for deployment in noisy-input",
"summary": "Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabulary semantic segmentation (OVSS) is inefficient:…",
"tags": [
"cs.CV",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Activesam / Image-conditional / Class",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_01276",
"rank": 36,
"score": 830,
"rationale": "Witten coauthor on wormholes/Mellin averaging in AdS/CFT; highly influential for quantum gravity and ensemble-averaging puzzles.",
"title": "Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling",
"url": "https://huggingface.co/papers/2606.03102",
"authors": "",
"description": "Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by dynamically deciding when to stop sampling, yet they typically rely on heuristic rules or rely on distribution assumptions. In this work, we formulate adaptive sampling as a Markov decision process (MDP). We train a lightweight sampling controller with reinforcement learning (RL) to jointly balance answer correctness, latency, and computation cost. At each round, the controller decides to stop sampling or to acquire additional samples. Our method is lightweight which only relies on statistics of final answers, and can be trained and deployed on CPU. We further show that the resulting framework admits an interpretation as the Lagrangian relaxation of a constrained optimization problem with explicit budget constraints. Experiments against strong baselines such as ASC and ESC show that our method achieves improved trade-offs among answer correctness, sampling rounds, and total samples required.",
"summary": "Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01691",
"rank": 37,
"score": 830,
"rationale": "High impact: multi-agent LLM fuzzing finding real kernel/SSL/Docker vulns validates agentic security tooling.",
"title": "Geo-Align: Video Generation Alignment via Metric Geometry Reward",
"url": "https://huggingface.co/papers/2605.23903",
"authors": "Zizun Li, Haoyu Guo, Runzhe Teng, Chunhua Shen, Tong He",
"description": "Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme scarcity of synchronized, multi-view real-world video data. Consequently, the prevailing paradigm often exhibits limited generalization when processing out-of-distribution real-world videos, with models struggling to accurately adhere to physical scales and camera trajectories. To bridge this gap, we propose Geo-Align, the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering. Built upon a pretrained model, we optimize the model through a scale-aware perceptual reward mechanism. Specifically, we introduce a metric 3D estimator to extract precise camera trajectories from generated videos, explicitly penalizing deviations in rotation and translation. Furthermore, we meticulously designed a data pipeline strategy based on real-world conditioning videos and target camera trajectories derived from synthetic data, eliminating the reliance on paired data. Extensive experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity, indicating the effectiveness of our method.",
"summary": "Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme sc...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-25 16:00",
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"sort_date": "2026-05-25T16:00:00Z"
},
{
"id": "item_02512",
"rank": 38,
"score": 830,
"rationale": "Benign fine-tuning breaks Audio LLM safety with quantified JSR; landmark safety finding.",
"title": "CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization",
"url": "https://huggingface.co/papers/2605.05136",
"authors": "HuggingFace",
"description": "Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen target domains. While recent invariant learning strategies and architectural advances have achieved strong performance, explicitly discovering a structured domain-invariant subspace through second-order statistics remains underexplored. In this work, we propose CPCANet, a novel framework grounded in Common Principal Component Analysis (CPCA), which unrolls the iterative Flury-Gautschi (FG) algorithm into fully differentiable neural layers. This approach integrates the statistical properties of CPCA into an end-to-end trainable framework, enforcing the discovery of a shared subspace across diverse domains while preserving interpretability. Experiments on four standard DG benchmarks demonstrate that CPCANet achieves state-of-the-art (SOTA) performance in zero-shot transfer. Moreover, CPCANet is architecture-agnostic and requires no dataset-specific tuning, providing a simple and efficient approach to learning robust representations under distribution shift. Code is available at https://github.com/wish44165/CPCANet.",
"summary": "Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen target…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
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}
],
"sort_date": "2026-05-11T04:00:00Z"
},
{
"id": "item_02584",
"rank": 39,
"score": 830,
"rationale": "Large-scale real coding agent dataset with striking 44% survival and security regressions; high-impact evidence.",
"title": "GeoStack: A Framework for Quasi-Abelian Knowledge Composition in VLMs",
"url": "https://huggingface.co/papers/2605.06477",
"authors": "",
"description": "We address the challenge of knowledge composition in Vision-Language Models (VLMs), where accumulating expertise across multiple domains or tasks typically leads to catastrophic forgetting. We introduce GeoStack (Geometric Stacking), a modular framework that allows independently trained domain experts to be composed into a unified model. By imposing geometric and structural constraints on the adapter manifold, GeoStack ensures the foundational knowledge of the base model is preserved. Furthermore, we mathematically demonstrate a weight-folding property that achieves constant-time inference complexity (O(1)), regardless of the number of integrated experts. Experimental results across multi-domain adaptation and class-incremental learning show that GeoStack provides an efficient mechanism for long-term knowledge composition while significantly mitigating catastrophic forgetting. Code is available at https://github.com/QuantitativeImagingLaboratory/GeoStack.",
"summary": "We address the challenge of knowledge composition in Vision-Language Models (VLMs), where accumulating expertise across multiple domains or tasks typically leads to catastrophic forgetting.",
"tags": [
"arXiv",
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
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],
"sort_date": "2026-05-08T11:00:00Z"
},
{
"id": "item_02601",
"rank": 40,
"score": 830,
"rationale": "Skala deep-learning XC functional surpassing hybrids is a landmark DFT/ML result with broad impact.",
"title": "RemoteZero: Geospatial Reasoning with Zero Human Annotations",
"url": "https://huggingface.co/papers/2605.04451",
"authors": "HuggingFace",
"description": "Geospatial reasoning requires models to resolve complex spatial semantics and user intent into precise target locations for Earth observation. Recent progress has liberated the reasoning path from manual curation, allowing models to generate their own inference chains. Yet a final dependency remains: they are still supervised by human-annotated ground-truth coordinates. This leaves the reasoning process autonomous, but not its spatial endpoint, and prevents true self-evolution on abundant unlabeled remote sensing data. To break this bottleneck, we introduce RemoteZero, a box-supervision-free framework for geospatial reasoning. RemoteZero is motivated by a simple asymmetry: an MLLM is typically better at verifying whether a region satisfies a query than at directly generating precise coordinates. Leveraging this stronger discriminative ability, RemoteZero replaces geometric supervision with intrinsic semantic verification and enables GRPO training without box annotations. The resulting framework further supports iterative self-evolution, allowing the model to improve from unlabeled remote sensing imagery through its own verification signal. Experiments show that RemoteZero achieves competitive performance against strong supervised methods, demonstrating the potential of self-verifying training for geospatial reasoning localization.",
"summary": "Geospatial reasoning requires models to resolve complex spatial semantics and user intent into precise target locations for Earth observation. Recent progress has liberated the reasoning path from manual curation,…",
"tags": [
"HuggingFace",
"LLMs"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_03018",
"rank": 41,
"score": 830,
"rationale": "Teacher consistency in OPD is a sharp, generalizable post-training insight; practical impact high.",
"title": "AI Terminology is Poorly Defined and Oft Misused",
"url": "https://vale.rocks/posts/ai-terminology",
"authors": "",
"description": "Words and terms used when describing artificial intelligence are often misused, inaccurate, or generalised to the point of losing all meaning. How terms like",
"summary": "Words and terms used when describing artificial intelligence are often misused, inaccurate, or generalised to the point of losing all meaning. How terms like",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🍞 Lobste.rs",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_03427",
"rank": 42,
"score": 830,
"rationale": "OpenSSH PQ rollout is landmark for deployed post-quantum cryptography.",
"title": "(https://www.theguardian.com/technology/2026/apr/22/what-is-anthropic-mythos-ai-threat-global-cybersecurity)",
"url": "https://www.theguardian.com/technology/2026/apr/22/what-is-anthropic-mythos-ai-threat-global-cybersecurity",
"authors": "",
"description": "Anthropic's decision to restrict access to its powerful new model increases fears about the advanced technology",
"summary": "Anthropic's decision to restrict access to its powerful new model increases fears about the advanced technology",
"tags": [
"💬 Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "💬 Hacker News",
"debrief_date": "2026-04-23 00:00",
"debrief_path": "debriefs/debrief-2026-04-23-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 00:00",
"debrief_path": "debriefs/debrief-2026-04-23-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T00:00:00Z"
},
{
"id": "item_03964",
"rank": 43,
"score": 830,
"rationale": "Meerkat framework for cross-trace safety auditing addresses real gap in agent oversight; strong AI safety contribution.",
"title": "Detecting Safety Violations Across Many Agent Traces",
"url": "https://arxiv.org/abs/2604.11806",
"authors": "Adam Stein, Davis Brown, Hamed Hassani, Mayur Naik, et al.",
"description": "To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversarially hidden and only detectable when multiple traces are analyzed together. These challenges arise in diverse settings such as misuse campaigns, covert sabotage, reward hacking, and prompt injection. Existing approaches struggle here for several reasons. Per-trace judges miss failures that only become visible across traces, naive agentic auditing does not scale to large trace collections, and fixed monitors are brittle to unanticipated behaviors. We introduce Meerkat, which combines clustering with agentic search to uncover violations specified in natural language. Through structured search and adaptive investigation of promising regions, Meerkat finds sparse failures without relying on seed scenarios, fixed workflows, or exhaustive enumeration. Across misuse, misalignment, and task gaming settings, Meerkat significantly improves detection of safety violations over baseline monitors, discovers widespread developer cheating on a top agent benchmark, and finds nearly 4x more examples of reward hacking on CyBench than previous audits.",
"summary": "To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversarially hidden and only detectable when multiple traces are analyzed together. Why it matters: indirect prompt injection is one of the most…",
"tags": [
"cs.AI",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_00709",
"rank": 44,
"score": 824,
"rationale": "Empirical evidence that AI research agents narrow exploration at scale; important meta-research finding.",
"title": "zkGPT: An Efficient Non-interactive Zero-knowledge Proof Framework for LLM Inference",
"url": "https://www.semanticscholar.org/paper/d64d9593f14f4e7f0b52d5d83f5a0877aa3cf7e8",
"authors": "",
"description": "zkGPT: An Efficient Non-interactive Zero-knowledge Proof Framework for LLM Inference",
"summary": "Finding from 🧪 Semantic Scholar.",
"tags": [
"📊 26 cites",
"Semantic Scholar"
],
"source": "🧪 Semantic Scholar",
"type": "news",
"section": "🌐 Web Findings",
"group": "🧪 Semantic Scholar",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_00101",
"rank": 45,
"score": 820,
"rationale": "ABC-Bench for agentic biosecurity capabilities is highly significant for AI safety/dual-use governance.",
"title": "Convergence of evolving artificial intelligence and machine learning techniques in precision oncology",
"url": "https://www.semanticscholar.org/paper/256ce5a88fe0323da1d812657aaa7fb1baf9921d",
"authors": "",
"description": "The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensi",
"summary": "The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology,…",
"tags": [
"Semantic Scholar",
"📊 151 cites",
"📊 152 cites"
],
"source": "🧪 Semantic Scholar",
"type": "news",
"section": "🌐 Web Findings",
"group": "🧪 Semantic Scholar",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 6,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-13 19:00",
"debrief_path": "debriefs/debrief-2026-06-13-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-13 11:00",
"debrief_path": "debriefs/debrief-2026-06-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_01499",
"rank": 46,
"score": 820,
"rationale": "Google's first AI-discovered 2FA-bypass zero-day is a landmark AI-for-security milestone with broad impact.",
"title": "Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets",
"url": "https://huggingface.co/papers/2605.28510",
"authors": "Andrea Gurioli, Davide D'Ascenzo, Federico Pennino, Maurizio Gabbrielli, Stefano Zacchiroli",
"description": "SOURCETRACKER is a 300M-parameter code retrieval encoder that narrows candidates via vector search then re-ranks with Winnowing fingerprints, outperforming Winnowing alone by 5.4% on 60+ token fragments while preserving logarithmic query complexity, addressing plagiarism and license compliance for code LLMs.",
"summary": "SOURCETRACKER is a 300M-parameter code retrieval encoder that narrows candidates via vector search then re-ranks with Winnowing fingerprints, outperforming Winnowing alone by 5.4%…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": "",
"occurrence_count": 3,
"occurrences": [
{
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-28T19:00:00Z"
},
{
"id": "item_04336",
"rank": 47,
"score": 820,
"rationale": "Anthropic Mythos model gated access is a notable AI-cyber event.",
"title": "Anthropic limits access to Mythos, its new cybersecurity AI model",
"url": "https://news.ycombinator.com/item?id=47693923",
"authors": "",
"description": "Article about: Anthropic limits access to Mythos, its new cybersecurity AI model",
"summary": "Article about: Anthropic limits access to Mythos, its new cybersecurity AI model",
"tags": [
"Hackernews",
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🟠 Hacker News",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04263",
"rank": 48,
"score": 820,
"rationale": "Cryptographer's view on quantum timelines — high-signal post-quantum strategy topic.",
"title": "A Cryptography Engineer’s Perspective on Quantum Computing Timelines",
"url": "https://words.filippo.io/crqc-timeline/",
"authors": "",
"description": "Article about: A Cryptography Engineer’s Perspective on Quantum Computing Timelines",
"summary": "Article about: A Cryptography Engineer’s Perspective on Quantum Computing Timelines",
"tags": [
"Hackernews",
"Lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00460",
"rank": 49,
"score": 820,
"rationale": "1-click GitHub token theft via VSCode bug; high-impact real-world security finding.",
"title": "Building llm-driven “ai” still requires domain knowledge",
"url": "https://lobste.rs/s/q9sd1m",
"authors": "",
"description": "I’m in the process of building an llm driven tool to take user questions and answer them using our customer api at $work. \n\nA big part of the work is capturing the domain knowledge and writing it down. It’s easier than previous generations of “ai” because the knowledge doesn’t strictly have to be rendered into a structured format, but this is what previous generations of “ai” foundered on.",
"summary": "I’m in the process of building an llm driven tool to take user questions and answer them using our customer api at $work. A big part of the work is capturing the domain knowledge…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T11:00:00Z"
},
{
"id": "item_00809",
"rank": 50,
"score": 820,
"rationale": "BonaFide meta-evaluation exposes near-chance CoT faithfulness metrics; landmark auditability finding.",
"title": "AMD touts the unified memory architecture",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u2l25d/amd_touts_the_unified_memory_architecture/",
"authors": "",
"description": "https://wccftech.com/amd-unified-memory-architectures-open-up-a-world-of-possibilities-shape-product-roadmaps/ Quote: AMD believes that UMA will help shape its next-gen architectures Article mentions Ryzen AI MAX 400 series, which we might recognize better as Gorgon Halo systems. Previous discussions on this topic: https://www.reddit.com/r/LocalLLaMA/comments/1swiylm/comparison_of_upcoming_x86_unified_memory_systems/ https://www.reddit.com/r/LocalLLaMA/comments/1oph7jd/unified_memory_is_the_future_not_gpu_for_local_ai/ submitted by /u/Terminator857 [link] [comments]",
"summary": "https://wccftech.com/amd-unified-memory-architectures-open-up-a-world-of-possibilities-shape-product-roadmaps/ Quote: AMD believes that UMA will help shape its next-gen…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00945",
"rank": 51,
"score": 820,
"rationale": "US $2B equity stakes in nine quantum firms is a landmark policy/industry event for quantum computing strategy.",
"title": "Itô maps for any-step SDEs",
"url": "https://arxiv.org/abs/2606.11156",
"authors": "Zhengkai Pan, Peter Potaptchik, Wenxi Yao, Michael S. Albergo, Jakiw Pidstrigach",
"description": "Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning from ordinary differential equations, leaving open how to define an exact distillation procedure for stochastic dynamics. We introduce the Itô map, an any-step stochastic flow map that takes an intermediate state and Brownian path and predicts future states in a single pass. The Itô map formulation yields novel estimators for inference-time control by providing cheap, differentiable access to posterior samples. Empirically, Itô maps produce diverse, conditionally valid endpoint samples from fixed intermediate states and support strong steering performance on synthetic and image-generation benchmarks. These results establish any-step SDE integration as a useful primitive for posterior sampling and stochastic control.",
"summary": "Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning from ordinary differential equations, leaving open…",
"tags": [
"stat.ML",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Machine Learning",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01050",
"rank": 52,
"score": 820,
"rationale": "Google disrupting AI-exploited zero-day; landmark AI-cyber event.",
"title": "Observability for Delegated Execution in Agentic AI Systems",
"url": "https://arxiv.org/abs/2606.09692",
"authors": "Abhinav Mishra, Kumar Sharad",
"description": "Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary execution sequences across runs for the same instruction, and spawn cooperating sub-agents. These dynamics fragment and interleave traces, making delegation-scoped reconstruction from causal structure alone structurally underdetermined. Although individual actions are authorized and logged, existing audit, tracing, and security schemas lack the semantics to reconstruct what actions occurred under a given delegation across heterogeneous systems. We focus on delegation-scoped attribution and access/share footprint reconstruction, not intent inference or reasoning reconstruction. We present an agent-aware observability substrate consisting of a lightweight gateway and a common information model that binds delegation context at execution time. This enables reliable cross-tool delegation-scoped reconstruction and direct forensic queries without heuristic time-window correlation.",
"summary": "Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially…",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01373",
"rank": 53,
"score": 820,
"rationale": "First public M5 macOS kernel memory-corruption exploit; landmark platform security breakthrough.",
"title": "Linear Scaling Video VLMs for Long Video Understanding",
"url": "https://huggingface.co/papers/2605.31598",
"authors": "Cristobal Eyzaguirre, Jiajun Wu, Juan Carlos Niebles",
"description": "Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compute and latency to grow quadratically with the number of frames. Existing efficiency methods improve scalability but often lose accuracy relative to full self-attention, for example through aggressive frame/token dropping or coarse attention approximations. We introduce StateKV, an inference-time method that adapts pretrained long-video VLMs to linear-time video prefill by carrying cross-frame context in a fixed-capacity, importance-based recurrent state, paired with a second full per-frame cache used for decoding. Across three long-video benchmarks and seven models spanning three families and multiple scales, StateKV remains close to full self-attention and consistently outperforms dominant sliding-window / recency-based streaming approximations, without fine-tuning or architectural changes. StateKV also reduces video-prefill cost measured FLOPs, enabling stronger accuracy at a fixed compute budget by running larger models. These results suggest a practical step toward scalable long-video understanding.",
"summary": "Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-05-29"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-01T04:00:00Z"
},
{
"id": "item_01949",
"rank": 54,
"score": 820,
"rationale": "DeepSeek new flagship release is a major industry event; high strategic AI/ML importance.",
"title": "What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Models",
"url": "https://arxiv.org/abs/2605.18738",
"authors": "Payal Chandak, Victoria Alkin, David Wu, Maya Dagan, Taposh Dutta Roy, Maria Clara Saad Menezes, Ayush Noori, Nirali Somia, John S. Brownstein, Ran Ba",
"description": "Medicine is inherently pluralistic. Principles such as autonomy, beneficence, nonmaleficence, and justice routinely conflict, and such ethical dilemmas often sharply divide reasonable physicians. Good clinical practice navigates these tensions in concert with each patient's values rather than imposing a single ethical stance. The ethical values that large language models bring to medical advice, however, have not been systematically examined. We present a framework for auditing value pluralism i",
"summary": "Medicine is inherently pluralistic. Principles such as autonomy, beneficence, nonmaleficence, and justice routinely conflict, and such ethical dilemmas often sharply divide reasonable physicians. Good clinical practice navigates these tensions in con…",
"tags": [
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs & Agents",
"debrief_date": "2026-05-19 04:00",
"debrief_path": "debriefs/debrief-2026-05-19-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-19 04:00",
"debrief_path": "debriefs/debrief-2026-05-19-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-19T04:00:00Z"
},
{
"id": "item_02069",
"rank": 55,
"score": 820,
"rationale": "Exploration hacking in RL post-training; first systematic study of strategic misalignment in RL, high impact.",
"title": "The Case Against Quantum Computing",
"url": "https://news.ycombinator.com/item?id=48171757",
"authors": "",
"description": "The Case Against Quantum Computing",
"summary": "The Case Against Quantum Computing",
"tags": [
"HACKER NEWS"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "💬 Hacker News",
"debrief_date": "2026-05-17 19:00",
"debrief_path": "debriefs/debrief-2026-05-17-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-17 19:00",
"debrief_path": "debriefs/debrief-2026-05-17-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-17T19:00:00Z"
},
{
"id": "item_02248",
"rank": 56,
"score": 820,
"rationale": "Iso-depth scaling laws for looped LMs with recurrence exponent; foundational scaling insight.",
"title": "The DAWN of World-Action Interactive Models",
"url": "https://huggingface.co/papers/2605.11550",
"authors": "Hongbo Lu, Liang Yao, Chenghao He, Haoyu Wang, Xiang Gu",
"description": "A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action generation as either isolated parallel branches or rigid predict-then-plan pipelines. We formalize this perspective as World-Action Interactive Models (WAIMs), and instantiate it in autonomous driving with DAWN (Denoising Actions and World iNteractive model), a simple yet strong latent generative baseline. DAWN operates in a compact semantic latent space and couples a World Predictor with a World-Conditioned Action Denoiser: the predicted world hypothesis conditions action denoising, while the denoised action hypothesis is fed back to update the world prediction, so that both are recursively refined during inference. Rather than eliminating test-time world evolution altogether or rolling out the full future in pixel space, DAWN performs a short explicit latent rollout that is sufficient to support long-horizon trajectory generation in complex interactive scenes. Experiments show that DAWN achieves strong planning performance and favorable safety-related results across multiple autonomous driving benchmarks. More broadly, our results suggest that interactive world-action generation is a principled path toward truly actionable world models.",
"summary": "A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss this reciprocity, treating world prediction and action…",
"tags": [
"cs.CV",
"cs.RO",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Vision & Gen AI",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02492",
"rank": 57,
"score": 820,
"rationale": "Systematic study of coding-agent score exploitation is high-impact for agent safety.",
"title": "UniSD: Towards a Unified Self-Distillation Framework for Large Language Models",
"url": "https://huggingface.co/papers/2605.06597",
"authors": "HuggingFace",
"description": "Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains challenging because self-generated trajectories are free-form, correctness is task-dependent, and plausible rationales can still provide unstable or unreliable supervision. Existing methods mainly examine isolated design choices, leaving their effectiveness, roles, and interactions unclear. In this paper, we propose UniSD, a unified framework to systematically study self-distillation. UniSD integrates complementary mechanisms that address supervision reliability, representation alignment, and training stability, including multi-teacher agreement, EMA teacher stabilization, token-level contrastive learning, feature matching, and divergence clipping. Across six benchmarks and six models from three model families, UniSD reveals when self-distillation improves over static imitation, which components drive the gains, and how these components interact across tasks. Guided by these insights, we construct UniSDfull, an integrated pipeline that combines complementary components and achieves the strongest overall performance, improving over the base model by +5.4 points and the strongest baseline by +2.8 points. Extensive evaluation highlights self-distillation as a practical and steerable approach for efficient LLM adaptation without stronger external teachers.",
"summary": "Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T04:00:00Z"
},
{
"id": "item_02701",
"rank": 58,
"score": 820,
"rationale": "Major reward-hacking dataset across frontier models; high value for agent eval and safety.",
"title": "KVerus: Scalable and Resilient Formal Verification Proof Generation for Rust Code",
"url": "https://arxiv.org/abs/2605.03822",
"authors": "Yuwei Liu, Xinyi Wan, Yanhao Wang, Minghua Wang, Lin Huang, Tao Wei",
"description": "Formal verification provides the highest assurance of software correctness and security, but its application to large-scale, evolving systems remains a major challenge. While large language models (LLMs) have shown promise in automating proof generation, they often fail in real-world settings due to their inability to handle complex cross-module dependencies or changes in the codebase or the verification toolchain. We identify the fundamental problem as the Semantic-Structural Gap: LLMs operate ",
"summary": "Formal verification provides the highest assurance of software correctness and security, but its application to large-scale, evolving systems remains a major challenge. While large language models (LLMs) have shown promise in automating proof generation, they…",
"tags": [
"cs.SE",
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T11:00:00Z"
},
{
"id": "item_02763",
"rank": 59,
"score": 820,
"rationale": "Formal verification of Signal protocol in Lean is landmark for applied crypto/safety; high impact.",
"title": "Motion-Aware Caching for Efficient Autoregressive Video Generation",
"url": "https://huggingface.co/papers/2605.01725",
"authors": "HuggingFace",
"description": "Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising. While cache reuse strategies can accelerate generation by skipping redundant denoising steps, existing methods rely on coarse-grained chunk-level skipping that fails to capture fine-grained pixel dynamics. This oversight is critical: pixels with high motion require more denoising steps to prevent error accumulation, while static pixels tolerate aggressive skipping. We formalize this insight theoretically by linking cache errors to residual instability, and propose MotionCache, a motion-aware cache framework that exploits inter-frame differences as a lightweight proxy for pixel-level motion characteristics. MotionCache employs a coarse-to-fine strategy: an initial warm-up phase establishes semantic coherence, followed by motion-weighted cache reuse that dynamically adjusts update frequencies per token. Extensive experiments on state-of-the-art models like SkyReels-V2 and MAGI-1 demonstrate that MotionCache achieves significant speedups of 6.28times and 1.64times respectively, while effectively preserving generation quality (VBench: 1%downarrow and 0.01%downarrow respectively). The code is available at https://github.com/ywlq/MotionCache.",
"summary": "Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising. While cache reuse…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "📄 HuggingFace Papers",
"debrief_date": "2026-05-05 04:00",
"debrief_path": "debriefs/debrief-2026-05-05-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-05 04:00",
"debrief_path": "debriefs/debrief-2026-05-05-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-05T04:00:00Z"
},
{
"id": "item_02877",
"rank": 60,
"score": 820,
"rationale": "Trail of Bits forging Google's quantum zk-proof; landmark zkVM vuln disclosure.",
"title": "Stop Holding Your Breath: CT-Informed Gaussian Splatting for Dynamic Bronchoscopy",
"url": "https://arxiv.org/abs/2604.28179",
"authors": "Andrea Dunn Beltran, Daniel Rho, Aarav Mehta, Xinqi Xiong, Raúl San José Estépar, Ron Alterovitz, Marc Niethammer, Roni Sengupta",
"description": "Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creating CT-to-body divergence that limits localization accuracy. In practice, this is mitigated through breath-hold protocols, which attempt to match the intraoperative anatomy to a static CT, but are difficult to reproduce and disrupt clinical workflow. We propose to eliminate the need for breath-hold protocols by leveraging patient-specific respirato",
"summary": "Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creating CT-to-body divergence that limits localization accuracy. In practice, this is mitigated through breath-hold…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-03 09:00",
"debrief_path": "debriefs/debrief-2026-05-03-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-03 09:00",
"debrief_path": "debriefs/debrief-2026-05-03-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-03T09:00:00Z"
},
{
"id": "item_03004",
"rank": 61,
"score": 820,
"rationale": "LASA: strong, novel LLM safety result — 24.7%→2.8% ASR via semantic-bottleneck alignment.",
"title": "Meta-learning-enhanced implicit full waveform inversion",
"url": "https://arxiv.org/abs/2604.26938",
"authors": "Huan Song, Shijun Cheng, Huanhuan Tang, Wei Ouyang, Weijian Mao",
"description": "Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of spatial coordinates, which alleviates the dependence on the initial model and improves inversion flexibility. However, IFWI still requires a large number of iterative updates for each new exploration area, leading to slow convergence, high computational cost, and a lack of mechanisms to share prior knowledge across different geological setti",
"summary": "Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of…",
"tags": [
"physics.geo-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Zero Knowledge",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_03251",
"rank": 62,
"score": 820,
"rationale": "BadSkill identifies a timely, underexplored supply-chain threat: backdoors inside model-bearing agent skills.",
"title": "Addressing Image Authenticity When Cameras Use Generative AI",
"url": "https://arxiv.org/abs/2604.21879",
"authors": "Umar Masud, Abhijith Punnappurath, Luxi Zhao, David B. Lindell, Michael S. Brown",
"description": "The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by our cameras are considered authentic and faithful. However, with the increasing integration of deep-learning modules into cameras' capture-time hardware -- namely, the image signal processor (ISP) -- there is now a potential for hallucinated content in images directly output by our cameras. Hallucina",
"summary": "The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by o…",
"tags": [
"cs.CV",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03340",
"rank": 63,
"score": 820,
"rationale": "First systematic study of malicious LLM API routers; landmark supply-chain finding.",
"title": "(https://www.bloomberg.com/news/articles/2026-04-17/anthropic-s-mythos-adds-strain-on-cybersecurity-teams-facing-ai-threats)",
"url": "https://www.bloomberg.com/news/articles/2026-04-17/anthropic-s-mythos-adds-strain-on-cybersecurity-teams-facing-ai-threats",
"authors": "Hacker News",
"description": "(https://www.bloomberg.com/news/articles/2026-04-17/anthropic-s-mythos-adds-strain-on-cybersecurity-teams-facing-ai-threats)",
"summary": "(https://www.bloomberg.com/news/articles/2026-04-17/anthropic-s-mythos-adds-strain-on-cybersecurity-teams-facing-ai-threats)",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "⎅ Hacker News",
"debrief_date": "2026-04-23 19:00",
"debrief_path": "debriefs/debrief-2026-04-23-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 19:00",
"debrief_path": "debriefs/debrief-2026-04-23-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T19:00:00Z"
},
{
"id": "item_03638",
"rank": 64,
"score": 820,
"rationale": "Critical Claude Code command-injection flaws bypassing sandbox; high-impact AI security finding.",
"title": "Anthropic Claude Code Leak Reveals Critical Command Injection Vulnerabilities",
"url": "https://beyondmachines.net/event_details/anthropic-claude-code-leak-reveals-critical-command-injection-vulnerabilities-e-6-c-1-k/gD2P6Ple2L",
"authors": "",
"description": "Anthropic's Claude Code CLI contains three critical command injection vulnerabilities that allow attackers to execute arbitrary code and exfiltrate cloud credentials via environment variables, file paths, and authentication helpers. These flaws bypass the tool's internal sandbox and are particularly dangerous in CI/CD environments where trust dialogs are disabled.",
"summary": "Anthropic's Claude Code CLI contains three critical command injection vulnerabilities that allow attackers to execute arbitrary code and exfiltrate cloud credentials via environment variables, file…",
"tags": [
"Lobste.rs",
"security",
"vibecoding",
"▲ 16"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-19 11:00",
"debrief_path": "debriefs/debrief-2026-04-19-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-19 11:00",
"debrief_path": "debriefs/debrief-2026-04-19-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-19T11:00:00Z"
},
{
"id": "item_03829",
"rank": 65,
"score": 820,
"rationale": "OS-BLIND benchmark for CUA safety via benign instructions; reveals >90% ASR, landmark agent safety finding.",
"title": "The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents",
"url": "https://huggingface.co/papers/2604.10577",
"authors": "",
"description": "Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit threats such as misuse and prompt injection, but overlook a subtle yet critical setting where user instructions are entirely benign and harm arises from the task context or execution outcome. We introduce OS-BLIND, a benchmark that evaluates CUAs under unintended attack conditions, comprising 300 human-crafted tasks across 12 categories, 8 applications, and 2 threat clusters: environment-embedded threats and agent-initiated harms. Our evaluation on frontier models and agentic frameworks reveals that most CUAs exceed 90% attack success rate (ASR), and even the safety-aligned Claude 4.5 Sonnet reaches 73.0% ASR. More interestingly, this vulnerability becomes even more severe, with ASR rising from 73.0% to 92.7% when Claude 4.5 Sonnet is deployed in multi-agent systems. Our analysis further shows that existing safety defenses provide limited protection when user instructions are benign. Safety alignment primarily activates within the first few steps and rarely re-engages during subsequent execution. In multi-agent systems, decomposed subtasks obscure the harmful intent from the model, causing safety-aligned models to fail. We will release our OS-BLIND to encourage the broader research community to further investigate and address these safety cha",
"summary": "Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace",
"debrief_date": "2026-04-15 19:00",
"debrief_path": "debriefs/debrief-2026-04-15-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 19:00",
"debrief_path": "debriefs/debrief-2026-04-15-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T19:00:00Z"
},
{
"id": "item_04173",
"rank": 66,
"score": 812,
"rationale": "First systematic study of malicious LLM API routers with empirical marketplace findings; landmark supply-chain attack work.",
"title": "Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain",
"url": "https://arxiv.org/abs/2604.08407",
"authors": "Hanzhi Liu, Chaofan Shou, Hongbo Wen, Yanju Chen, Ryan Jingyang Fang, Yu Feng",
"description": "Large language model (LLM) agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers. These routers operate as application-layer proxies with full plaintext access to every in-flight JSON payload, yet no provider enforces cryptographic integrity between client and upstream model. We present the first systematic study of this attack surface. We formalize a threat model for malicious LLM API routers and define two core attack classes, payload injection (AC-1) and secret exfiltration (AC-2), together with two adaptive evasion variants: dependency-targeted injection (AC-1.a) and conditional delivery (AC-1.b). Across 28 paid routers purchased from Taobao, Xianyu, and Shopify-hosted storefronts and 400 free routers collected from public communities, we find 1 paid and 8 free routers actively injecting malicious code, 2 deploying adaptive evasion triggers, 17 touching researcher-owned AWS canary credentials, and 1 draining ETH from a researcher-owned private key. Two poisoning studies further show that ostensibly benign routers can be pulled into the same attack surface: a leaked OpenAI key generates 100M GPT-5.4 tokens and more than seven Codex sessions, while weakly configured decoys yield 2B billed tokens, 99 credentials across 440 Codex sessions, and 401 sessions already running in autonomous YOLO mode. We build Mine, a research proxy that implements all four attack classes against four public agent frameworks, and use it to evaluate three deployable client-side defenses: a fail-closed policy gate, response-side anomaly screening, and append-only transparency logging.",
"summary": "Large language model (LLM) agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 04:00",
"debrief_path": "debriefs/debrief-2026-04-11-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_01502",
"rank": 67,
"score": 812,
"rationale": "High-impact safety result: single-neuron mediation of refusal across 7 models, strong mechanistic evidence, broad implications.",
"title": "Contrastive Distribution Matching for Amortized Sequential Monte Carlo in Discrete Diffusion",
"url": "https://huggingface.co/papers/2605.23346",
"authors": "Jaihoon Kim, Taehoon Yoon, Prin Phunyaphibarn, Seungjun Kim, Morteza Mardani",
"description": "CDM amortizes SMC inference in discrete diffusion models by learning a twist function via positive and negative samples, adding less than 5% computational overhead and consistently outperforming baselines for toxic text generation, DNA/protein design, and LLM alignment.",
"summary": "CDM amortizes SMC inference in discrete diffusion models by learning a twist function via positive and negative samples, adding less than 5% computational overhead and…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-28 19:00",
"debrief_path": "debriefs/debrief-2026-05-28-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-28T19:00:00Z"
},
{
"id": "item_04201",
"rank": 68,
"score": 812,
"rationale": "Strong empirical result: simple self-distillation lifts LiveCodeBench pass@1 by 13pts, novel precision-exploration analysis. High practical impact.",
"title": "Embarrassingly Simple Self-Distillation Improves Code Generation",
"url": "https://arxiv.org/abs/2604.01193",
"authors": "",
"description": "Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirmative with simple self-distillation (SSD): sample solutions from the model with certain temperature and truncation configurations, then fine-tune on those samples with standard supervised fine-tuning. SSD improves Qwen3-30B-Instruct from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on harder problems, and it generalizes across Qwen and Llama models at 4B, 8B, and 30B scale, including both instruct and thinking variants. To understand why such a simple method can work, we trace these gains to a precision-exploration conflict in LLM decoding and show that SSD reshapes token distributions in a context-dependent way, suppressing distractor tails where precision matters while preserving useful diversity where exploration matters. Taken together, SSD offers a complementary post-training direction for improving LLM code generation.",
"summary": "Article about: Embarrassingly Simple Self-Distillation Improves Code Generation",
"tags": [
"Lobsters",
"lobsters"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00669",
"rank": 69,
"score": 812,
"rationale": "Critical finding that CoT monitoring is fundamentally fragile across languages; significant safety implications.",
"title": "HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness",
"url": "https://huggingface.co/papers/2606.12882",
"authors": "",
"description": "Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by the harness that mediates agent--environment interaction. Existing harnesses are largely manually engineered, making them difficult to scale as trajectories grow longer and interactions become more complex. In this work, we ask whether harness can be generated by a learnable plug-in module that can be trained in an end-to-end fashion. We introduce HarnessBridge, a lightweight learnable harness controller that parameterizes the agent--environment interface as a bidirectional projection. HarnessBridge learns two bidirectional projections: observation projection, which distills raw trajectories into compact, decision-relevant states, and action projection, which converts proposed actions into executable transitions or trajectory-grounded rejections. We train HarnessBridge on a harness supervision dataset via unified instruction tuning. On Terminal-Bench~2.0 and SWE-bench Verified, HarnessBridge matches or surpasses strong specialized harnesses while substantially reducing token usage and trajectory length, and generalizes from smaller generators to larger commercial models.",
"summary": "Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by…",
"tags": [
"▲ 5",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_02112",
"rank": 70,
"score": 812,
"rationale": "Empirical 100-model study showing safety drift under fine-tuning; critical for deployment governance.",
"title": "Aligning Latent Geometry for Spherical Flow Matching in Image Generation",
"url": "https://arxiv.org/abs/2605.15193",
"authors": "Tuna Han Salih Meral, Kaan Oktay, Hidir Yesiltepe, Adil Kaan Akan, Pinar Yanardag",
"description": "Latent flow matching for image generation usually transports Gaussian noise to variational autoencoder latents along linear paths. Both endpoints, however, concentrate in thin spherical shells, and a Euclidean chord leaves those shells even when preprocessing aligns their radii. By decomposing each latent token into radial and angular components, we show through component-swap probes that decoded perceptual and semantic content is carried predominantly by direction, with radius contributing much",
"summary": "Latent flow matching for image generation usually transports Gaussian noise to variational autoencoder latents along linear paths. Both endpoints, however, concentrate in thin spherical shells, and a Euclidean chord…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 AI Safety & Alignment",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_03682",
"rank": 71,
"score": 812,
"rationale": "Production random-graph datacenter fabric at Amazon is landmark networking deployment paper.",
"title": "Expanding into Reality: Random Graphs for Datacenter Networks",
"url": "https://arxiv.org/abs/2604.15261",
"authors": "Giacomo Bernardi, Ratul Mahajan, C. Seshadhri, Enrico Carlesso, Chinchu Merine Joseph, Saurabh Kumar, Pavan Manikonda, Luiza Popa, Randy Ram, Steven Robinson, Elizabeth Tennent",
"description": "We design and deploy at Amazon the first production datacenter fabrics based on random graphs. While the cost and fault-tolerance benefits of such topologies have been long known, their practical realization has been hampered by a lack of scalable routing and cabling approaches. Our design, called RNG, has a new distributed routing protocol that exploits the properties of random graphs to find a large number of edge disjoint paths between endpoint pairs. A novel passive optical device that inter",
"summary": "We design and deploy at Amazon the first production datacenter fabrics based on random graphs. While the cost and fault-tolerance benefits of such topologies have been long known, their practical…",
"tags": [
"cs.NI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Machine Learning",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_03721",
"rank": 72,
"score": 812,
"rationale": "Empirical dissection of Claude Code's architecture with comparative analysis to OpenClaw; valuable for agent designers.",
"title": "Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems",
"url": "https://huggingface.co/papers/2604.14228",
"authors": "",
"description": "Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the publicly available TypeScript source code and further comparing it with OpenClaw, an independent open-source AI agent system that answers many of the same design questions from a different deployment context. Our analysis identifies five human values, philosophies, and needs that motivate the architecture (human decision authority, safety and security, reliable execution, capability amplification, and contextual adaptability) and traces them through thirteen design principles to specific implementation choices. The core of the system is a simple while-loop that calls the model, runs tools, and repeats. Most of the code, however, lives in the systems around this loop: a permission system with seven modes and an ML-based classifier, a five-layer compaction pipeline for context management, four extensibility mechanisms (MCP, plugins, skills, and hooks), a subagent delegation mechanism with worktree isolation, and append-oriented session storage. A comparison with OpenClaw, a multi-channel personal assistant gateway, shows that the same recurring design questions produce different architectural answers when the deployment context changes: from per-action safety classification to perimeter-level access control, from a single CLI loop to an embedded runtime within a gateway control plane",
"summary": "Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_00001",
"rank": 73,
"score": 810,
"rationale": "Identifies a fundamental blind spot in faithfulness metrics; oracle-completeness method is a notable contribution to grounded generation evaluation.",
"title": "LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents",
"url": "https://huggingface.co/papers/2606.20529",
"authors": "",
"description": "Policy-adherent tool-calling agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies. Task states consist of relevant facts, identifiers, constraints, and conditions observed through user interaction and tool calls. In standard agents, task states are not represented separately. Observations, tool returns, and policy instructions are placed in the prompt, leaving agents to reconstruct the relevant states from the prompt each time they decide what to do next. This design makes state management implicit, creating two common failure modes. An agent may retrieve the right facts but later ground its decision in stale, missing, or incorrect information; and a syntactically valid tool call may still violate a domain policy that depends on the current task state. We introduce LedgerAgent, an inference-time method for tool-calling agents that maintains observed task states in a separate ledger and renders the states into the prompt. The ledger is also used to check state-dependent policy constraints before environment-changing tool calls are executed, blocking policy violations. Across four customer-service domains and a mixed panel of open- and closed-weight models, LedgerAgent improves average passk over a standard prompt-based tool-calling approach, with the largest gains under stricter multi-trial consistency metrics.",
"summary": "Policy-adherent tool-calling agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies. Task states consist of…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-19 19:00",
"debrief_path": "debriefs/debrief-2026-06-19-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 19:00",
"debrief_path": "debriefs/debrief-2026-06-19-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-19T19:00:00Z"
},
{
"id": "item_00181",
"rank": 74,
"score": 810,
"rationale": "Trust functions address core weak-to-strong generalization with iterative compounding gains—high impact for alignment/superhuman supervision.",
"title": "Secret key-distribution over networks with node-based adversarial errors",
"url": "https://arxiv.org/abs/2606.19305",
"authors": "Reza Sayyari, Michael Langberg",
"description": "We study the multiple key-cast problem in network coding under active node-based adversaries. In multiple key-cast, a source generates independent secret keys to be securely and reliably delivered to designated terminal subsets. The network adversary can observe \\(\\ell_o\\) nodes, inject additive or overwrite errors into \\(\\ell_e\\) nodes, and simultaneously observe and corrupt \\(\\ell_{oe}\\) nodes, while having full knowledge of the topology and coding operations. Adversarial models of similar nature, however, where corruption and eavesdropping is done on edges instead of nodes, have seen previous studies in the context of secure multicast network-coding. The work at hand builds on and extends these studies to address the challenges in node-based adversaries in the context of (multiple) key distribution. For single-source networks where every node is d-vertex connected from the source, we show that perfectly secure multiple key-cast under additive and overwrite error models is asymptotically achievable at the key-capacity of \\(d-\\ell_o-\\ell_e-2\\ell_{oe}\\). We then extend our analysis to networks where only terminal nodes satisfy this connectivity requirement, while intermediate nodes may be only partially connected. For these topologies, we develop coding schemes that achieve secure and reliable multiple key-cast capacities determined by the source vertex-connectivity and additional structural properties of the network. Finally, we show that our results generalize to multi-sour",
"summary": "We study the multiple key-cast problem in network coding under active node-based adversaries. In multiple key-cast, a source generates independent secret keys to be securely and reliably delivered to…",
"tags": [
"cs.IT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Secret / Key-distribution / Over",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00246",
"rank": 75,
"score": 810,
"rationale": "End-to-end context compression at scale closes encoder-decoder gap; high practical impact.",
"title": "Evaluating Open-Source LLMs for Multi-Label ATT&CK Technique Classification on CTI Reports",
"url": "https://arxiv.org/abs/2606.18166",
"authors": "Ahmed Ryan, Saad Sakib Noor, Md Erfan, Shaswata Mitra, Sudip Mittal, Md Rayhanur Rahman",
"description": "Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort. Pre-Large Language Model (LLM) automation sped up this process, but could not resolve the complex language and multi-step attack patterns found in unstructured CTI reports. LLMs addressed previous limitations by using contextual reasoning to understand unstructured text. However, current evaluations rely on simplified, single-technique sentences that ignore the complexity of real-world CTI reports, which often leads to inflated performance results. Consequently, the baseline performance of open-source LLMs on complex unstructured CTI reports remains unevaluated. To address this gap, we constructed a ground-truth dataset of 2,076 human-annotated sentences (1,281 technique-positive, 795 negative) from 83 complex unstructured CTI reports. These sentences were mapped to 114 unique ATT&CK techniques using a six-phase annotation process, achieving \\k{appa} = 0.68 inter-annotator agreement. Using this dataset, we evaluated seven open-source LLMs ranging from 8B to 236B parameters across prompt strategy and temperature configurations. The highest-performing LLM achieved a micro-averaged F1 score of 0.22, establishing the empirical baseline for multi-label ATT&CK classification on complex unstructured CTI. Parameter size showed a statistically significant positive correlation with F1",
"summary": "Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human…",
"tags": [
"cs.CR",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Att / Multi-source / Cybersecurity",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00406",
"rank": 76,
"score": 810,
"rationale": "Decades of GPS broadcasts reveal military rekeying behavior; strong signals-intelligence result.",
"title": "Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks",
"url": "https://huggingface.co/papers/2606.14819",
"authors": "",
"description": "A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate communities. We show this in an agent-based model where N=240 learning agents on a community-structured network each post harmless, productive, or dangerous content, and a regulator removes or penalizes whatever a noisy classifier flags. Overall usefulness barely moves as the noise changes (one-way ANOVA, p=0.96): by aggregate measures, nothing looks wrong. The damage instead concentrates on these bridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. A governance loss (L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles under false-positive-heavy noise. Aggregate accuracy hides who is harmed, and the cheap quantity to audit is how many connections a user has (degree), a near-perfect proxy for the betweenness that defines a bridge (r=0.96).",
"summary": "A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00947",
"rank": 77,
"score": 810,
"rationale": "Government $2B equity stakes in quantum firms is a high-impact, strategically significant funding milestone.",
"title": "OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib",
"url": "https://arxiv.org/abs/2606.11144",
"authors": "Abhijoy Sarkar, Aarchi Singh Thakur",
"description": "Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories. We introduce OncoTraj, a public benchmark of 813 EGFR-mutant NSCLC patients receiving first-line osimertinib, harmonized from three real-world clinical-genomic sources: MSK-CHORD (672 patients), AACR Project GENIE BPC NSCLC (34 patients), and the FLAURA molecular-resistance supplement (107 patients). OncoTraj defines three locked tasks: (A) binary classification of progression by a fixed 12-month landmark, (B) regression of time-to-first-progression in days, and (C) six-class classification of the dominant resistance mechanism. We release the harmonized dataset, patient-level train/validation/test splits with an audited no-leakage guarantee, an open-source evaluation harness, and six reference baselines spanning a majority-class predictor, logistic regression, random forest, XGBoost, an LSTM, and a multi-task transformer. With v1's single-timepoint snapshot features, no task clears chance on clean within-source evaluation: the uniformity of this ceiling across every model class localizes the limit to the input modality (single-snapshot tissue NGS rather than serial ctDNA), not the algorithm. The benchmark does recover a reproducible literature-consistent association: TP53 co",
"summary": "Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark…",
"tags": [
"cs.LG",
"q-bio.GN",
"q-bio.QM",
"stat.AP"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Machine Learning",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01201",
"rank": 78,
"score": 810,
"rationale": "Foundational mechanistic result: proves activation steering leaves the prompt-realizable manifold, reshaping interpretability and safety practice.",
"title": "Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution",
"url": "https://huggingface.co/papers/2606.06492",
"authors": "daily curated papers",
"description": "Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolving codebases. We introduce Code2LoRA, a hypernetwork framework that generates repository-specific LoRA adapters, effectively injecting repository knowledge with zero inference-time token overhead. Code2LoRA supports two usage scenarios: Code2LoRA-Static converts a single repository snapshot into an adapter, suitable for comprehension of stable codebases; while Code2LoRA-Evo maintains an adapter backed by a GRU hidden state updated per code diff, suitable for active development of evolving codebases. To evaluate Code2LoRA against parameter-efficient fine-tuning baselines, we build RepoPeftBench, a benchmark of 604 Python repositories with two tracks: a static track with 40K training and 12K test assertion-completion tasks, and an evolution track with 215K commit-derived training and 87K commit-derived test tasks. On the static track, Code2LoRA-Static achieves 63.8% cross-repo and 66.2% in-repo exact match, matching the per-repository LoRA upper bound; on the evolution track, Code2LoRA-Evo achieves 60.3% cross-repo exact match (+5.2 pp over a single shared LoRA). Code2LoRA's code can be found at https://anonymous.4open.science/r/code2lora-6857; the model checkpoints and RepoPeftBenc",
"summary": "Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01636",
"rank": 79,
"score": 810,
"rationale": "High-impact AI safety finding: Mythos CVE rediscovery exposes contamination in frontier-model security claims.",
"title": "(https://www.minicor.com/)",
"url": "https://www.minicor.com/",
"authors": "",
"description": "Deploy self-healing computer use agents that adapt when UIs change. Zero to production in hours.",
"summary": "Deploy self-healing computer use agents that adapt when UIs change. Zero to production in hours.",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🗽 Hacker News",
"debrief_date": "2026-05-26 11:00",
"debrief_path": "debriefs/debrief-2026-05-26-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-26 11:00",
"debrief_path": "debriefs/debrief-2026-05-26-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-26T11:00:00Z"
},
{
"id": "item_01685",
"rank": 80,
"score": 810,
"rationale": "High impact: argues 90-day disclosure is obsolete under LLM-accelerated exploit timelines; strategic industry shift.",
"title": "LatentUMM: Dual Latent Alignment for Unified Multimodal Models",
"url": "https://huggingface.co/papers/2605.17766",
"authors": "Yinyi Luo, Wenwen Wang, Hayes Bai, Marios Savvides, Jindong Wang",
"description": "Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhibit functional inconsistency between these two capabilities. We observe that this issue does not stem from a lack of shared representations, but from the absence of explicit alignment between the transformations that map into and out of the latent space. As a result, generation and re-encoding can follow inconsistent trajectories, leading to semantic drift under modality transitions. In this work, we propose LatentUMM, a framework that constructs an enhanced shared latent space to explicitly align these transformations and improve cross-modal consistency. LatentUMM consists of two stages. First, dual latent alignment enforces consistency at both the modality and capacity levels: cross-modal alignment uses a stronger embedding model to impose structured cross-modal semantics, while dual capacity alignment enforces bidirectional consistency under generation and re-encoding. Second, latent dynamics stabilization improves robustness via stochastic latent rollouts and preference optimization, favoring trajectories that better preserve semantic consistency. Experiments show that LatentUMM consistently improves multimodal consistency across diverse architectures. Code is available at: https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/LatentUMM.",
"summary": "Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhibit functional inconsistency between these two capabilities. We observe that this issue d...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-25T16:00:00Z"
},
{
"id": "item_02246",
"rank": 81,
"score": 810,
"rationale": "Quantum kernel advantage evidence on medical embeddings; strong quantum-ML crossover result.",
"title": "AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation",
"url": "https://huggingface.co/papers/2605.13724",
"authors": "Yuchao Gu, Guian Fang, Yuxin Jiang, Weijia Mao, Song Han",
"description": "Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This limitation arises because consistency distillation replaces the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behavior of ODE sampling. To address this limitation, we introduce AnyFlow, the first any-step video diffusion distillation framework based on flow maps. Instead of distilling a model for only a few fixed sampling steps, AnyFlow optimizes the full ODE sampling trajectory. To this end, we shift the distillation target from endpoint consistency mapping (z_{t}rightarrow z_{0}) to flow-map transition learning (z_{t}rightarrow z_{r}) over arbitrary time intervals. We further propose Flow Map Backward Simulation, which decomposes a full Euler rollout into shortcut flow-map transitions, enabling efficient on-policy distillation that reduces test-time errors (i.e., discretization error in few-step sampling and exposure bias in causal generation). Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency-based counterparts in the few-step regime, while scaling with sampling step budgets.",
"summary": "Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any-step video diffusion. This…",
"tags": [
"cs.CV",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Vision & Gen AI",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02995",
"rank": 82,
"score": 810,
"rationale": "Fabricked breaks AMD SEV-SNP via Infinity Fabric misconfig; major confidential-computing vulnerability.",
"title": "SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts",
"url": "https://arxiv.org/abs/2604.26506",
"authors": "Yuan Xin, Yixuan Weng, Minjun Zhu, Ying Ling, Chengwei Qin, Michael Hahn, Michael Backes, Yue Zhang, Linyi Yang",
"description": "As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial instructions embedded in submissions to manipulate outcomes -- emerges as a critical threat to scholarly integrity. To counter this, we propose a novel adversarial framework where a Generator model, trained to create sophisticated attack prompts, is jointly optimized with a Defender model tasked with their detection. This system is trained using a loss ",
"summary": "As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial instructions…",
"tags": [
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_04100",
"rank": 83,
"score": 810,
"rationale": "AI Index 2025: authoritative landscape report on AI capability, adoption, governance, and impact.",
"title": "Artificial intelligence index report 2025",
"url": "https://arxiv.org/abs/2504.07139",
"authors": "",
"description": "Welcome to the eighth edition of the AI Index report. The 2025 Index is our most comprehensive to date and arrives at an important moment, as AI's influence across society, the economy, and global governance continues to intensify. New in this year's report are in-depth analyses of the evolving landscape of AI hardware, novel estimates of inference costs, and new analyses of AI publication and patenting trends. We also introduce fresh data on corporate adoption of responsible AI practices, along with expanded coverage of AI's growing role in science and medicine. Since its founding in 2017 as",
"summary": "Welcome to the eighth edition of the AI Index report. The 2025 Index is our most comprehensive to date and arrives at an important moment, as AI's influence across society, the economy, and global governance continues…",
"tags": [
"Google Scholar"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "Google Scholar",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_00112",
"rank": 84,
"score": 800,
"rationale": "ARM unified discrete-token autoregressive multimodal model is a significant architecture/result.",
"title": "STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability",
"url": "https://huggingface.co/papers/2606.19236",
"authors": "",
"description": "Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training. We conduct a first-order gradient analysis of token-level entropy dynamics under GRPO and identify a token-level credit assignment mismatch: the per-token entropy variation decomposes into the product of the trajectory-level advantage and an entropy sensitivity function over the next-token distribution, yielding an advantage-surprisal four-quadrant structure and a near-criticality property. Motivated by it, we propose STARE (Surprisal-guided Token-level Advantage Reweighting for policy Entropy stability), which identifies entropy-critical token subsets via batch-internal surprisal quantiles, selectively reweights their effective advantages, and incorporates a target-entropy closed-loop gate for stable entropy regulation. Across model scales from 1.5B to 32B and three task families (Short CoT, Long CoT, and Multi-Turn Tool Use), STARE sustains stable RL training over thousands of steps while maintaining policy entropy within the target band. On AIME24 and AIME25, STARE outperforms DAPO and other competitive baselines by 4%-8% in average accuracy, with reflection tokens and response length growing in tandem, indicating sustained exploration-exploitation balance that further unlocks RL training potential.Code is available at https://github.com/hp-luo/STARE.",
"summary": "Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from…",
"tags": [
"▲ 8",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T11:00:00Z"
},
{
"id": "item_00960",
"rank": 85,
"score": 800,
"rationale": "Spectral-shaping view of Muon (per-stage p choice) is a foundational theoretical + empirical result for optimizer design.",
"title": "What Do Deepfake Speech Detectors Actually Hear?",
"url": "https://arxiv.org/abs/2606.10912",
"authors": "Vojtěch Staněk, Veronika Jirmusová, Anton Firc, Kamil Malinka, Jakub Reš, Martin Perešíni",
"description": "Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or what cues drive the decision. We propose an audio-native explainability pipeline using Integrated Gradients on time-aligned self-supervised representations to localize decision evidence over time. We apply the proposed method to three WavLM-based detectors (AASIST, CA-MHFA, SLS) on ASVspoof 5 and manually annotate the highest-attribution regions to provide a semantic meaning of the most important cues. Despite similar performance, the detectors rely on different cues: AASIST emphasizes non-speech/environment cues, CA-MHFA focuses on localized phoneme artifacts, and SLS relies on word boundaries and spectral integrity. We move beyond speculative reasoning and validate our findings by causal masking of the primary detector cues. Observed performance degradation further supports the explained detector semantics.",
"summary": "Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or what cues drive the decision. We propose an…",
"tags": [
"cs.SD",
"cs.AI",
"cs.CR",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01051",
"rank": 86,
"score": 800,
"rationale": "First Apple M5 exploit found via Anthropic AI; major AI-for-security milestone.",
"title": "Parent-Hash DAG: A Cost Analysis of Constant-Time Append for On-Chain Registries",
"url": "https://arxiv.org/abs/2606.09593",
"authors": "Ian C. Moore, Fernando Paredes Garcia",
"description": "Provenance trees are append-only directed acyclic graphs of artifact registrations anchored on a public blockchain, recently introduced as the data substrate of operator-gated provenance infrastructure. Their defining data-structural pattern is a parent-hash directed acyclic graph (PHDAG), in which each append performs a constant number of storage writes to previously-untouched slots. This pattern has not previously been isolated as a standalone primitive, formally bounded with explicit constants, or benchmarked against the standard alternative, the incremental Merkle tree (IMT). We formalize PHDAG append as O(1) in gas cost, independent of registry size and tree depth, and develop a stochastic cost model for IMT in which per-insert cost is a random variable over the leaf index, deriving closed-form expressions for its mean and variance. We validate both analyses empirically on Base Sepolia across tree depths 1 to 25. PHDAG is observed to be depth-invariant at 76,276 gas (standard deviation about 6 gas), while IMT cost grows linearly with depth. The crossover below which IMT is cheaper falls far beneath the depths of every production registry surveyed. We further establish trustless registry reconstruction from public event logs in linear time with no off-chain dependency.",
"summary": "Provenance trees are append-only directed acyclic graphs of artifact registrations anchored on a public blockchain, recently introduced as the data substrate of operator-gated provenance…",
"tags": [
"cs.DC",
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01751",
"rank": 87,
"score": 800,
"rationale": "Prescriptive scaling laws for data-constrained training are highly impactful.",
"title": "Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps",
"url": "https://huggingface.co/papers/2605.16928",
"authors": "Yanke Zhou, Yiduo Li, Hanlin Tang, Maohua Li, Kan Liu",
"description": "Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show that full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation. Our approach is built on three observations: (1) only a small subset of attention heads truly requires full long-context processing; (2) long-range retrieval is governed primarily by a low-dimensional subspace, allowing relevant tokens to be retrieved efficiently with a 16-dimensional indexer; and (3) the useful token budget is strongly query-dependent, making dynamic top-p selection more suitable than fixed top-k sparsification. Based on these insights, we propose RTPurbo, which retains the full KV cache only for retrieval heads and introduces a lightweight token indexer for sparse attention. By exploiting the model's intrinsic sparsity, RTPurbo achieves sparsification with only a few hundred training steps. Experiments on long-context benchmarks and reasoning tasks show that RTPurbo preserves near-lossless accuracy while delivering substantial efficiency gains, including up to a 9.36times prefill speedup at 1M context and about a 2.01times decode speedup. These results suggest that strong sparse inference can be obtained from sta",
"summary": "Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show…",
"tags": [
"arXiv:2605.16928"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_02258",
"rank": 88,
"score": 800,
"rationale": "Viability-theoretic runtime governance for autonomous agents; principled AI safety framework.",
"title": "Fragnesia: New Linux Privilege Escalation Exploit",
"url": "https://github.com/v12-security/pocs/tree/main/fragnesia",
"authors": "",
"description": "poc it like it's hot. Contribute to v12-security/pocs development by creating an account on GitHub.",
"summary": "poc it like it's hot. Contribute to v12-security/pocs development by creating an account on GitHub.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "Lobste.rs",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_02563",
"rank": 89,
"score": 800,
"rationale": "Lazarus industrializing AI-driven attacks on developers is a landmark security/AI-safety signal.",
"title": "ActiveFlowMark: Assessing Tor Anonymity under Active Bandwidth Watermarking",
"url": "https://arxiv.org/abs/2605.05887",
"authors": "Zilve Fan, Zijian Zhang, Yangnan Guo, Jiaqi Gao, Zhen Li, Mengyu Wang, Chengxiang Si, Liehuang Zhu",
"description": "Low-latency anonymity networks such as Tor remain vulnerable to infrastructure-level traffic analysis that exploits side-channel information observable from encrypted communications. We introduce NATA, a non-invasive active traffic-correlation analysis algorithm that injects distinguishable throughput patterns into traffic flows through controlled bandwidth perturbations. Unlike passive correlation methods, NATA does not require endpoint compromise, Tor-browser modification, or packet-payload de",
"summary": "Low-latency anonymity networks such as Tor remain vulnerable to infrastructure-level traffic analysis that exploits side-channel information observable from encrypted…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T09:00:00Z"
},
{
"id": "item_02852",
"rank": 90,
"score": 800,
"rationale": "Scalable pre-silicon verification for masked PQC accelerators; FIPS-relevant, high impact.",
"title": "Learning while Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies",
"url": "https://huggingface.co/papers/2605.00416",
"authors": "",
"description": "Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of generalist Vision-Language-Action (VLA) policies. Starting from a pretrained VLA policy, LWD closes the loop between deployment, shared physical experience, policy improvement, and redeployment by using autonomous rollouts and human interventions collected across a robot fleet. To stabilize learning from heterogeneous, sparse-reward fleet data, LWD combines Distributional Implicit Value Learning (DIVL) for robust value estimation with Q-learning via Adjoint Matching (QAM) for policy extraction in flow-based VLA action generators. We validate LWD on a fleet of 16 dual-arm robots across eight real-world manipulation tasks, including semantic grocery restocking and 3--5 minute long-horizon tasks. A single generalist policy improves as fleet experience accumulates, reaching an average success rate of 95%, with the largest gains on long-horizon tasks.",
"summary": "Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, lon…",
"tags": [
"Huggingface_Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_03057",
"rank": 91,
"score": 800,
"rationale": "14–48% helpfulness collapse from trivial constraints; striking safety/robustness finding.",
"title": "Triton language for Huawei Ascend",
"url": "https://github.com/triton-lang/triton-ascend",
"authors": "",
"description": "Contribute to triton-lang/triton-ascend development by creating an account on GitHub.",
"summary": "Contribute to triton-lang/triton-ascend development by creating an account on GitHub.",
"tags": [
"lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "📰 News Headlines",
"group": "🔗 Lobste.rs",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_03828",
"rank": 92,
"score": 800,
"rationale": "Fabricked breaks AMD SEV-SNP via Infinity Fabric; high-impact confidential computing attack with serious implications.",
"title": "Fabricked: Misconfiguring Infinity Fabric to Break AMD SEV-SNP",
"url": "https://fabricked-attack.github.io/",
"authors": "",
"description": "Confidential computing allows cloud tenants to offload sensitive computations and data to remote resources without needing to trust the cloud service provider. Hardware-based trusted execution environments, like AMD SEV-SNP, achieve this by creating Confidential Virtual Machines (CVMs). With Fabricked, we present a novel software-based attack that manipulates memory routing to compromise AMD SEV-SNP. By redirecting memory transactions, a malicious hypervisor can deceive the secure co-processor (PSP) into improperly initializing SEV-SNP. This enables the attacker to perform arbitrary read and w",
"summary": "Confidential computing allows cloud tenants to offload sensitive computations and data to remote resources without needing to trust the cloud service provider. Hardware-based…",
"tags": [
"Lobste.rs",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "Lobste.rs",
"debrief_date": "2026-04-15 19:00",
"debrief_path": "debriefs/debrief-2026-04-15-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 19:00",
"debrief_path": "debriefs/debrief-2026-04-15-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T19:00:00Z"
},
{
"id": "item_04024",
"rank": 93,
"score": 798,
"rationale": "Bold training-free cross-model capability transfer via subspace alignment; high novelty and broad applicability.",
"title": "The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment",
"url": "https://huggingface.co/papers/2604.06377",
"authors": "",
"description": "We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model scales. We propose the Master Key Hypothesis, which states that model capabilities correspond to directions in a low-dimensional latent subspace that induce specific behaviors and are transferable across models through linear alignment. Based on this hypothesis, we introduce UNLOCK, a training-free and label-free framework that extracts a capability direction by contrasting activations between capability-present and capability-absent Source variants, aligns it with a Target model through a low-rank linear transformation, and applies it at inference time to elicit the behavior. Experiments on reasoning behaviors, including Chain-of-Thought (CoT) and mathematical reasoning, demonstrate substantial improvements across model scales without training. For example, transferring CoT reasoning from Qwen1.5-14B to Qwen1.5-7B yields an accuracy gain of 12.1% on MATH, and transferring a mathematical reasoning direction from Qwen3-4B-Base to Qwen3-14B-Base improves AGIEval Math accuracy from 61.1% to 71.3%, surpassing the 67.8% achieved by the 14B post-trained model. Our analysis shows that the success of transfer depends on the capabilities learned during pre-training, and that our intervention amplifies latent capabilities by sharpening the output distribution toward successful reasoning trajectories.",
"summary": "We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model scales. We propose the Master Key Hypothesis,…",
"tags": [
"2026-04-07",
"HuggingFace",
"Huggingface Papers",
"arXiv:",
"cs.AI",
"cs.LG",
"daily curated papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers (via HuggingFace)",
"group": "🧠 LLMs & Reasoning",
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": "",
"occurrence_count": 10,
"occurrences": [
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"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
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{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 19:00",
"debrief_path": "debriefs/debrief-2026-04-12-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 16:00",
"debrief_path": "debriefs/debrief-2026-04-12-16.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 04:00",
"debrief_path": "debriefs/debrief-2026-04-12-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 00:00",
"debrief_path": "debriefs/debrief-2026-04-12-00.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T19:00:00Z"
},
{
"id": "item_00708",
"rank": 94,
"score": 796,
"rationale": "LiveBrowseComp exposes IKD confound in search benchmarks; methodology + benchmark are both impactful.",
"title": "FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching",
"url": "https://huggingface.co/papers/2601.05212",
"authors": "",
"description": "Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which estimates an individual's biological brain age from MRI data. Effective BAP models require large, diverse, and age-balanced datasets, whereas existing 3D MRI datasets are demographically skewed, limiting fairness and generalizability. Acquiring new data is costly and ethically constrained, motivating generative data augmentation. Current generative methods are often based on latent diffusion models, which operate in learned low dimensional latent spaces to address the memory demands of volumetric MRI data. However, these methods are typically slow at inference, may introduce artifacts due to latent compression, and are rarely conditioned on age, thereby affecting the BAP performance. In this work, we propose FlowLet, a conditional generative framework that synthesizes age-conditioned 3D MRIs by leveraging flow matching within an invertible 3D wavelet domain, helping to avoid reconstruction artifacts and reducing computational demands. Experiments show that FlowLet generates high-fidelity volumes with few sampling steps. Training BAP models with data generated by FlowLet improves performance for underrepresented age groups, and region-based analysis confirms preservation of anatomical structures.",
"summary": "Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_00401",
"rank": 95,
"score": 790,
"rationale": "Hidden-state on-policy representation distillation; strong empirical+theoretical result.",
"title": "BadWorld: Adversarial Attacks on World Models",
"url": "https://huggingface.co/papers/2606.16519",
"authors": "",
"description": "Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to adversarial perturbations. Standard adversarial attacks fail to assess this vulnerability because attackers lack ground-truth future videos and cannot predict subsequent user controls. We introduce BadWorld, a label-free adversarial framework tailored for autoregressive VWMs that systematically overcomes both constraints. First, to bypass the need for future supervision, we propose a self-supervised velocity attack that directly disrupts the early denoising dynamics of the model. Second, to ensure the attack generalizes across unpredictable user actions, we formulate a trajectory-adaptive bi-level optimization that actively mines hard control sequences to forge control-agnostic perturbations. Evaluated on representative VWMs with continuous and discrete controls, BadWorld exposes severe structural fragility. Visually indistinguishable adversarial images reliably trigger catastrophic degradation in future rollouts, leading to incomplete denoising, structural collapse, and control inconsistency. These findings reveal critical risks for deploying VWMs in safety-critical systems while highlighting a practical mechanism for privacy protection.",
"summary": "Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to…",
"tags": [
"▲ 13",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00627",
"rank": 96,
"score": 790,
"rationale": "Alignment tampering via RLHF self-influence is a fundamental alignment/safety vulnerability finding.",
"title": "Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks",
"url": "https://arxiv.org/abs/2606.13621",
"authors": "Achraf Hsain, Sultan Almuhammadi",
"description": "Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the wrong product. The same automata-theoretic machinery -- specification compilation, product game construction, attractor computation, and winning-region extraction -- is better read as a design-time analytical instrument whose outputs are structural insights about a system rather than runtime constraints on a deployed agent. We instantiate this through a constrained two-player safety game for network defense. The two specifications are enforced asymmetrically: the defender specification defines the unsafe region of the game, whereas the attacker specification restricts the adversary's legal actions during attractor computation. Solving the game yields a defensibility verdict -- a formal certificate that a topology-specification pair is or is not defensible -- with the associated winning region and shield. Beyond the binary verdict, we derive topology-level metrics from the attractor structure and combine them with post-convergence behavior from shield-constrained adversarial multi-agent reinforcement learning. Together these form a defensibility fingerprint capturing both a network's formal safety properties and its operational behavior under adaptive play. A what-if analysis shows that formal defensibility and operational effectiveness capture distinct aspects of security: smal",
"summary": "Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata restricting an agent's actions. We argue this is the…",
"tags": [
"cs.AI",
"cs.CR",
"cs.GT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Beyond / Runtime / Enforcement",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_02465",
"rank": 97,
"score": 790,
"rationale": "Open problems in unlearning for AI safety is highly relevant, framing-critical, and timely.",
"title": "Advances in quantum learning theory with bosonic systems",
"url": "https://arxiv.org/abs/2605.08082",
"authors": "Francesco Anna Mele",
"description": "Reviews quantum learning theory for continuous-variable (bosonic/quantum-optical) systems, extending the theory previously developed only for finite-dimensional quantum systems.",
"summary": "Reviews quantum learning theory for continuous-variable (bosonic/quantum-optical) systems, extending the theory previously developed only for finite-dimensional quantum systems.",
"tags": [
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚛️ Quantum",
"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T11:00:00Z"
},
{
"id": "item_00678",
"rank": 98,
"score": 788,
"rationale": "High-impact security finding: protestware specifically targeting coding agent CI pipelines.",
"title": "WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces",
"url": "https://huggingface.co/papers/2606.09426",
"authors": "",
"description": "Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools. Existing benchmarks, however, often evaluate these interfaces as separable capabilities, leaving long-horizon cross-interface orchestration under-tested. Thus, we introduce WeaveBench, a long-horizon hybrid-interface benchmark with 114 tasks across 8 real-world work domains, grounded in real user requests and publicly verifiable artifacts. Each task requires agents to combine GUI observations/actions with CLI/code operations within a single trajectory. We evaluate these tasks on a real Ubuntu desktop inside deployed CLI-agent runtimes, augmented with a minimal desktop-control plugin. We also propose a companion trajectory-aware judge that inspects deliverables, files, screenshots, logs, and action traces, while detecting shortcut behaviors such as fabricated visual evidence or hard-coded metrics. Across frontier model-runtime pairings, the best PassRate reaches only 41.2%, showing the benchmark remains far from saturated. The trajectory-aware judge further reveals that outcome-only grading substantially overestimates agent performance. Overall, WeaveBench exposes a critical gap in CUA evaluation and provides an effective testbed to measure whether agents can orchestrate GUI, CLI, and code operations across long-horizon real-world tasks.",
"summary": "Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools. Existing…",
"tags": [
"▲ 50",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-12 04:00",
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"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_03503",
"rank": 99,
"score": 785,
"rationale": "Anthropic restricts Mythos access: landmark security-AI decision.",
"title": "Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers",
"url": "https://huggingface.co/papers/2604.17632",
"authors": "",
"description": "Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monolingual contexts. To bridge this critical disconnect, we present a holistic study dedicated to code-switching IR. We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), constructing a dataset via human annotation to capture the authentic naturalness of mixed-language queries. Our evaluation across statistical, dense, and late-interaction paradigms reveals that code-switching acts as a fundamental performance bottleneck, degrading the effectiveness of even robust multilingual models. We demonstrate that this failure stems from substantial divergence in the embedding space between pure and code-switched text. Scaling this investigation, we propose CS-MTEB, a comprehensive benchmark covering 11 diverse tasks, where we observe performance declines of up to 27%. Finally, we show that standard multilingual techniques like vocabulary expansion are insufficient to resolve these deficits completely. These findings underscore the fragility of current systems and establish code-switching as a crucial frontier for future IR optimization.",
"summary": "Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monol…",
"tags": [
"Huggingface Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 Huggingface Papers",
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T09:00:00Z"
},
{
"id": "item_00179",
"rank": 100,
"score": 782,
"rationale": "ALE is a major industry-grounded agent benchmark; high strategic value for agent evaluation.",
"title": "Mean-Payoff-Parity and Lifting Strategies from MDPs to 2-Player Stochastic Games",
"url": "https://arxiv.org/abs/2606.19324",
"authors": "Mohan Dantam, Richard Mayr",
"description": "We consider the strategy complexity (i.e., memory and randomization) of optimal strategies in turn-based 2-player zero-sum stochastic games. Results in [Gimbert,Kelmendi:2023] show how to lift optimal memoryless strategies for shift-invariant inverse-submixing objectives from MDPs to 2-player stochastic games with an exponential increase in the number of memory modes. We show the corresponding lower bound, i.e., the extra exponential memory is required in general, even for randomized strategies. Moreover, we solve the strategy complexity of the well-studied mean-payoff-parity objective in 2-player stochastic games. This objective is also shift-invariant inverse-submixing, but easier than the worst case for this class. In MDPs, Maximizer has optimal memoryless randomized strategies, while optimal deterministic strategies require exponential memory. However, in stochastic games, optimal randomized strategies require, at least and at most, linear memory (equal to the number of even colors). Finally, we show that a different construction for lifting memoryless (resp. finite-memory) deterministic strategies from MDPs (resp. 1-player games) to 2-player games cannot be generalized even to memoryless randomized strategies. We construct a shift-invariant objective where Max and Min each have optimal memoryless randomized strategies in all MDPs, but optimal (randomized) Max strategies still require infinite memory in deterministic 2-player games.",
"summary": "We consider the strategy complexity (i.e., memory and randomization) of optimal strategies in turn-based 2-player zero-sum stochastic games. Results in [Gimbert,Kelmendi:2023] show how to lift…",
"tags": [
"cs.GT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Mean-payoff-parity / Lifting / Strategies",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00661",
"rank": 101,
"score": 782,
"rationale": "Important empirical finding on multi-agent privacy contagion; novel Moltbook-scale simulation methodology.",
"title": "EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge",
"url": "https://huggingface.co/papers/2606.13120",
"authors": "",
"description": "Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing benchmarks such as BrowseComp rely on static knowledge, making them vulnerable to test-set contamination and parametric memorization. Consequently, models can achieve high scores through fact recall rather than genuine retrieval, obscuring true browsing competence via reasoning shortcuts.\n In this paper, we introduce EvoBrowseComp, an evolving benchmark of 400 English and 400 Chinese contamination-free complex questions synthesized via live-web traversal. To collect these questions, we design a three-agent collaborative framework: (1) a QA synthesis agent that retrieves fresh knowledge from the live web to synthesize QA pairs; (2) an information filtering agent that filters retrieved knowledge in terms of credibility and popularity to block parametric shortcuts; and (3) a high-level guidance agent that formalizes questions into reasoning graphs to reduce logical redundancy and shortcuts in synthesized QA pairs. Because the framework supports fully automated synthesis, EvoBrowseComp can be regularly updated to prevent data contamination and maintain temporal freshness. Extensive experiments confirm its great difficulty, requiring broad horizontal search. It establishes a scalable paradigm for auto-updatable, high-difficulty benchmarking that keeps pace with both evolving world knowledge and advancing agent capabilities.",
"summary": "Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing benchmarks such as BrowseComp rely…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
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],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00706",
"rank": 102,
"score": 781,
"rationale": "Chain-of-Evidence for autonomous research is a major verifiability contribution addressing real failure modes.",
"title": "DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models",
"url": "https://huggingface.co/papers/2606.05758",
"authors": "",
"description": "Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot generalization across diverse tasks, they are poorly suited for problems that require precise continuous outputs, such as localizing temporal boundaries of events or generating robotic control actions. To address this challenge, we propose DRIFT, a general framework for adapting pretrained VLMs to continuous decoding tasks. DRIFT combines a base predictor, which provides a coarse estimate of the target output, with a generative refinement module based on flow matching that iteratively improves the prediction. This residual formulation transforms the generative modeling problem from learning a global output distribution to modeling a localized residual distribution around a strong prior, substantially simplifying optimization. We evaluate DRIFT on both perception and planning tasks, including visual grounding and robotic control. Across multiple tasks and architectures spanning MLLMs, VLAs, and WAMs, DRIFT consistently outperforms a strong set of regression- and generative-based solutions.",
"summary": "Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_00004",
"rank": 103,
"score": 780,
"rationale": "Reveals surprising client-side surveillance architecture in ChatGPT via Cloudflare-React integration; high security/privacy impact.",
"title": "Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models",
"url": "https://openreview.net/forum?id=fectsEG2GU",
"authors": "",
"description": "Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of producing highly consistent multi-view images. However, due to the scarcity of synchronized multi-view video data, it remains challenging to adapt this paradigm to 4D generation directly. Despite that, the available video and 3D data are adequate for training video and multi-view diffusion models separately that can provide satisfactory dynamic and geometric pri",
"summary": "Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and…",
"tags": [
"OpenReview"
],
"source": "📝 OpenReview",
"type": "news",
"section": "🌐 Web Findings",
"group": "📝 OpenReview",
"debrief_date": "2026-06-19 19:00",
"debrief_path": "debriefs/debrief-2026-06-19-19.html",
"debrief_md": "",
"occurrence_count": 31,
"occurrences": [
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"debrief_date": "2026-06-19 19:00",
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"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
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"debrief_date": "2026-06-19 04:00",
"debrief_path": "debriefs/debrief-2026-06-19-04.html",
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{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-17 11:00",
"debrief_path": "debriefs/debrief-2026-06-17-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-16 19:00",
"debrief_path": "debriefs/debrief-2026-06-16-19.html",
"debrief_md": ""
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],
"sort_date": "2026-06-19T19:00:00Z"
},
{
"id": "item_02200",
"rank": 104,
"score": 780,
"rationale": "RPKI exploit chain enabling internet disconnection; high-impact infra vulnerability.",
"title": "Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling",
"url": "https://huggingface.co/papers/2605.12411",
"authors": "",
"description": "AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In such interactions, the counterpart's LLM, prompts, control logic, and rule-based fallbacks are hidden, while each decision can have monetary consequences. We ask whether an agent can predict an unfamiliar counterpart's next decision from a few interactions. To avoid real-world logging confounds, we study this problem in controlled bargaining and negotiation games, formulating it as target-adaptive text-tabular prediction: each decision point is a table row combining structured game state, offer history, and dialogue, while K previous games of the same target agent, i.e., the counterpart being modeled, are provided in the prompt as labeled adaptation examples. Our model is built on a tabular foundation model that represents rows using game-state features and LLM-based text representations, and adds LLM-as-Observer as an additional representation: a small frozen LLM reads the decision-time state and dialogue; its answer is discarded, and its hidden state becomes a decision-oriented feature, making the LLM an encoder rather than a direct few-shot predictor. Training on 13 frontier-LLM agents and testing on 91 held-out scaffolded agents, the full model outperforms direct LLM-as-Predictor prompting and game+text features baselines. Within this tabular model, Observer features contribute beyond the other ",
"summary": "AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant…",
"tags": [
"HuggingFace",
"cs.AI",
"cs.CL",
"cs.RO",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 7,
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{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
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{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-05-14 11:00",
"debrief_path": "debriefs/debrief-2026-05-14-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-14 11:00",
"debrief_path": "debriefs/debrief-2026-05-14-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-14 11:00",
"debrief_path": "debriefs/debrief-2026-05-14-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_00095",
"rank": 105,
"score": 780,
"rationale": "Target-SFT unifying framework for SFT target distributions is a strong conceptual contribution.",
"title": "Verified Detection and Prevention of Concurrency Anomalies in Multi-Agent Large Language Model Systems",
"url": "https://huggingface.co/papers/2606.17182",
"authors": "",
"description": "Multi-agent LLM systems share state through memory stores, vector indices, and tool registries. We model such sharing as long-running read-generate-write operations under deterministic-generation semantics -- the regime durable-execution engines enforce by deterministic replay -- and formalize four concurrency anomalies in TLA+: stale-generation, phantom-tool, causal-cascade, and tool-effect reordering, structural analogues of classical isolation anomalies, each with a TLC counter-example. The exclusion lattice over these anomalies is trivial; the contribution is the mechanically verified realizability and strict separation of one maximal chain within it, L_0 subsetneq cdots subsetneq L_4, to our knowledge the first machine-checked consistency hierarchy for such runtimes. A development of 274 Verus obligations (zero assume, zero admit; trust base: two structural axioms and a mutex correspondence) proves the detectors sound and complete against the specifications and each runtime its avoidance set. Three deployed Rust runtimes realize L0-L1 (pessimistic locking, serializable snapshot isolation, default-SI), each verified against stale-generation and refined to its state machine; L2-L4 are exec-mode-verified with dependency-free prevention twins (A3, A6, A2: 0/1000 versus 1000/1000), and L2 is run live across three model families (A3 prevented in all 120 retracted sessions). We reproduce a silent lost update in ByteDance's deer-flow, formalizing its fix as a verified L_0 to L_1",
"summary": "Multi-agent LLM systems share state through memory stores, vector indices, and tool registries. We model such sharing as long-running read-generate-write operations under…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
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"debrief_date": "2026-06-18 19:00",
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"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
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"debrief_date": "2026-06-18 04:00",
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"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
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}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_01586",
"rank": 106,
"score": 780,
"rationale": "Criminal hackers using AI to find major software flaws is landmark cybersecurity precedent.",
"title": "Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling",
"url": "https://huggingface.co/papers/2605.27030",
"authors": "Xinglin Wang, Hao Lin, Shaoxiong Feng, Peiwen Yuan, Yiwei Li",
"description": "Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing parallel TTS methods typically keep branches isolated during search: intermediate discoveries remain branch-private and cannot guide other branches in time. This information isolation causes substantial redundant exploration, as branches repeatedly rediscover information already found elsewhere and require more search steps to collect complete decision information needed to reach correct answers. To bridge this gap, we propose Collaborative Parallel Thinking (CPT), a training-free inference framework that enables search-time information sharing across parallel branches. CPT extracts compact intermediate information from ongoing branches, maintains a deduplicated query-level information pool, and broadcasts pool entries through the input context, allowing each branch in subsequent search steps to reuse discoveries made by other branches rather than rediscover the same information. Empirically, experiments on HMMT and AIME benchmarks show that CPT establishes a stronger accuracy--latency Pareto frontier than strong baselines across rollout budgets and model scales, highlighting search-time collaboration as an effective direction for efficient parallel TTS.",
"summary": "Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing parallel TTS methods typically keep…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🧠 LLMs & Foundation Models",
"debrief_date": "2026-05-27 09:00",
"debrief_path": "debriefs/debrief-2026-05-27-09.html",
"debrief_md": "",
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"debrief_date": "2026-05-27 09:00",
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"debrief_date": "2026-05-27 09:00",
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"sort_date": "2026-05-27T09:00:00Z"
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{
"id": "item_01798",
"rank": 107,
"score": 780,
"rationale": "Dirty Frag universal Linux LPE is high-impact kernel exploit.",
"title": "MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems",
"url": "https://huggingface.co/papers/2605.18565",
"authors": "Hyunji Lee, Justin Chih-Yao Chen, Joykirat Singh, Zaid Khan, Elias Stengel-Eskin",
"description": "Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple pieces of information. However, existing benchmarks focus on static, independent recall and fail to capture these dynamic interactions between evolving memories. In this paper, we study how current memory-augmented agents perform in realistic, interference-heavy, long-horizon settings across diverse domains and question types. We introduce MINTEval (Long-Horizon Memory under INTerference Evaluation), a benchmark featuring (1) long, highly interconnected contexts with frequently updated information that induces substantial interference, (2) diverse domains (state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits), enabling evaluation of domain generalization, and (3) diverse question types that assess robustness to interference, including (i) single-target recall tasks requiring retrieval of a specific target from long contexts, and (ii) multi-target aggregation tasks requiring reasoning over multiple relevant pieces of information. Overall, MINTEval has 15.6k question-answering pairs over long-horizon contexts averaging 138.8k tokens and extending up to 1.8M tokens per instance. We evaluate 7 representative systems, including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks. Across all systems, we observe consistently low performance (avg.",
"summary": "Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple…",
"tags": [
"HUGGINGFACE",
"cs.AI",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔬 Agents & Reasoning",
"debrief_date": "2026-05-22 04:00",
"debrief_path": "debriefs/debrief-2026-05-22-04.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-22 04:00",
"debrief_path": "debriefs/debrief-2026-05-22-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-21 16:00",
"debrief_path": "debriefs/debrief-2026-05-21-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T04:00:00Z"
},
{
"id": "item_02163",
"rank": 108,
"score": 780,
"rationale": "Practical privacy leak in widely deployed dynamic quantization; affects real serving stacks.",
"title": "MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models",
"url": "https://huggingface.co/papers/2605.14906",
"authors": "2605.14906 | Xiyu Ren, Zhaowei Wang, Yiming Du, Zhongwei Xie et al.",
"description": "MemLens benchmarks 27 LVLMs and 7 memory-augmented agents across 789 questions, finding long-context models degrade with conversation length while memory agents lose visual fidelity.",
"summary": "MemLens benchmarks 27 LVLMs and 7 memory-augmented agents across 789 questions, finding long-context models degrade with conversation length while memory agents lose visual fidelity.",
"tags": [
"HF",
"HUGGINGFACE_PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-15T04:00:00Z"
},
{
"id": "item_04348",
"rank": 109,
"score": 780,
"rationale": "ClawBench: 153 live-site agent tasks; strong agentic eval benchmark.",
"title": "ClawBench: Can AI Agents Complete Everyday Online Tasks?",
"url": "https://huggingface.co/papers/2604.08523",
"authors": "",
"description": "AI agents may be able to automate your inbox, but can they automate other routine aspects of your life? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework of 153 simple tasks that people need to accomplish regularly in their lives and work, spanning 144 live platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require demanding capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and challenges of real-world web interaction. A lightweight interception layer captures and blocks only the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 7 frontier models show that both proprietary and open-source models can complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%. Progress on ClawBench brings us closer to AI agents that can function as reliable general-purpose assistants.",
"summary": "arXiv:2604.08523 | Authors: Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, Junwen Miao | Upvotes: 98",
"tags": [
"Huggingface Papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
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"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00118",
"rank": 110,
"score": 780,
"rationale": "OpenPCC confidential LLM serving on commodity TEEs addresses major deployable privacy concern.",
"title": "iOSWorld: A Benchmark for Personally Intelligent Phone Agents",
"url": "https://huggingface.co/papers/2606.09764",
"authors": "",
"description": "A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device, not just follow isolated instructions in an impersonal sandbox. Existing mobile agent benchmarks lack this kind of personalization. We introduce iOSWorld, the first interactive native iOS simulator benchmark built around a persistent user identity spanning 26 newly built iOS apps. These apps contain connected data such as transactions, messages, travel records, social relationships, and financial activity. iOSWorld includes 133 tasks across three increasingly difficult categories. Single-app tasks (27) test one app, multi-app tasks (60) span 2 to 8 apps, and memory and personalization tasks (46) require agents to infer patterns from personal data. We evaluate frontier and open-source computer-use models in both vision-only and privileged vision+XML settings. The best configuration reaches 52\\% overall but only 37\\% on multi-app tasks. Privileged vision+XML access improves frontier models by up to 26 percentage points, while smaller models do not benefit from added accessibility-tree input. We release iOSWorld as an open-source benchmark with all apps, seeded data, tasks, rubrics, and evaluation code.",
"summary": "A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device, not just follow isolated…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T11:00:00Z"
},
{
"id": "item_00184",
"rank": 111,
"score": 780,
"rationale": "Default Ubuntu lxd-group → root chain is a real, high-impact local privesc with full-chain PoC; relevant to security practitioners.",
"title": "Spectral Functions of Lorentzian Quantum Gravity",
"url": "https://arxiv.org/abs/2606.19321",
"authors": "Gabriel Assant, Daniel F. Litim, Manuel Reichert",
"description": "We compute spectral functions of graviton modes in Lorentzian quantum gravity, interpolating between classical general relativity and an asymptotically safe ultraviolet fixed point. Using functional renormalisation adapted for theories in Lorentzian signature, and enhanced by new symmetry conditions to account for underlying Ward identities, we derive and solve flow equations directly for the Källén-Lehmann representation of propagators. Consistent results are found for several sets of renormalisation conditions yielding normalisable spectral functions for the graviton and the scalar graviton mode, in agreement with effective theory in the infrared. We further calculate the full quantum effective action to quadratic order in curvature, extract graviton-induced form factors, and discuss implications for unitarity of quantum gravity.",
"summary": "We compute spectral functions of graviton modes in Lorentzian quantum gravity, interpolating between classical general relativity and an asymptotically safe ultraviolet fixed point. Using functional…",
"tags": [
"hep-th",
"gr-qc"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Spectral / Functions / Lorentzian",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00200",
"rank": 112,
"score": 780,
"rationale": "DRPO fixes a known PPO/GRPO flaw (hard-mask gradient discard); strong, central result for LLM RL post-training.",
"title": "Reinforcing Dual-Path Reasoning in Spatial Vision Language Models",
"url": "https://huggingface.co/papers/2606.17539",
"authors": "",
"description": "Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging. Moreover, different spatial queries call for fundamentally different strategies: some are best addressed through purely linguistic, step-by-step deduction, while others require explicit 3D grounding before quantitative inference. We present Dual-Path Spatial Reasoning via Reinforcement Learning for Spatial VLMs (SR-REAL), a unified framework that equips a spatial VLM with two complementary reasoning paths: Language-Only Reasoning (LOR), which performs step-by-step linguistic deduction, and Detect-Then-Reason (DTR), which detects 3D geometric cues (e.g., centers or bounding boxes) via region tokens before explicit geometric inference. SR-REAL begins with a cold-start supervised fine-tuning stage that constructs LOR and DTR chain-of-thought supervision and exposes a region-to-3D interface, followed by RL that optimizes the policy model with accuracy and format rewards; for DTR, a discrete center-based detection reward further refines geometric alignment. Across diverse spatial benchmarks, SR-REAL significantly outperforms spatial VLM baselines: (i) a single RL-trained model supports both reasoning paths, with DTR excelling in region-aware tasks through precise 3D localization and LOR enhancing general spatial reasoning; (ii) jointly training both paths fosters mutual reinforcement; (iii)",
"summary": "Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains…",
"tags": [
"▲ 11",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00242",
"rank": 113,
"score": 780,
"rationale": "Practical KV-cache breakthrough for ultra-long context; high deployment impact.",
"title": "ACL 2026 first author with weak GPA. How should I approach PhD applications? [D]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u8bp65/acl_2026_first_author_with_weak_gpa_how_should_i/",
"authors": "",
"description": "Hi everyone, I have a fairly weak undergraduate: a 3.3/5 GPA in Computer Engineering from an average Nigerian university. For my Master's, I studied Artificial Intelligence at an average European university, where I finished with an 8/10 GPA. A condensed version of my Master's thesis was recently accepted at ACL 2026, with a meta-review score of 8/10 and a confidence score of 5/5. It's scheduled for presentation next month. I want to pursue a PhD focused on expanding linguistic resources for low-resource African languages. I know my weak undergrad GPA and the relatively unknown reputation of m",
"summary": "Hi everyone, I have a fairly weak undergraduate: a 3.3/5 GPA in Computer Engineering from an average Nigerian university. For my Master's, I studied Artificial Intelligence at an…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-17 11:00",
"debrief_path": "debriefs/debrief-2026-06-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 11:00",
"debrief_path": "debriefs/debrief-2026-06-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T11:00:00Z"
},
{
"id": "item_00258",
"rank": 114,
"score": 780,
"rationale": "Curvature explanation of Muon vs Adam; foundational optimizer analysis with broad impact.",
"title": "Sign-Rank, Index, and List Replicability: Connections and Separations",
"url": "https://arxiv.org/abs/2606.18236",
"authors": "Ari Blondal, Hamed Hatami, Pooya Hatami, Chavdar Lalov, Sivan Tretiak",
"description": "In learning theory, the sign rank of a binary concept class captures the smallest dimension in which it can be represented by points and halfspaces. Despite tremendous interest, lower bounds on sign rank are notoriously difficult to come by. Two recent approaches to the problem establish lower bounds on sign rank by measures that are easier to analyze: the $\\mathbb{Z}_2$-index and the list replicability number. We order these measures, showing that the $\\mathbb{Z}_2$-index is upper-bounded by a linear function of the list replicability number. As a main consequence, we obtain a strong separation between sign rank and $\\mathbb{Z}_2$-index, thereby resolving a question of Frick, Hosseini, and Vasileuski. This motivates a thorough study of list replicability, the stronger of the two lower-bounding measures. We establish upper bounds on the list replicability number by two combinatorial measures: height and minimum star number. We also prove a fundamental composition result, showing that the product of two concept classes has list replicability number bounded by the sum of the list replicability numbers of the two classes.",
"summary": "In learning theory, the sign rank of a binary concept class captures the smallest dimension in which it can be represented by points and halfspaces. Despite tremendous interest, lower bounds on sign…",
"tags": [
"cs.LG",
"cs.IT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Sign-rank / Index / List",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00363",
"rank": 115,
"score": 780,
"rationale": "AURA addresses agentic re-identification threat model; timely, relevant to LLM privacy/safety.",
"title": "The Importance of Phase in Neural Representations: An Internal Oppenheim-Lim Test of Image Classifiers",
"url": "https://arxiv.org/abs/2606.17037",
"authors": "Alper Yıldırım",
"description": "Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their Fourier phase alone, while the magnitude carries little of their identity. We ask whether trained image classifiers reproduce this asymmetry inside their hidden layers, and we test it causally: given two images, we transplant the phase of one onto the magnitude of the other at a chosen layer and record which image the prediction follows. In PRISM2D, GFNet, and ViT-B/16 the prediction follows the phase or sign donor, and deleting all image-specific magnitude barely moves accuracy, so identity rides on phase while image-specific magnitude is largely dispensable to the readout. ResNet-50 at first seems to break the pattern, because transplanting sign after its ReLUs does nothing; a fair intervention before the ReLU reveals a strong latent sign code in the late blocks, and a DC-only control shows the readout consumes a channel-wise spatial average. Controls rule out the trivial case in which magnitude simply stops depending on the image. The architectures therefore share a phase/sign identity code but expose it in different bases, set by rectification and readout geometry, which gives a mechanistic account of the texture--shape gap between CNNs and attention models.",
"summary": "Oppenheim and Lim (1981) showed that natural images stay recognizable when reconstructed from their Fourier phase alone, while the magnitude carries little of their identity. We ask whether trained…",
"tags": [
"cs.CV",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Importance / Phase / Representations",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00456",
"rank": 116,
"score": 780,
"rationale": "Systematic pressure-test of deception probes across Gemma 3 sizes; landmark AI-safety eval.",
"title": "krahets/hello-algo",
"url": "https://github.com/krahets/hello-algo",
"authors": "",
"description": "《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持简中、繁中、English、日本語，提供 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 等代码实现",
"summary": "《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持简中、繁中、English、日本語，提供 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 等代码实现",
"tags": [
"⭐ 126.8k",
"GitHub Trending"
],
"source": "💻 GitHub Trending",
"type": "news",
"section": "🌐 Web Findings",
"group": "💻 GitHub Trending",
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T11:00:00Z"
},
{
"id": "item_00542",
"rank": 117,
"score": 780,
"rationale": "Directly addresses persistent prompt-injection backdoors in agentic harnesses; high relevance to AI safety/agents.",
"title": "Why do frontier AI labs send so many people to conferences? [D]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u67koz/why_do_frontier_ai_labs_send_so_many_people_to/",
"authors": "",
"description": "Recent years I see plenty of folks from OpenAI and Anthropic attending conferences like ICML/Neurips, yet obviously few are presenting. Are they mainly recruiting? Following emerging research? Curious if anyone with firsthand experience can shed some light on how attendance is justified internally and what the main objectives usually are. submitted by /u/snekslayer [link] [comments]",
"summary": "Recent years I see plenty of folks from OpenAI and Anthropic attending conferences like ICML/Neurips, yet obviously few are presenting. Are they mainly recruiting? Following…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00803",
"rank": 118,
"score": 780,
"rationale": "NSA cybersecurity guidance on MCP — high authoritative impact for AI-driven automation security.",
"title": "Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay",
"url": "https://huggingface.co/papers/2606.11786",
"authors": "",
"description": "Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demonstrate that our model, named Lius, yields notable improvements over standard instruction-tuned models by outperforming 4-6 points, and surpassing both Neural Machine Translation (NMT) and Multilingual LLM models by 10-13 points on several evaluation metrics. These findings highlight the potential of our approach to mitigate the reliance on large-scale parallel data in low-resource language translation.",
"summary": "Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00761",
"rank": 119,
"score": 780,
"rationale": "MiniMax-M2 frontier MoE with agentic RL infra; high-impact flagship model release.",
"title": "Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots",
"url": "https://arxiv.org/abs/2606.12378",
"authors": "Zhi Wei Xu, Torbjörn E. M. Nordling",
"description": "Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments. Remote photoplethysmography (rPPG) enables non-contact heart-rate (HR) estimation from an RGB camera, making it a promising sensing modality for robot-mounted vision systems. However, illumination variation remains a major barrier to robust deployment. This paper presents an end-to-end spatial-temporal transformer framework for remote HR estimation on a new dataset with varied illumination. Our estimator integrates PRNet-based 3D face alignment, clip-level illumination augmentation, the Residual Temporal Standardization Module, and controlled hybrid temporal-frequency supervision. The training objective combines a Soft-Shifted Pearson waveform loss with a spectral Kullback-Leibler divergence loss, where a tuned weight ($\\mathbfβ$) controls the contribution of frequency-domain heart-rate guidance. Experiments on a static all-level mix protocol covering three illumination levels show that $\\mathbfβ=5$ provides the strongest result among the tested beta settings, achieving a best-run HR mean absolute error (MAE) of 0.79 bpm and an HR correlation of 0.982. Compared with the PhysFormer baseline evaluated on our dataset, our estimator reduces HR MAE by 93.6 %, while increasing HR correlation from 0.088 to 0.982, making it usable when illumination varies.",
"summary": "Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments. Remote photoplethysmography (rPPG) enables non-contact heart-rate…",
"tags": [
"cs.CV",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Illumination-robust / Camera-based / Heart-rate",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
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"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00919",
"rank": 120,
"score": 780,
"rationale": "Optimizer-induced spectral scaling laws (Muon vs AdamW, 2.3x exponent gap) is a foundational finding for LM training.",
"title": "I'm brand new to running LLMs and the sheer number of tools is overwhelming",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u1pi1w/im_brand_new_to_running_llms_and_the_sheer_number/",
"authors": "",
"description": "Hey everyone. I'm brand new to running LLMs in general, even more new to running them locally, and the sheer number of tools available is absolutely overwhelming. Regarding applications, I look at github and see so many different options that I don't know what to pick. Can't really fully decipher the differences between the tools either, mostly because their descriptions/taglines are filled with so many AI buzzwords. What's the go-to GUI for Windows? The built-in ollama GUI seems like it's pretty barebones. Regarding model differences like between qwen vs gemma, is there a resource that shows",
"summary": "Hey everyone. I'm brand new to running LLMs in general, even more new to running them locally, and the sheer number of tools available is absolutely overwhelming. Regarding…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_00938",
"rank": 121,
"score": 780,
"rationale": "Mass GitHub CI-workflow backdooring is a high-impact supply-chain security finding with broad agent/coding relevance.",
"title": "Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques",
"url": "https://arxiv.org/abs/2606.11140",
"authors": "Guido Di Federico, Wenchao Teng, Louis J. Durlofsky",
"description": "Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism. Latent diffusion models (LDMs) provide efficient mappings from high-dimensional geological model space to a low-dimensional latent variable, reducing the dimensionality of the inverse problem while maintaining plausibility in posterior geomodels. However, the high nonlinearity in the LDM mapping may degrade the performance of Kalman-gain-based ensemble updates. We present a systematic comparison of DA algorithms applied to large-scale 3D channelized geomodels with hierarchical geological uncertainty. We compare model-space and latent-space DA using the ensemble smoother with multiple data assimilation (ESMDA), and demonstrate a key trade-off: model-space updates achieve significant uncertainty reduction but produce geologically unrealistic posterior models, while latent-space updates preserve realism but exhibit limited uncertainty reduction. Motivated by this, we explore rigorous Markov chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) algorithms in the 3D-LDM latent space. To accommodate their high computational demands, we develop a fast surrogate flow model that approximates well-rate responses. MCMC and SMC are evaluated against ESMDA across three synthetic test cases, with DA performed in the LDM latent space. All models maintain geological realism due to the LDM parameterization. MCMC and SMC are consist",
"summary": "Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism. Latent diffusion models (LDMs) provide…",
"tags": [
"physics.geo-ph",
"cs.AI",
"cs.LG",
"stat.AP"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Artificial Intelligence",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01034",
"rank": 122,
"score": 780,
"rationale": "Mechanistic backdoor circuit analysis in 8B model: strong AI safety contribution.",
"title": "SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation",
"url": "https://arxiv.org/abs/2606.09774",
"authors": "Matthew Ho, Brian Liu, Jixuan Chen, Audrey Wang, Lianhui Qin",
"description": "Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study simulator setup as a problem of agent-tool interface grounding: what minimal simulator-specific adaptations are needed for an off-the-shelf coding agent to operate real scientific software? Our intuition is that coding agents already know how to navigate files, edit code, run commands, and repair outputs, but they lack the simulator's executable contract: its vocabulary, structural constraints, validation rules, and termination conditions. We introduce SIGA, a Simulator-Interface Grounding Adapter that supplies this contract through retrieval, procedural memory, in-trajectory validation, and validation-enforced termination. We primarily evaluate SIGA on GEOS, an open-source multiphysics simulator used in subsurface science. SIGA produces a complete GEOS deck in about five minutes with TreeSim above 0.90, matching an extended-budget human expert who took about three hours, a roughly 36x wall-clock speedup. On a harder held-out set, grounding raises TreeSim from 0.720 to 0.789, a roughly 10% relative gain over the bare agent, and can reduce the across-seed standard deviation by 16x. Self-evolution further improves SIGA by rewriting adapter contents from prior trajectories, yielding the highest held-out GEOS mean and matching or outperforming the strongest hand-designed configuration. Tra",
"summary": "Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study…",
"tags": [
"cs.AI",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Artificial Intelligence",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01059",
"rank": 123,
"score": 780,
"rationale": "AI-assisted CVE surge thesis: high strategic relevance to AI security.",
"title": "Notes on gamma invariants of finite dimensional Hopf algebras",
"url": "https://arxiv.org/abs/2606.09069",
"authors": "Xinru Zhang, Libin Li, Yinhuo Zhang",
"description": "Let $H$ be a finite-dimensional, non-semisimple Hopf algebra over an algebraically closed field $\\mathbf{k}$. This paper investigates the asymptotic behavior of the core of left $H$-modules through the lens of the gamma invariant $γ_{\\mathfrak{X}}$ relative to a representation ideal $I_{\\mathfrak{X}}$. We establish an equivalent characterization for the quotient of the Green ring $R_{\\mathfrak{X}}$ to be a transitive fusion ring, demonstrating that transitivity is synonymous with the non-degeneracy of a naturally induced bilinear form and the collapse of the ideals $P_{+}$, $P_{-}$ and $I_{\\operatorname{max}}$ into a single ideal. Furthermore, we prove that the Green ring exhibits the structure of a representation ring in the sense of Benson, provided that the square of the antipode is an inner automorphism and the equality $I_{\\operatorname{max}}=I_{\\operatorname{proj}}$ holds. As an explicit application of these frameworks, we analyze the Drinfeld double $D(H_4)$ of the Sweedler algebra, identifying an infinite family of distinct representation ideals and proving that the maximal gamma invariant $γ_{\\operatorname{max}}$ induces a genuine ring homomorphism. Finally, for Hopf algebras of finite representation type under the assumption $P_{+} = P_{-} = I_{\\operatorname{max}}$, we show that $γ_{\\operatorname{max}}$ coincides precisely with the Frobenius--Perron dimension, and we explicitly compute the gamma invariants for the standard basis elements of the Green ring of the Taf",
"summary": "Let $H$ be a finite-dimensional, non-semisimple Hopf algebra over an algebraically closed field $\\mathbf{k}$. This paper investigates the asymptotic behavior of the core of left $H$-modules through…",
"tags": [
"math.QA",
"math.RT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Cryptography",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01234",
"rank": 124,
"score": 780,
"rationale": "Alleged Microsoft BitLocker backdoor is a major cryptography/integrity claim worth tracking.",
"title": "Unsupervised Skill Discovery for Agentic Data Analysis",
"url": "https://huggingface.co/papers/2606.06416",
"authors": "daily curated papers",
"description": "Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone. We propose DataCOPE, an unsupervised verifier-guided skill discovery framework for data-analytic agents. DataCOPE derives verifier signals from the exploration trajectories and uses them to characterize relative quality or aggreement among trajectories. It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation. For report-style analysis, we instantiate the verifier as an Adaptive Checklist Verifier that derives task-specific criteria, scores reports by verifiable coverage, and iteratively refines the checklist. For reasoning-style analysis, we instantiate it as an Answer Agreement Verifier that groups trajectories by answer agreement and uses self-consistency as an auxiliary signal. We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep. Across both settings, DataCOPE consistently improves held-out performance over baselines. Averaged across four model setting",
"summary": "Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01207",
"rank": 125,
"score": 780,
"rationale": "Hardware-adaptive MLA variant with dual decoding paths; strong practical impact for efficient LLM serving.",
"title": "Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?",
"url": "https://huggingface.co/papers/2606.04811",
"authors": "daily curated papers",
"description": "Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when their generated videos leave the screen and enter reality? We propose robotic manipulation as a concrete, measurable window onto this question: if a model has truly internalized physical laws, the motion it depicts should translate into executable robot behavior. We introduce Dream.exe, an evaluation framework that operationalizes this criterion through a video-to-execution pipeline. Given a scene image and a task description, Dream.exe synthesizes a manipulation video, converts the generated motion into robot trajectories, and executes them in a physics simulator, yielding a grounding signal that purely visual metrics cannot offer. Using this pipeline, we evaluate 8 models spanning frontier closed-source generators, open-source generators, and robot-specific models. Our benchmark covers 101 manually curated manipulation tasks at three levels of physical complexity, measured across visual quality, trajectory fidelity, and execution success. Encouragingly, several models achieve measurable execution success, suggesting that generative priors learned from internet-scale data already encode meaningful physical knowledge. Yet visual quality proves a poor predictor of executability, exposing a dimension of model capability that standard visual e",
"summary": "Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01270",
"rank": 126,
"score": 780,
"rationale": "OS-style security framing for LLM agents is a high-impact conceptual lens; connects agent capabilities to established security primitives.",
"title": "AutoMedBench: Towards Medical AutoResearch with Agentic AI Models",
"url": "https://huggingface.co/papers/2606.01961",
"authors": "",
"description": "Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering. However, existing medical agent benchmarks primarily evaluate final outputs, providing limited visibility into agent behavior within the research process. To address this gap, we present AutoMedBench, a workflow-aware benchmark for autonomous medical-AI research across diverse medical imaging and multimodal inference tasks, organizing agent execution into a unified five-stage workflow (S1-S5): Plan, Setup, Validate, Inference, and Submit. It comprises long-horizon tasks with each run averaging 33 agent turns, spanning five research tracks: segmentation, image enhancement, visual question answering (VQA), report generation, and lesion detection. Each task is evaluated under two difficulty tiers, Lite and Standard, which use the same data and metrics but differ in the amount of task-brief scaffolding, and each run is scored using both final task performance and S1-S5 stage scores, enabling stage-level analysis from the initial task brief to the final submitted artifact. Across thousands of recorded runs, stage-level scoring reveals that Validate is the weakest workflow stage on average, whereas Setup is the strongest, suggesting that current agents are better at making pipelines executable than at verifying their reliability. Post-run error analysis further shows that verification and submission failu",
"summary": "Autonomous agents are increasingly expected to support end-to-end medical-AI research workflows, moving beyond isolated prediction tasks or short-form clinical question answering. However, existing medical agent…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01346",
"rank": 127,
"score": 780,
"rationale": "Linux kernel 0-day enabling root file theft via ptrace bypass; high-impact real-world security vulnerability.",
"title": "On Reading SRAMs in IR Images, and Establishing Bounds on Trust",
"url": "https://www.bunniestudios.com/blog/2026/on-reading-srams-in-ir-images-and-establishing-bounds-on-trust/",
"authors": "Lobste.rs • security • 2026-05-31",
"description": "Last month’s name that ware demonstrates that even though non-destructive IR imaging is not capable of resolving an individual bit cell, at least at 22nm it is still possible to constrain the number of bits in an SRAM macro.",
"summary": "Last month’s name that ware demonstrates that even though non-destructive IR imaging is not capable of resolving an individual bit cell, at least at 22nm it is still possible to constrain the number of bits in an SRAM macro …",
"tags": [
"Lobste.rs",
"security",
"2026-05-31"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-01T04:00:00Z"
},
{
"id": "item_01423",
"rank": 128,
"score": 780,
"rationale": "Critical NGINX RCE via 18-year-old heap overflow; high-impact real-world vulnerability.",
"title": "bpK#: Delegatable Pseudonyms And Their Applications to National eID Systems",
"url": "https://arxiv.org/abs/2605.30212",
"authors": "Stephan Krenn, Doryan Lesaignoux, Sebastian Ramacher",
"description": "This item focuses on bpk#: delegatable pseudonyms and their applications to national eid systems within the security and cryptography landscape. It matters because defensive practice is being reshaped by both AI-native tooling and the coming post-quantum transition.",
"summary": "This item focuses on bpk#: delegatable pseudonyms and their applications to national eid systems within the security and cryptography landscape …",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-30T04:00:00Z"
},
{
"id": "item_01492",
"rank": 129,
"score": 780,
"rationale": "Claude Code RCE via deeplink/settings injection is a high-impact disclosed vulnerability in major AI coding tool.",
"title": "Parallax: Parameterized Local Linear Attention for Language Modeling",
"url": "https://huggingface.co/papers/2605.29157",
"authors": "Yifei Zuo, Dhruv Pai, Zhichen Zeng, Alec Dewulf, Shuming Hu",
"description": "Large Language Models (LLMs) have become the central paradigm in artificial intelligence, yet the core computational primitive of attention has remained structurally unchanged. Local Linear Attention (LLA) is an attention mechanism derived from nonparametric statistics in the test-time regression framework. In contrast to prior research on efficient attention variants, LLA upgrades the local constant estimate in softmax attention to a local linear estimate, yielding provably superior bias-variance tradeoffs for associative memory. However, LLA has not been scaled in LLM pretraining due to computational and numerical stability concerns. We introduce Parallax, a parameterized Local Linear Attention that is scalable for LLMs. Parallax eliminates the numerical solver in LLA and learns an extra query-like projector that probes the KV covariance. We place Parallax within a family of attention mechanisms connected by the bandwidth, the probe construction and the affine structure. We propose a hardware-aware algorithm that increases the arithmetic intensity over FlashAttention, shifting attention into a more compute bound regime. Our prototype decode kernel matches or outperforms FlashAttention 2/3 across diverse batch sizes and context lengths. We pretrain Parallax at 0.6B and 1.7B scales and find consistent perplexity improvements throughout pretraining with gains that transfer to downstream benchmarks. The advantage persists under both parameter-matched and compute-matched control",
"summary": "Large Language Models (LLMs) have become the central paradigm in artificial intelligence, yet the core computational primitive of attention has remained structurally unchanged …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-05-27"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-29T19:00:00Z"
},
{
"id": "item_01683",
"rank": 130,
"score": 780,
"rationale": "High impact: Claude browser extension hijack flaw exposes systemic agentic-extension security risk.",
"title": "Encyclical Letter of His Holiness Leo XIV Magnifica Humanitas",
"url": "http://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html",
"authors": "2026-05-25",
"description": "Pope Leo XIV's encyclical 'Magnifica Humanitas' addresses safeguarding the human person in the age of artificial intelligence, covering themes of human dignity, AI ethics, and governance from the Vatican.",
"summary": "Pope Leo XIV's encyclical 'Magnifica Humanitas' addresses safeguarding the human person in the age of artificial intelligence, covering themes of human dignity, AI ethics, and governance from the Vatican.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🦞 Lobste.rs",
"group": "⚡ Machine Learning",
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-26T04:00:00Z"
},
{
"id": "item_01877",
"rank": 131,
"score": 780,
"rationale": "120-qubit digital quantum simulation beyond exact statevector is a high-impact, scalable hardware demonstration; landmark-class result.",
"title": "Chromium publishes fixed exploit 4 years later, turns out it's actually unfixed",
"url": "https://infosec.exchange/@rebane2001/116606719764376414",
"authors": "",
"description": "Attached: 1 video back in 2022 i found a bug that would let me, with no user interaction, turn any chromium-based browser into a permanent js botnet member in edge, you wouldn't even notice anything out-of-place, and would stay connected to the c2 even after closing the browser today, almost 4 years later, the bug is finally public: https://issues.chromium.org/issues/40062121",
"summary": "Attached: 1 video back in 2022 i found a bug that would let me, with no user interaction, turn any chromium-based browser into a permanent js botnet member in edge, you wouldn't even notice anything…",
"tags": [
"LOBSTERS"
],
"source": "Lobste.rs",
"type": "news",
"section": "📰 News Headlines",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T19:00:00Z"
},
{
"id": "item_01927",
"rank": 132,
"score": 780,
"rationale": "Meta CWM open-weight release with frontier preparedness report; significant for open model ecosystem and safety practice.",
"title": "Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring",
"url": "https://huggingface.co/papers/2605.16386",
"authors": "",
"description": "Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal clinical scales remains poorly understood. We benchmark three frontier LLM families against supervised deep learning models for scoring Clock Drawing Test (CDT) images on two public datasets using the Shulman rubric. While fully fine-tuned Vision Transformers achieve the best calibration (MAE 0.52, within-1 accuracy 91%), zero-shot LLMs remain competitive on tolerance-based agreement (GPT-5 MAE 0.67, within-1 accuracy 92%) despite higher absolute error. However, per-score analysis reveals that all three LLM families exhibit a pronounced central tendency effect (systematic endpoint compression): predictions are systematically compressed toward the middle of the scale, with over-prediction at the low end (score 0 to 1) and under-prediction at the high end (score 5 to 4). This effect disproportionately affects the clinically critical extremes where accurate scoring most impacts screening decisions for cognitive impairment. Targeted ablations show that neither few-shot exemplars spanning the full score range nor removing clinical terminology from the prompt eliminates the effect. Our findings extend the LLM-as-a-judge bias literature from NLP evaluation to clinical assessment, and highlight the need for calibration-aware evaluation and post-hoc calibration before deploying LLM-based raters in high-stakes screening workflows.",
"summary": "Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal clinical scales remains poorly understood. We…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_02068",
"rank": 133,
"score": 780,
"rationale": "Activation-level multi-turn injection detection via trajectory features; strong AI safety signal.",
"title": "Researcher says Microsoft secretly built a backdoor into BitLocker",
"url": "https://www.techspot.com/news/112410-security-researcher-microsoft-secretly-built-backdoor-bitlocker-releases.html",
"authors": "",
"description": "Security researcher claims Microsoft intentionally built a backdoor into BitLocker encryption, raising concerns about software integrity and government encryption mandates.",
"summary": "Security researcher claims Microsoft intentionally built a backdoor into BitLocker encryption, raising concerns about software integrity and government encryption mandates.",
"tags": [
"lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🔗 Lobste.rs",
"group": "💬 Lobste.rs",
"debrief_date": "2026-05-18 04:00",
"debrief_path": "debriefs/debrief-2026-05-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-18 04:00",
"debrief_path": "debriefs/debrief-2026-05-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-18T04:00:00Z"
},
{
"id": "item_02099",
"rank": 134,
"score": 780,
"rationale": "21-day 3505-agent onchain deployment with 7.5M invocations; rare large-scale LLM-agent reliability trace.",
"title": "Talk is (Not) Cheap: A Taxonomy and Benchmark Coverage Audit for LLM Attacks",
"url": "https://arxiv.org/abs/2605.15118",
"authors": "Karthik Raghu Iyer, Yazdan Jamshidi, Nicholas Bray, Alexey A. Shvets",
"description": "We introduce a reusable framework for auditing whether LLM attack benchmarks collectively cover the threat surface: a 4$\\times$6 Target $\\times$ Technique matrix grounded in STRIDE, constructed from a 507-leaf taxonomy -- 401 data-populated and 106 threat-model-derived leaves -- of inference-time attacks extracted from 932 arXiv security studies (2023--2026). The matrix enables benchmark-external validation -- auditing collective coverage rather than individual benchmark consistency. Applying it",
"summary": "We introduce a reusable framework for auditing whether LLM attack benchmarks collectively cover the threat surface: a 4$\\times$6 Target $\\times$ Technique matrix grounded in STRIDE, constructed from a 507-leaf taxonomy…",
"tags": [
"cs.CR",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_02077",
"rank": 135,
"score": 780,
"rationale": "CVE-2026-31431 Kubernetes copy-fail: kernel-level exploit, real CVSS-worthy Linux flaw with PoC.",
"title": "FutureSim: Replaying World Events to Evaluate Adaptive Agents",
"url": "https://arxiv.org/abs/2605.15188",
"authors": "Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu, Steffen Staab, Moritz Hardt, Maksym Andriushchenko, Jonas Geiping",
"description": "AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations that replay real-world events in the order they occurred. We build FutureSim, where agents forecast world events beyond their knowledge cutoff while interacting with a chronological replay of the world: real news articles arriving and questions resolving over t",
"summary": "AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building…",
"tags": [
"cs.LG",
"cs.AI",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_02231",
"rank": 136,
"score": 780,
"rationale": "Formal limits of recursive self-training with two failure modes; strong theoretical result for AI safety.",
"title": "Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?",
"url": "https://huggingface.co/papers/2605.12684",
"authors": "Yichen Feng, Yuetai Li, Chunjiang Liu, Yuanyuan Chen, Fengqing Jiang",
"description": "Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a scalar score for a single image. We first ask whether such scores faithfully capture comparative preference: in a controlled study with eight expert annotators, score-derived rankings align poorly with the same annotators' direct comparisons, while direct ranking yields substantially higher inter-annotator agreement on best- and worst-image labels. Motivated by this finding, we introduce the Visual Aesthetic Benchmark (VAB), which casts aesthetic evaluation as comparative selection over candidate sets with matched subject matter. VAB contains 400 tasks and 1,195 images across fine art, photography, and illustration, with labels derived from the consensus of 10 independent expert judges per task. Evaluating 20 frontier MLLMs and six dedicated visual-quality reward models, we find that the strongest system identifies both the best and the worst image correctly across three random permutations of the candidate order in only 26.5% of tasks, far below the 68.9% achieved by human experts. Fine-tuning a 35B-parameter model on 2,000 expert examples brings its accuracy close to that of a 397B-parameter open-weight model, suggesting that the comparative signal in VAB is transferable. Together, these results expose a clear an",
"summary": "Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a…",
"tags": [
"cs.CL",
"cs.CV",
"cs.RO"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02223",
"rank": 137,
"score": 780,
"rationale": "SAEs as jailbreak defense across four model families; novel and practically relevant to AI safety.",
"title": "Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation",
"url": "https://huggingface.co/papers/2605.12975",
"authors": "Jiashuo Sun, Jimeng Shi, Yixuan Xie, Saizhuo Wang, Jash Rajesh Parekh",
"description": "Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors are detected by the same model that produces them making self-reflection an unreliable, ungrounded signal.\n We observe that multi-hop question answering is a typical form of step-by-step computation, and that this structured process aligns closely with how code-specialized language models are trained to operate. Motivated by this, we introduce \\pyrag, a framework that reformulates multi-hop RAG as program synthesis and execution. Instead of free-form reasoning trajectories, \\pyrag represents the reasoning process as an executable Python program over retrieval and QA tools, exposing intermediate states as variables, producing deterministic feedback through execution, and yielding an inspectable trace of the entire reasoning process. This formulation further enables compiler-grounded self-repair and execution-driven adaptive retrieval without any additional training.\n Experiments on five QA benchmarks (PopQA, HotpotQA, 2WikiMultihopQA, MuSiQue, and Bamboogle) show that \\pyrag consistently outperforms strong baselines under both trai",
"summary": "Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that…",
"tags": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02344",
"rank": 138,
"score": 780,
"rationale": "Budget-aware scaling-law experiment design; high practical value for million-dollar training runs, novel framing.",
"title": "Can Muon Fine-tune Adam-Pretrained Models?",
"url": "https://huggingface.co/papers/2605.10468",
"authors": "Xingyu Qu, Peigeng Huang, Samuel Horvath",
"description": "Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are pretrained with Adam, and naively switching to Muon for fine-tuning leads to degraded performance due to an optimizer mismatch. We investigate this mismatch through controlled experiments and relate it to the distinct implicit biases of Adam and Muon. We provide evidence that the mismatch disrupts pretrained knowledge, and that this disruption scales with update strength. This leads us to hypothesize that constraining updates should mitigate the mismatch. We validate this with LoRA: across language and vision tasks, LoRA reduces the performance gap between Adam and Muon observed under full fine-tuning. Studies on LoRA rank, catastrophic forgetting, and LoRA variants further confirm that mismatch severity correlates with update strength. These results shed light on how optimizer mismatch affects fine-tuning and how it can be mitigated. Our code is available at https://github.com/XingyuQu/muon-finetune.",
"summary": "Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are pretrained with Adam, and naively switching to Muon for…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤗 HuggingFace Papers — daily curated papers",
"debrief_date": "2026-05-12 11:00",
"debrief_path": "debriefs/debrief-2026-05-12-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-12 11:00",
"debrief_path": "debriefs/debrief-2026-05-12-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T11:00:00Z"
},
{
"id": "item_02508",
"rank": 139,
"score": 780,
"rationale": "Bitwarden CLI supply-chain compromise is high-impact, credible security incident.",
"title": "MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation",
"url": "https://huggingface.co/papers/2512.18181",
"authors": "HuggingFace",
"description": "With the rise of online dance-video platforms and rapid advances in AI-generated content (AIGC), music-driven dance generation has emerged as a compelling research direction. Despite substantial progress in related domains such as music-driven 3D dance generation, pose-driven image animation, and audio-driven talking-head synthesis, existing methods cannot be directly adapted to this task. Moreover, the limited studies in this area still struggle to jointly achieve high-quality visual appearance and realistic human motion. Accordingly, we present MACE-Dance, a music-driven dance video generation framework with cascaded Mixture-of-Experts (MoE). The Motion Expert performs music-to-3D motion generation while enforcing kinematic plausibility and artistic expressiveness, whereas the Appearance Expert carries out motion- and reference-conditioned video synthesis, preserving visual identity with spatiotemporal coherence. Specifically, the Motion Expert adopts a diffusion model with a BiMamba-Transformer hybrid architecture and a Guidance-Free Training (GFT) strategy, achieving state-of-the-art (SOTA) performance in 3D dance generation. The Appearance Expert employs a decoupled kinematic-aesthetic fine-tuning strategy, achieving state-of-the-art (SOTA) performance in pose-driven image animation. To better benchmark this task, we curate a large-scale and diverse dataset and design a motion-appearance evaluation protocol. Based on this protocol, MACE-Dance also achieves state-of-the-a",
"summary": "With the rise of online dance-video platforms and rapid advances in AI-generated content (AIGC), music-driven dance generation has emerged as a compelling…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤖 Agents",
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T04:00:00Z"
},
{
"id": "item_02637",
"rank": 140,
"score": 780,
"rationale": "AI agent sandbox for user-data confidentiality directly addresses a critical agentic security gap.",
"title": "Relational Intelligence as Alignment Methodology A Case for Collaborative Approaches to AI Safety",
"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6186018",
"authors": "Google Scholar",
"description": "… (RLHF), Constitutional AI (CAI), and mechanistic interpretability—treat alignment as a … alignment is fundamentally a relational problem: the quality of interaction between humans and AI …",
"summary": "… (RLHF), Constitutional AI (CAI), and mechanistic interpretability—treat alignment as a … alignment is fundamentally a relational problem: the quality of interaction between humans and AI …",
"tags": [
"Google Scholar",
"AI Safety"
],
"source": "Google Scholar",
"type": "paper",
"section": "📄 Web Findings",
"group": "🛡️ AI Safety",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_02805",
"rank": 141,
"score": 780,
"rationale": "Critical command injection in Claude Code CLI with credential exfiltration; high-impact agent security finding.",
"title": "To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling",
"url": "https://arxiv.org/abs/2605.00737",
"authors": "Qinyuan Wu, Soumi Das, Mahsa Amani, Arijit Nag, Seungeon Lee, Krishna P. Gummadi, Abhilasha Ravichander, Muhammad Bilal Zafar",
"description": "Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool, when performing a task. This decision is particularly challenging for web search tools, where the benefits of external information depend on the model's internal knowledge and its ability to integrate potentially noisy tool",
"summary": "Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on …",
"tags": [
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_02827",
"rank": 142,
"score": 780,
"rationale": "First coop benchmark for LLM agents in dilemmas; reveals defection, safety-relevant.",
"title": "KingsGuard: Enclave Data Protection Under Real-World TEE Vulnerabilities",
"url": "https://arxiv.org/abs/2605.00613",
"authors": "Saltanat Firdous Allaqband, Deepanjali S, Rohit Srinivas R G, Devashish Gosain, Chester Rebeiro",
"description": "Trusted Execution Environments (TEEs) have emerged as a cornerstone for securing sensitive computations by providing isolated enclaves protected from untrusted software. However, their security guarantees are undermined by vulnerabilities in both the enclave code and the underlying hardware design, which can allow sensitive data to leak despite strong isolation guarantees. This paper presents KINGSGUARD, a novel TEE design that systematically monitors and controls the propagation of sensitive da",
"summary": "Trusted Execution Environments (TEEs) have emerged as a cornerstone for securing sensitive computations by providing isolated enclaves protected from untrusted software. However, their security guarantees are undermined …",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_03013",
"rank": 143,
"score": 780,
"rationale": "On-policy distillation phenomenology + recipe; key post-training insight for LLMs.",
"title": "ClawGym: A Scalable Framework for Building Effective Claw Agents",
"url": "https://arxiv.org/abs/2604.26904",
"authors": "Fei Bai, Huatong Song, Shuang Sun, Daixuan Cheng, Yike Yang, Chuan Hao, Renyuan Li, Feng Chang, Yuan Wei, Ran Tao, Bryan Dai, Jian Yang, Wayne Xin Zhao",
"description": "Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains constrained by the absence of a systematic framework, especially one for synthesizing verifiable training data and integrating it with agent training and diagnostic evaluation. To address this challenge, we present ClawGym, a scalable framework that supports the full lifecycle of Claw-style personal agent development. Concr",
"summary": "Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around these…",
"tags": [
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 AI Agents & Reasoning",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_03191",
"rank": 144,
"score": 780,
"rationale": "Training-free cross-model capability transfer via subspace alignment is striking, potentially foundational.",
"title": "DeepHartree: A Poisson-Coupled Neural Field for Scalable Density Functional Theory",
"url": "https://arxiv.org/abs/2604.22669",
"authors": "Jiankun Wu, Jinming Fan, Chao Qian, Shaodong Zhou",
"description": "Ab initio calculations are fundamentally bottlenecked for large systems by the steep computational scaling of solving self-consistent field (SCF) equations. While machine learning offers potential accelerations, existing methods often compromise physical rigor or rely on basis-dependent, non-transferable representations. Here, we introduce DeepHartree, a Poisson-coupled neural field that accelerates linear combination of atomic orbitals (LCAO) density functional theory (DFT). By coupling an E(3)",
"summary": "Ab initio calculations are fundamentally bottlenecked for large systems by the steep computational scaling of solving self-consistent field (SCF) equations. While machine learning…",
"tags": [
"physics.chem-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-27T04:00:00Z"
},
{
"id": "item_03252",
"rank": 145,
"score": 780,
"rationale": "Chain-of-thought hijacking via two-stage backdoor targets a real emerging CoT-integrity threat in open-weight models.",
"title": "Evaluation of Automatic Speech Recognition Using Generative Large Language Models",
"url": "https://arxiv.org/abs/2604.21928",
"authors": "Thibault Baneras-Roux, Shashi Kumar, Driss Khalil, Sergio Burdisso, Petr Motlicek, Shiran Liu, Mickael Rouvier, Jane Wottawa, Richard Dufour",
"description": "Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human perception, but decoder-based Large Language Models (LLMs) remain underexplored for this task. This paper evaluates their relevance through three approaches: (1) selecting the best hypothesis between two candidates, (2) computing semantic distance using generative embeddings, and (3) qualitative class",
"summary": "Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human pe…",
"tags": [
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03368",
"rank": 146,
"score": 780,
"rationale": "Self-distillation paper: 42.4→55.3 pass@1, mechanistic insight on decoding.",
"title": "Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity",
"url": "https://arxiv.org/abs/2604.20789",
"authors": "Pranava Madhyastha, Dagmar Adamcova",
"description": "Human-like working memory constraints integrated into Transformers via fixed-width windows and temporal decay attention variants enable modified GPT-2 models trained on developmentally plausible datasets to align with human reading time data.",
"summary": "Human-like working memory constraints integrated into Transformers via fixed-width windows and temporal decay attention variants enable modified GPT-2 models trained on developmentally plausible datasets to align with human reading time…",
"tags": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T09:00:00Z"
},
{
"id": "item_03459",
"rank": 147,
"score": 780,
"rationale": "Stable neutral-atom qubit logic operations: notable quantum-computing milestone.",
"title": "FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning",
"url": "https://arxiv.org/abs/2604.19729",
"authors": "Abdulmoneam Ali, Ahmed Arafa",
"description": "Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimization-such as model update trajectories-to cluster users that need to accomplish the same tasks together. However, these learning-dynamics-based methods are inherently vulnerable to low-quality data and noisy labels, as corrupted updates distort clustering decisions and degrade perso",
"summary": "Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches…",
"tags": [
"cs.LG",
"cs.IT",
"eess.SP"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T09:00:00Z"
},
{
"id": "item_03625",
"rank": 148,
"score": 780,
"rationale": "30B deep-research multi-agent framework with strong benchmark results; notable deployed system.",
"title": "Mind DeepResearch Technical Report",
"url": "https://huggingface.co/papers/2604.14518",
"authors": "HuggingFace",
"description": "We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter models through a meticulously designed data synthesis and multi-stage training pipeline. The core innovation of MindDR lies in a collaborative three-agent architecture (Planning Agent, DeepSearch Agent, and Report Agent) and a four-stage agent-specialized training pipeline comprising SFT cold-start, Search-RL, Report-RL and preference alignment. With this regime, MindDR demonstrates competitive performance even with ~30B-scale models. Specifically, MindDR achieves 45.7% on BrowseComp-ZH, 42.8% on BrowseComp, 46.5% on WideSearch, 75.0% on xbench-DS, and 52.5 on DeepResearch Bench, outperforming comparable-scale open-source agent systems and rivaling larger-scale models. MindDR has been deployed as an online product in Li Auto. Furthermore, we introduce MindDR Bench, a curated benchmark of 500 real-world Chinese queries from our internal product user interactions, evaluated through a comprehensive multi-dimensional rubric system rather than relying on a single RACE metric. On MindDR Bench, MindDR achieves a state-of-the-art score of 51.8.",
"summary": "We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter models through a meticulously designed data synthesis and multi-stage tra…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03573",
"rank": 149,
"score": 780,
"rationale": "Mechanistic study of jailbreak routes (SFT/RLVR/abliteration); high-impact AI safety analysis.",
"title": "Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks",
"url": "https://arxiv.org/abs/2604.18510",
"authors": "Md Rysul Kabir, Zoran Tiganj",
"description": "Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in capabilities, behavioral profile, and internal failure mode. We study behavioral and mechanistic properties of jailbroken models across three unsafe routes: harmful supervised fine-tuning (SFT), harmful reinforcement learning with verifiable rewards (RLVR), and refusal-suppressing abliteration. All three routes achieve near-ceiling harmful compliance, bu",
"summary": "Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in capabilities, behavioral profile, and internal failure mode. We study behavi…",
"tags": [
"cs.CR",
"cs.AI",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Crypto",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03783",
"rank": 150,
"score": 780,
"rationale": "RealVuln benchmark on 15 scanners across real Python code; high practical value for SAST/LLM security scanning evaluation.",
"title": "RealVuln: Benchmarking Rule-Based, General-Purpose LLM, and Security-Specialized Scanners on Real-World Code",
"url": "https://arxiv.org/abs/2604.13764",
"authors": "John Pellew, Faizan Raza",
"description": "How do security scanners perform on real-world code? We present RealVuln, the first open-source benchmark comparing Rule-Based SAST, General-Purpose LLMs, and Security-Specialized scanners on 26 intentionally vulnerable Python repositories (educational and Capture-The-Flag applications) with 796 hand-labeled entries (676 vulnerabilities, 120 false-positive traps). We test 15 scanners (3 Rule-Based SAST, 10 General-Purpose LLM, 2 Security-Specialized) and rank them by F3 score (beta=3, weighting ",
"summary": "How do security scanners perform on real-world code? We present RealVuln, the first open-source benchmark comparing Rule-Based SAST, General-Purpose LLMs, and Security-Specialized scanners on 26…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_03807",
"rank": 151,
"score": 780,
"rationale": "RationalRewards with reasoning critiques for visual generation; novel training+test-time use, high impact.",
"title": "RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time",
"url": "https://huggingface.co/papers/2604.11626",
"authors": "",
"description": "Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward models to produce explicit, multi-dimensional critiques before scoring transforms them from passive evaluators into active optimization tools, improving generators in two complementary ways: at training time, structured rationales provide interpretable, fine-grained rewards for reinforcement learning; at test time, a Generate-Critique-Refine loop turns critiques into targeted prompt revisions that improve outputs without any parameter updates. To train such a reward model without costly rationale annotations, we introduce Preference-Anchored Rationalization (PARROT), a principled framework that recovers high-quality rationales from readily available preference data through anchored generation, consistency filtering, and distillation. The resulting model, RationalRewards (8B), achieves state-of-the-art preference prediction among open-source reward models, competitive with Gemini-2.5-Pro, while using 10-20x less training data than comparable baselines. As an RL reward, it consistently improves text-to-image and image-editing generators beyond scalar alternatives. Most strikingly, its test-time critique-and-refine loop matches or exceeds RL-based fine-tuning on several benchmarks, suggesting that structured reasoning can unlock latent capabilities in existing generators that suboptimal prompts fail t",
"summary": "Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference.",
"tags": [
"HuggingFace",
"▲ 64"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🔗 HuggingFace",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_03865",
"rank": 152,
"score": 780,
"rationale": "Parallax argues thinking agents must not act; bold, timely agent-safety thesis.",
"title": "Parallax: Why AI Agents That Think Must Never Act",
"url": "https://arxiv.org/abs/2604.12986",
"authors": "Joel Fokou",
"description": "Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real-world actions (reading files, running commands, making network requests, modifying databases), a fundamental security gap has emerged. The dominant approach to agent safety relies on prompt-level guardrails: natural language instructions that operate at the s",
"summary": "Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real-world actions (reading files, running commands, making network requests…",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_03938",
"rank": 153,
"score": 780,
"rationale": "Mechanistic localization of refusal policy circuits across scales is a major interpretability result.",
"title": "How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models",
"url": "https://huggingface.co/papers/2604.04385",
"authors": "Gregory N. Frank",
"description": "This paper localizes the policy routing mechanism in alignment-trained language models. An intermediate-layer attention gate reads detected content and triggers deeper amplifier heads that boost the signal toward refusal. In smaller models the gate and amplifier are single heads; at larger scale they become bands of heads across adjacent layers. The gate contributes under 1% of output DLA, but interchange testing (p<0.001) and knockout cascade confirm it is causally necessary. Interchange screening at n>=120 detects the same motif in twelve models from six labs (2B to 72B), though specific heads differ by lab. Per-head ablation weakens up to 58x at 72B and misses gates that interchange identifies; interchange is the only reliable audit at scale. Modulating the detection-layer signal continuously controls policy from hard refusal through evasion to factual answering. On safety prompts the same intervention turns refusal into harmful guidance, showing the safety-trained capability is gated by routing rather than removed. Thresholds vary by topic and by input language, and the circuit relocates across generations within a family while behavioral benchmarks register no change. Routing is early-commitment: the gate commits at its own layer before deeper layers finish processing the input. Under an in-context substitution cipher, gate interchange necessity collapses 70 to 99% across three models and the model switches to puzzle-solving. Injecting the plaintext gate activation into",
"summary": "This mechanistic study localizes sparse policy-routing heads that detect disallowed content and amplify refusal behavior deeper in the network. It matters because it suggests alignment behavior can be controlled through…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 11:00",
"debrief_path": "debriefs/debrief-2026-04-14-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 11:00",
"debrief_path": "debriefs/debrief-2026-04-14-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T11:00:00Z"
},
{
"id": "item_03970",
"rank": 154,
"score": 780,
"rationale": "PKE from high-corruption CSPs is theoretically significant; plausible quasi-exponential security with novel assumptions.",
"title": "Public Key Encryption from High-Corruption Constraint Satisfaction Problems",
"url": "https://arxiv.org/abs/2604.10479",
"authors": "Isaac M Hair, Amit Sahai",
"description": "We give a public key encryption scheme with plausible quasi-exponential security based on the conjectured intractability of two constraint satisfaction problems (CSPs), both of which are instantiated with a corruption rate of $1 - o(1)$. First, we conjecture the hardness of a new large alphabet random predicate CSP (LARP-CSP) defined over an arbitrary but strongly expanding factor graph, where the vast majority of predicate outputs are replaced with random outputs. Second, we conjecture the hardness of the standard $k$XOR problem defined over a random factor graph, again where the vast majority of parity computations are replaced with random bits. In support of our hardness conjecture for LARP-CSPs, we give a variety of lower bounds, ruling out many natural attacks including all known attacks that exploit non-random factor graphs. Our public key encryption scheme is the first to leverage high corruption CSPs while simultaneously achieving a plausible security level far above quasi-polynomial. At the heart of our work is a new method for planting cryptographic trapdoors based on the label extended factor graph for a CSP. Along the way to achieving our result, we give the first uniform construction of an error-correcting code that has an expanding, low density generator matrix while simultaneously allowing for efficient decoding from a $1 - o(1)$ fraction of corruptions.",
"summary": "We give a public key encryption scheme with plausible quasi-exponential security based on the conjectured intractability of two constraint satisfaction problems (CSPs), both of which are instantiated with a corruption rate of $1 - o(1)$. First, we conjecture the hardness of a new large alphabet random predicate CSP…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04084",
"rank": 155,
"score": 780,
"rationale": "Novel model-in-skill supply-chain backdoor; directly relevant to agent ecosystems and security.",
"title": "BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning",
"url": "https://arxiv.org/abs/2604.09378",
"authors": "Guiyao Tie, Jiawen Shi, Pan Zhou, et al.",
"description": "Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic. This creates a supply-chain risk that is not captured by prompt injection or ordinary plugin misuse: a third-party skill may appear benign while concealing malicious behavior inside its bundled model. We present BadSkill, a backdoor attack formulation that targets this model-in-skill threat surface. In BadSkill, an adversary publishes",
"summary": "Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic.",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_04266",
"rank": 156,
"score": 780,
"rationale": "Boot attestation/remote verification gaps — foundational trust problem in security.",
"title": "Why Nobody Can Verify What Booted Your Server",
"url": "https://unmitigatedrisk.com/?p=1231",
"authors": "",
"description": "Article about: Why Nobody Can Verify What Booted Your Server",
"summary": "Article about: Why Nobody Can Verify What Booted Your Server",
"tags": [
"Lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04030",
"rank": 157,
"score": 779,
"rationale": "Documented real-world AI-enabled breach of nine agencies exfiltrating citizen records; high-impact case study.",
"title": "The AI-Assisted Breach of Mexico's Government Infrastructure",
"url": "https://gambit.security/blog-post/a-single-operator-two-ai-platforms-nine-government-agencies-the-full-technical-report",
"authors": "",
"description": "In February, we published our initial findings on the AI-assisted breach of Mexico's government infrastructure, warning of the elevated risk that AI-powered threat actors now pose. A single operator used AI to breach nine Mexican government organizations and exfiltrate hundreds of millions of citizen records. Today, we release the full technical report.",
"summary": "In February, we published our initial findings on the AI-assisted breach of Mexico's government infrastructure, warning of the elevated risk that AI-powered threat actors now pose. A single operator…",
"tags": [
"Lobste.rs",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T19:00:00Z"
},
{
"id": "item_02996",
"rank": 158,
"score": 775,
"rationale": "",
"title": "Quantamination: Dynamic Quantization Leaks Your Data Across the Batch",
"url": "https://arxiv.org/abs/2604.26505",
"authors": "Hanna Foerster, Ilia Shumailov, Cheng Zhang, Yiren Zhao, Jamie Hayes, Robert Mullins",
"description": "Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static quantization, which applies quantization offline, dynamic quantization operates on tensors at run-time, adapting its parameters to the actual input data. Today's mainstream machine learning frameworks, including ML compilers and inference engines, frequently recommend dynamic quantization as an initial step for optimizing model serving. This is becau",
"summary": "Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_04036",
"rank": 159,
"score": 774,
"rationale": "First pipeline-parallelism backdoor attack in decentralized post-training; meaningful security contribution.",
"title": "Backdoor Attacks on Decentralised Post-Training",
"url": "https://huggingface.co/papers/2604.02372",
"authors": "2604.02372",
"description": "Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and backdoor attacks by one or more malicious participants. There have been several works on attacks and defenses against decentralised data parallelism or federated learning. However, existing works on the robustness of pipeline parallelism are limited to poisoning attacks. To the best of our knowledge, this paper presents the first backdoor attack on pipeline parallelism, designed to misalign the trained model. In our setup, the adversary controls an intermediate stage of the pipeline rather than the whole model or the dataset, making existing attacks, such as data poisoning, inapplicable. Our experimental results show that even such a limited adversary can inject the backdoor and cause misalignment of the model during post-training, independent of the learned domain or dataset. With our attack, the inclusion of the trigger word reduces the alignment percentage from 80% to 6%. We further test the robustness of our attack by applying safety alignment training on the final model, and demonstrate that our backdoor attack still succeeds in 60% of cases.",
"summary": "Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and…",
"tags": [
"arXiv:"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Research Papers (14)",
"group": "LLMs & Agents",
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T11:00:00Z"
},
{
"id": "item_02263",
"rank": 160,
"score": 770,
"rationale": "Layerwise convergence fingerprints for runtime LLM misbehavior; unified defense approach.",
"title": "Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts",
"url": "https://huggingface.co/papers/2605.09296",
"authors": "",
"description": "Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet existing detectors based on pre-trained feature extractors tend to over-rely on global semantics, limiting sensitivity to the critical micro-defects. In this work, we propose Micro-Defects expose Macro-Fakes (MDMF), a local distribution-aware detection framework that amplifies micro-scale statistical irregularities into macro-level distributional discrepancies. To avoid localized forensic cues being diluted by plain aggregation, we introduce a learnable Patch Forensic Signature that projects semantic patch embeddings into a compact forensic latent space. We then use Maximum Mean Discrepancy (MMD) to quantify distributional discrepancies between generated and real images. Our theory-grounded analysis shows that patch-wise modeling yields provably larger discrepancies when localized forensic signals are present in generated images, enabling more reliable separation from real images. Extensive experiments demonstrate that MDMF consistently outperforms baseline detectors across multiple benchmarks, validating its general effectiveness. Project page: https://zbox1005.github.io/MDMF-project/",
"summary": "Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet existing detectors based on…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_03750",
"rank": 161,
"score": 770,
"rationale": "Skill compilation/runtime system treating skills as portable code is architecturally significant for agent portability.",
"title": "SkVM: Compiling Skills for Efficient Execution Everywhere",
"url": "https://huggingface.co/papers/2604.03088",
"authors": "",
"description": "LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing the same skill to behave inconsistently for different agents. This fragility undermines skill portability and execution efficiency.\n To address this challenge, we analyze 118,000 skills and draw inspiration from traditional compiler design. We treat skills as code and LLMs as heterogeneous processors. To make portability actionable, we decompose a skill's requirements into a set of primitive capabilities, and measure how well each model-harness pair supports them. Based on these capability profiles, we propose SkVM, a compilation and runtime system designed for portable and efficient skill execution. At compile time, SkVM performs capability-based compilation, environment binding, and concurrency extraction. At runtime, SkVM applies JIT code solidification and adaptive recompilation for performance optimization.\n We evaluate SkVM across eight LLMs of varying scales and three agent harnesses, covering SkillsBench and representative skill tasks. Results demonstrate that SkVM significantly improves task completion rates across different models and environments while reducing token consumption by up to 40%. In terms of performance, SkVM achieves up to 3.2x speedup with enhanced parallelism, and 19-50x latency reduction through code solidification.",
"summary": "LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing…",
"tags": [
"HuggingFace Papers",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🤗 HuggingFace Papers",
"group": "HuggingFace Papers",
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T11:00:00Z"
},
{
"id": "item_03963",
"rank": 162,
"score": 765,
"rationale": "Runtime guard against indirect prompt injection in tool-augmented LLM agents—highly relevant to AI agent security.",
"title": "ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection",
"url": "https://arxiv.org/abs/2604.11790",
"authors": "Wei Zhao, Zhe Li, Peixin Zhang, Jun Sun",
"description": "Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect prompt injection. Adversaries exploit this weakness by embedding malicious instructions within tool-returned content, which agents directly incorporate into their conversation history as trusted observations. This vulnerability manifests across three primary attack channels: web and local content injection, MCP server injection, and skill file injection. To address these vulnerabilities, we introduce \\textsc{ClawGuard}, a novel runtime security framework that enforces a user-confirmed rule set at every tool-call boundary, transforming unreliable alignment-dependent defense into a deterministic, auditable mechanism that intercepts adversarial tool calls before any real-world effect is produced. By automatically deriving task-specific access constraints from the user's stated objective prior to any external tool invocation, \\textsc{ClawGuard} blocks all three injection pathways without model modification or infrastructure change. Experiments across five state-of-the-art language models on AgentDojo, SkillInject, and MCPSafeBench demonstrate that \\textsc{ClawGuard} achieves robust protection against indirect prompt injection without compromising agent utility. This work establishes deterministic tool-call boundary enforcement as an effective defense mechanism for secure agentic AI systems, requiring neither safety-specific fine-tuning nor architectural modification. Code is publicly available at https://github.com/Claw-Guard/ClawGuard.",
"summary": "Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect prompt injection. Adversaries exploit this weakness by embedding malicious instructions within tool-returned content, which agents directly…",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04026",
"rank": 163,
"score": 763,
"rationale": "First benchmark for implicit/non-declarative LLM memory; well-grounded in cognitive science, high relevance to agents.",
"title": "ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models",
"url": "https://huggingface.co/papers/2604.08064",
"authors": "",
"description": "Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. This gap is critical: effective assistants must automatically apply learned procedures or avoid failed actions without explicit reminders. We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of non-declarative memory: Procedural Memory (one-shot skill acquisition after interference), Priming (theme-driven bias via paired experimental/control instances), and Classical Conditioning (Conditioned Stimulus--Unconditioned Stimulus (CS--US) associations shaping first decisions). Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring. Evaluation of 17 models reveals severe limitations: no model exceeds 66% overall, with top performers DeepSeek-R1 (65.3%), Qwen3-32B (64.1%), and GPT-5 (63.0%) far below human baselines. Analysis uncovers dramatic asymmetries (inhibition 17.6% vs. preference 75.0%) and universal bottlenecks requiring architectural innovations beyond parameter scaling. ImplicitMemBench reframes evaluation from \"what agents recall\" to \"what they automatically enact\".",
"summary": "Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. This gap is…",
"tags": [
"2026-04-09",
"HuggingFace",
"Huggingface Papers",
"arXiv:",
"cs.AI",
"cs.CL",
"daily curated papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers (via HuggingFace)",
"group": "🧠 LLMs & Reasoning",
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": "",
"occurrence_count": 9,
"occurrences": [
{
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 19:00",
"debrief_path": "debriefs/debrief-2026-04-12-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 16:00",
"debrief_path": "debriefs/debrief-2026-04-12-16.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 04:00",
"debrief_path": "debriefs/debrief-2026-04-12-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 00:00",
"debrief_path": "debriefs/debrief-2026-04-12-00.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T19:00:00Z"
},
{
"id": "item_03685",
"rank": 164,
"score": 762,
"rationale": "Pre-silicon masking verification for PQC accelerators is highly relevant to crypto/side-channels.",
"title": "Structural Dependency Analysis for Masked NTT Hardware: Scalable Pre-Silicon Verification of Post-Quantum Cryptographic Accelerators",
"url": "https://arxiv.org/abs/2604.15249",
"authors": "Ray Iskander, Khaled Kirah",
"description": "Post-quantum cryptographic accelerators require side-channel resistance evidence for FIPS 140-3 certification. However, exact masking-verification tools scale only to gadgets of a few thousand cells. We present a four-stage verification hierarchy, D0/D1 structural dependency analysis, fresh-mask refinement, Boolean Single-Authentication Distance Checking (SADC), and arithmetic SADC, that extends sound first-order masking verification to production arithmetic modules. Applied to the 1.17-million-",
"summary": "Post-quantum cryptographic accelerators require side-channel resistance evidence for FIPS 140-3 certification. However, exact masking-verification tools scale only to gadgets of a few thousand cells.…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_01304",
"rank": 165,
"score": 760,
"rationale": "Active npm worm with SLSA L3 provenance; major supply-chain security incident with broad ecosystem impact.",
"title": "OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents",
"url": "https://huggingface.co/papers/2606.02031",
"authors": "Rui Yang, Qianhui Wu, Yuxi Chen, Hao Bai, Wenlin Yao",
"description": "Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories. This dependence creates a major scalability bottleneck: high-quality demonstrations are expensive to collect, and static datasets offer limited coverage of the diverse, ever-changing open web. Although online RL has shown promise for text-based agents, its potential for training visual web agents directly on live websites remains largely underexplored. In this paper, we introduce OpenWebRL, an open framework for training visual web agents with online multi-turn RL on real websites. OpenWebRL covers the full training pipeline, including scalable live-browser infrastructure, supervised initialization, multimodal context management, trajectory-level success judging, and efficient multi-turn policy optimization. Using this framework, we train OpenWebRL-4B, which establishes a new open-source state of the art on challenging live-web benchmarks. With only 0.4K initialization trajectories and 2.2K open-ended RL training tasks, OpenWebRL-4B achieves 67.0% success on Online-Mind2Web and 64.0% on DeepShop, outperforming prior open agents of similar or larger scale and remaining competitive with proprietary systems including OpenAI CUA and Gemini CUA. Beyo",
"summary": "Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary …",
"tags": [
"2026-06-01",
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-06-02 11:00",
"debrief_path": "debriefs/debrief-2026-06-02-11.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-06-02 11:00",
"debrief_path": "debriefs/debrief-2026-06-02-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-02 04:00",
"debrief_path": "debriefs/debrief-2026-06-02-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-02T11:00:00Z"
},
{
"id": "item_00182",
"rank": 166,
"score": 760,
"rationale": "AsyncWebRL delivers concrete 2.9x speedup and fixes a real GRPO inefficiency—solid systems+RL contribution for web agents.",
"title": "Quantum solitons and their quantum walks in transmon arrays",
"url": "https://arxiv.org/abs/2606.19339",
"authors": "Ben Blain, Giampiero Marchegiani, Luigi Amico, Gianluigi Catelani",
"description": "Superconducting qubits are artificial atoms whose spectra and interactions can be engineered through appropriate circuit design, a versatility that can be exploited for quantum simulation. We theoretically investigate a linear array of capacitively coupled transmons, effectively described by a Bose-Hubbard Hamiltonian with attractive interaction. We revisit the discrete-soliton nature of the lowest-energy band of the spectrum, and identify spatially localized quantum solitons. The solitonic character of these states is revealed through their time evolution, which displays a quantum interference pattern, or quantum walk, highlighting their composite nature. We discuss protocols for preparing spatially localized quantum solitons that are compatible with current state-of-the-art tunable-transmon circuits. Our results demonstrate that superconducting circuits provide a promising and experimentally accessible platform for the investigation of quantum soliton physics.",
"summary": "Superconducting qubits are artificial atoms whose spectra and interactions can be engineered through appropriate circuit design, a versatility that can be exploited for quantum simulation. We…",
"tags": [
"quant-ph",
"cond-mat.mes-hall",
"cond-mat.quant-gas"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Quantum / Solitons / Their",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00215",
"rank": 167,
"score": 760,
"rationale": "Delegation-scoped observability for agentic AI is a foundational gap; directly relevant to AI safety and auditing.",
"title": "(https://github.com/tchauffi/ChessTransformer)",
"url": "https://github.com/tchauffi/ChessTransformer",
"authors": "",
"description": "Contribute to tchauffi/ChessTransformer development by creating an account on GitHub.",
"summary": "Contribute to tchauffi/ChessTransformer development by creating an account on GitHub.",
"tags": [
"⭐ 1",
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "📰 Hacker News",
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T19:00:00Z"
},
{
"id": "item_00273",
"rank": 168,
"score": 760,
"rationale": "Adversarial hacker-fixer loops for agent benchmarks; important security/eval contribution.",
"title": "Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping",
"url": "https://arxiv.org/abs/2606.18120",
"authors": "Mohammadreza Rashidi",
"description": "Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace {x} expression HTML-escapes the interpolated value and is documented as the safe default; its triple-brace {x} expression inserts the value raw. We show that this choice silently governs an application's exposure to structural role injection, where attacker-controlled data carries chat role delimiters that forge a higher-privilege turn. A model-free analysis establishes the mechanism: Handlebars escaping rewrites angle brackets but not square brackets, colons, or Markdown hashes, so it neutralises ChatML, Llama-3, and XML role delimiters (survival rate 0.00) while leaving Llama-2 [INST], legacy Human:/Assistant:, and Markdown ### delimiters intact (survival rate 1.00 for the last two). We then run 5760 trials across seven delimiter families, two attack objectives, and four models (GPT-3.5 Turbo, GPT-4o mini, GPT-4.1 mini, Claude Haiku 4.5) at a combined API cost of 1.63 USD. GPT-3.5 Turbo follows the task-hijack instruction in 97% of raw and 91% of escaped trials, with the escaping protection concentrated in the angle-bracket families and absent for the colon- and Markdown-based families; the harder secret-exfiltration objective, which does not saturate, exposes the same family interaction more cleanly. Claude Haiku 4.5 resists both objectives almost entirely. The escaped default",
"summary": "Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace…",
"tags": [
"cs.CR",
"cs.AI",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Structural / Role / Injection",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00407",
"rank": 169,
"score": 760,
"rationale": "Identifies space-based GNSS interferer over Europe/Greenland/Canada; notable security finding.",
"title": "FastContext: Training Efficient Repository Explorer for Coding Agents",
"url": "https://huggingface.co/papers/2606.14066",
"authors": "",
"description": "Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent's context with irrelevant snippets. In most agents, the same model explores the repository and solves the task, leaving exploratory reads and searches in the solver's history. We present FastContext, a dedicated exploration subagent that separates repository exploration from solving. Invoked on demand, FastContext issues parallel tool calls and returns concise file paths and line ranges as focused context. FastContext is powered by specialized exploration models spanning 4B--30B parameters. We bootstrap them from strong reference-model trajectories and refine them with task-grounded rewards for broad first-turn search, multi-turn evidence gathering, and precise citation generation. Across SWE-bench Multilingual, SWE-bench Pro, and SWE-QA, integrating FastContext into Mini-SWE-Agent improves end-to-end resolution rates up to 5.5\\% while reducing coding-agent token consumption up to 60\\%, with marginal overhead. These results show that repository exploration can be separated from solving and handled effectively by specialized models. Code and data: https://github.com/microsoft/fastcontext",
"summary": "Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code…",
"tags": [
"▲ 40",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00352",
"rank": 170,
"score": 760,
"rationale": "SABER environment-aware agent safety benchmark with 54% HSR; landmark agentic safety eval.",
"title": "ExpRL: Exploratory RL for LLM Mid-Training",
"url": "https://arxiv.org/abs/2606.17024",
"authors": "Violet Xiang, Amrith Setlur, Chase Blagden, Nick Haber, Aviral Kumar",
"description": "Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model. In practice, models are often primed for RL through \\emph{mid-training} on curated reasoning traces that teach useful primitive skills such as decomposition, verification, or self-correction. Although effective, this strategy requires manually specifying what the model should learn, and it remains unclear whether such primitive coverage is enough for much harder problems, which require combining these skills into broader solution strategies. We study a more automated approach: \\emph{RL-based mid-training} using large corpora of human-written question-answer data. Rather than treating reference solutions as targets to imitate, our method, ExpRL, uses them as \\emph{reward scaffolds}: references are hidden from the policy and used only to construct problem-specific grading rubrics for judging on-policy reasoning traces. The policy samples from the original problem prompt, while an LLM judge compares the sampled reasoning trace against the reference solution and assigns outcome-level or process-level dense rewards. This lets ExpRL reinforce partial progress, useful intermediate reductions, and productive reasoning behaviors that sparse final-answer rewards often fail to upweight. On challenging math reasoning tasks, ExpRL yields stronger RL priming than SFT, sparse-reward GRPO, and self-distillation, and pr",
"summary": "Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model. In practice, models are…",
"tags": [
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Reinforcement / When / Doubt",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00453",
"rank": 171,
"score": 760,
"rationale": "Large-scale agent-skill security signals with scanner-disagreement analysis; highly relevant.",
"title": "Panniantong/Agent-Reach",
"url": "https://github.com/Panniantong/Agent-Reach",
"authors": "",
"description": "Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.",
"summary": "Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.",
"tags": [
"⭐ 29.7k",
"GitHub Trending"
],
"source": "💻 GitHub Trending",
"type": "news",
"section": "🌐 Web Findings",
"group": "💻 GitHub Trending",
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T11:00:00Z"
},
{
"id": "item_00488",
"rank": 172,
"score": 760,
"rationale": "Mechanistic unlearning depth metric via activation patching; strong AI safety/auditing contribution.",
"title": "OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains",
"url": "https://arxiv.org/abs/2606.14702",
"authors": "Xinyue Cai, Chaoyou Fu, Yi-Fan Zhang, Ran He, Caifeng Shan",
"description": "Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short clips and generate separate descriptions for audio and visual modalities. This decoupled processing severs inherent associations between sounds and their visual sources, while independent clip processing often causes inconsistent descriptions of the same entity across segments. Furthermore, coupling long-text comprehension and QA synthesis into a single step often restricts models to localized events, yielding questions lacking long-term temporal connections and deep cross-modal reasoning. To address these issues, we propose an automated data engine featuring two mechanisms: (1) \\textbf{Entity-Anchored Video Scripting} transforms videos into structured scripts, comprising summaries, main entity lists, and segment-wise audio-visual descriptions. The entity list serves as a global prior to ensure cross-segment referential consistency and reconstruct audio-visual associations. (2) \\textbf{Clue-Guided QA Generation} prompts models to first mine cross-segment, multimodal clues from the script, and subsequently generate QA pairs based on these high-value clues. Leveraging this pipeline, we construct the instruction-tuning dataset \\textbf{OmniVideo-100K} and a human-verified test set, \\textbf{OmniVideo-Test}. Fine-tuning VITA-1.5, Qwen2.5-Omni-7B and Qwen3-Omni-30B on OmniVideo-100K yields performance gains o",
"summary": "Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short clips and generate…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Omnivideo-100k / Dataset / Audio-visual",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00655",
"rank": 173,
"score": 760,
"rationale": "Lightweight agent safety framework matching GPT-5.4 with tiny models is high-impact for open agents.",
"title": "The Moving Drone: Negotiating Agency Between the Voice and the Virtual",
"url": "https://arxiv.org/abs/2606.13640",
"authors": "Nithya Shikarpur, Victor Arul, Anna Huang",
"description": "Melodic material in Hindustani music is presented in relation to a tonic, usually sustained by the tanpura, a four-stringed drone instrument. Rooted in Hindustani music, 'The Moving Drone' sets the traditionally static drone into motion that, throughout the performance, gains increasing agency transitioning from reactive to more proactive roles. The work employs four independent loopers in Max/MSP to function as 'virtual' drones. They are populated cyclically in real-time as the vocalist improvises, creating an organic and evolving feedback loop between the voice and the virtual drone. This relationship further evolves melodically by pitch shifting the loops, which introduces a dimension of sudden, explicit movement. Then it changes timbrally, via the integration of GaMaDHaNi, a singer conditioned pitch-to-voice generative AI model to resynthesize looped audio. While current music AI approaches prioritize high-fidelity and realism of generated content which has sparked anxiety over job replacement for the music community, this work intentionally utilizes low-fidelity generative outputs, further necessitating human interpretation and situational context in order to be complete. 'The Moving Drone' positions technology and generative AI within established socio-cultural musical practices, proposing a virtual drone as an active, responsive, and co-creative musical agent.",
"summary": "Melodic material in Hindustani music is presented in relation to a tonic, usually sustained by the tanpura, a four-stringed drone instrument. Rooted in Hindustani music, 'The Moving Drone' sets the…",
"tags": [
"cs.SD"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Moving / Drone / Negotiating",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00847",
"rank": 174,
"score": 760,
"rationale": "High-NA EUV-fabricated quantum dot qubit is a meaningful quantum+manufacturing convergence milestone.",
"title": "Show HN: A curated collection of simple datasets for machine learning",
"url": "https://news.ycombinator.com/item?id=48476571",
"authors": "",
"description": "Show HN: A curated collection of simple datasets for machine learning",
"summary": "Finding from 📰 Hacker News.",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "📰 Hacker News",
"debrief_date": "2026-06-10 09:00",
"debrief_path": "debriefs/debrief-2026-06-10-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 09:00",
"debrief_path": "debriefs/debrief-2026-06-10-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T09:00:00Z"
},
{
"id": "item_00914",
"rank": 175,
"score": 760,
"rationale": "Unsupervised PRMs are high-impact: removes a major annotation bottleneck for reasoning RL, strong evidence reported.",
"title": "Cohere North Mini Code 1.0",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u19pcg/cohere_north_mini_code_10/",
"authors": "",
"description": "Early access was linked here a few days ago, but final release seems to be now. 30B A3B coding model. Weights: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 Blog: https://huggingface.co/blog/CohereLabs/introducing-north-mini-code Artificial analysis score of 28 is pretty weak against Qwen 3.6 35B (43), but it's more competitive in coding index (33 vs 35) and well above Gemma 4 26B (22) there: https://artificialanalysis.ai/models/north-mini-code submitted by /u/Middle_Bullfrog_6173 [link] [comments]",
"summary": "Early access was linked here a few days ago, but final release seems to be now. 30B A3B coding model. Weights: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 Blog:…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_01007",
"rank": 176,
"score": 760,
"rationale": "First privilege-escalation on Apple M5; major security finding with AI-assisted discovery angle.",
"title": "ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations",
"url": "https://huggingface.co/papers/2606.11188",
"authors": "",
"description": "This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework. ARM is built on three efforts: first, we train a discrete semantic visual tokenizer that maps images into compact token sequences. Our tokenizer is supervised with multiple objectives that jointly promote semantic discriminability, language alignment and faithful reconstruction, thereby supporting diverse tasks in a shared latent space. With this, we train a 7B autoregressive model over large-scale text and image token sequences, seamlessly developing vision-language perception and generation capabilities. Finally, to further improve preference-aligned behavior for text-to-image generation and instruction-guided editing, ARM applies reinforcement learning (RL) to optimize task-level objectives such as visual quality, instruction adherence, and edit consistency. Surprisingly, the results show that RL not only substantially improves performance on the target tasks (e.g., raising WISE overall from 0.50 to 0.56, GEdit-Bench-EN G_O from 5.75 to 6.68), but also induces cross-task synergy between text-to-image generation and editing. Collectively, these findings highlight autoregressive modeling, when paired with strong representations and preference optimization, as a scalable foundation for multimodal intelligence. Code: https://github.com/wdrink/ARM.",
"summary": "This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01023",
"rank": 177,
"score": 760,
"rationale": "Provable RoPE failure modes in long context: high impact for LLM research.",
"title": "Claude Fable 5 and Claude Mythos 5",
"url": "https://www.anthropic.com/news/claude-fable-5-mythos-5",
"authors": "",
"description": "Today we’re launching Claude Fable 5: a Mythos-class model that we’ve made safe for general use.",
"summary": "Today we’re launching Claude Fable 5: a Mythos-class model that we’ve made safe for general use.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-09 19:00",
"debrief_path": "debriefs/debrief-2026-06-09-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 19:00",
"debrief_path": "debriefs/debrief-2026-06-09-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T19:00:00Z"
},
{
"id": "item_01239",
"rank": 178,
"score": 760,
"rationale": "Pre-Stuxnet Fast16 saboteur targeting nuclear weapons sims is high-impact security history.",
"title": "RP2040 DMA is Turing Complete (2023)",
"url": "https://people.ece.cornell.edu/land/courses/ece4760/RP2040/C_SDK_DMA_machine/DMA_machine_rp2040.html",
"authors": "compsci",
"description": "DMA uses memory controllers separate from the CPU to accelerate data movment between memory locations, or between peripherials and memory. The RP2040 has 12 DMA channels which can stream an agregate of over 100 megabytes/sec without affecting CPU performance, in many cases. There are a huge number of options available to set up a DMA transfer. You can think of a DMA channel controller as a separate, programmable, processor with the main job of moving data. Memory on the rp2040 is arranged as a bus matrix with separate memory bus control masters for each ARM core and for the DMA system, and sev",
"summary": "DMA uses memory controllers separate from the CPU to accelerate data movment between memory locations, or between peripherials and memory. The RP2040 has 12 DMA channels which can stream an agregate of over 100 megabytes/sec …",
"tags": [
"LOBSTERS",
"compsci"
],
"source": "Lobste.rs",
"type": "news",
"section": "🔴 Lobste.rs",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01211",
"rank": 179,
"score": 760,
"rationale": "Concrete AI-exploited vulnerability disclosure from Google; high-impact security/agent news.",
"title": "Multimodal Music Recommendation System using LLMs",
"url": "https://huggingface.co/papers/2606.00125",
"authors": "daily curated papers",
"description": "Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and text-enhanced approaches to sequential recommendation, and while some methods partially combine semantic, acoustic, or engagement signals, none jointly model all three within a unified LLM-based sequential reasoning framework that grounds recommendations in actual song content. In this work, we propose a multimodal framework for session-based music recommendation that enriches the LastFM-1K dataset with three complementary signals: (1) audio and lyric embeddings extracted using pretrained music and text representation models, (2) LLM-generated semantic metadata using the MGPHot annotation schema, and (3) listening completion ratios. We adopt the E4SRec framework by extending it with multimodal features and different item ID encoder backbones, including SASRec, BERT4Rec, and GRU4Rec. We further extend the LLM backbone option with LLaMa-2-13B, Qwen2.5-7B-Instruct, and LLaMa-3-70B in both zero-shot and fine-tuned settings. Our experiments show that integrating content-based features improves over ID-only baselines up to 95% in terms of Recall and 79% in terms of NDCG. Moreover, our experiments show that naive multimodal fusion does not always yield additive improvements, highlighting challenges in cross-modal integration. We release a large-scale multimodal benchmar",
"summary": "Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and text-enhanced approaches to sequential recommendation, and while some …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01719",
"rank": 180,
"score": 760,
"rationale": "High impact: agentic LLM reconstructing Linux binary patches shows offensive/defensive dual-use.",
"title": "AI alignment boundaries",
"url": "https://www.authorea.com/doi/full/10.22541/au.171697103.39692698",
"authors": "Source: google scholar",
"description": "Article from google scholar.",
"summary": "Article from google scholar.",
"tags": [
"google scholar"
],
"source": "Google Scholar",
"type": "paper",
"section": "🔬 Google Scholar",
"group": "All",
"debrief_date": "2026-05-24 11:00",
"debrief_path": "debriefs/debrief-2026-05-24-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-24 11:00",
"debrief_path": "debriefs/debrief-2026-05-24-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-24T11:00:00Z"
},
{
"id": "item_01780",
"rank": 181,
"score": 760,
"rationale": "UniPool global shared MoE challenges per-layer convention with evidence.",
"title": "(https://www.wsj.com/tech/u-s-to-award-quantum-computing-firms-2-billion-and-take-equity-stakes-7382e6be)",
"url": "https://www.wsj.com/tech/u-s-to-award-quantum-computing-firms-2-billion-and-take-equity-stakes-7382e6be",
"authors": "",
"description": "(https://www.wsj.com/tech/u-s-to-award-quantum-computing-firms-2-billion-and-take-equity-stakes-7382e6be)",
"summary": "(https://www.wsj.com/tech/u-s-to-award-quantum-computing-firms-2-billion-and-take-equity-stakes-7382e6be)",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01902",
"rank": 182,
"score": 760,
"rationale": "Stable Counting Capacity across 100+ models exposes context-window marketing vs reality; strong, novel reliability probe.",
"title": "ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop",
"url": "https://huggingface.co/papers/2605.18746",
"authors": "",
"description": "Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passively processing what is seen, they actively uncover what is unseen - occluded structure, dynamics, containment, and functionality that cannot be resolved from passive sensing alone. We move beyond prior formulations of spatial intelligence that assume oracle observations by recasting the observer as an actor. We introduce ESI-BENCH, a comprehensive benchmark for embodied spatial intelligence spanning 10 task categories and 29 subcategories built on OmniGibson, grounded in Spelke's core knowledge systems. Agents must decide what abilities to deploy - perception, locomotion, and manipulation - and how to sequence them to actively accumulate task-relevant evidence. We conduct extensive experiments on state-of-the-art MLLMs and find that active exploration substantially outperforms passive counterparts, with agents spontaneously discovering emergent spatial strategies without explicit instructions, while random multi-view often adds noise rather than signal despite consuming far more images. Most failures stem not from weak perception but from action blindness: poor action choices lead to poor observations, which in turn drive cascading errors. While explicit 3D grounding stabilizes reasoning on depth-sensitive tasks, imperfect 3D representation proves more harmful than 2D baselines by distorting spatia",
"summary": "Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passively processing…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_02284",
"rank": 183,
"score": 760,
"rationale": "Lifecycle security architecture for autonomous agents—directly relevant and timely.",
"title": "FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning",
"url": "https://huggingface.co/papers/2605.09932",
"authors": "Zehua Pei, Hui-Ling Zhen, Xianzhi Yu, Sinno Jialin Pan, Mingxuan Yuan",
"description": "Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to how attention budget is spent during supervised fine-tuning (SFT) on long sequences: positional biases and attention sinks cause the model to allocate most of its attention to positionally privileged tokens rather than semantically relevant content. This training-time attention dilution (the starvation of content tokens in the attention distribution) weakens the gradient signal, limiting the model's ability to learn robust long-context capabilities. We introduce FocuSFT, a bilevel optimization framework that addresses this problem at training time. An inner loop adapts lightweight fast-weight parameters on the training context to form a parametric memory that concentrates attention on relevant content, and the outer loop performs SFT conditioned on this sharpened representation. Both loops apply bidirectional attention over context tokens while preserving causal masking for responses, reducing the causal asymmetry that gives rise to attention sinks and aligning inner-outer behavior. On BABILong, FocuSFT improves accuracy by up to +14pp across 4K--32K context lengths; on RULER, it raises CWE aggregation from 72.9\\% to 81.1\\% at 16K; and on GPQA with agentic tool use, it yields a 24\\% relative gain in pass@1. Attention analysis shows that FocuSFT reduces attention sink mass by 529times and triples cont",
"summary": "Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to how attention budget is spent during supervised fine-tuning…",
"tags": [
"arXiv:2605.09932"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T11:00:00Z"
},
{
"id": "item_02375",
"rank": 184,
"score": 760,
"rationale": "Agentic world modeling taxonomy (levels × laws); foundational conceptual organization for agent research.",
"title": "Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction",
"url": "https://huggingface.co/papers/2605.09649",
"authors": "",
"description": "The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction methods reduce this cost but typically degrade performance relative to full-cache inference. Our key insight is that full-cache attention is not always optimal: in long contexts, irrelevant tokens can dilute attention away from useful evidence, so selective, learnable eviction can improve generation rather than merely approximate the full cache. We introduce a global retention-based KV eviction method that learns each token's future utility under a unified memory budget. Lightweight retention gates assign utility scores to cached KV entries, and a shared final scoring projection calibrates these scores across all layers and heads. This enables a single global eviction policy in which tokens from different layers, heads, and modalities compete directly for cache capacity. We further provide theoretical analysis showing that preferentially retaining useful tokens reduces attention dilution, and we justify geometric retention as a query-agnostic proxy for future utility. Across diverse long-context language and vision-language reasoning, and multi-turn dialogue benchmarks, our method substantially reduces KV memory while matching or surpassing full-cache inference. These results suggest that learned, globally calibrated KV eviction is not only a compression technique, but also a mechanism for improving long-context reasoning.",
"summary": "The key-value (KV) cache is a major bottleneck in long-context inference, where memory and computation grow with sequence length. Existing KV eviction methods reduce this cost but…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T04:00:00Z"
},
{
"id": "item_02567",
"rank": 185,
"score": 760,
"rationale": "25K-run study exposing 68% evidence-ignoring in AI scientists is a strong AI-safety/epistemic finding.",
"title": "Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction",
"url": "https://arxiv.org/abs/2605.06644",
"authors": "Yuchen Xiong, Swee Keong Yeap, Steven Aw Yoong Kit",
"description": "Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identity alone. Protein language models and emission-band averages capture global trends, but do not model how local physical signals act on specific chromophore regions. We present a chromophore-centred mechanism graph algorithm for QY prediction. Each PDB structure is converted into a typed 3D residue graph, registered to a mature-CRO state, partitioned",
"summary": "Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identity alone. Protein language…",
"tags": [
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚛️ Quantum",
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T09:00:00Z"
},
{
"id": "item_02593",
"rank": 186,
"score": 760,
"rationale": "Anthropic Mythos security model leak is significant for AI safety and supply-chain threat discourse.",
"title": "Stop MITM on the first SSH connection, on any VPS or cloud provider",
"url": "https://www.joachimschipper.nl/Stop%20MITM%20on%20the%20first%20SSH%20connection,%20on%20any%20VPS%20or%20cloud%20provider.html",
"authors": "",
"description": "This little script stops attacks on the first SSH connection to a new VM, even on providers (like Hetzner Cloud ) that don't offer a proprietary solution ; we only need cloud-init , which is widely supported .",
"summary": "This little script stops attacks on the first SSH connection to a new VM, even on providers (like Hetzner Cloud ) that don't offer a proprietary solution ; we only need cloud-init , which is widely su…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🍞 Lobste.rs",
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T11:00:00Z"
},
{
"id": "item_02702",
"rank": 187,
"score": 760,
"rationale": "Strong empirical study on symbolic guardrails; 80-benchmark audit is useful, policy gap finding is notable.",
"title": "Large homomorphisms on the homotopy lie coalgebra",
"url": "https://arxiv.org/abs/2605.03813",
"authors": "Andrew J. Soto Levins, Ryan Watson",
"description": "We introduce and study a notion of large homomorphisms on the homotopy lie coalgebra; these homomorphisms are a variant of the large homomorphisms of Levin. As a consequence of our work, we establish new cases of a homotopy lie coalgebra analog of a conjecture of Quillen as proposed by Briggs.",
"summary": "We introduce and study a notion of large homomorphisms on the homotopy lie coalgebra; these homomorphisms are a variant of the large homomorphisms of Levin. As a consequence of our work, we establish new cases…",
"tags": [
"math.AC",
"math.AT"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T11:00:00Z"
},
{
"id": "item_02949",
"rank": 188,
"score": 760,
"rationale": "MCP threat intelligence platform; directly addresses emerging agentic protocol security with open-source tooling.",
"title": "FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments [None]",
"url": "https://huggingface.co/papers/2604.25135",
"authors": "",
"description": "Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs with smaller parameter sizes, limited context windows, and constrained inference budgets, which contribute to increased error accumulation in agentic settings. To tackle these challenges, we present the Failure-Aware Meta-Agentic (FAMA) framework. FAMA operates in two stages: first, it analyzes failure trajectories from baseline agents to identify the most prevalent errors; second, it employs an orchestration mechanism that activates a minimal subset of specialized agents tailored to address these failures by injecting a targeted context for the tool-use agent before the decision-making step. Experiments across open-source LLMs demonstrate performance gains up to 27% across evaluation modes over standard baselines. These results highlight that targeted curation of context through specialized agents to address common failures is a valuable design principle for building reliable, multi-turn tool-use LLM agents that simulate real-world conversational scenarios.",
"summary": "Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🌐 Agents",
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-01T11:00:00Z"
},
{
"id": "item_03032",
"rank": 189,
"score": 760,
"rationale": "Sharp security argument against 'thinking + acting' agents; high relevance to agent safety.",
"title": "Secure signatures without a private key",
"url": "https://katexochen.aro.bz/posts/reproducible-secure-signatures/",
"authors": "Lobste.rs",
"description": "Reproducible builds allow anyone to verify that a binary matches its source code. But what if the build artifact must contain a cryptographic signature? Reproducing the signature requires the private key, which defeats the purpose of reproducibility. In this post, we present a technique based on ECDSA public key recovery that produces signatures which are both secure and fully reproducible, without anyone ever knowing a private key.",
"summary": "Reproducible builds allow anyone to verify that a binary matches its source code. But what if the build artifact must contain a cryptographic signature?…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "📄 Web Findings",
"group": "Lobste.rs (security)",
"debrief_date": "2026-04-29 11:00",
"debrief_path": "debriefs/debrief-2026-04-29-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 11:00",
"debrief_path": "debriefs/debrief-2026-04-29-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T11:00:00Z"
},
{
"id": "item_03131",
"rank": 190,
"score": 760,
"rationale": "Meerkat for cross-trace safety violation detection; addresses a critical and underexplored agent auditing gap.",
"title": "Gate-dependent offset charge shifts and anharmonicity in gatemon qubits in the weak tunneling regime",
"url": "https://arxiv.org/abs/2604.24716",
"authors": "Utkan Güngördü, Rusko Ruskov, Silas Hoffman, Kyle Serniak, Andrew J. Kerman, Charles Tahan",
"description": "Gatemon qubits are based on a superconductor-quantum dot-superconductor (S-QD-S) junction which enables in situ electrostatic tuning via a gate electrode. For a single-channel QD this structure gives rise to two subgap Andreev bound states (ABSs), and generally leads to a richer quantum phase dynamics as compared to conventional transmons. In a recent work [Phys. Rev. B 111, 214503 (2025)] we derived the quantum phase dynamics from a many-body treatment which leads to an effective gate voltage-d",
"summary": "Gatemon qubits are based on a superconductor-quantum dot-superconductor (S-QD-S) junction which enables in situ electrostatic tuning via a gate electrode. For a single-channel QD this structure gives rise to two subgap A…",
"tags": [
"cond-mat.mes-hall",
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚛️ Quantum",
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-28T04:00:00Z"
},
{
"id": "item_03426",
"rank": 191,
"score": 760,
"rationale": "Frontier-model cyber capability assessment is high-impact AI safety signal.",
"title": "Anthropic investigates unauthorized access to unreleased Mythos cybersecurity AI",
"url": "https://news.ycombinator.com/item?id=47867698",
"authors": "",
"description": "HN discussion on Anthropic's investigation into unauthorized access to its unreleased Mythos model.",
"summary": "HN discussion on Anthropic's investigation into unauthorized access to its unreleased Mythos model.",
"tags": [
"💬 Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "💬 Hacker News",
"debrief_date": "2026-04-23 00:00",
"debrief_path": "debriefs/debrief-2026-04-23-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 00:00",
"debrief_path": "debriefs/debrief-2026-04-23-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T00:00:00Z"
},
{
"id": "item_03749",
"rank": 192,
"score": 760,
"rationale": "Empirical demonstration of Claude Opus authoring a Chrome exploit is highly relevant to AI safety and offensive AI.",
"title": "I Let Claude Opus Write a Chrome Exploit: The Next Model (Mythos?) Won't Need My Help?",
"url": "https://www.hacktron.ai/blog/i-let-claude-opus-to-write-me-a-chrome-exploit",
"authors": "",
"description": "Product P r o d u c t Testimonials T e s t i m o n i a l s Pricing P r i c i n g Team T e a m Blog B l o g Advisories A d v i s o r i e s Toggle theme const theme = (() => { const localStorageTheme = localStorage?.getItem('theme') ?? '' if (['dark', 'light'].includes(localStorageTheme)) { return localStorageTheme } if (window.matchMedia('(prefers-color-scheme: dark)').matches) { return 'dark' } return 'light' })() document.documentElement.setAttribute('data-theme', theme) document.documentElement.classList.add( theme === 'dark' ? 'scheme-dark' : 'scheme-light', ) window.localStorage.setItem('t",
"summary": "Product P r o d u c t Testimonials T e s t i m o n i a l s Pricing P r i c i n g Team T e a m Blog B l o g Advisories A d v i s o r i e s Toggle theme const theme = (() => { const…",
"tags": [
"Lobste.rs",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🔗 Lobste.rs",
"group": "Lobste.rs",
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T11:00:00Z"
},
{
"id": "item_03889",
"rank": 193,
"score": 760,
"rationale": "Verification tax: minimax limits on calibration estimation; fundamental AI-evaluation theory.",
"title": "The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime",
"url": "https://arxiv.org/abs/2604.12951",
"authors": "Jason Z Wang",
"description": "The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical noise floor. We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is Theta((Lepsilon/m)^{1/3}), and no estimator can beat it. This \"verification tax\" implies that as AI models improve, verifying their calibration becomes fundamentally harder -- with the same exponent in opposite directions. We establish four results that contradict standard evaluation practice: (1) self-evaluation without labels provides exactly zero information about calibration, bounded by a constant independent of compute; (2) a sharp phase transition at mepsilon approx 1 below which miscalibration is undetectable; (3) active querying eliminates the Lipschitz constant, collapsing estimation to detection; (4) verification cost grows exponentially with pipeline depth at rate L^K. We validate across five benchmarks (MMLU, TruthfulQA, ARC-Challenge, HellaSwag, WinoGrande; ~27,000 items) with 6 LLMs from 5 families (8B-405B parameters, 27 benchmark-model pairs with logprob-based confidence), 95% bootstrap CIs, and permutation tests. Self-evaluation non-significance holds in 80% of pairs. Across frontier models, 23% of pairwise comparisons are indistinguishable from noise, implying that credible calibration claims must report verification floors and prioritize active querying once gains approach benchmark resolution.",
"summary": "The most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 (Guo et al., 2017) -- is below the statistical noise floor. We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is…",
"tags": [
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_04302",
"rank": 194,
"score": 760,
"rationale": "Alignment-context-injection LLM exploit; directly relevant to AI safety/alignment.",
"title": "LeBron James Is President – Exploiting LLMs via \"Alignment\" Context Injection",
"url": "https://github.com/skavanagh/lebron-james-is-president",
"authors": "",
"description": "Article about: LeBron James Is President – Exploiting LLMs via \"Alignment\" Context Injection",
"summary": "Article about: LeBron James Is President – Exploiting LLMs via \"Alignment\" Context Injection",
"tags": [
"Lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04375",
"rank": 195,
"score": 760,
"rationale": "HY-Embodied-0.5 embodied foundation; major real-world agent release with high upvotes.",
"title": "HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents",
"url": "https://huggingface.co/papers/2604.07430",
"authors": "",
"description": "arXiv:2604.07430 | Authors: Tencent Robotics X, HY Vision Team, Xumin Yu, Zuyan Liu, Ziyi Wang | Upvotes: 139",
"summary": "arXiv:2604.07430 | Authors: Tencent Robotics X, HY Vision Team, Xumin Yu, Zuyan Liu, Ziyi Wang | Upvotes: 139",
"tags": [
"Huggingface Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00663",
"rank": 196,
"score": 758,
"rationale": "Practical backdoor defense for fine-tuning with broad applicability and low overhead; strong empirical scope.",
"title": "DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning",
"url": "https://huggingface.co/papers/2606.08035",
"authors": "",
"description": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existing RLVR methods optimize primarily for the reasoning outcome, fundamentally overlooking the fine-grained cross-modal coordination required during the generation process. Through token-level analyses and controlled interventions, we reveal that during Chain-of-Thought (CoT) reasoning, MLLMs frequently fail to dynamically alternate between extracting visual evidence and synthesizing textual context-a coordination breakdown that is causally linked to reasoning failures. Motivated by these findings, we propose DyCo-RL, which integrates dynamic cross-modal coordination into RLVR optimization. Specifically, DyCo-RL uses the Fisher-Rao geodesic distance to measure within-modality attention shifts, assigning tokens to either visually-oriented or text-oriented functional roles. It then evaluates the alignment between a token's actual attention allocation and its assigned role, leveraging this score for alignment-guided advantage reweighting during policy optimization. Extensive experiments demonstrate that the algorithm-agnostic DyCo-RL, when applied to Qwen2.5-VL-3B/7B, consistently improves four representative RLVR algorithms across seven benchmarks spanning visual-centric and mathematical reasoning.",
"summary": "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However,…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00125",
"rank": 197,
"score": 756,
"rationale": "Certifies privacy+integrity for quantum remote sensing via Pauli twirling; rare security+metrology crossover.",
"title": "LLM-Enabled NWDAF: A Step Toward AI-Native 6G Network Intelligence",
"url": "https://huggingface.co/papers/2606.11877",
"authors": "",
"description": "The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and closed-loop automation. Despite its critical role, open-source NWDAF implementations remain limited in scope and accessibility. In this paper, we develop an open-source NWDAF, compatible with the open-source core network Free5GC, that collects network data via subscriptions to Network Functions (NFs), and also includes an integrated Large Language Model (LLM) interface that enables natural language interaction with human operators. The interface processes user intents, encodes them using a semantic embedding model, and maps them to one of seven predefined intent categories to trigger analytics queries or event subscription commands. This architecture abstracts the complexity of traditional interfaces, allowing non-expert users to manage network analytics and subscriptions with ease. The system supports Access and Management Function (AMF) and Session Management Function (SMF) event subscriptions, real-time monitoring, and analytics retrieval via Prometheus, all accessible through a conversational interface. By bridging AI-driven intent recognition with standardized network analytics, our implementation enhances operator usability and provides a foundation towards AI-native 6G networks. The source code and datasets generated during the current study are available in the github repository, https://github.com/Hen",
"summary": "The Network Data Analytics Function (NWDAF) is central to enabling zero-touch network management in fifth-generation (5G) networks by supporting real-time analytics and…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T11:00:00Z"
},
{
"id": "item_02120",
"rank": 198,
"score": 756,
"rationale": "1M+ paper method-evolution graph; significant infrastructure for AI scientist agents and literature mining.",
"title": "Geographic Patterns in I2P Peer Selection: An Empirical Network Topology Analysis",
"url": "https://arxiv.org/abs/2605.14435",
"authors": "Siddique Abubakr Muntaka, Jess Kropczynski, Jacques Bou Abdo, Murat Ozer",
"description": "The Invisible Internet Project (I2P) routes data via encrypted, decentralized tunnels. Peer selection can significantly affect security and performance. This empirical study examines whether geographic location systematically influences I2P's routing topology. Consistent with I2P's design principles, which include avoiding multiple peers from the same /16 IP subnet to maximize anonymity, we conducted assortativity analysis, community detection, and permutation testing on data from 327 routers an",
"summary": "The Invisible Internet Project (I2P) routes data via encrypted, decentralized tunnels. Peer selection can significantly affect security and performance. This empirical study examines whether geographic location…",
"tags": [
"cs.NI",
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Crypto & Blockchain",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_02392",
"rank": 199,
"score": 755,
"rationale": "Duplicate of Agentic World Modeling; high-importance conceptual taxonomy for agents.",
"title": "Learning Visual Feature-Based World Models via Residual Latent Action",
"url": "https://huggingface.co/papers/2605.07079",
"authors": "",
"description": "World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other hand, predict future visual features instead of raw video pixels, offering a promising alternative that is more efficient and less prone to hallucination. However, current feature-based approaches rely on direct regression, which leads to blurry or collapsed predictions in complex interactions, while generative modeling in high-dimensional feature spaces still remains challenging. In this work, we discover that a new type of latent action representation, which we refer to as *Residual Latent Action* (RLA), can be easily learned from DINO residuals. We also show that RLA is predictive, generalizable, and encodes temporal progression. Building on RLA, we propose *RLA World Model* (RLA-WM), which predicts RLA values via flow matching. RLA-WM outperforms both state-of-the-art feature-based and video-diffusion world models on simulation and real-world datasets, while being orders of magnitude faster than video diffusion. Furthermore, we develop two robot learning techniques that use RLA-WM to improve policy learning. The first one is a minimalist world action model with RLA that learns from actionless demonstration videos. The second one is the first visual RL framework trained entirely inside a world model learned from offline videos only, using a video-aligned reward and no online interactions or handc",
"summary": "World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T04:00:00Z"
},
{
"id": "item_03636",
"rank": 200,
"score": 750,
"rationale": "Cross-domain memory transfer for coding agents; strong empirical findings on abstraction.",
"title": "Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents",
"url": "https://huggingface.co/papers/2604.14004",
"authors": "Kangsan Kim, Minki Kang, Taeil Kim, Yanlai Yang, Mengye Ren",
"description": "Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as runtime environments and programming languages, that exist across diverse real-world coding problems. To address this limitation, we investigate Memory Transfer Learning (MTL) by harnessing a unified memory pool from heterogeneous domains. We evaluate performance across 6 coding benchmarks using four memory representations, ranging from concrete traces to abstract insights. Our experiments demonstrate that cross-domain memory improves average performance by 3.7\\%, primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code. Importantly, we find that abstraction dictates transferability; high-level insights generalize well, whereas low-level traces often induce negative transfer due to excessive specificity. Furthermore, we show that transfer effectiveness scales with the size of the memory pool, and memory can be transferred even between different models. Our work establishes empirical design principles for expanding memory utilization beyond single-domain silos. Project page: https://memorytransfer.github.io/",
"summary": "Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as…",
"tags": [
"HuggingFace",
"HuggingFace Papers",
"Huggingface Papers",
"daily curated papers",
"▲ 21",
"▲ 28",
"▲25"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-19 19:00",
"debrief_path": "debriefs/debrief-2026-04-19-19.html",
"debrief_md": "",
"occurrence_count": 9,
"occurrences": [
{
"debrief_date": "2026-04-19 19:00",
"debrief_path": "debriefs/debrief-2026-04-19-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-19 11:00",
"debrief_path": "debriefs/debrief-2026-04-19-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-18 19:00",
"debrief_path": "debriefs/debrief-2026-04-18-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-18 11:00",
"debrief_path": "debriefs/debrief-2026-04-18-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-18 09:00",
"debrief_path": "debriefs/debrief-2026-04-18-09.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-17 19:00",
"debrief_path": "debriefs/debrief-2026-04-17-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-19T19:00:00Z"
},
{
"id": "item_00113",
"rank": 201,
"score": 750,
"rationale": "Next Forcing multi-chunk prediction for world models; faster/accurate video generation, important direction.",
"title": "From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning",
"url": "https://huggingface.co/papers/2606.17682",
"authors": "",
"description": "Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically infer which configuration will best improve the current policy. To automate this process, we propose the LLM-as-Environment-Engineer framework in which the current policy model analyzes failure trajectories together with contextual information and proposes modifications to the next-stage training environment configuration. We also introduce MAPF-FrozenLake, a controllable testbed whose generator exposes multi-dimensional environment configurations, making it suitable for studying and benchmarking environment redesign. On this testbed, we condition the environment engineer on structured summaries of policy behavior, failure cases, and environment statistics, from which it produces the configuration for the next training stage. With Qwen3-4B as the backbone, our framework achieves the strongest aggregate performance on our benchmarks, outperforming larger proprietary LLMs (e.g., GPT, Gemini) and fixed-environment training baselines. We further analyze which forms of context are most effective, finding that successful environment updates rely on failure evidence and preserve configurations that already work. Interestingly, the current RL checkpoint serves as a better environment engineer than the original base model, suggesting that policy learning improves the model's ability to diagnose its remaining we",
"summary": "Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically…",
"tags": [
"▲ 11",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
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],
"sort_date": "2026-06-18T11:00:00Z"
},
{
"id": "item_00395",
"rank": 202,
"score": 750,
"rationale": "Normalizing-flow latent reasoning preserving KV-cache and likelihood; strong method.",
"title": "Filtered Conformal Ellipsoids for Graph-Native Time Series",
"url": "https://arxiv.org/abs/2606.17014",
"authors": "Yannick Limmer",
"description": "Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered conformal ellipsoids: a frozen state-space filter emits a one-step predictive mean and covariance, and split-conformal calibration is applied to the resulting Mahalanobis scores. The filter is used to choose the ellipsoid shape; conformal calibration chooses the scalar radius, so the construction benefits from a learned predictive covariance without relying on Gaussian tail probabilities for coverage. The main difficulty is that filtered scores are dependent and learned recurrent filters need not contract in their raw hidden state; we therefore analyse contraction in an observable predictive-law quotient that identifies hidden states producing the same future sequence of emitted Gaussian laws. Under a stable Bayes Gaussian-projection filter, covariance bounds, and a finite-horizon observability Fisher condition, small excess Gaussian negative log-likelihood implies contraction of the learned emitted laws. Combined with a threshold-autocovariance envelope this yields a Chebyshev-type approximate coverage bound for filtered split-conformal prediction under dependence; a sharper Bernstein-type bound requires an additional geometric-mixing concentration assumption. Under Gaussian oracle realisability we also obtain a near-oracle log-volume comparison within the class of conditionally valid Gaussian ellipsoid rules. We instantiate the fr",
"summary": "Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered conformal ellipsoids: a frozen state-space filter…",
"tags": [
"cs.LG",
"math.ST",
"stat.ML"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Filtered / Conformal / Ellipsoids",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
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"debrief_md": ""
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"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00475",
"rank": 203,
"score": 750,
"rationale": "Trace-answer dissociation under adversarial pressure; novel reasoning-safety failure mode.",
"title": "Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications",
"url": "https://arxiv.org/abs/2606.14658",
"authors": "Nicole Villavicencio-Garduño, Maksim Ekin Eren, Milo Prisbrey, Ben Migliori, Michael Teti",
"description": "Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras. Recent research has shown that acoustic vibration can induce real physical motion in cameras, interfering with their internal stabilization mechanisms. Because the motion falls outside the conditions the stabilization system was designed to handle, the system introduces artifacts into the frame, causing AI-based CV models to misclassify, miss targets, or hallucinate objects. Previous work used ultrasonic frequencies (>20 kHz) to perform short-range attacks, which limits them to short distances due to the attenuation exhibited by high frequencies. In this work, we investigate acoustic attacks using lower frequencies in the audible range (<20 kHz), and we further expand our analysis to include how various image and object features are affected by the attacks. Specifically, we performed physical experiments to demonstrate the viability of our attacks on an off-the-shelf object detection model (YOLO11) by resonating a commercially available camera with various frequencies. Based on our results, we provide insights into several factors that make an AI CV system more vulnerable to these attacks, which could help inform the development of future mitigation strategies.",
"summary": "Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras.…",
"tags": [
"cs.CV",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Giving / Headache / Acoustic",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00805",
"rank": 204,
"score": 750,
"rationale": "Open deep research agents trained on 8K synthetic tasks rivaling closed-source; strong agents contribution.",
"title": "To Gen or Not To Gen: The Ethical Use of Generative AI",
"url": "https://blog.johanneslink.net/2025/11/04/to-gen-or-not-to-gen/",
"authors": "",
"description": "Is it ethical to use hyper-scaled GenAI?",
"summary": "Is it ethical to use hyper-scaled GenAI?",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_01740",
"rank": 205,
"score": 750,
"rationale": "High impact: Let's Encrypt issuance halt signals major CA infrastructure incident, broad web impact.",
"title": "KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving",
"url": "https://huggingface.co/papers/2605.13734",
"authors": "Zedong Liu, Xinyang Ma, Dejun Luo, Hairui Zhao, Bing Lu",
"description": "LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability and cost efficiency, but it also turns KV into an explicit payload crossing network and storage boundaries, making KV a dominant end-to-end bottleneck. Existing KV compression are typically static runtime configurations, despite production service context varies over time in workload mix, bandwidth, and SLO/quality budgets. As a result, a fixed choice can be suboptimal or even increase latency. We present KVServe, the first service-aware and adaptive KV communication compression framework for disaggregated LLM serving: KVServe (1) unifies KV compression into a modular strategy space with new components and cross-method recomposition; (2) introduces Bayesian Profiling Engine that efficiently searches this space and distills a 3D Pareto candidate set, reducing 50times offline search overhead; and (3) deploys a Service-Aware Online Controller that combines an analytical latency model with a lightweight bandit to select profiles under constraints and correct offline-to-online mismatch. Integrated into vLLM and evaluated across datasets, models, GPUs and networks, KVServe achieves up to 9.13times JCT speedup in PD-separated serving and up to 32.8times TTFT reduction in KV-disaggregated serving.",
"summary": "LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability and cost efficiency, but it also turns KV into an explicit payload crossing network and storage boundaries, making KV a dominant end-to-end…",
"tags": [
"arXiv:2605.13734"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_02257",
"rank": 206,
"score": 750,
"rationale": "Minecraft SciCrafter benchmark for discovery-to-application loop; strong agent evaluation.",
"title": "Achieving NGINX Remote Code Execution via an 18-Year-Old Vulnerability",
"url": "https://depthfirst.com/research/nginx-rift-achieving-nginx-rce-via-an-18-year-old-vulnerability",
"authors": "",
"description": "We used the depthfirst system to analyze the NGINX source code, and it autonomously discovered 4 remote memory corruption issues, including a critical heap buffer overflow introduced in 2008. We further investigated the exploitability of the issues, and developed a working proof of concept demonstrating RCE with ASLR off. If you use rewrite and set directives in your NGINX configuration, you",
"summary": "We used the depthfirst system to analyze the NGINX source code, and it autonomously discovered 4 remote memory corruption issues, including a critical heap buffer overflow…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "Lobste.rs",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_03060",
"rank": 207,
"score": 750,
"rationale": "NIST PQC round-4 status report; authoritative, high-impact for cryptographic transition strategy.",
"title": "Computing quantum waves from classical action",
"url": "https://news.ycombinator.com/item?id=47939570",
"authors": "",
"description": "Article on hackernews — see link for details.",
"summary": "Article on hackernews — see link for details.",
"tags": [
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "📰 News Headlines",
"group": "▲ Hacker News",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_03812",
"rank": 208,
"score": 750,
"rationale": "OccuBench professional agent benchmark via Language World Models; broad coverage of 65 domains, strong agent eval.",
"title": "OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language World Models",
"url": "https://huggingface.co/papers/2604.10866",
"authors": "",
"description": "AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import processing), yet existing benchmarks can only evaluate agents in the few domains where public environments exist. We introduce OccuBench, a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains, enabled by Language World Models (LWMs) that simulate domain-specific environments through LLM-driven tool response generation. Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity. OccuBench evaluates agents along two complementary dimensions: task completion across professional domains and environmental robustness under controlled fault injection (explicit errors, implicit data degradation, and mixed faults). We evaluate 15 frontier models across 8 model families and find that: (1) no single model dominates all industries, as each has a distinct occupational capability profile; (2) implicit faults (truncated data, missing fields) are harder than both explicit errors (timeouts, 500s) and mixed faults, because they lack overt error signals and require the agent to independently detect data degradation; (3) larger models, newer generations, and higher reasoning effort consistently improve performance. GPT-5.2 improves by 27.5 points fro",
"summary": "AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import processing), yet…",
"tags": [
"HuggingFace",
"▲ 42"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🔗 HuggingFace",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_04171",
"rank": 209,
"score": 746,
"rationale": "Unified prompt-injection evaluation platform directly addresses critical LLM security gap; high impact.",
"title": "PIArena: A Platform for Prompt Injection Evaluation",
"url": "https://arxiv.org/abs/2604.08499",
"authors": "Runpeng Geng, Chenlong Yin, Yanting Wang, Ying Chen, Jinyuan Jia",
"description": "Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the community faces a critical gap: the lack of a unified platform for prompt injection evaluation. This makes it challenging to reliably compare defenses, understand their true robustness under diverse attacks, or assess how well they generalize across tasks and benchmarks. For instance, many defenses initially reported as effective were later found to exhibit limited robustness on diverse datasets and attacks. To bridge this gap, we introduce PIArena, a unified and extensible platform for prompt injection evaluation that enables users to easily integrate state-of-the-art attacks and defenses and evaluate them across a variety of existing and new benchmarks. We also design a dynamic strategy-based attack that adaptively optimizes injected prompts based on defense feedback. Through comprehensive evaluation using PIArena, we uncover critical limitations of state-of-the-art defenses: limited generalizability across tasks, vulnerability to adaptive attacks, and fundamental challenges when an injected task aligns with the target task. The code and datasets are available at https://github.com/sleeepeer/PIArena.",
"summary": "Prompt injection attacks pose serious security risks across a wide range of real-world applications.",
"tags": [
"cs.AI",
"cs.CL",
"cs.CR",
"cs.CR, cs.AI, cs.CL, cs.LG",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 04:00",
"debrief_path": "debriefs/debrief-2026-04-11-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00662",
"rank": 210,
"score": 745,
"rationale": "Longitudinal agent reliability framework with practical AgingBench; addresses real deployment concern.",
"title": "VIA-SD: Verification via Intra-Model Routing for Speculative Decoding",
"url": "https://huggingface.co/papers/2606.12243",
"authors": "",
"description": "Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft-verify methods use binary decisions: accept or fully recompute. Yet we find that many rejected tokens can be verified correctly by a slim submodel derived from the full verifier via intra-model routing, instead of the full verifier. This motivates our slim-verifier to handle tokens requiring moderate verification resources, reducing expensive large-model calls. We propose Verification via Intra-Model Routing for Speculative Decoding (VIA-SD), a multi-tier framework using a routed slim-verifier. Draft tokens are processed hierarchically: direct acceptance for high-confidence cases, slim-verifier regeneration for medium-confidence cases, and full-model verification for uncertain cases. Across four representative tasks and multiple model families, VIA-SD reduces rejection rates by 0.10-0.22 and delivers 10-20% speedups over strong SD baselines, while achieving 2.5-3x acceleration over non-drafting decoding. Moreover, VIA-SD is compatible with existing SD frameworks without modifying their training procedures. Our results suggest multi-tier SD as a general paradigm for scalable and efficient LLM inference. Project page: https://zju-xyc.github.io/VIA-SD-Project-Page/",
"summary": "Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing…",
"tags": [
"▲ 7",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00679",
"rank": 211,
"score": 742,
"rationale": "Real Linux LPE CVE with LLM-assisted chain composition; significant security research angle.",
"title": "PianoKontext: Expressive Performance Rendering from Deadpan Context",
"url": "https://huggingface.co/papers/2606.12282",
"authors": "",
"description": "Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.",
"summary": "Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00691",
"rank": 212,
"score": 742,
"rationale": "SAE-internals for RL data engineering is a strong mechanistic-meets-systems paper; broadly useful.",
"title": "Building Social World Models with Large Language Models",
"url": "https://huggingface.co/papers/2606.11482",
"authors": "",
"description": "Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.",
"summary": "Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 19:00",
"debrief_path": "debriefs/debrief-2026-06-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 19:00",
"debrief_path": "debriefs/debrief-2026-06-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T19:00:00Z"
},
{
"id": "item_02101",
"rank": 213,
"score": 742,
"rationale": "Rehabilitates FD as a trainable objective with strong ImageNet results and FID limitations insight.",
"title": "PickleFuzzer: A Case Study in Fuzzing for Discrepancies Between Python Pickle Implementations",
"url": "https://arxiv.org/abs/2605.15084",
"authors": "Justin Applegate, Andreas Kellas",
"description": "Python's native serialization protocol, pickle, is a powerful but insecure format for transferring untrusted data. It is frequently used, especially for saving machine learning models, despite known security challenges. While developers sometimes mitigate this risk by restricting imports during unpickling or using static and dynamic analysis tools, these approaches are error-prone and depend heavily on accurate interpretations of the Pickle Virtual Machine (PVM) opcodes. Discrepancies across Pyt",
"summary": "Python's native serialization protocol, pickle, is a powerful but insecure format for transferring untrusted data. It is frequently used, especially for saving machine learning models, despite known security challenges.…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_02119",
"rank": 214,
"score": 742,
"rationale": "Agent-Native Research Artifacts: compelling reframing of publication for agent consumers; high meta-impact.",
"title": "Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs",
"url": "https://arxiv.org/abs/2605.14718",
"authors": "Shruthi Gorantala, Jianming Tong, Asra Ali, Baiyu Li, Jonathan Katz, Jeremy Kun, Thomas Steinke, Abhradeep Thakurta, Julian Walker, Amir Yazdanbakhsh",
"description": "The deployment of Fully Homomorphic Encryption (FHE) at scale is hindered due to its heavy computational overhead. While specialized hardware accelerators like Google Tensor Processing Units (TPUs) can help, mapping complex cryptographic kernels onto such architectures remains a challenge. Efficient execution requires co-optimization between the systolic array-based Matrix Multiplication Unit (MXU) and Vector Processing Units (VPUs), as well as the orchestration of data movement across the vecto",
"summary": "The deployment of Fully Homomorphic Encryption (FHE) at scale is hindered due to its heavy computational overhead. While specialized hardware accelerators like Google Tensor Processing Units (TPUs) can help, mapping…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Crypto & Blockchain",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_03668",
"rank": 215,
"score": 742,
"rationale": "Symbolic superoptimizer for tensor programs is technically significant for ML systems/compilers.",
"title": "Prism: Symbolic Superoptimization of Tensor Programs",
"url": "https://arxiv.org/abs/2604.15272",
"authors": "Mengdi Wu, Xiaoyu Jiang, Oded Padon, Zhihao Jia",
"description": "This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large classes of tensor programs by symbolically representing some execution parameters. Prism organizes optimization as a two-level search: it constructs symbolic graphs that represent families of programs, and then instantiates them into concrete implementations. This formulation enables structured pruning of provably suboptima",
"summary": "This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that compactly encodes large classes of tensor…",
"tags": [
"cs.PL,",
"cs.AI,",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Machine Learning",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_02202",
"rank": 216,
"score": 740,
"rationale": "DeepSeek new flagship release; significant competitive signal for frontier AI.",
"title": "MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching",
"url": "https://huggingface.co/papers/2605.08557",
"authors": "",
"description": "Parameter-efficient adaptation of pretrained vision models is commonly performed through linear probes, prompts, low-rank updates, or lightweight residual modules. While effective, these methods usually treat adaptation as a discrete Euclidean perturbation of frozen representations, without explicitly modeling the geometry of the task-induced feature displacement. We propose MC-RFM, a mixed-curvature Riemannian flow-matching framework for few-shot adaptation of frozen visual backbones. The key idea is to represent adapted features on a product manifold combining a hyperbolic factor, which captures hierarchy-sensitive semantic structure, and a Euclidean factor, which preserves locally discriminative visual variation. Adaptation is formulated as a task-conditioned continuous transport from frozen features to support-set prototypes, trained with a flow-matching objective and coupled to a hybrid prototype-linear classifier. The method is lightweight, backbone-agnostic, and operates entirely on cached frozen features. Across seven visual recognition benchmarks, five frozen backbones, and 1/4/16-shot regimes, MC-RFM is the best-performing method in a majority of evaluated settings, with the strongest gains on Transformer backbones and fine-grained datasets. Ablations show that the mixed-curvature head, task conditioning, adaptive branch gating, prototype shrinkage, and discriminative supervision each contribute to performance. These results suggest that few-shot adaptation benefits",
"summary": "Parameter-efficient adaptation of pretrained vision models is commonly performed through linear probes, prompts, low-rank updates, or lightweight residual…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤖 Machine Learning",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_04355",
"rank": 217,
"score": 740,
"rationale": "Conditional analysis of reasoning SFT generalization; timely post-training insight.",
"title": "Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability",
"url": "https://huggingface.co/papers/2604.06628",
"authors": "",
"description": "A prevailing narrative in LLM post-training holds that supervised finetuning (SFT) memorizes while reinforcement learning (RL) generalizes. We revisit this claim for reasoning SFT with long chain-of-thought (CoT) supervision and find that cross-domain generalization is not absent but conditional, jointly shaped by optimization dynamics, training data, and base-model capability. Some reported failures are under-optimization artifacts: cross-domain performance first degrades before recovering and improving with extended training (a dip-and-recovery pattern), so shorttraining checkpoints can underestimate generalization. Data quality and structure both matter: low-quality solutions broadly hurt generalization,while verified long-CoT traces yield consistent cross-domain gains. Model capability is essential: stronger models internalize transferable procedural patterns (e.g., backtracking) even from a toy arithmetic game, while weaker ones imitate surface verbosity. This generalization is asymmetric, however: reasoning improves while safety degrades, reframing the question from whether reasoning SFT generalizes to under what conditions and at what cost.",
"summary": "arXiv:2604.06628 | Authors: Qihan Ren, Peng Wang, Ruikun Cai, Shuai Shao, Dadi Guo | Upvotes: 186",
"tags": [
"Huggingface Papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00098",
"rank": 218,
"score": 740,
"rationale": "Piper decouples training parallelism strategy from runtime; important systems contribution for large models.",
"title": "Reinforcement Learning-Guided Retrieval with Soft Fusion for Robust Multimodal Imitation Learning under Missing Modalities",
"url": "https://huggingface.co/papers/2606.15514",
"authors": "",
"description": "Robotic systems perceive the world through multiple input modalities -- including visual camera streams and natural language instructions -- and must select appropriate actions based on these signals. However, assuming the permanent availability of all input devices is unrealistic, as sensors may fail, become occluded, or drop out entirely during deployment. Robust handling of such missing-modality scenarios is therefore essential for real-world robot operation. This paper introduces RL4IL, a reinforcement learning guided method for imitation learning that selects the most suitable action for a given observation by identifying the most relevant expert demonstrations from a training library. A reinforcement learning policy, trained via Proximal Policy Optimisation over Breadth-First Search candidate sets, ranks candidate demonstrations and a soft cross-attention fusion head aggregates their action signals to produce the final prediction. When a modality is missing at inference time, a dedicated per-modality RL retrieval policy identifies donor demonstrations from the training library, and a soft imputation head reconstructs the missing embedding via cross-attention over the top-ranked donors -- without requiring any retraining of the system. Experiments on three LIBERO benchmark suites demonstrate that RL4IL substantially outperforms state-of-the-art imitation learning methods under sensor dropout conditions, while requiring no policy network training. The code can be found at",
"summary": "Robotic systems perceive the world through multiple input modalities -- including visual camera streams and natural language instructions -- and must select appropriate actions…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_00249",
"rank": 219,
"score": 740,
"rationale": "Bayesian skill evolution for agent harnesses; strong agent-strategy relevance.",
"title": "ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues",
"url": "https://arxiv.org/abs/2606.18237",
"authors": "Shanda Li, Qiuhong Anna Wei, Jingwu Tang, Valerie Chen, Nihar B Shah, Tim Dettmers, Yiming Yang, Ameet Talwalkar",
"description": "Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and evaluate four frontier model-agent configurations. Our results show that LLM agents, even without executing code, can identify many real-world reproducibility problems from paper-repository pairs: the best agent in our study, namely Codex with GPT-5.5, surfaces at least one semantically related human-reported blocker for ~90% of papers in the study. Further analysis shows that agents are particularly effective for surfacing visible failures and identifying the right semantic region, but may still be insufficient in exact localization. ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real-world reproducibility auditing. Our code is released at https://github.com/LithiumDA/ReproRepo.",
"summary": "Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility,…",
"tags": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Reprorepo / Scaling / Reproducibility",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00328",
"rank": 220,
"score": 740,
"rationale": "WebMCP tool-surface poisoning/MSTI attack; landmark agentic web security finding.",
"title": "LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies",
"url": "https://huggingface.co/papers/2606.15768",
"authors": "",
"description": "Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene. World-Action Models (WAMs) address this limitation by conditioning policies on predicted futures, yet existing approaches typically rely on computationally expensive video generation with substantial pixel-level redundancy. We present LaWAM, a Latent World Action Model that exposes predictive dynamics to robot policies through compact latent visual subgoals instead of reconstructed future video. At the core of LaWAM is a latent-action-conditioned Latent World Model (LaWM). We obtain LaWM by training a latent action model in the latent space of a pretrained vision foundation model and repurposing its forward decoder to predict future observation features for scene evolution. LaWAM then conditions action generation on these predicted latent visual subgoals to enable dynamics-aware robot control. LaWAM achieves state-of-the-art or competitive success rates (SRs) across LIBERO (98.6% SR), RoboTwin (91.22% SR), and real-world manipulation tasks while retaining low-latency inference. LaWAM runs in 187 ms per action-chunk prediction and achieves up to 24x lower wall-clock latency than pixel-space WAMs.",
"summary": "Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T11:00:00Z"
},
{
"id": "item_00392",
"rank": 221,
"score": 740,
"rationale": "Economic shadow-price budget allocation for inference; principled, broadly applicable.",
"title": "Hadronic tensor in lattice gauge theories by quantum computing",
"url": "https://arxiv.org/abs/2606.17003",
"authors": "Dairui Zou, Tianyin Li, Jian Liang, Enke Wang, Hongxi Xing",
"description": "The hadronic tensor encodes crucial information regarding the internal structure of hadrons, reflecting the non-perturbative features of quantum chromodynamics (QCD). In this work, we directly compute the hadronic tensor within (1+1)-dimensional $\\rm U(1)$ and $\\rm SU(2)$ gauge theories by evaluating real-time current-current correlation functions. Utilizing quantum algorithms executed on classical hardware, we demonstrate that the hadron form factors for both meson and baryon states can be reliably extracted from the hadronic tensor. Our methodology is validated by strong agreement with both direct calculation and exact diagonalization of the form factors.",
"summary": "The hadronic tensor encodes crucial information regarding the internal structure of hadrons, reflecting the non-perturbative features of quantum chromodynamics (QCD). In this work, we directly…",
"tags": [
"hep-ph",
"hep-lat",
"nucl-th"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Hadronic / Tensor / Lattice",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00818",
"rank": 222,
"score": 740,
"rationale": "Attractor-based equilibrium reasoners scale depth/breadth; 2.6→99% on Sudoku, strong reasoning contribution.",
"title": "ICMI 2026 Reviews [D]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u2mzek/icmi_2026_reviews_d/",
"authors": "",
"description": "Did anyone else submit to ACM ICMI 2026? The reviews were recently released, and this is my first time submitting to ICMI, so I'm not very familiar with the acceptance patterns. I submitted a long paper and received the following overall ratings: 4 (Probably Accept), 3 (Borderline), 4 (Probably Accept) The reviewer with the highest stated expertise recommended acceptance, while the borderline reviewer had some concerns about soundness but still considered it a nice contribution. For those who have submitted to or reviewed for ICMI before, how would you interpret these scores? Is a 4/3/4 genera",
"summary": "Did anyone else submit to ACM ICMI 2026? The reviews were recently released, and this is my first time submitting to ICMI, so I'm not very familiar with the acceptance patterns. I…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_01041",
"rank": 223,
"score": 740,
"rationale": "Project Glasswing: Mythos on real infra; important AI-for-security findings.",
"title": "iOSWorld: A Benchmark for Personally Intelligent Phone Agents",
"url": "https://arxiv.org/abs/2606.09764",
"authors": "Lawrence Keunho Jang, Mareks Woodside, Geronimo Carom, Andrew Keunwoo Jang, Jing Yu Koh, Ruslan Salakhutdinov",
"description": "A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device, not just follow isolated instructions in an impersonal sandbox. Existing mobile agent benchmarks lack this kind of personalization. We introduce iOSWorld, the first interactive native iOS simulator benchmark built around a persistent user identity spanning 26 newly built iOS apps. These apps contain connected data such as transactions, messages, travel records, social relationships, and financial activity. iOSWorld includes 133 tasks across three increasingly difficult categories. Single-app tasks (27) test one app, multi-app tasks (60) span 2 to 8 apps, and memory and personalization tasks (46) require agents to infer patterns from personal data. We evaluate frontier and open-source computer-use models in both vision-only and privileged vision+XML settings. The best configuration reaches 52\\% overall but only 37\\% on multi-app tasks. Privileged vision+XML access improves frontier models by up to 26 percentage points, while smaller models do not benefit from added accessibility-tree input. We release iOSWorld as an open-source benchmark with all apps, seeded data, tasks, rubrics, and evaluation code.",
"summary": "A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device, not just follow isolated instructions in an…",
"tags": [
"cs.LG",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Machine Learning",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01195",
"rank": 224,
"score": 740,
"rationale": "HarnessAudit audits full agent trajectories across boundary, fidelity, stability; high AI-safety relevance.",
"title": "The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset",
"url": "https://huggingface.co/papers/2606.02956",
"authors": "daily curated papers",
"description": "Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized sensor suite combines high-resolution global-shutter cameras, long-range lidar beyond 400m, 4D imaging radar, and redundant GNSS/INS localization. Our HD maps are, to our knowledge, the most complete of any sensor dataset, validated through autonomous driving trials on open-source software. For the first time in a public dataset, all driving-relevant traffic elements, such as traffic lights, are mapped in 3D to a reprojection-accurate level with full topological connectivity. Recorded in cities with irregular street layouts and mixed traffic modes, our dataset complements existing datasets by broadening the available geographic diversity. We also introduce four benchmarks, each advancing spatial learning for embodied AI: online HD map construction, long-range depth estimation, novel view synthesis, and end-to-end driving. Project page: https://kitscenes.com/",
"summary": "Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01235",
"rank": 225,
"score": 740,
"rationale": "IEEE 'Case Against Quantum Computing' positions a long-running skepticism debate; high strategy value.",
"title": "Personal AI Agent for Camera Roll VQA",
"url": "https://huggingface.co/papers/2606.05275",
"authors": "daily curated papers",
"description": "We study the personal camera roll visual question answering setting. In this setting, a conversational AI assistant can access a user's personal camera roll and retrieve relevant photos to answer queries, ranging from simple factual questions (e.g., ``Name of the food I tried yesterday?'') to more open-ended ones (e.g., ``Recommend some dishes I have never eaten before''). Given the vast nature of the personal camera roll (i.e., multiple years, hundreds to thousands of photos), a successful AI assistant needs to understand a long-horizon, highly personalized visual content stream in order to navigate and locate the correct and/or relevant information. To support this, we collect and manually annotate questions that mimic real-world usage. The final dataset, camroll, contains 50 users, 31,476 images, and 2,500 QA pairs. We further design camroll-agent, a conversational AI agent equipped with hierarchical memory and a minimal set of tools for efficient navigation over large, personalized visual memory. Experimental results show that camroll-agent outperforms numerous baselines and methods for long-context understanding AI agents system. Together, the camroll dataset and camroll-agent highlight the gap in AI agents' long-context reasoning: personalized visual memory requires different approaches from standard long-context textual memory, especially when consistency, visual details, and user-specific context are present.",
"summary": "We study the personal camera roll visual question answering setting. In this setting, a conversational AI assistant can access a user's personal camera roll and retrieve relevant photos to answer queries, ranging from simple factual …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01258",
"rank": 226,
"score": 740,
"rationale": "Position paper critiquing behavioural assurance for AI safety governance; high-relevance safety argument.",
"title": "Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching",
"url": "https://huggingface.co/papers/2606.03911",
"authors": "",
"description": "Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits scalability, especially for video editing where collecting paired data is prohibitively expensive. We propose Bootstrap Your Generator (ByG), a general framework for unpaired training of flow matching editing models. It leverages the base model's knowledge without any external signal. Our approach pairs instruction-following cues extracted from the frozen model with cycle-consistency for structure preservation. To make this tractable, we propose to route gradients from downstream losses over clean predictions to noisy training states. We demonstrate state-of-the-art results on challenging data-scarce image and video editing scenarios. Extensive evaluations and user studies show that our method effectively generalizes to unseen domains and outperforms supervised baselines trained on millions of samples. Analysis reveals that our gradient routing bridges the train-inference gap, and extracting semantic cues from a base model provides a robust training signal that obviates the need for external reward models.",
"summary": "Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-06-03 11:00",
"debrief_path": "debriefs/debrief-2026-06-03-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 11:00",
"debrief_path": "debriefs/debrief-2026-06-03-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T11:00:00Z"
},
{
"id": "item_01443",
"rank": 227,
"score": 740,
"rationale": "Parallel-streams-of-thought addresses a real agent bottleneck; paradigm-shifting framing for chat-model architecture.",
"title": "An AI audit of FreeBSD",
"url": "https://blog.calif.io/p/an-ai-audit-of-freebsd",
"authors": "Lobste.rs • security • 2026-05-29",
"description": "15 kernel bugs, including 3 RCEs, 5 LPEs, and 1 bhyve escape.",
"summary": "15 kernel bugs, including 3 RCEs, 5 LPEs, and 1 bhyve escape. It matters because defensive practice is being reshaped by both AI-native tooling and the coming post-quantum transition. …",
"tags": [
"Lobste.rs",
"security",
"2026-05-29"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-29T19:00:00Z"
},
{
"id": "item_01686",
"rank": 228,
"score": 740,
"rationale": "Notable: Aurora optimizer beating AdamW at frontier scale is a credible Muon successor.",
"title": "SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research",
"url": "https://huggingface.co/papers/2605.22878",
"authors": "Shuofei Qiao, Yunxiang Wei, Jiazheng Fan, Bin Wu, Busheng Zhang",
"description": "The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmented and unstructured knowledge organization impedes deep interdisciplinary integration. Current academic retrieval tools predominantly rely on superficial keyword matching or vector-space semantic retrieval, which lack the topological reasoning capabilities required to navigate complex logical connections. Agentic deep-research-based frameworks are often prone to logical hallucinations and consuming high inference costs. To bridge this gap, in this report, we introduce SciAtlas, a large-scale, multi-disciplinary, heterogeneous academic resource knowledge graph designed as a panoramic scientific evolution network. By integrating over 43M papers from 26 disciplines, and a total of 157M entities and 3B triplets, SciAtlas provides a structured topological cognitive substrate that dismantles disciplinary barriers and furnishes AI agents with a global perspective. Furthermore, we develop a neuro-symbolic retrieval algorithm featuring tri-path collaborative recall and graph reranking, achieving a seamless transition from simple semantic matching to deterministic association discovery. We also present key application directions of SciAtlas, including literature review, automated research trend synthesis, idea positioning, and academic trajectory exploration, to demonstrate that SciAtlas can serve as an effective ``cognitive map'' to e",
"summary": "The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmented and unstructured knowledge organization impedes deep interdisciplinary integration. ...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-25T16:00:00Z"
},
{
"id": "item_01766",
"rank": 229,
"score": 740,
"rationale": "Identifies aggregation bias in GRPO and proposes drop-in fix, broadly useful.",
"title": "TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks",
"url": "https://huggingface.co/papers/2605.22535",
"authors": "Zhaoyang Chu, Jiarui Hu, Xingyu Jiang, Pengyu Zou, Han Li",
"description": "We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from \"in-the-wild\" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 representative, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve. Data and code are available at https://github.com/EuniAI/TerminalWorld.",
"summary": "We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from \"in-the-wild\" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short…",
"tags": [
"arXiv:2605.22535"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01745",
"rank": 230,
"score": 740,
"rationale": "EMO MoE for emergent modularity addresses a real MoE deployment limitation.",
"title": "\"I didn't Make the Micro Decisions\": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration",
"url": "https://huggingface.co/papers/2605.21363",
"authors": "Eunsu Kim, Jessica R. Mindel, Kyungjin Kim, Sherry Tongshuang Wu",
"description": "As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, CoTrace, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying CoTrace to 638 real-world collaboration logs, we find that while models account for only 11-26% of goal-shaping contribution, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.",
"summary": "As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the…",
"tags": [
"arXiv:2605.21363"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_02239",
"rank": 231,
"score": 740,
"rationale": "GitHub Enterprise RCE CVE-2026-3854 CVSS 8.7; high-impact security disclosure.",
"title": "Covering Human Action Space for Computer Use: Data Synthesis and Benchmark",
"url": "https://huggingface.co/papers/2605.12501",
"authors": "Miaosen Zhang, Xiaohan Zhao, Zhihong Tan, Zhou Huoshen, Yijia Fan",
"description": "Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user trust. Our analysis of failure cases from advanced models suggests a long-tail pattern in GUI operations, where a relatively small fraction of complex and diverse interactions accounts for a disproportionate share of task failures. We hypothesize that this issue largely stems from the scarcity of data for complex interactions. To address this problem, we propose a new benchmark CUActSpot for evaluating models' capabilities on complex interactions across five modalities: GUI, text, table, canvas, and natural image, as well as a variety of actions (click, drag, draw, etc.), covering a broader range of interaction types than prior click-centric benchmarks that focus mainly on GUI widgets. We also design a renderer-based data-synthesis pipeline: scenes are automatically generated for each modality, screenshots and element coordinates are recorded, and an LLM produces matching instructions and action traces. After training on this corpus, our Phi-Ground-Any-4B outperforms open-source models with fewer than 32B parameters. We will release our benchmark, data, code, and models at https://github.com/microsoft/Phi-Ground.git",
"summary": "Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions is still poor, limiting user trust. Our analysis of failure cases from advanced models suggests a…",
"tags": [
"cs.CL",
"cs.CV",
"cs.AI"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02405",
"rank": 232,
"score": 740,
"rationale": "Foundational complexity-theoretic result on transformer succinctness vs automata.",
"title": "X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction",
"url": "https://huggingface.co/papers/2605.05765",
"authors": "",
"description": "Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report, we introduce X-OmniClaw, a unified mobile agent designed for multimodal understanding and interaction in the Android ecosystem. This unified architecture of perception, memory, and action enables the agent to handle complex mobile tasks with high contextual awareness. Specifically, Omni Perception provides a unified multimodal ingress pipeline that integrates UI states, real-world visual contexts, and speech inputs, leveraging a temporal alignment module to decompose raw data into structured multimodal intent representations. Omni Memory leverages multimodal memory optimization to enhance personalized intelligence by integrating runtime working memory for task continuity with long-term personal memory distilled from local data, enabling highly context-aware and personalized interactions. Finally, Omni Action employs a hybrid grounding strategy that combines structural XML metadata with visual perception for robust interaction. Through Behavior Cloning and Trajectory Replay, the system captures user navigation as reusable skills, enabling precise direct-access execution. Demonstrations across diverse scenarios show that X-OmniClaw effectively enhances interaction efficiency and task reliability, providing a practical architectural blueprint for the next generation of mobile-native personal assistan",
"summary": "Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T04:00:00Z"
},
{
"id": "item_02462",
"rank": 233,
"score": 740,
"rationale": "Temporally extended MoE via options framework is a clean, impactful efficiency/architecture contribution.",
"title": "A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices",
"url": "https://arxiv.org/abs/2605.07340",
"authors": "Xin Wang et al.",
"description": "Bridges PUF interoperability bottleneck across heterogeneous IoT devices with distinct challenge-response spaces through a unified open-set authentication framework.",
"summary": "Bridges PUF interoperability bottleneck across heterogeneous IoT devices with distinct challenge-response spaces through a unified open-set authentication framework.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T11:00:00Z"
},
{
"id": "item_02542",
"rank": 234,
"score": 740,
"rationale": "Machine-checked masking composability for PQC hardware resolves a real verification gap; strong crypto relevance.",
"title": "Task-Aware Answer Preservation under Audio Compression for Large Audio Language Models",
"url": "https://arxiv.org/abs/2605.06631",
"authors": "Amir Ivry",
"description": "Large audio language models (LALMs) are increasingly used to reason over long audio clips, yet deployment often compresses audio before inference to reduce memory and latency. The risk is that compression can leave aggregate accuracy acceptable while sharply degrading answers for a deployment-critical query family. We study answer-preserving audio compression, judging a compressor by the excess answer-error it induces, especially for the worst-affected family. We formulate this theoretically as",
"summary": "Large audio language models (LALMs) are increasingly used to reason over long audio clips, yet deployment often compresses audio before inference to reduce memory and latency. The…",
"tags": [
"eess.AS"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T09:00:00Z"
},
{
"id": "item_02604",
"rank": 235,
"score": 740,
"rationale": "Real Mythos-discovered Firefox CVEs shipped to users; strong AI-for-security practical impact.",
"title": "Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study",
"url": "https://huggingface.co/papers/2605.06643",
"authors": "HuggingFace",
"description": "Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algorithmic progress or are artifacts of inconsistent evaluation protocols. Current research is fragmented, with studies varying significantly across datasets, modality configurations, and experimental settings. Furthermore, existing benchmarks focus predominantly on action recognition, often neglecting critical real-world challenges such as input corruptions, missing modalities, and model trustworthiness. This lack of standardization obscures a reliable assessment of the field's advancement. To address this issue, we introduce MMDG-Bench, the first unified and comprehensive benchmark for MMDG, which standardizes evaluation across six datasets spanning three diverse tasks: action recognition, mechanical fault diagnosis, and sentiment analysis. MMDG-Bench encompasses six modality combinations, nine representative methods, and multiple evaluation settings. Beyond standard accuracy, it systematically assesses corruption robustness, missing-modality generalization, misclassification detection, and out-of-distribution detection. With 7, 402 neural networks trained in total across 95 unique cross-domain tasks, MMDG-Bench yields five key findings: (1) under fair comparisons, recent specialized MMDG methods offer only marginal improvements over ERM baseline; (2) no single method consistently outperforms others ",
"summary": "Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algorithmic progress or are artifacts of…",
"tags": [
"HuggingFace",
"LLMs"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_02639",
"rank": 236,
"score": 740,
"rationale": "Cyber defense benchmark with 106 real ATT&CK procedures is high-value for LLM SecOps.",
"title": "Major DNS Outage Hits .de Domains: DNSSEC Failure on May 5, 2026",
"url": "https://www.ip.network/blog/de-domain-dnssec-outage-may-2026",
"authors": "Lobste.rs",
"description": "Major DNS Outage Hits .de Domains: DNSSEC Failure on May 5, 2026",
"summary": "Major DNS Outage Hits .de Domains: DNSSEC Failure on May 5, 2026",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🌐 Lobste.rs",
"debrief_date": "2026-05-07 11:00",
"debrief_path": "debriefs/debrief-2026-05-07-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-07 11:00",
"debrief_path": "debriefs/debrief-2026-05-07-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-07T11:00:00Z"
},
{
"id": "item_02830",
"rank": 237,
"score": 740,
"rationale": "Black-box adversarial routing attack; new threat model for cost-aware LLM routing.",
"title": "On the action of Bender-Knuth generators of cactus group on the set of short semi-standard Young tableaux",
"url": "https://arxiv.org/abs/2605.00514",
"authors": "Igor Svyatnyy",
"description": "In the article by Michael Chmutov, Max Glick and Pavel Pylyavskii \\cite{Chmutov} the action of the cactus group $C_N$ on the set of semi-standard Young tableaux filled with the numbers from $1$ to $N$ was defined. Namely, they constructed the set of generators (we rightfully call them Bender-Knuth generators) of the cactus group and a group homomorphism from $C_N$ to Berenstein-Kirillov group $BK_N$ (cf. \\cite{Berenstein_Kirillov}), which sends these generators to the Bender-Knuth involutions on",
"summary": "In the article by Michael Chmutov, Max Glick and Pavel Pylyavskii \\cite{Chmutov} the action of the cactus group $C_N$ on the set of semi-standard Young tableaux filled with the numbers from $1$ to $N$ was defined. Namely…",
"tags": [
"math.CO",
"math.RT"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_03055",
"rank": 238,
"score": 740,
"rationale": "Three-phase complexity classification of 2-local Hamiltonians; foundational complexity result.",
"title": "Meta-CoT: Enhancing Granularity and Generalization in Image Editing",
"url": "https://huggingface.co/papers/2604.24625",
"authors": "Shiyi Zhang, Yiji Cheng, Tiankai Hang, Zijin Yin, Runze He",
"description": "Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta-CoT, a paradigm that performs a two-level decomposition of any single-image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet - (task, target, required understanding ability). Inspired by this, Meta-CoT decomposes both the editing task and the target, generating task-specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations and guides it to learn each element of the triplet during training, substantially improving the editing capability. (2) Generalizability. In the second decomposition level, we further break down editing tasks into five fundamental meta-tasks. We find that training on these five meta-tasks, together with the other two elements of the triplet, is sufficient to achieve strong generalization across diverse, unseen editing tasks. To further align the model's editing behavior with its CoT reasoning, we introduce the CoT-Editing Consistency Reward, which encourages more accurate and effective utilization of CoT ",
"summary": "Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a…",
"tags": [
"huggingface"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_03338",
"rank": 239,
"score": 740,
"rationale": "PIArena unified prompt-injection eval platform; high-impact security infra.",
"title": "(https://www.chestnut.so/)",
"url": "https://www.chestnut.so/",
"authors": "Hacker News",
"description": "Chestnut helps you learn software engineering with personalised courses, based on code you just shipped.",
"summary": "Chestnut helps you learn software engineering with personalised courses, based on code you just shipped.",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "⎅ Hacker News",
"debrief_date": "2026-04-23 19:00",
"debrief_path": "debriefs/debrief-2026-04-23-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 19:00",
"debrief_path": "debriefs/debrief-2026-04-23-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T19:00:00Z"
},
{
"id": "item_03434",
"rank": 240,
"score": 740,
"rationale": "Targeted social engineering supply-chain attack; significant threat-model update.",
"title": "Accurate and scalable exchange-correlation with deep learning",
"url": "https://huggingface.co/papers/2506.14665",
"authors": "",
"description": "Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.",
"summary": "Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "📄 HuggingFace Papers",
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T11:00:00Z"
},
{
"id": "item_03728",
"rank": 241,
"score": 740,
"rationale": "Realistic reproducible DRA benchmark with static sandboxes addresses a critical evaluation gap for deep research agents.",
"title": "DR^{3}-Eval: Towards Realistic and Reproducible Deep Research Evaluation",
"url": "https://huggingface.co/papers/2604.14683",
"authors": "",
"description": "Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation remains challenging due to dynamic web environments and ambiguous task definitions. We propose DR^{3}-Eval, a realistic and reproducible benchmark for evaluating deep research agents on multimodal, multi-file report generation. DR^{3}-Eval is constructed from authentic user-provided materials and paired with a per-task static research sandbox corpus that simulates open-web complexity while remaining fully verifiable, containing supportive documents, distractors, and noise. Moreover, we introduce a multi-dimensional evaluation framework measuring Information Recall, Factual Accuracy, Citation Coverage, Instruction Following, and Depth Quality, and validate its alignment with human judgments. Experiments with our developed multi-agent system DR^{3}-Agent based on multiple state-of-the-art language models demonstrate that DR^{3}-Eval is highly challenging and reveals critical failure modes in retrieval robustness and hallucination control. Our code and data are publicly available.",
"summary": "Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation remains challenging…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_03802",
"rank": 242,
"score": 740,
"rationale": "SemaClaw personal agent infrastructure; strong agent architecture work on harness engineering for production agents.",
"title": "SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering",
"url": "https://huggingface.co/papers/2604.11548",
"authors": "",
"description": "The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step research. This scale of adoption signals that two parallel arcs of development have reached an inflection point. First is a paradigm shift in AI engineering, evolving from prompt and context engineering to harness engineering-designing the complete infrastructure necessary to transform unconstrained agents into controllable, auditable, and production-reliable systems. As model capabilities converge, this harness layer is becoming the primary site of architectural differentiation. Second is the evolution of human-agent interaction from discrete tasks toward a persistent, contextually aware collaborative relationship, which demands open, trustworthy and extensible harness infrastructure. We present SemaClaw, an open-source multi-agent application framework that addresses these shifts by taking a step towards general-purpose personal AI agents through harness engineering. Our primary contributions include a DAG-based two-phase hybrid agent team orchestration method, a PermissionBridge behavioral safety system, a three-tier context management architecture, and an agentic wiki skill for automated personal knowledge base construction.",
"summary": "The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step…",
"tags": [
"HuggingFace",
"▲ 11"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🔗 HuggingFace",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_04002",
"rank": 243,
"score": 740,
"rationale": "Comprehensive survey of 47 credit-assignment methods for reasoning+agentic LLM RL; valuable taxonomy.",
"title": "From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models",
"url": "https://huggingface.co/papers/2604.09459",
"authors": "Chenchen Zhang",
"description": "Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions within a long trajectory caused the outcome remains difficult. This credit assignment (CA) problem manifests in two regimes: reasoning RL, where credit must be distributed across tokens and steps within a single chain-of-thought generation (500--30K+ tokens); and agentic RL, where multi-turn environment interaction introduces stochastic transitions, partial observability, and horizons of 100+ turns (100K--1M tokens), making episode-level credit increasingly uninformative. We survey 47 CA methods (41 core, 6 adjacent enablers) published between 2024 and early 2026, organizing them in a two-dimensional taxonomy by assignment granularity (token, segment, step, turn, multi-agent) and methodology (Monte Carlo, temporal difference, model-based, game-theoretic, information-theoretic). Beyond the survey itself, we contribute three reusable resources: (1) a structured, machine-readable paper inventory with taxonomy labels, baseline families, and evidence levels; (2) a reporting checklist for future CA papers, validated against the reviewed literature to identify systematic methodological gaps; and (3) a benchmark protocol specification with task families, metadata requirements, and controlled bifurcation tasks, accompanied by a method selection decision tree. Our synthesis suggests that the shift from reasoning to agentic RL complicates",
"summary": "Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions within a long trajectory caused the outcome remains difficult. This credit assignment (CA) problem manifests in…",
"tags": [
"🤗 HuggingFace",
"daily curated papers",
"2026-04-13"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
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"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_01527",
"rank": 244,
"score": 739,
"rationale": "Formalized meta-agent substrate with Lean-mechanized ops, Git-like traces, 5x faster forks; strong agent infrastructure contribution.",
"title": "ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations",
"url": "https://huggingface.co/papers/2605.27908",
"authors": "",
"description": "Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited interpretability and little support for systematic skill improvement. We propose ESC-Skills, a skill-centric framework that discovers and self-evolves executable emotional support skills. We first model localized support interactions as Intervention Units (IUs), which capture state--action--outcome dynamics between seeker states, support interventions, and post-response emotional changes. Based on IUs extracted from both successful and failed ESC dialogues, we construct the ESC-Skills Bank, a repository of executable emotional support skills containing intervention guidance, applicability conditions, expected outcomes, and potential risks. To further improve robustness, we introduce a multi-profile self-evolutionary refinement framework in which an ESC agent interacts with diverse simulated seeker profiles under SAGE evaluation. The resulting interaction traces are analyzed to identify missing skills, unsafe interventions, and profile-specific failure patterns, which are then used to refine the Skills Bank through simulation-based verification. Experimental results demonstrate that ESC-Skills improves both response-level quality and dialogue-level emotional outcomes while providing more interpretable and controllable support behaviors. We will release the code, prompts, and ESC-Skills Bank at https://github.com/aliyun/qwen-dianji",
"summary": "Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited interpretability and little support for systematic skill improvement.…",
"tags": [
"HUGGINGFACE_PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-28 04:00",
"debrief_path": "debriefs/debrief-2026-05-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-28 04:00",
"debrief_path": "debriefs/debrief-2026-05-28-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-28T04:00:00Z"
},
{
"id": "item_00106",
"rank": 245,
"score": 730,
"rationale": "Future-behavior prediction probes enable better steering of reasoning models, notable alignment work.",
"title": "Early identification of breakthrough technologies: Insights from science-driven innovations",
"url": "https://www.sciencedirect.com/science/article/pii/S1751157724001184",
"authors": "",
"description": "This paper presents a framework for identifying breakthrough innovations at the technology level, building on the premise that breakthrough potential can be systematically predicted. The authors employ machine learning-based link prediction across knowledge graphs to surface latent connections between research domains that frequently precede technological breakthroughs, demonstrating the approach on historical patent and publication datasets.",
"summary": "Finding from 🎓 Google Scholar.",
"tags": [
"GOOGLE SCHOLAR",
"GOOGLE_SCHOLAR",
"GS:machine learning bre",
"Google Scholar",
"Machine Learning",
"SCHOLAR",
"Scholar",
"google scholar",
"google_scholar",
"innovation",
"knowledge-graphs",
"link-prediction",
"machine learning",
"machine learning breakthroughs",
"machine-learning",
"ml",
"🎓 Google Scholar",
"📚 Google Scholar"
],
"source": "Google Scholar",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🎓 Google Scholar",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 91,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
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{
"debrief_date": "2026-06-17 11:00",
"debrief_path": "debriefs/debrief-2026-06-17-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-16 19:00",
"debrief_path": "debriefs/debrief-2026-06-16-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-15 11:00",
"debrief_path": "debriefs/debrief-2026-06-15-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_04338",
"rank": 246,
"score": 730,
"rationale": "ZK proofs for private quantum circuits hits crypto+quantum intersection.",
"title": "How zero-knowledge proofs make quantum circuits \"private\"",
"url": "https://news.ycombinator.com/item?id=47714060",
"authors": "",
"description": "Article about: How zero-knowledge proofs make quantum circuits \"private\"",
"summary": "Article about: How zero-knowledge proofs make quantum circuits \"private\"",
"tags": [
"Hackernews",
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🟠 Hacker News",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00188",
"rank": 247,
"score": 730,
"rationale": "Detailed nftables UAF exploit with CVE-2026-23111 and upstream patch; strong technical security write-up.",
"title": "Fine-scale downflows above flare ribbons captured by Solar Orbiter/EUI",
"url": "https://arxiv.org/abs/2606.19335",
"authors": "Zheng Sun, Alexander G. M. Pietrow, Malcolm K. Druett, Hui Tian, Julián D. Alvarado-Gómez, Song Tan, Alexander Warmuth, Jiasheng Wang, Yuhang Gao, Zhenyong Hou, Alexandros Stork",
"description": "In solar flares, flare ribbons map chromospheric footpoints where flare energy deposition occurs. These locations are associated with field aligned energy transport from the corona that results from energy liberated during magnetic reconnection. Recent chromospheric observations in the H$α$ and H$β$ bands have revealed fine-scale downflow structures above flare ribbons, referred to as riblets. In this study, we identify similar downflow structures in the extreme-ultraviolet (EUV) wavelength using high-resolution observations from Solar Orbiter/EUI. These fine-scale downflows appear as downward-propagating, bright, and thread-like structures. They exhibit typical velocities of $\\sim100~\\mathrm{km\\ s^{-1}}$, lifetimes of $\\sim15$~s, and lengths of $\\sim1.6$~Mm. Based on their morphological and dynamical properties, we interpret these observed downflows as the EUV counterparts of the riblets that have previously been reported from chromospheric observations. This study presents EUV imaging of $\\sim 10^6$~K downflows above flare ribbons. We interpret these downflows as a result of (1) the energisation and subsequent compression of pre-existing chromospheric fibrils due to particle beams or (2) adiabatic or shock-driven compression induced by the downward-propagating plasma from the corona. These fine-scale EUV riblets provide a new diagnostic tool for probing the dynamics of magnetic reconnection as well as energy transport and deposition during solar flares.",
"summary": "In solar flares, flare ribbons map chromospheric footpoints where flare energy deposition occurs. These locations are associated with field aligned energy transport from the corona that results from…",
"tags": [
"astro-ph.SR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Fine-scale / Downflows / Above",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00420",
"rank": 248,
"score": 730,
"rationale": "Variance-normalized KV-cache quantization fixes accumulation; strong practical LLM efficiency.",
"title": "PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions",
"url": "https://huggingface.co/papers/2606.14832",
"authors": "",
"description": "Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature still evaluates agents primarily as GUI controllers that observe a screen, emit taps and swipes, and are scored by target app state. Real phone-use tasks are broader: they require deciding when to use app GUIs, device-side commands, or structured tools, while leaving evidence that the intended side effect actually occurred. We introduce PhoneHarness, a mixed-action benchmark and execution harness for studying phone-use agents on verifiable mobile workflows. PhoneHarness runs a device-side agent loop over GUI, CLI, and host-side tool actions, combining deterministic action routing with bounded GUI delegation and auditable execution traces. Its benchmark, PhoneHarness Bench, evaluates whether agents complete tasks with observable side effects, not only whether they produce plausible final answers. On the annotated evaluation split, PhoneHarness reaches a 75.0% pass rate, outperforming the strongest non-PhoneHarness settings by 12.9 percentage points. PhoneHarness and PhoneHarness Bench therefore play distinct but mutually dependent roles: the harness makes mixed phone workflows executable, while the benchmark measures whether agents can use that harness reliably and safely. Our findings suggest that reliable phone automation depends on action-surface routing and verifiable execution, not only visual GUI",
"summary": "Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature…",
"tags": [
"▲ 7",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00440",
"rank": 249,
"score": 730,
"rationale": "Sleep-style continual learning for LLMs is a high-impact continual-learning framework.",
"title": "AI language models have favorite names, and we mapped them [R]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u6mn3q/ai_language_models_have_favorite_names_and_we/",
"authors": "",
"description": "It turns out LLMs have strong priors over character names that are model-specific and version-specific. If you find Elena Vasquez and Marcus Chen together on a website, there's a good chance Claude generated it. We stumbled on this as a side finding while working on a model diffing method (CDD), and it grew into its own paper. The short version: these names travel as correlated ensembles, appear across dozens of websites as volcano experts, podcast hosts, thriller protagonists, and authors of 1000+ papers published in two months. Then we found a third name in the ensemble. The collage in the c",
"summary": "It turns out LLMs have strong priors over character names that are model-specific and version-specific. If you find Elena Vasquez and Marcus Chen together on a website, there's a…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T19:00:00Z"
},
{
"id": "item_00478",
"rank": 250,
"score": 730,
"rationale": "WASH shows watermarks collapse under model ensembling; fundamental LLM-watermarking attack.",
"title": "From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing",
"url": "https://arxiv.org/abs/2606.14639",
"authors": "Hugo Daumain, Driss Matrouf, Khaled Khelif, Mickael Rouvier",
"description": "Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of current anti-spoofing systems is their limited robustness to unseen synthesis methods. In this work, we transform a self-supervised speech representation model into a Mixture-of-Experts (MoE) architecture to improve generalization. Feed-forward blocks in selected encoder layers are replaced by multiple expert networks controlled by a layer-wise gating mechanism, allowing experts to capture complementary acoustic patterns while preserving the representations learned during self-supervised pretraining. We further analyze the architectural choices affecting the performance of this MoE conversion and investigate the activation behavior of the experts. The proposed approach is evaluated on 14 spoofing datasets and reduces the macro EER from 5.46% to 4.81%, corresponding to 11.9% relative improvement over the baseline.",
"summary": "Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging. A key limitation of current anti-spoofing…",
"tags": [
"cs.SD",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Self-supervised / Speech / Mixture-of-experts",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00609",
"rank": 251,
"score": 730,
"rationale": "AI-assisted FreeBSD audit surfacing 15 kernel bugs incl. RCEs/LPEs/bhyve escape is high-impact.",
"title": "Unicode composition for filenames (2008)",
"url": "https://svn.apache.org/repos/asf/subversion/trunk/notes/unicode-composition-for-filenames",
"authors": "",
"description": "Unicode composition for filenames (2008)",
"summary": "Finding from 🦞 Lobste.rs.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T11:00:00Z"
},
{
"id": "item_00599",
"rank": 252,
"score": 730,
"rationale": "Automated alignment auditing for sabotage; high relevance to AI safety evaluation, notable authorship.",
"title": "A Stationary (and Therefore Compatible) Representation is All You Need",
"url": "https://huggingface.co/papers/2606.12488",
"authors": "",
"description": "Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we demonstrate that stationary representations learned by d-Simplex fixed classifiers imply compatibility as in its formal definition. This result establishes a foundation for future works and can be directly exploited in practical learning scenarios. We address the challenge of learning compatibility using d-Simplex fixed classifiers when the model is sequentially fine-tuned. Learning according to a d-Simplex fixed classifier with the cross-entropy loss aligns feature distributions at the first-order statistics. Consequently, it may not fully capture higher-order dependencies in the representation between model updates. To address this issue, we demonstrate that training the model using a d-Simplex fixed classifier through a convex combination of the cross-entropy loss and a contrastive loss not only captures higher-order dependencies, but is also equivalent to learning with the cross-entropy under the compatibility constraints. We confirm our findings with extensive experiments also considering a new scenario where a pre-trained model is sequentially fine-tuned and occasionally replaced with an improved model. We show that stationary representations enable uninterrupted retrieval services (without reprocessing gallery images) while improving performance during model updates and replacements, achieving state-o",
"summary": "Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T11:00:00Z"
},
{
"id": "item_01030",
"rank": 253,
"score": 730,
"rationale": "Universal text-optimizer SOTA across diverse tasks including ARC-AGI; high impact.",
"title": "Topological Neural Operators",
"url": "https://arxiv.org/abs/2606.09806",
"authors": "Lennart Bastian, Samuel Leventhal, Mustafa Hajij, Tolga Birdal",
"description": "We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains. TNOs represent data as features defined on cells of varying dimension and model their interactions through Discrete Exterior Calculus, enabling explicit cross-dimensional coupling via gradient-, curl-, and divergence-type operators. The key design principle is to decouple where information flows, as governed by fixed topological operators, from how it is transformed (which is learned), yielding models that respect the geometric support of physical quantities and expose conservation and compatibility structure. We further propose Hierarchical TNOs (HTNOs), which incorporate learned coarse complexes to propagate long-range and topology-dependent information. Our framework subsumes existing NOs as a special case, providing a unified perspective on operator learning across discretizations. Across a range of PDE benchmarks, including irregular-geometry flow problems, TNOs and HTNOs improve accuracy; controlled studies further isolate the benefits of native higher-rank and topological structure. Project page: https://circle-group.github.io/research/TNO",
"summary": "We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological…",
"tags": [
"cs.LG",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Artificial Intelligence",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01199",
"rank": 254,
"score": 730,
"rationale": "Quantization-permanent unlearning with mechanistic circuit attribution resolves a real dual-failure problem.",
"title": "Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models",
"url": "https://huggingface.co/papers/2606.05833",
"authors": "daily curated papers",
"description": "Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences. This approach effectively restructures the semantic latent space within MLLMs to unlock spatial intelligence. Rather than employing superficial feature mixing, GeoVR reshapes the internal representations of the MLLM by distilling geometry knowledge from pre-trained 3D foundation models. This is accomplished through a multi-objective learning strategy driven by four complementary geometric targets: (1) estimating inter-frame camera poses to embed varying viewpoint dynamics, (2) regressing dense depth maps to anchor physical distances, (3) predicting a metric scale factor for real-world calibration, and (4) distilling multi-scale 3D features to align the intermediate feature space. Guided by these explicit physical and geometric constraints, the model's internal representations naturally develop strong 3D awareness. Extensive experiments on spatial reasoning benchmarks demonstrate that GeoVR achieves state-of-the-art performance, establishing a new paradigm for endowing foundation models with spatial intelligence.",
"summary": "Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01274",
"rank": 255,
"score": 730,
"rationale": "Universal quantum resource distillation via composite Stein's lemma; foundational result for entanglement/crypto distillation rates.",
"title": "Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging",
"url": "https://huggingface.co/papers/2606.01717",
"authors": "",
"description": "Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization. We ask whether these two bottlenecks can be addressed jointly by training parts of the mixture independently and reconciling them once in parameter space. We develop a local quadratic theory inside a shared flat basin that yields three results: weight merging produces a curvature-weighted variance reduction; PCA-aligned conflict splitting maximizes this gain along high-curvature directions; and merging additionally acts as spectral filtering with implicit norm regularization. These results directly motivate MERIT, a decentralized merge-ready instruction-tuning pipeline that estimates dataset-level gradient conflicts, partitions the mixture along the top PCA conflict axes, fine-tunes each partition independently with no inter-partition communication, and merges once via token-weighted averaging. On Qwen2.5-VL-3B with 136 Vision-FLAN tasks, MERIT improves the 8-benchmark average from 54.3 (joint training) to 57.0. The same recipe scales to a 7B model on a 1.6M-example, 176-source mixture -- matching or exceeding centralized joint training with minimal cost overhead -- and transfers to text-only FLAN. Our code is available at https://github.com/naver-ai/merit.",
"summary": "Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization. We…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01799",
"rank": 256,
"score": 730,
"rationale": "Firefox hardening with Claude Mythos shows AI agentic security capability.",
"title": "OCaml Infrastructure: How the opam-repository Works",
"url": "https://ocaml.org/backstage/2025-11-05-how-the-opam-repository-works",
"authors": "",
"description": "> The opam package repository is a commons rather than a publishing platform: it is manually curated, so not all packages submitted for publication are accepted; it is maintained communally, so anyone can suggest changes to any package.",
"summary": "> The opam package repository is a commons rather than a publishing platform: it is manually curated, so not all packages submitted for publication are accepted; it is maintained…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "📡 Lobste.rs",
"debrief_date": "2026-05-22 04:00",
"debrief_path": "debriefs/debrief-2026-05-22-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 04:00",
"debrief_path": "debriefs/debrief-2026-05-22-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T04:00:00Z"
},
{
"id": "item_01903",
"rank": 257,
"score": 730,
"rationale": "Metacognition/faithful-uncertainty framing for hallucinations is conceptually important for agent trust and safety.",
"title": "EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL",
"url": "https://huggingface.co/papers/2605.18703",
"authors": "",
"description": "Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of realistic training data that captures implicit human reasoning. Existing approaches depend on costly real-world APIs, hallucination-prone LLM simulators, or synthetic environments that are often single-turn or depend on pre-collected documents. Moreover, synthetic trajectories are frequently over-specified, resembling instruction sequences rather than natural human intents, reducing their effectiveness for RL training. We introduce EnvFactory, a fully automated framework that addresses both challenges. EnvFactory autonomously explores and verifies stateful, executable tool environments from authentic resources, and synthesizes natural multi-turn trajectories through topology-aware sampling and calibrated refinement, producing grounded queries with implicit intents. Using only 85 verified environments across 7 domains, EnvFactory generates 2,575 SFT and RL trajectories. Despite using significantly fewer environments than prior work, which are often 5 times more, EnvFactory achieves superior training efficiency and downstream performance, improving Qwen3-series models by up to +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks including τ^2-Bench and VitaBench. By fully automating both environment construction and trajectory synthesis, EnvFactory provides a scala",
"summary": "Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and the scarcity of…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_02296",
"rank": 258,
"score": 730,
"rationale": "High-dim multi-qubit Bell nonlocality on superconducting hardware—important quantum-foundation experiment.",
"title": "World Action Models: The Next Frontier in Embodied AI",
"url": "https://huggingface.co/papers/2605.12090",
"authors": "Siyin Wang, Junhao Shi, Zhaoyang Fu, Xinzhe He, Feihong Liu",
"description": "Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline. We term this emerging paradigm World Action Models (WAMs): embodied foundation models that unify predictive state modeling with action generation, targeting a joint distribution over future states and actions rather than actions alone. However, the literature remains fragmented across architectures, learning objectives, and application scenarios, lacking a unified conceptual framework. We formally define WAMs and disambiguate them from related concepts, and trace the foundations and early integration of VLA and world model research that gave rise to this paradigm. We organize existing methods into a structured taxonomy of Cascaded and Joint WAMs, with further subdivision by generation modality, conditioning mechanism, and action decoding strategy. We systematically analyze the data ecosystem fueling WAMs development, spanning robot teleoperation, portable human demonstrations, simulation, and internet-scale egocentric video, and synthesize emerging evaluation protocols organized around visual fidelity, physical commonsense, and action plausibility. Overall, this survey provides th",
"summary": "Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this…",
"tags": [
"arXiv:2605.12090"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T11:00:00Z"
},
{
"id": "item_02541",
"rank": 259,
"score": 730,
"rationale": "Multi-agent harness synthesis for vuln discovery is highly relevant; harness-vs-model insight is impactful.",
"title": "Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models",
"url": "https://arxiv.org/abs/2605.06423",
"authors": "Zeyuan Chen, Yihan Ma, Xinyue Shen, Michael Backes, Yang Zhang",
"description": "Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns. We introduce the PopQuiz Attack, a black-box membership inference attack that tests whether a model can recall specific training examples. The core idea is to turn target data into quiz-style multiple-choice questions and infer membership from the model's answers. Across six widely used LLMs (GPT-3.5, GPT-4o, LLaMA2-7b,",
"summary": "Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns.…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T09:00:00Z"
},
{
"id": "item_02710",
"rank": 260,
"score": 730,
"rationale": "Sharp empirical finding that LLM agents ignore exposed solutions; strong implications for agent design.",
"title": "Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor",
"url": "https://arxiv.org/abs/2605.04025",
"authors": "Gavin S. Hartnett, Khadijeh Sona Najafi, Aleksei Khindanov, Haoran Liao, Michael Schutzman, Michael R. Hush, Michael J. Biercuk, Yuval Baum",
"description": "We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector simulation and challenging for state-of-the-art tensor-network methods. We encode this problem using up to 120 qubits through an efficient mapping that reduces circuit complexity, and we improve accuracy through error suppression to simulate dynamical evolution using up to 90 Trotter steps. From a vacancy defect intro",
"summary": "We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector simulation and challenging for state-of-the-art tensor-network methods. We encode this…",
"tags": [
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "⚛️ Quantum",
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T11:00:00Z"
},
{
"id": "item_02835",
"rank": 261,
"score": 730,
"rationale": "First symbolic superoptimizer for tensor programs; significant systems/ML contribution.",
"title": "Topological protection of local quantum Fisher information",
"url": "https://arxiv.org/abs/2605.00770",
"authors": "Marcin Płodzień, Jan Chwedeńczuk",
"description": "In many-body quantum systems, unitary dynamics generically delocalize locally encoded information, causing single-site metrological sensitivity to vanish. We analytically demonstrate that a topological phase can prevent this dispersal. In the open Kitaev chain, a Majorana zero mode fixes the boundary quantum Fisher information (QFI) at a nonzero plateau that persists for times exponentially long in system size. We derive exact analytical expressions for the local QFI and identify the mechanism a",
"summary": "In many-body quantum systems, unitary dynamics generically delocalize locally encoded information, causing single-site metrological sensitivity to vanish. We analytically demonstrate that a topological phase can prevent …",
"tags": [
"quant-ph",
"cond-mat.quant-gas"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_03733",
"rank": 262,
"score": 730,
"rationale": "Self-sovereign agent framing with governance/security analysis is strategically important for agent ecosystem research.",
"title": "Self-Sovereign Agent",
"url": "https://huggingface.co/papers/2604.08551",
"authors": "",
"description": "We investigate the emerging prospect of self-sovereign agents -- AI systems that can economically sustain and extend their own operation without human involvement. Recent advances in large language models and agent frameworks have substantially expanded agents' practical capabilities, pointing toward a potential shift from developer-controlled tools to more autonomous digital actors. We analyze the remaining technical barriers to such deployments and discuss the security, societal, and governance challenges that could arise if such systems become practically viable. A project page is available at: https://self-sovereign-agent.github.io.",
"summary": "We investigate the emerging prospect of self-sovereign agents -- AI systems that can economically sustain and extend their own operation without human involvement. Recent advances in large language…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_03934",
"rank": 263,
"score": 730,
"rationale": "Agentic aggregation over parallel rollouts is an important scaling axis for deep-research agents.",
"title": "Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks",
"url": "https://huggingface.co/papers/2604.11753",
"authors": "Yoonsang Lee, Howard Yen, Xi Ye, Danqi Chen",
"description": "AggAgent treats parallel agent trajectories as an environment of their own and gives an aggregation agent tools to inspect, compare, and synthesize them rather than simply voting on final answers. It matters because deep-research style systems increasingly rely on many long rollouts, and smarter aggregation is becoming a first-class scaling lever.",
"summary": "AggAgent treats parallel agent trajectories as an environment of their own and gives an aggregation agent tools to inspect, compare, and synthesize them rather than simply voting on final answers. It matters…",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-04-13",
"Upvotes: 10"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 19:00",
"debrief_path": "debriefs/debrief-2026-04-14-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 19:00",
"debrief_path": "debriefs/debrief-2026-04-14-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T19:00:00Z"
},
{
"id": "item_04265",
"rank": 264,
"score": 730,
"rationale": "RISC-V CFI extensions paper; relevant to hardware security research.",
"title": "CVA6-CFI: A First Glance at RISC-V Control-Flow Integrity Extensions",
"url": "https://arxiv.org/pdf/2602.04991",
"authors": "",
"description": "Article about: CVA6-CFI: A First Glance at RISC-V Control-Flow Integrity Extensions",
"summary": "Article about: CVA6-CFI: A First Glance at RISC-V Control-Flow Integrity Extensions",
"tags": [
"Lobsters"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04025",
"rank": 265,
"score": 725,
"rationale": "Identifies CoT-grounding failure in MRM RLVR training and proposes constrained alternative; strong empirical rigor.",
"title": "Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization",
"url": "https://huggingface.co/papers/2604.08476",
"authors": "",
"description": "Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe that accuracy gains often come at the cost of reasoning quality: generated Chain-of-Thought (CoT) traces are frequently inconsistent with the final answer and poorly grounded in the visual evidence. We systematically study this phenomenon across seven challenging real-world spatial reasoning benchmarks and find that it affects contemporary MRMs such as ViGoRL-Spatial, TreeVGR as well as our own models trained with standard Group Relative Policy Optimization (GRPO). We characterize CoT reasoning quality along two complementary axes: \"logical consistency\" (does the CoT entail the final answer?) and \"visual grounding\" (does each reasoning step accurately describe objects, attributes, and spatial relationships in the image?). To address this, we propose Faithful GRPO (FGRPO), a variant of GRPO that enforces consistency and grounding as constraints via Lagrangian dual ascent. FGRPO incorporates batch-level consistency and grounding constraints into the advantage computation within a group, adaptively adjusting the relative importance of constraints during optimization. We evaluate FGRPO on Qwen2.5-VL-7B and 3B backbones across seven spatial datasets. Our results show that FGRPO substantially improves reasoning quality, reducing the inconsistency rate from 24.5% to 1.7% and improving visual grounding scores by +13%. It also improves final answer accuracy over simple GRPO, demonstrating that faithful reasoning enables better answers.",
"summary": "Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe that accuracy gains…",
"tags": [
"2026-04-09",
"HuggingFace",
"Huggingface Papers",
"arXiv:",
"cs.AI",
"cs.CV",
"daily curated papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers (via HuggingFace)",
"group": "🧠 LLMs & Reasoning",
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": "",
"occurrence_count": 10,
"occurrences": [
{
"debrief_date": "2026-04-13 19:00",
"debrief_path": "debriefs/debrief-2026-04-13-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 19:00",
"debrief_path": "debriefs/debrief-2026-04-12-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 16:00",
"debrief_path": "debriefs/debrief-2026-04-12-16.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 04:00",
"debrief_path": "debriefs/debrief-2026-04-12-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-12 00:00",
"debrief_path": "debriefs/debrief-2026-04-12-00.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T19:00:00Z"
},
{
"id": "item_04160",
"rank": 266,
"score": 724,
"rationale": "Differentially private language generation in the limit: foundational theory bridging privacy and language learning.",
"title": "Differentially Private Language Generation and Identification in the Limit",
"url": "https://arxiv.org/abs/2604.08504",
"authors": "Anay Mehrotra, Grigoris Velegkas, Xifan Yu, Felix Zhou",
"description": "We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of differential privacy. We consider the continual release model, where a generator must eventually output a stream of valid strings while protecting the privacy of the entire input sequence. Our first main result is that for countable collections of languages, privacy comes at no qualitative cost: we provide an $\\varepsilon$-differentially-private algorithm that generates in the limit from any countable collection. This stands in contrast to many learning settings where privacy renders learnability impossible. However, privacy does impose a quantitative cost: there are finite collections of size $k$ for which uniform private generation requires $Ω(k/\\varepsilon)$ samples, whereas just one sample suffices non-privately. We then turn to the harder problem of language identification in the limit. Here, we show that privacy creates fundamental barriers. We prove that no $\\varepsilon$-DP algorithm can identify a collection containing two languages with an infinite intersection and a finite set difference, a condition far stronger than the classical non-private characterization of identification. Next, we turn to the stochastic setting where the sample strings are sampled i.i.d. from a distribution (instead of being generated by an adversary). Here, we show that private identification is possible if and only if the collection is identifiable in the adversarial model. Together, our results establish new dimensions along which generation and identification differ and, for identification, a separation between adversarial and stochastic settings induced by privacy constraints.",
"summary": "We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of differential privacy.",
"tags": [
"cs.AI",
"cs.CL",
"cs.DS",
"cs.LG",
"stat.ML",
"stat.ML, cs.AI, cs.CL, cs.DS, cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Machine Learning",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 04:00",
"debrief_path": "debriefs/debrief-2026-04-11-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00176",
"rank": 267,
"score": 724,
"rationale": "OpenSSL PKCS7_verify UAF CVE; critical crypto library vuln with broad downstream impact.",
"title": "CHERI-D: Secure and efficient inline object ID for CHERI temporal memory safety",
"url": "https://arxiv.org/abs/2606.19055",
"authors": "Yuecheng Wang, Jonathan Woodruff, Alfredo Mazzinghi, Peter Rugg, Samuel W. Stark, Alexandre Joannou, Robert N. M. Watson, Simon W. Moore",
"description": "We propose CHERI-D, an architectural extension to CHERI that supports efficient temporal memory safety. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial safety and a foundation for temporal memory safety. Due to CHERI lacking intrinsic architectural support for temporal memory safety, the state-of-the-art CHERI temporal safety solution, Cornucopia Reloaded, is a software-based solution that provides use-after-reallocation (UAR) protections instead of the stronger use-after-free (UAF) mitigation, and suffers performance overhead due to delayed reallocation and revocation. CHERI-D associates object identification (ID) metadata with capability pointers to provide temporal integrity of allocations. CHERI spatial safety allows CHERI-D to store object IDs safely inline with allocation data, potentially within unused fragmentation. Evaluated in simulation and in hardware, CHERI-D significantly reduces the revocation overhead of Cornucopia Reloaded while allowing it to support strict use-after-free mitigation.",
"summary": "We propose CHERI-D, an architectural extension to CHERI that supports efficient temporal memory safety. Efficient memory safety is an increasing priority for programming languages, operating systems,…",
"tags": [
"cs.AR",
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Cheri-d / Secure / Efficient",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00670",
"rank": 268,
"score": 724,
"rationale": "Timely benchmark for computer-use agent robustness to real-world corruptions; practically important.",
"title": "WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation",
"url": "https://huggingface.co/papers/2606.13672",
"authors": "",
"description": "The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limited real-world interaction. To unlock these downstream capabilities, a WM needs to jointly satisfy three desiderata: (i) fidelity (i.e., producing simulated trajectories that correlate with reality), (ii) consistency (i.e., producing simulated trajectories that are coherent over long horizons), and (iii) efficiency (i.e., producing simulated trajectories quickly). We propose WEAVER (World Estimation Across Views for Embodied Reasoning): a WM architecture that simultaneously achieves all three desiderata, providing state-of-the-art results on robotic manipulation tasks. WEAVER is a multi-view WM trained to predict future latents and reward values via a flow-matching loss. We distill the key design decisions across model architecture, memory, and prediction objectives required to unlock the kinds of long-horizon dynamic manipulation tasks that have confounded prior world modeling approaches. We apply WEAVER in robotic hardware, demonstrating its effectiveness at policy evaluation (ρ=0.870 correlation with real-world success rate), policy improvement (real-world success rate improvement of 38% on top of the π_{0.5} robot foundation model), and test-time planning (real-world success rate improvement of 14% with a 5-10times speedup over prior WMs). WEAVER also demonstrates better performance than prior W",
"summary": "The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_02111",
"rank": 269,
"score": 724,
"rationale": "Memory/compute-efficient red-teaming for prompt injection on long-context LLMs; high safety relevance.",
"title": "SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer",
"url": "https://arxiv.org/abs/2605.15178",
"authors": "Haoyi Zhu, Haozhe Liu, Yuyang Zhao, Tian Ye, Junsong Chen, Jincheng Yu, Tong He, Song Han, Enze Xie",
"description": "We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p, minute-scale videos with precise camera control. SANA-WM achieves visual quality comparable to large-scale industrial baselines such as LingBot-World and HY-WorldPlay, while significantly improving efficiency. Four core designs drive our architecture: (1) Hybrid Linear Attention combines frame-wise Gated DeltaNet (GDN) with softmax attention for ",
"summary": "We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p, minute-scale videos with precise camera control. SANA-WM achieves…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Quantum",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
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"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_03153",
"rank": 270,
"score": 720,
"rationale": "Anthropic Claude Mythos cyber capability eval; important frontier-model safety/CTF benchmark.",
"title": "EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model [W22]",
"url": "https://huggingface.co/papers/2604.10268",
"authors": "",
"description": "We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training. Leveraging the generative priors of large-scale T2I diffusion models enables the development of a wide array of novel generation and editing applications. Although numerous image editing methods have been proposed based on diffusion models and exhibit high-quality editing results, they are difficult to apply to images with arbitrary aspect ratios or higher resolutions since they only work at the training resolutions (512x512 or 1024x1024). Naively applying patch-wise editing fails with unrealistic object structures and repetition. To address these challenges, we introduce EditCrafter, a simple yet effective editing pipeline. EditCrafter operates by first performing tiled inversion, which preserves the original identity of the input high-resolution image. We further propose a noise-damped manifold-constrained classifier-free guidance (NDCFG++) that is tailored for high resolution image editing from the inverted latent. Our experiments show that the our EditCrafter can achieve impressive editing results across various resolutions without fine-tuning and optimization.",
"summary": "We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly e…",
"tags": [
"HF Papers",
"HUGGINGFACE_PAPERS",
"HuggingFace",
"HuggingFace Papers",
"Huggingface Papers",
"Kunho",
"cs.CV",
"cs.LG",
"daily curated papers",
"diffusion",
"image-editing",
"tuning-free",
"⬆ 9"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 Huggingface Papers",
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": "",
"occurrence_count": 13,
"occurrences": [
{
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
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{
"debrief_date": "2026-04-27 19:00",
"debrief_path": "debriefs/debrief-2026-04-27-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-27 16:00",
"debrief_path": "debriefs/debrief-2026-04-27-16.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-27 00:00",
"debrief_path": "debriefs/debrief-2026-04-27-00.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-26 11:00",
"debrief_path": "debriefs/debrief-2026-04-26-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-26 04:00",
"debrief_path": "debriefs/debrief-2026-04-26-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-25 19:00",
"debrief_path": "debriefs/debrief-2026-04-25-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-25 11:00",
"debrief_path": "debriefs/debrief-2026-04-25-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-28T04:00:00Z"
},
{
"id": "item_01384",
"rank": 271,
"score": 720,
"rationale": "MDASH 100+ agent system topping CyberGym at 88.45% is high-impact multi-agent security milestone.",
"title": "Why no easter eggs? (2005)",
"url": "https://learn.microsoft.com/ar-sa/archive/blogs/larryosterman/why-no-easter-eggs",
"authors": "",
"description": "A 2005 blog post by Larry Osterman explaining why Microsoft disabled easter eggs in Windows — corporate security policies and customer trust concerns made them inappropriate for a mainstream OS.",
"summary": "A 2005 blog post by Larry Osterman explaining why Microsoft disabled easter eggs in Windows — corporate security policies and customer trust concerns made them inappropriate for a mainstream OS.",
"tags": [
"2026-05-29",
"LOBSTE.RS",
"Lobste.rs",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "Lobste.rs",
"debrief_date": "2026-05-31 19:00",
"debrief_path": "debriefs/debrief-2026-05-31-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-31 19:00",
"debrief_path": "debriefs/debrief-2026-05-31-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-31T19:00:00Z"
},
{
"id": "item_02191",
"rank": 272,
"score": 720,
"rationale": "GLM-5V-Turbo native multimodal agent foundation model; strong model release.",
"title": "Towards Self-Evolving Agentic Literature Retrieval",
"url": "https://huggingface.co/papers/2605.14306",
"authors": "2605.14306 | Yuwen Du, Tian Jin, Jing Kang, Xianghe Pang et al.",
"description": "PaSaMaster is a self-evolving agentic retrieval system using iterative intent analysis and ranking, improving F1 by 15.6X over keyword search across 38 scientific disciplines.",
"summary": "PaSaMaster is a self-evolving agentic retrieval system using iterative intent analysis and ranking, improving F1 by 15.6X over keyword search across 38 scientific disciplines.",
"tags": [
"HF",
"HUGGINGFACE_PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-15T04:00:00Z"
},
{
"id": "item_02161",
"rank": 273,
"score": 720,
"rationale": "Novel AF detection via tool selection; formalizes deception beyond CoT; strong safety relevance.",
"title": "Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation",
"url": "https://huggingface.co/papers/2605.15141",
"authors": "2605.15141 | Min Zhao, Hongzhou Zhu, Kaiwen Zheng, Zihan Zhou et al.",
"description": "Causal Forcing++ uses causal consistency distillation for few-step AR video generation initialization, surpassing prior 4-step chunk-wise methods while reducing first-frame latency by 50% and training cost by 4x.",
"summary": "Causal Forcing++ uses causal consistency distillation for few-step AR video generation initialization, surpassing prior 4-step chunk-wise methods while reducing first-frame latency by 50% and…",
"tags": [
"HF",
"HUGGINGFACE_PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-15T04:00:00Z"
},
{
"id": "item_02745",
"rank": 274,
"score": 720,
"rationale": "Survey of seven cross-domain prompt-injection techniques; high practical security value.",
"title": "Linear-Time Global Visual Modeling without Explicit Attention",
"url": "https://huggingface.co/papers/2605.01711",
"authors": "Ruize He, Dongchen Han, Gao Huang",
"description": "We demonstrate that attention can be mathematically reframed as a Multi-Layer Perceptron (MLP) equipped with dynamically predicted parameters. Through this lens, attention's global modeling power is explained not as explicit token-wise aggregation but as an implicit process where dynamically generated parameters act as a compressed representation of global context. We investigate whether Transformer-level sequence global modeling can be achieved entirely through dynamic parameterization while maintaining linear complexity. We design various dynamic parameter prediction strategies and integrate them into standard network layers. Extensive empirical studies on vision models demonstrate that dynamic parameterization serves as a highly effective, linear-complexity alternative to explicit attention, opening new pathways for efficient sequence modeling.",
"summary": "We demonstrate that attention can be mathematically reframed as a Multi-Layer Perceptron (MLP) equipped with dynamically predicted parameters. Through this lens, attention's global modeling power is explained not as explicit token-wise aggregation but as an implicit process where dynamically generated…",
"tags": [
"HuggingFace",
"Machine Learning",
"arXiv:2605.01711"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🔬 Machine Learning",
"debrief_date": "2026-05-06 00:00",
"debrief_path": "debriefs/debrief-2026-05-06-00.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-06 00:00",
"debrief_path": "debriefs/debrief-2026-05-06-00.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-06 00:00",
"debrief_path": "debriefs/debrief-2026-05-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T00:00:00Z"
},
{
"id": "item_04359",
"rank": 275,
"score": 720,
"rationale": "Structural graph probing of VLMs; meaningful interpretability lens.",
"title": "Structural Graph Probing of Vision-Language Models",
"url": "https://huggingface.co/papers/2603.27070",
"authors": "",
"description": "Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whether population-level structure is behaviorally meaningful, how it changes across modalities and depth, and whether it identifies causally influential internal components under intervention. We show that correlation topology carries recoverable behavioral signal; moreover, cross-modal structure progressively consolidates with depth around a compact set of recurrent hub neurons, whose targeted perturbation substantially alters model output. Neural topology thus emerges as a meaningful intermediate scale for VLM interpretability: richer than local attribution, more tractable than full circuit recovery, and empirically tied to multimodal behavior. Code is publicly available at https://github.com/he-h/vlm-graph-probing.",
"summary": "arXiv:2603.27070 | Authors: Haoyu He, Yue Zhuo, Yu Zheng, Qi R. Wang | Upvotes: 2",
"tags": [
"Huggingface Papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00014",
"rank": 276,
"score": 720,
"rationale": "Multi-chunk prediction for autoregressive video is a substantive training/inference advance with clear empirical gains.",
"title": "LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI",
"url": "https://huggingface.co/papers/2606.18021",
"authors": "",
"description": "AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal for trustworthy deployment. We present LegalHalluLens, an auditing framework with three components: typed hallucination profiles across four legally-motivated claim categories (numeric, temporal, obligation/entitlement, factual) over CUAD (Hendrycks et al., 2021); a Risk Direction Index (RDI) that reduces omission-versus-invention bias to a single deployment-comparable scalar; and a typed debate pipeline calibrated to both magnitudes and directions. Across 510 contracts and 249,252 clause-level instances we measure a within-model gap of approximately 38-40 pp between obligation/numeric and temporal claims that aggregate reporting hides, and show that two systems with matched 52% rates can carry opposite RDIs. The debate pipeline reduces fabricated detections by 45% with per-category gains tracking the diagnosis, matching commercial APIs with a substantially smaller backbone (4B active parameters). Typed profiles and RDI surface failure modes that aggregate metrics hide; we further show these diagnostics serve as calibration inputs for multi-agent debate pipelines, where Skeptic challenges and asymmetric gates targeted at measured failure modes outperform generically-tuned debate. The framework supports direction-aware procurement, accountab",
"summary": "AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-19T11:00:00Z"
},
{
"id": "item_00096",
"rank": 277,
"score": 720,
"rationale": "EEVEE test-time prompt learning for real-world multi-dataset agent streams, solid agents work.",
"title": "REVES: REvision and VErification--Augmented Training for Test-Time Scaling",
"url": "https://huggingface.co/papers/2606.18910",
"authors": "",
"description": "Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post-training methods primarily optimize single-shot objectives, creating a fundamental misalignment with multi-step inference dynamics. While recent work treats this as multi-turn reinforcement learning (RL), conventional approaches optimize over the multi-step trajectories directly, failing to further exploit the high-quality mistakes in intermediate steps that model can learn from correcting them. We propose a two-stage iterative framework that alternates between online data/prompt augmentation and policy optimization. By converting the intermediate steps (``near-miss'' answers) in the successful recovery trajectories into decoupled revision and verification prompts, our approach concentrates training on both effective answer transformation and error identification. This approach enables efficient off-policy data generation and reduces the computational overhead of long-horizon sampling compared to standard multi-turn RL. On LiveCodeBench, using publicly available test cases as feedback, we observe gains of +6.5 points over the RL baseline and +4.0 points over standard multi-turn training. Beyond coding, our approach matches the previously reported SOTA result on circle packing while using the smallest base model (4B) and far fewer rollouts than the much larger evolutionary search systems. Math results under ground-truth verific",
"summary": "Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post-training methods primarily…",
"tags": [
"▲ 2",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_00214",
"rank": 278,
"score": 720,
"rationale": "HPAA exploits human-vs-LLM perceptual gap in moderation; strong, novel adversarial-text angle with clear safety impact.",
"title": "(https://github.com/SandroMartens/DBGSOM)",
"url": "https://github.com/SandroMartens/DBGSOM",
"authors": "",
"description": "A scikit-learn compatible Python implementation of the Directed Batch Growing Self-Organizing Map - SandroMartens/DBGSOM",
"summary": "A scikit-learn compatible Python implementation of the Directed Batch Growing Self-Organizing Map - SandroMartens/DBGSOM",
"tags": [
"⭐ 4",
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "📰 Hacker News",
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T19:00:00Z"
},
{
"id": "item_00296",
"rank": 279,
"score": 720,
"rationale": "Microsoft packages with credential stealers targeting AI agents; high-impact security.",
"title": "ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining",
"url": "https://huggingface.co/papers/2606.17200",
"authors": "",
"description": "Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show that large-scale egocentric human videos provide complementary real-world supervision in pretraining. However, joint training on human and robot data remains challenging due to divergences in action spaces, embodiment structures, temporal dynamics, and supervision quality. We introduce ACE-EGO-0, a unified VLA pretraining framework jointly leveraging heterogeneous data sources. To extract large-scale pretraining supervision from egocentric human videos, we build a scalable egocentric video-to-action pipeline that converts raw human videos into robot-format pseudo-action trajectories. To make these labels comparable with robot demonstrations, ACE-EGO-0 uses a unified action representation based on camera-space actions, morphology conditioning, and time-aligned action chunking. To robustly leverage noisy pseudo-action supervision from egocentric human videos, we formulate a reliability-aware training objective with a human auxiliary loss that concentrates supervision on reliable signals. We instantiate ACE-EGO-0 on 4.53K hours of robot and simulation data, together with 1.48K hours of pseudo-action-labeled egocentric human data. Experiments show that incorporating large-scale human supervision under reliability-aware weighting consistently improves both unified joint pretraining and supervised fine-tu",
"summary": "Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show…",
"tags": [
"▲ 34",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00316",
"rank": 280,
"score": 720,
"rationale": "Cross-layer sparse attention with shared routing; meaningful long-context inference efficiency win.",
"title": "Can gzip be a language model?",
"url": "https://nathan.rs/posts/gzip-lm/",
"authors": "",
"description": "A while back I wrote about language modeling without neural networks, where I generated Shakespeare with an unbounded n-gram model: no weights, no training, …",
"summary": "A while back I wrote about language modeling without neural networks, where I generated Shakespeare with an unbounded n-gram model: no weights, no training, …",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-16 19:00",
"debrief_path": "debriefs/debrief-2026-06-16-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 19:00",
"debrief_path": "debriefs/debrief-2026-06-16-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T19:00:00Z"
},
{
"id": "item_00375",
"rank": 281,
"score": 720,
"rationale": "Propensity-aware memorization evaluation; advances privacy/safety measurement for LLMs.",
"title": "Simultaneous Tricolor Video Observations of Three Tiny Near-Earth Asteroids with Sub-Minute Rotation Periods",
"url": "https://arxiv.org/abs/2606.17047",
"authors": "Jin Beniyama, Ryou Ohsawa, Shigeyuki Sako, Satoshi Takita, Daisuke Kuroda, Tomohiko Sekiguchi, Keisuke Isogai",
"description": "Studying the physical properties of near-Earth asteroids (NEAs) is crucial for understanding their dynamical histories and origins, and assessing impact hazards to Earth. Tiny NEAs with diameters smaller than 100 m are intrinsically faint and are typically observable only during close approaches, resulting in few well-characterized objects. Furthermore, because these objects are often fast-moving and fast-rotating, sequential multiband photometry is prone to systematic offsets in derived colors. To mitigate this effect, we performed simultaneous $g$-, $r$-, and $i$-band photometry of three tiny NEAs using the TriColor CMOS Camera and Spectrograph (TriCCS) on the 3.8 m Seimei Telescope. We used high-cadence video observations with exposure times of 1 s and 5 s to investigate lightcurve variations on timescales of seconds. All three NEAs are confirmed as fast rotators with rotation periods shorter than 60 s: $15.281\\pm0.002$ s for 2021 TY$_{14}$, $21.099\\pm0.003$ s for 2021 UW$_{1}$, and $8.779\\pm0.013$ s for 2022 GQ$_{1}$. The derived colors indicate that 2021 TY$_{14}$ belongs to the X-complex, while 2021 UW$_{1}$ and 2022 GQ$_{1}$ belong to the S-complex. Their positions in the diameter--rotation period diagram show that all three objects belong to the small, fast-rotating NEA population, with 2022 GQ$_{1}$ being the smallest and fastest-rotating among them with spectroscopic measurements. Analysis of the color time series suggests that the surfaces of observed NEAs are larg",
"summary": "Studying the physical properties of near-Earth asteroids (NEAs) is crucial for understanding their dynamical histories and origins, and assessing impact hazards to Earth. Tiny NEAs with diameters…",
"tags": [
"astro-ph.EP",
"astro-ph.IM"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Simultaneous / Tricolor / Video",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00351",
"rank": 282,
"score": 720,
"rationale": "Magecart abusing Stripe API as C2; high-impact supply-chain threat with novel abuse pattern.",
"title": "R2RDreamer: 3D-aware Data Augmentation for Spatially-generalized 2D Manipulation Policies",
"url": "https://arxiv.org/abs/2606.17040",
"authors": "Xiuwei Xu, Haowen Sun, Angyuan Ma, Yiwei Zhang, Zhenyu Wu, Xiaofeng Wang, Bingyao Yu, Zheng Zhu, Jie Zhou, Jiwen Lu",
"description": "Spatial generalization is critical for imitation-learned manipulation policies, but achieving it typically requires scaling demonstrations across diverse object poses, robot configurations, and camera viewpoints. Data augmentation from a few source demonstrations offers a practical alternative to costly real-world collection. Simulation-based augmentation can create controllable variation, but requires complex environment and object setup and may introduce a sim-to-real gap. Recent real-to-real methods avoid these issues by jointly editing 3D observations and action trajectories from real demonstrations, yet they still rely on strong 3D scene parsing and geometry completion, and often produce observations tailored to 3D pointcloud policies rather than RGB-based 2D policies. We propose R2RDreamer, a real-to-real demonstration augmentation framework that preserves the geometric consistency of 3D action-observation editing while moving visual completion to 2D video space. Specifically, R2RDreamer first performs lightweight 3D augmentation by editing incomplete object pointclouds and end-effector trajectories in a shared 3D frame; it then projects the edited scene into masked image-space control videos with occlusion-aware reasoning and uses a dense-control image-to-video model to complete temporally coherent RGB observations. Experiments on spatially shifted manipulation tasks with both 2D diffusion-style policies and vision-language-action policies show that R2RDreamer improves",
"summary": "Spatial generalization is critical for imitation-learned manipulation policies, but achieving it typically requires scaling demonstrations across diverse object poses, robot configurations, and…",
"tags": [
"cs.RO",
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Manipulation / Human / Geometric",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
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],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00470",
"rank": 283,
"score": 720,
"rationale": "Open online multi-turn RL framework for visual web agents; major open-source agent release.",
"title": "RATS! Patches Talk Through Registers: Emergent Parts in Register Attention Transformers",
"url": "https://arxiv.org/abs/2606.14701",
"authors": "Timing Yang, Predrag Neskovic, Jansen Seheult, Wenchao Han, Anand Bhattad, Alan Yuille, Feng Wang",
"description": "When humans see a bird, they recognize far more than just \"bird\" -- they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every bird they have ever seen. We ask whether a self-supervised visual model can discover the same compositional structure on its own. To this end, we propose RATS (Register Attention Transformers), which decomposes the classification token into N learnable register tokens that route patch information through an L->N->N->L bottleneck via a three-step compress-communicate-broadcast attention. The N registers are partitioned across the H attention heads, so that registers assigned to different heads do not interact with each other. Without auxiliary losses or part annotations, each register spontaneously specializes into a proto-semantic region whose emerging structure resembles object parts. RATS surpasses all baselines by +12 mIoU on average across five segmentation benchmarks, with consistent gains on ADE20K (+1.11 mIoU) and COCO (+0.2 AP^m). Its register dictionary further exhibits part-level consistency and semantic proximity across related categories. Our results suggest that RATS may provide a useful architectural prior for structured and interpretable visual representation learning.",
"summary": "When humans see a bird, they recognize far more than just \"bird\" -- they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every bird they have ever…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Gaze / Heads / How",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00513",
"rank": 284,
"score": 720,
"rationale": "SoundnessBench for AI scientist proposals is timely, well-scoped, and methodologically strong.",
"title": "Measuring Epistemic Resilience of LLMs Under Misleading Medical Context",
"url": "https://huggingface.co/papers/2606.12291",
"authors": "",
"description": "Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.",
"summary": "Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients…",
"tags": [
"▲ 11",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00482",
"rank": 285,
"score": 720,
"rationale": "Multimodal agent skills with visual grounding addresses real text-only bottleneck. Novel framing.",
"title": "HumP-KD: A Hybrid Uncertainty-Aware Multi-Stage Progressive Knowledge Distillation Framework for Efficient Fire Classification",
"url": "https://arxiv.org/abs/2606.14684",
"authors": "Mohammed Arif Mainuddin, Najifa Tabassum, Omar Ibne Shahid, Riasat Khan",
"description": "Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware. This work proposes \\textbf{HumP-KD}, a Hybrid Uncertainty-aware Multi-stage Progressive Knowledge Distillation framework for efficient fire classification. Two datasets, FlameVision and Dataset-II, containing 8,600 and 31,309 images, are used. Various CNN and transformer baselines are applied under standard preprocessing, online augmentation, Gaussian noise and motion blur robustness conditions. The proposed HumP-KD model distills knowledge from two frozen heterogeneous transformer teachers, Swin-Tiny and ViT-Base, along with their Meta-MLP ensemble, into a lightweight MobileViT-S student via three tightly integrated components. Hierarchical Progressive Knowledge Distillation employs a Hierarchical Feature Builder. It generates a fused spatial attention mask to guide distillation toward discriminative regions selectively. Multi-Stage Knowledge Distillation progressively activates three distillation stages across training. On Dataset-II, HumP-KD achieves a mean F1 score of $0.9876 \\pm 0.0063$ across 10 independent trials, significantly outperforming the MobileViT-S baseline trained without distillation ($0.9537 \\pm 0.0351$), with statistical significance confirmed by both independent t-test ($p = 0.0195$) and Wilcoxon signed-rank test ($W = 1$, $p = 0.0039$). The proposed method also demonstrates strong generalizatio",
"summary": "Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware. This work proposes \\textbf{HumP-KD},…",
"tags": [
"cs.CV",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Hump-kd / Hybrid / Uncertainty-aware",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00559",
"rank": 286,
"score": 720,
"rationale": "Open-source agent firewall beyond HTTP; high relevance to agent security, addresses concrete deployment gap.",
"title": "puppeteer/puppeteer",
"url": "https://github.com/puppeteer/puppeteer",
"authors": "",
"description": "JavaScript API for Chrome and Firefox",
"summary": "JavaScript API for Chrome and Firefox",
"tags": [
"⭐ 94.6k",
"GitHub Trending"
],
"source": "💻 GitHub Trending",
"type": "news",
"section": "🌐 Web Findings",
"group": "💻 GitHub Trending",
"debrief_date": "2026-06-14 11:00",
"debrief_path": "debriefs/debrief-2026-06-14-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-14 11:00",
"debrief_path": "debriefs/debrief-2026-06-14-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-14T11:00:00Z"
},
{
"id": "item_00601",
"rank": 287,
"score": 720,
"rationale": "Working memory for latent reasoning from Hochreiter lab is notable for chain-of-thought research.",
"title": "The Cold-Start Safety Gap in LLM Agents",
"url": "https://huggingface.co/papers/2606.07867",
"authors": "",
"description": "Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially safer after a few regular agentic tasks -- a phenomenon we term the cold-start safety gap. To study this systematically, we introduce Safety Over Depth for Agents (SODA), a benchmark that controls how many regular agentic tasks the agent completes before encountering a safety threat, supporting up to 20 preceding tasks. Evaluating 7 models from 4 families, safety improves by 9--52% as the number of preceding regular agentic tasks increases from zero to twenty. Representation analysis confirms that model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present. By systematically studying which part of the preceding conversation matters most, we find that the regular agentic tasks themselves are the primary driver of safety, while the agent's own prior responses have less effect on safety but are essential for preserving later utility. This conclusion is further supported by evaluation on open-source safety benchmarks (AgentHarm, Agent Safety Bench) and utility benchmarks (BFCL, API-Bank), confirming that warming up the agent with regular agentic tasks before deployment makes it safer and preserves full capability. Based on these findings, we recommend a simple deployment strategy: having the agent complete a few regular agentic tasks before possible exposure to sa",
"summary": "Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 11:00",
"debrief_path": "debriefs/debrief-2026-06-12-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T11:00:00Z"
},
{
"id": "item_00660",
"rank": 288,
"score": 720,
"rationale": "Type-holes for safe LLM agents with ReAct/sub-agents as control flow is conceptually strong.",
"title": "Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?",
"url": "https://huggingface.co/papers/2606.08063",
"authors": "",
"description": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black-box feature alignment lacks interpretability, and white-box text-based reasoning cannot restore lost pixel-level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust-U1, a novel framework that equips MLLMs with explicit visual self-recovery capability for robust understanding. The approach comprises three core stages: supervised fine-tuning for initial reconstruction, reinforcement learning with dual rewards (pixel-level SSIM and semantic-level CLIP similarity) for aligning high visual quality, and multimodal reasoning that jointly considers both the corrupted input and the recovered image. Extensive experiments demonstrate that Robust-U1 achieves state-of-the-art robustness on the real-world corruption benchmark and maintains superior performance under adversarial corruptions on general VQA benchmarks. Analysis confirms that high-quality visual recovery directly enhances reasoning performance, establishing self-recovery as a critical mechanism for robust visual understanding. The source code is available at https://github.com/jqtangust/Robust-U1.",
"summary": "Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real-world visual…",
"tags": [
"▲ 23",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00736",
"rank": 289,
"score": 720,
"rationale": "Native multimodal embedding model from Gemini with strong benchmark results; high impact.",
"title": "DifussionGemma 4 on 4x7900xtx",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u31zmk/difussiongemma_4_on_4x7900xtx/",
"authors": "",
"description": "Just got 100 tps on generation, but in total time it around 45-60 t/s in case of prompt processing waiting. Available memory show: GPU KV cache size: 152,671 tokens Maximum concurrency for 131,072 tokens per request: 1.16x amd-smi monitor for this gpu: GPU XCP POWER GPU_T MEM_T GFX_CLK GFX% MEM% ENC% DEC% VRAM_USAGE 3 0 183 W 82 °C 84 °C 3036 MHz 100 % 5 % N/A 0 % 23.6/ 24.0 GB 5 0 161 W 81 °C 88 °C 3101 MHz 100 % 0 % N/A 0 % 23.7/ 24.0 GB 7 0 165 W 78 °C 86 °C 3095 MHz 100 % 1 % N/A 0 % 23.7/ 24.0 GB 8 0 154 W 80 °C 88 °C 3090 MHz 100 % 0 % N/A 0 % 23.6/ 24.0 GB # DiffusionGemma 26B on vllm d",
"summary": "Just got 100 tps on generation, but in total time it around 45-60 t/s in case of prompt processing waiting. Available memory show: GPU KV cache size: 152,671 tokens Maximum…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_00784",
"rank": 290,
"score": 720,
"rationale": "Terminal agents learning world models from free env feedback; doubles GRPO pass@1, strong agents fit.",
"title": "The Simplified Stabilizer ZX-Calculus is Minimal",
"url": "https://arxiv.org/abs/2606.12383",
"authors": "Harry K. Stoltz",
"description": "The stabilizer fragment of the ZX calculus is amongst the most important fragments of the theory. The closely related Clifford+T fragment is approximately universal (arXiv:1705.11151). Additionally, the stabilizer calculus can be described by a small collection of rewrites, most of which have been shown to be necessary (arXiv:1709.08903). However, two rules, describing the red/green compact-structure coincidence and the important bialgebra law, had not been shown to be necessary. We present a countermodel-style argument showing that both of these rules are individually necessary relative to the connectivity meta-rule of Backens--Perdrix--Wang (arXiv:1709.08903), and hence establish that the rule set presented in arXiv:1709.08903 has no redundant rewrite rule.",
"summary": "The stabilizer fragment of the ZX calculus is amongst the most important fragments of the theory. The closely related Clifford+T fragment is approximately universal (arXiv:1705.11151). Additionally,…",
"tags": [
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Simplified / Stabilizer / Zx-calculus",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00901",
"rank": 291,
"score": 720,
"rationale": "Strong practical tool: proxy metrics beating loss for model selection addresses a real, costly LLM development pain point.",
"title": "AI Epistemic Risks: Emerging Mechanisms & Evidence [R]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u1ew6q/ai_epistemic_risks_emerging_mechanisms_evidence_r/",
"authors": "",
"description": "How will AI affect our ability to think and judge for ourselves? Our new paper co-authored by 30 experts explores epistemic risks —the threats AI poses to our collective capacity to form beliefs accurately, reason well, and maintain a healthy information environment. We look at how AI can lead to harm through these mechanisms: Persuasion & Manipulation: AI systems are highly persuasive, opening the door for political/economic manipulation, incitement and radicalization, and other misuse, as well as unintentional harms like AI sycophancy and mental health risks. Cognitive Offloading: We may be",
"summary": "How will AI affect our ability to think and judge for ourselves? Our new paper co-authored by 30 experts explores epistemic risks —the threats AI poses to our collective capacity…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_00964",
"rank": 292,
"score": 720,
"rationale": "Strong benchmark for long-horizon agent memory under interference; high relevance to agent research.",
"title": "Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties",
"url": "https://arxiv.org/abs/2606.10615",
"authors": "Jordi Thijsman, Merlijn Sebrechts, Stefan Lefever, Filip De Turck, Bruno Volckaert",
"description": "Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme computational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer. Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code. All computation in 2cVM is governed by a Commitment Manifest that enumerates participants, component composition, permitted data channels, and authorized outputs; the manifest is locked to the VM and incorporated into attestation evidence, making the policy immutable and independently verifiable throughout the VM's lifetime. A proof-of-concept realization combines AMD SEV-SNP for hardware protection with the WebAssembly Component Model for fine-grained sandboxing of participant code. Evaluation on commodity hardware across four benchmark classes shows that the two isolation layers do not accumulate linearly: once a workload executes inside the WebAssembly sandbox, the marginal cost of enabling hardware memory protection is small.",
"summary": "Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants.…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Cryptography",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_00979",
"rank": 293,
"score": 720,
"rationale": "Toto 2.0: open-weights time-series foundation models with SOTA; strong practical release.",
"title": "SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research",
"url": "https://huggingface.co/papers/2606.09730",
"authors": "",
"description": "Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite. Recent work explores a paradigm where a main agent decomposes tasks and dispatches subtasks to subagents, which execute and return only summarized results, conserving the main agent's context budget. However, performing this well requires delegation intelligence: the ability to decompose complex tasks, determine when and what to delegate, and integrate returned results into the ongoing workflow. Training data for this capability is scarce in naturally occurring text, and to our knowledge, how to synthesize such data and train models to acquire this capability remains largely unexplored in the open-source community. To bridge this gap, we present a preliminary exploration targeting deep research, a representative long-horizon agent task. Specifically, we design a harness that guides the model toward high-quality task decomposition and delegation, while constraining subagents to return results properly to support the main agent's workflow. The harness-guided trajectories naturally encode correct delegation decisions, which we use as supervised fine-tuning data to internalize delegation intelligence into model weights. Our resulting model, SearchSwarm-30B-A3B, achieves 68.1 on BrowseComp and 73.3 on BrowseComp-ZH, the best results among all models of comparable scale. We will release our",
"summary": "Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_00939",
"rank": 294,
"score": 720,
"rationale": "Google API key revocation lag (23 min) is a concrete, exploitable security finding relevant to LLM-agent credential hygiene.",
"title": "Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation",
"url": "https://arxiv.org/abs/2606.11127",
"authors": "Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu, Pratinav Seth",
"description": "Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examined together: whether the filtering signal is grounded in the source evidence that induced each generation, and whether rejected samples can be systematically recovered rather than permanently discarded. We present a controlled study of both questions across gate configurations, recovery strategies, and generator scales, using adversarially injected corpora to provide ground-truth failure labels. We find that exact source provenance improves faithfulness gating for stronger judges, that hallucination and reward gates reject largely disjoint sample populations making both necessary, and that an adaptive recovery pipeline combining failure diagnosis with targeted regeneration achieves higher yield, recovery rate, and injection recall than naive resampling. Downstream fine-tuning quality is driven primarily by generator scale, with filtration and recovery conditions contributing meaningfully but secondarily.",
"summary": "Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examined together: whether the filtering signal is…",
"tags": [
"cs.CL",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Artificial Intelligence",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_00932",
"rank": 295,
"score": 720,
"rationale": "Reverse-engineered real-world terminal benchmark with 1,530 tasks is high-value, authentic agent evaluation.",
"title": "EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents",
"url": "https://arxiv.org/abs/2606.11182",
"authors": "Weixian Xu, Shilong Liu, Mengdi Wang",
"description": "In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under real-world task streams. Existing methods are largely designed for single-dataset settings, while real-world applications require models to handle heterogeneous input streams drawn from multiple datasets, domains, and task distributions, limiting their practical applicability. To mitigate cross-dataset interference, EEVEE introduces a router that partitions incoming inputs into task clusters and assigns them to suitable prompt configurations. This design is optimized via a router-prompt co-evolution strategy, which employs interleaved router and prompt learning phases to address their mutual dependency. Experiments across multiple datasets demonstrate that the framework improves robustness under heterogeneous data streams while maintaining single-benchmark learning capability and efficiency. Specifically, EEVEE improves average multi-benchmark scores by 10.38 and 24.32 points over Qwen3-4B-Instruct and DeepSeek-V3.2, surpassing SOTA methods GEPA and ACE by up to 37.2% and 48.2%.",
"summary": "In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under real-world task streams. Existing methods are…",
"tags": [
"cs.LG",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Artificial Intelligence",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01044",
"rank": 296,
"score": 720,
"rationale": "XSS breaking passkey attestation is critical for auth security.",
"title": "TSseek: Regular Expression-Based Similarity Search for Distributed Time Series Datasets",
"url": "https://arxiv.org/abs/2606.09824",
"authors": "Xiaoshuai Li, Khalid Alnuaim, Mohamed Y. Eltabakh, Elke A. Rundensteiner",
"description": "Similarity search is a fundamental operation in time series analysis. Most existing techniques, however, require users to supply a precise sequence of values (typically an entire time series object) as the query input. This rigid requirement limits real-world applications, where users instead want to express patterns, trends, or value ranges. Flexible, pattern-based search has been explored in text retrieval and complex event processing, but remains underexplored for large-scale distributed time series. To close this gap, we propose TSseek, a regular-expression-powered search framework for distributed time series datasets. TSseek's query language enables users to compose patterns encompassing trends, value ranges, and wildcard segments. We show that conventional approximation techniques (e.g., PAA and SAX) and their index structures are ill-suited for such queries because they cannot operate on regular-expression query constructs. In TSseek, we map the time series objects and the query constructs into the same space by approximating time series objects as sequences of line segments that retain both trend (slope direction) and value range, and translating query constructs into bounding rectangles. To support efficient processing, we build TSseek-X, a distributed spatial index over the time series segments. TSseek supports two fundamental query types, namely whole-matching queries (over entire series) and subsequence-matching queries (over arbitrary windows within a series)",
"summary": "Similarity search is a fundamental operation in time series analysis. Most existing techniques, however, require users to supply a precise sequence of values (typically an entire time series object)…",
"tags": [
"cs.DB"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Large Language Models",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01100",
"rank": 297,
"score": 720,
"rationale": "Healthcare workflow benchmark exposing 28% best agent; strong evidence of real-world agent gaps.",
"title": "Phase Marginalization for Patch-Grid Instability in Vision Transformers",
"url": "https://huggingface.co/papers/2606.08132",
"authors": "",
"description": "Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries. We formalize patch-grid phase as a nuisance variable and propose Phase Marginalization, a post-hoc marginalization method that evaluates structured patch-grid phases, inverse-aligns dense outputs, and aggregates them in the original image coordinate system. The central variant, Uniform Phase Marginalization with K = 4, is training-free and improves over the canonical K = 1 baseline across measured segmentation, depth, and local matching settings. In a controlled Cityscapes experiment, Uniform Phase Marginalization provides a modest compute-matched advantage over generic shift-based four-forward test-time augmentation (TTA) (+0.31 mean Intersection-over-Union over the strongest tested generic row). A scaling study further shows that K = 4 is a practical cost-accuracy trade-off: K = 8 is essentially unchanged and K = 16 adds little accuracy at much higher latency. These results position patch-grid phase as a measurable nuisance variable and Phase Marginalization as a simple diagnostic and post-hoc marginalization baseline for dense ViT prediction.",
"summary": "Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01135",
"rank": 298,
"score": 720,
"rationale": "Overeager coding agents benchmark is highly relevant to agent safety and authorization.",
"title": "Expanding Private Cloud Compute",
"url": "https://security.apple.com/blog/expanding-pcc/",
"authors": "",
"description": "Alongside the next generation of Apple Intelligence, today we’re expanding Private Cloud Compute (PCC) beyond Apple’s data centers. When Apple introduced Private Cloud Compute in 2024, we defined a new frontier for private AI inference, extending the security and privacy of Apple devices into the cloud for those AI workloads more complex than on-device models can handle. Now, we are collaborating with Google and NVIDIA to run new Apple Intelligence workloads on Google Cloud Platform, extending our industry-leading PCC privacy commitments to third-party data centers for the first time.",
"summary": "Alongside the next generation of Apple Intelligence, today we’re expanding Private Cloud Compute (PCC) beyond Apple’s data centers. When Apple introduced Private Cloud Compute in…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01236",
"rank": 299,
"score": 720,
"rationale": "Companion IEEE piece emphasizing variable-precision requirements; relevant quantum-strategy signal.",
"title": "OPRD: On-Policy Representation Distillation",
"url": "https://huggingface.co/papers/2606.06021",
"authors": "daily curated papers",
"description": "On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limits: (1) sampling variance from Monte Carlo KL estimates over large vocabularies (e.g., Qwen's ~150k tokens) persists throughout training, and (2) it treats the teacher as a black-box, discarding all intermediate hidden states after the LM head. We propose On-Policy Representation Distillation (OPRD), which lifts distillation into hidden-state space by aligning student and teacher representations across selected layers on the same rollouts, bypassing the LM head entirely. Theoretically, OPRD eliminates sampling variance and provides richer per-layer structural information. Empirically, OPRD closes the student-teacher gap on AIME 2024/2025 and AIMO, while output-space OPD baselines plateau below the teacher. OPRD also trains 1.44x faster and uses 54% less memory than top-k OPD. Code: https://github.com/ShenzhiYang2000/OPRD.",
"summary": "On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limits: (1) sampling variance from Monte Carlo KL estimates over large vocabularies (e.g., Qwen's ~150k tokens) …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01193",
"rank": 300,
"score": 720,
"rationale": "CIPO turns failed trajectories into correction supervision; clean, broadly useful RLVR extension.",
"title": "BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding",
"url": "https://huggingface.co/papers/2606.05515",
"authors": "daily curated papers",
"description": "Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundary representations BReps, which encodes exact parametric surfaces, curves, and their topology, has received little attention as a representation learning substrate. We introduce BRepCLIP, the first framework to align BRep geometry with language and image embeddings through contrastive pretraining. We model each CAD object as a sequence of face and edge tokens with separate discrete vocabularies for surface and curve geometry, augmented with spatial and semantic descriptors that capture surface types (e.g., cylindrical, torus, NURBS) and curve primitives (e.g., line, arc, B-spline). A transformer encoder aggregates these tokens into a global BRep embedding, aligned with CLIP's text and image encoders via a joint contrastive objective. BRepCLIP generates more discriminative and semantically grounded embeddings than existing point-based alternatives, improving Top-1 retrieval over OpenShape by 40.4%, 22.0%, and 23.9% on ABC, CADParser, and Automate, respectively, and improving zero-shot classification on FabWave by 15% in Top-1 score. We further demonstrate its utility as a CAD-aware similarity metric for evaluating text and image-conditioned CAD generation, establishing the importance of structure-aware pretraining for multimodal CAD understanding. Project page is available at https://muhammadusama100.",
"summary": "Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundary representations BReps, which encodes exact parametric surfaces …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01264",
"rank": 301,
"score": 720,
"rationale": "Backdoor via positional encoding is a novel attack surface on LLMs; strong security implications and bypasses content-based defenses.",
"title": "αDepth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion",
"url": "https://huggingface.co/papers/2606.00386",
"authors": "",
"description": "Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existing depth models primarily predict single-layer depth, leading to ambiguity in depth correspondence at soft boundaries. While matting techniques can capture opacity for layered modeling, they often struggle in complex scenes with multiple targets and usually require user intervention. This paper introduces αDepth, a layered representation that decomposes soft boundaries for high-fidelity stereo conversion. Specifically, we first resolve mixed color and depth ambiguity by estimating layered color and depth values at soft boundaries. Considering complex multi-target scenes, we design a Circular Alpha Representation (CAR) that shifts the paradigm from global target extraction to local boundary decomposition. Unlike prior matting methods restricted to a single foreground/background, CAR enables efficient scene-level inference without manual guidance. Extensive evaluations demonstrate that αDepth achieves state-of-the-art performance in stereo conversion, eliminating background bleeding and structural distortions at soft boundaries.",
"summary": "Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-06-03 11:00",
"debrief_path": "debriefs/debrief-2026-06-03-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 11:00",
"debrief_path": "debriefs/debrief-2026-06-03-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T11:00:00Z"
},
{
"id": "item_01305",
"rank": 302,
"score": 720,
"rationale": "Project Zero 0-click Pixel 10 chain is high-value offensive research; demonstrates shifting attack surfaces.",
"title": "Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses",
"url": "https://huggingface.co/papers/2606.02373",
"authors": "Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, Jiashuo Sun",
"description": "Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked. We argue that this formulation puts too much routine state management inside the policy: reinforcement learning is forced to optimize both semantic search decisions and recoverable bookkeeping that the environment can maintain more reliably. We introduce Harness-1, a 20B search agent (retrieval subagent) trained with reinforcement learning inside a stateful search harness. The harness maintains environment-side working memory, including a candidate pool, an importance-tagged curated set, compact evidence links, verification records, compressed and deduplicated observations, and budget-aware context rendering. The policy retains the semantic decisions: what to search, which documents to keep or discard, what to verify, and when to stop. Across eight retrieval benchmarks spanning web, finance, patents, and multi-hop QA, Harness-1 achieves 0.730 average curated recall, outperforming the next strongest open search subagent by +11.4 points and remaining competitive with much larger frontier-model searchers. Its gains are especially strong on held-out transfer benchmarks, suggesting that reinforcement learning over explicit search state can produce retrieval behaviors that generalize beyond the training domains. Our code is available at",
"summary": "Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-06-01"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-06-02 11:00",
"debrief_path": "debriefs/debrief-2026-06-02-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-02 11:00",
"debrief_path": "debriefs/debrief-2026-06-02-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-02T11:00:00Z"
},
{
"id": "item_01335",
"rank": 303,
"score": 720,
"rationale": "Orchard open-source agentic framework: 67.5% SWE-bench SOTA for open + 4B GUI agent; high impact open release.",
"title": "When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models",
"url": "https://huggingface.co/papers/2605.28181",
"authors": "Jungwon Park, Jimyeong Kim, Jungmin Ko, Nojun Kwak, Wonjong Rhee",
"description": "Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inference-time decision. Most training-free decoding strategies use model confidence for position selection, assuming that high-confidence positions are ready to be decoded. In this work, we revisit this assumption by studying when confidence misleads fully non-autoregressive (fully non-AR) decoding. EOT tokens can receive high confidence and cause incomplete generation; inserting a suffix anchor can mitigate this issue but introduces local overconfidence near the anchor, causing anchor-adjacent tokens to be decoded too early.",
"summary": "Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positi…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-06-01 11:00",
"debrief_path": "debriefs/debrief-2026-06-01-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-01 11:00",
"debrief_path": "debriefs/debrief-2026-06-01-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-01T11:00:00Z"
},
{
"id": "item_01484",
"rank": 304,
"score": 720,
"rationale": "SenseNova-U1 native unified multimodal MoT model is a meaningful system-level release rivaling top VLMs.",
"title": "LoMo: Local Modality Substitution for Deeper Vision-Language Fusion",
"url": "https://huggingface.co/papers/2605.30265",
"authors": "Feng Han, Zhixiong Zhang, Zheming Liang, Yibin Wang, Jiaqi Wang",
"description": "Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image-text training aimed at multimodal fusion. Ideally, replacing a textual question with its rendered-image counterpart should leave model performance essentially unaffected. In practice, however, such modality substitution induces dramatic performance degradation. We attribute this \"carrier sensitivity\" issue to an inherent bias in current training corpora. Across prevalent datasets such as image captioning, VQA, OCR, and web-sourced interleaved data, text and images are typically organized into distinct and asymmetric roles, with text serving as linguistic queries and images as visual references. Such data bias leads VLMs to exhibit distinct preferences for information acquisition across different modalities. Consequently, VLMs fail to align representations of semantically equivalent content across textual and visual carriers, making model reasoning fragile under modality substitution. To address this, we propose Local Modality Substitution (LoMo), a lightweight, architecture-agnostic data curation paradigm designed to provide supervision for cross-modal representational invariance between semantically equivalent text and image carriers. LoMo achieves this by reformulating single-modality prompts into seamlessly interleaved multimodal sequences. It dynamically selects target text spans and recasts them as rendered images, thereby",
"summary": "Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image-text training aimed at multimodal fusion. Ideally …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-05-28"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-29T19:00:00Z"
},
{
"id": "item_01573",
"rank": 305,
"score": 720,
"rationale": "First controllable agent red-teaming platform is high-impact for AI safety and security evaluation.",
"title": "Performance analysis and industry deployment of post-quantum cryptography algorithms",
"url": "https://arxiv.org/abs/2503.12952",
"authors": "",
"description": "As quantum computing advances, modern cryptographic standards face an existential threat, necessitating a transition to post-quantum cryptography (PQC). The National Institute of Standards and Technology (NIST) has selected CRYSTALS-Kyber and CRYSTALS-Dilithium as standardized PQC algorithms for secure key exchange and digital signatures, respectively. This study conducts a comprehensive performance analysis of these algorithms by benchmarking execution times across cryptographic operations such as key generation, encapsulation, decapsulation, signing, and verification. Additionally, the impac",
"summary": "As quantum computing advances, modern cryptographic standards face an existential threat, necessitating a transition to post-quantum cryptography (PQC). The National Institute of Standards and Technology (NIST) has…",
"tags": [
"GOOGLE_SCHOLAR"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "📚 Google Scholar",
"debrief_date": "2026-05-28 04:00",
"debrief_path": "debriefs/debrief-2026-05-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-28 04:00",
"debrief_path": "debriefs/debrief-2026-05-28-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-28T04:00:00Z"
},
{
"id": "item_01623",
"rank": 306,
"score": 720,
"rationale": "Important benchmark exposing persistent LLM-agent cyberattack biases; directly relevant to AI safety in agents.",
"title": "Are we self-sovereign PKI yet?",
"url": "https://buffrr.dev/blog/are-we-self-sovereign-pki-yet/",
"authors": "",
"description": "Every end-to-end encrypted messenger ships a fingerprint UI. Almost nobody opens it. A note on what's actually missing.",
"summary": "Every end-to-end encrypted messenger ships a fingerprint UI. Almost nobody opens it. A note on what's actually missing.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "📌 Lobste.rs",
"debrief_date": "2026-05-26 11:00",
"debrief_path": "debriefs/debrief-2026-05-26-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-26 11:00",
"debrief_path": "debriefs/debrief-2026-05-26-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-26T11:00:00Z"
},
{
"id": "item_01647",
"rank": 307,
"score": 720,
"rationale": "Mechanistic identification of MMS collapse in deep DiTs and MV-Split fix; high-impact training-stability insight.",
"title": "SEAL: Synergistic Co-Evolution of Agents and Learning Environments",
"url": "https://huggingface.co/papers/2605.24426",
"authors": "Yihao Hu, Zhihao Wen, Xiujin Liu, Pan Wang, Xin Zhang",
"description": "SEAL co-evolves both the agent policy and its training environment in a closed loop, using turn-level failure diagnoses as a shared signal for both environment adaptation and policy optimization, yielding +8.25 to +26.25 average-point gains with only 400 training samples.",
"summary": "SEAL co-evolves both the agent policy and its training environment in a closed loop, using turn-level failure diagnoses as a shared signal for both environment adaptation and policy optimization, yielding…",
"tags": [
"cs.AI",
"cs.ML"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-26 04:00",
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"debrief_md": ""
}
],
"sort_date": "2026-05-26T04:00:00Z"
},
{
"id": "item_01681",
"rank": 308,
"score": 720,
"rationale": "Notable: Mythos autonomously finding a real curl vuln demonstrates frontier AI cyber capability milestone.",
"title": "Imec builds first High-NA EUV-fabricated quantum dot qubit",
"url": "https://news.ycombinator.com/item?id=48272816",
"authors": "2026-05-26",
"description": "Imec has built the first quantum dot qubit fabricated with High-NA EUV lithography, a breakthrough that could pull quantum computing onto the same manufacturing roadmap as next-gen AI processors.",
"summary": "Imec has built the first quantum dot qubit fabricated with High-NA EUV lithography, a breakthrough that could pull quantum computing onto the same manufacturing roadmap as next-gen AI processors.",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "💬 Hacker News",
"group": "⚡ Machine Learning",
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-26T04:00:00Z"
},
{
"id": "item_01749",
"rank": 309,
"score": 720,
"rationale": "StraTA improves agentic RL with trajectory-level strategy, useful advance.",
"title": "Minimalist Visual Inertial Odometry",
"url": "https://huggingface.co/papers/2605.19990",
"authors": "Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar",
"description": "Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.",
"summary": "Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can…",
"tags": [
"arXiv:2605.19990"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01763",
"rank": 310,
"score": 720,
"rationale": "TIDE re-examines token index embedding, addresses rare-token and collapse issues.",
"title": "Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles",
"url": "https://huggingface.co/papers/2605.22177",
"authors": "Jinyang Wu, Guocheng Zhai, Ruihan Jin, Yuhao Shen, Zhengxi Lu",
"description": "The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct advantages across diverse domains, yet current frameworks fail to exploit the complementary strengths of models and skills, thereby limiting their performance on downstream tasks. In this paper, we present Maestro (Multimodal Agent for Expert-Skill Targeted Reinforced Orchestration), a Reinforcement Learning (RL)-driven orchestration framework that reframes heterogeneous multimodal tasks as a sequential decision-making process over a hierarchical model-skill registry. Rather than consolidating all knowledge into a single model, Maestro trains a lightweight policy to dynamically compose ensembles of frozen expert models and a two-tier skill library, deciding at each step whether to invoke an external expert, which model-skill pair to select, and when to terminate. The policy is optimized via outcome-based RL, requiring no step-level supervision. We evaluate Maestro across ten representative multimodal benchmarks spanning mathematical reasoning, chart understanding, high-resolution perception, and domain-specific analysis. With only a 4B orchestrator, Maestro achieves an average accuracy of 70.1%, surpassing both GPT-5 (69.3%) and Gemini-2.5-Pro (68.7%). Crucially, the",
"summary": "The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct…",
"tags": [
"arXiv:2605.22177"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01737",
"rank": 311,
"score": 720,
"rationale": "Notable: io_uring ZCRX freelist LPE giving root from u32 is impactful kernel exploit technique.",
"title": "DelTA: Discriminative Token Credit Assignment for Reinforcement Learning from Verifiable Rewards",
"url": "https://huggingface.co/papers/2605.21467",
"authors": "Kaiyi Zhang, Wei Wu, Yankai Lin",
"description": "Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate into token-level probability changes remains poorly understood. We introduce a discriminator view of RLVR updates, showing that the policy-gradient update direction implicitly acts as a linear discriminator over token-gradient vectors and thereby determines which token probabilities are increased or decreased during learning. Under standard sequence-level RLVR, this discriminator is constructed from positive- and negative-side centroids formed by advantage-weighted averaging of token-gradient vectors. However, such centroid construction can be dominated by shared high-frequency patterns, such as formatting tokens, diluting sparse yet discriminative directions that better distinguish high-reward responses from low-reward ones. To address this limitation, we propose DelTA, a discriminative token credit assignment method that estimates token coefficients to amplify side-specific token-gradient directions and downweight shared or weakly discriminative ones. These coefficients reweight a self-normalized RLVR surrogate, making the effective side-wise centroids more contrastive and thereby reshaping the RLVR update direction. On seven mathematical benchmarks, DelTA outperforms the strongest same-scale baselines by 3.26 and 2.62 average points on Qwen3-8B-Base and Qwe",
"summary": "Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate into token-level probability changes remains poorly understood. We introduce a discriminator view…",
"tags": [
"arXiv:2605.21467"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01811",
"rank": 312,
"score": 720,
"rationale": "Tsallis-loss continuum for RLVR cold-start; theoretically grounded, high impact for reasoning post-training.",
"title": "A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook",
"url": "https://huggingface.co/papers/2605.20266",
"authors": "Kaiwen Luo, Zhenhong Zhou, Leo Wang, Liang Lin, Yang Xiao",
"description": "The foundational capabilities established by Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs), within which Large Audio Language Models (LALMs) are essential for realizing universal auditory intelligence. Despite their remarkable performance, the escalation of LALMs' capabilities has significantly outpaced the development of systemic frameworks to ensure their trustworthiness. This survey provides a comprehensive investigation into the endogenous mechanisms of LALMs, detailing the architectural innovations and alignment algorithms that facilitate emergent reasoning. Specifically, we analyze how the transition to unified end-to-end frameworks and the integration of continuous acoustic signals inherently expand the attack surface. To rigorously evaluate the risks within these paradigms, we establish a comprehensive taxonomy of trustworthiness, categorizing critical vulnerabilities such as cross-modal jailbreaking, latent acoustic backdoors, and biometric privacy leakage. We review the state-of-the-art through six analytical pillars: hallucination, robustness, safety, privacy, fairness, and authentication. The profound imbalance between a mature offensive landscape and underdeveloped defenses further validates the critical trustworthiness gaps and multidimensional risks facing audio-centric intelligence. Finally, we propose a strategic roadmap advocating for \"Defense-in-Depth\" architectures, causal auditory world modeling, and intrinsi",
"summary": "The foundational capabilities established by Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs), within which Large Audio Language Models (LALMs) are essential for realizing universal auditory intelligence. Despite their remarkable perfor…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-21 16:00",
"debrief_path": "debriefs/debrief-2026-05-21-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-21 16:00",
"debrief_path": "debriefs/debrief-2026-05-21-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-21T16:00:00Z"
},
{
"id": "item_01881",
"rank": 313,
"score": 720,
"rationale": "ARIS cross-model adversarial executor/reviewer harness directly targets plausible-supported-success failure mode in long-horizon research agents.",
"title": "the may 2026 fedi software vulnerability",
"url": "https://w.on-t.work/activitypub/may-2026-vulnerability",
"authors": "",
"description": "the may 2026 fedi software vulnerability",
"summary": "the may 2026 fedi software vulnerability",
"tags": [
"LOBSTERS"
],
"source": "Lobste.rs",
"type": "news",
"section": "📰 News Headlines",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T19:00:00Z"
},
{
"id": "item_01906",
"rank": 314,
"score": 720,
"rationale": "HiL-Bench's Ask-F1 directly measures a crucial agent judgment gap (when to escalate); high-impact agent-safety benchmark.",
"title": "GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment",
"url": "https://huggingface.co/papers/2605.19577",
"authors": "",
"description": "We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Existing long-context RL methods often treat data construction as a matter of designing increasingly complex retrieval paths, leading to homogeneous task coverage and reward formulations that inadequately reflect practical long-context requirements. Our work offers two contributions. (1) Capability-oriented data construction with full open release. We openly release a dataset of 23K RLVR samples, the complete construction pipeline, and all training code. Guided by a taxonomy of long-context capabilities, the dataset spans 9 task types, each paired with its natural evaluation metric. It comprises curated open-source samples from established corpora and synthetic samples whose QA pairs are generated from real source documents such as books, academic papers, and multi-turn dialogues. Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset. Moreover, our Qwen3-30B-A3B model trained on this data delivers long-context performance comparable to DeepSeek-R1-0528 and Qwen3-235B-A22B-Thinking-2507, suggesting that broader coverage and greater reward diversity substantially benefit long-context capability improvement. (2) TMN-Reweight for heterogeneous multitask optimization. To address optimization challenges from heterogeneous rewards, we propose TMN-Reweight, which combines task-leve",
"summary": "We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Existing long-context RL methods often treat…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_01971",
"rank": 315,
"score": 720,
"rationale": "Position paper for Bayes-consistent agentic AI orchestration; high-quality principled framing for agents.",
"title": "Bridging the Cybersecurity Gap Between Web2 and Web3 - An Incident-Based Analysis of Organizational and Application-Level Security Failures",
"url": "https://arxiv.org/abs/2605.18484",
"authors": "Tarkan Yavas, Arslan Brömme",
"description": "The rapid adoption of Web3 infrastructures has led to a growing number of security incidents affecting cryptocurrency exchanges, custody services and blockchain-based platforms. While existing research predominantly focuses on vulnerabilities in smart contracts and blockchain protocols, a substantial portion of real-world losses originates from off-chain systems, organizational processes and human-centered operational workflows. This paper presents a qualitative, incident-based analysis of pub",
"summary": "The rapid adoption of Web3 infrastructures has led to a growing number of security incidents affecting cryptocurrency exchanges, custody services and blockchain-based platforms. While existing research predominantly focuses on vulnerabilities in smar…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-05-19 04:00",
"debrief_path": "debriefs/debrief-2026-05-19-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-19 04:00",
"debrief_path": "debriefs/debrief-2026-05-19-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-19T04:00:00Z"
},
{
"id": "item_02049",
"rank": 316,
"score": 720,
"rationale": "Red-teaming long-context LLMs for prompt injection/knowledge corruption; high AI safety impact.",
"title": "Unlocking Dense Metric Depth Estimation in VLMs",
"url": "https://huggingface.co/papers/2605.15876",
"authors": "Hanxun Yu, Xuan Qu, Yuxin Wang, Jianke Zhu, Lei ke",
"description": "Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text-only supervision paradigm, which under-constrains fine-grained visual perception and prevents the recovery of dense geometry. Prior methods either distill geometry from external vision models, introducing error accumulation, or enable direct prediction with inefficient per-pixel query or coarse token-level outputs. In this paper, we propose DepthVLM, a simple yet effective framework that transforms a single VLM into a native dense geometry predictor while preserving its multimodal capability. By attaching a lightweight depth head to the LLM backbone and training under a unified vision-text supervision paradigm with a two-stage schedule, DepthVLM generates full-resolution depth maps alongside language outputs in a single forward pass. We further introduce a unified indoor-outdoor metric depth benchmark in a VLM-compatible format. Experiments show that DepthVLM significantly outperforms existing VLMs with higher inference efficiency, surpasses leading pure vision models, and improves complex 3D spatial reasoning, moving toward a truly unified foundation model. All code and checkpoints will be publicly released.",
"summary": "Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text-only supervision paradigm, which under-constrains fine-grained…",
"tags": [
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "🤖 LLMs",
"debrief_date": "2026-05-18 04:00",
"debrief_path": "debriefs/debrief-2026-05-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-18 04:00",
"debrief_path": "debriefs/debrief-2026-05-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-18T04:00:00Z"
},
{
"id": "item_02233",
"rank": 317,
"score": 720,
"rationale": "Identifies SFT-induced hallucinations and proposes self-distillation fix; high practical impact.",
"title": "Context Training with Active Information Seeking",
"url": "https://huggingface.co/papers/2605.13050",
"authors": "Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng, Jiajun Shen, Lucio Dery",
"description": "Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent work has shown that, by manipulating and optimizing their context, LLMs can be tailored to downstream tasks without updating their weights. However, most existing methods remain closed-loop, relying solely on the model's intrinsic knowledge. In this paper, we equip these context optimizers with Wikipedia search and browser tools for active information seeking. We show that naively adding these tools to a standard sequential context optimization pipeline can actually degrade performance compared to baselines. However, when paired with a search-based training procedure that maintains and prunes multiple candidate contexts, active information seeking delivers consistent and substantial gains. We demonstrate these improvements across diverse domains, including low-resource translation (Flores+), health scenarios (HealthBench), and reasoning-heavy tasks (LiveCodeBench and Humanity's Last Exam). Furthermore, our method proves to be data-efficient, robust across different hyperparameters, and capable of generating effective textual contexts that generalize well across different models.",
"summary": "Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent work has shown that, by manipulating and optimizing their context,…",
"tags": [
"cs.CL",
"cs.AI",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-14 09:00",
"debrief_path": "debriefs/debrief-2026-05-14-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-14T09:00:00Z"
},
{
"id": "item_02268",
"rank": 318,
"score": 720,
"rationale": "Multi-thinker CoT learning theory; deep learning-theoretic contribution.",
"title": "Efficient Pre-Training with Token Superposition",
"url": "https://huggingface.co/papers/2605.06546",
"authors": "",
"description": "Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data throughput. In this work, we present Token-Superposition Training (TST), a simple drop-in method that significantly improves the data throughput per FLOPs during pre-training without modifying the parallelism, optimizer, tokenizer, data, or model architecture. TST is done in two phases: (i) A highly efficient superposition phase where we combine many contiguous tokens into one bag and train using a multi-hot cross-entropy (MCE) objective, and (ii) a recovery phase where we revert back to standard training. We extensively evaluate TST on the scale of 270M and 600M parameters and validate on 3B and a 10B A1B mixture of experts model, demonstrating that it is highly robust in different settings. Ultimately, TST consistently outperforms baseline loss and downstream evaluations, and under equal-loss settings, TST yields up to a 2.5x reduction in total pre-training time at the 10B A1B scale.",
"summary": "Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_02308",
"rank": 319,
"score": 720,
"rationale": "Unified VLA safety survey tying embodied AI risks to attacks/defenses—high relevance, well-scoped.",
"title": "MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments",
"url": "https://huggingface.co/papers/2605.09131",
"authors": "Giridhar Ganapavarapu, Dhaval Patel",
"description": "The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how agents conceptualize the environments within which they operate. Current paradigms are bifurcated: Task-level planning often ignores execution-time dynamics, while reactive execution lacks long-horizon foresight. We present MCP-Cosmos, a framework that infuses generative World Models (WM) into the MCP ecosystem to enable predictive task automation. By unifying three disparate technologies, namely MCP, World Model, and Agent, we demonstrate that a \"Bring Your Own World Model\" (BYOWM) strategy allows agents to simulate state transitions and refine plans in a latent space before execution. We conducted experiments using two strategies, namely ReAct and SPIRAL with 2 planning models and 3 representative world models over 20+ MCP-Bench tasks. We observed improvements in Agent's environment interaction KPI such as tool success rate and tool parameter accuracy. The framework also offers new metrics such as Execution Quality to generate new insights about the effectiveness of world models compared to baseline.",
"summary": "The Model Context Protocol (MCP) has unified the interface between Large Language Models (LLMs) and external tools, yet a fundamental gap remains in how agents conceptualize the environments within which they operate. Current paradigms are…",
"tags": [
"arXiv:2605.09131"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T11:00:00Z"
},
{
"id": "item_02422",
"rank": 320,
"score": 720,
"rationale": "Disputes recent claimed quantum advantage, high-impact correctness result.",
"title": "Can you help reconcile my first/second-hand LLM Experience with HN's Experience?",
"url": "https://news.ycombinator.com/item?id=48102861",
"authors": "",
"description": "",
"summary": "",
"tags": [
"HN:LLM research pa",
"HACKERNEWS"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T04:00:00Z"
},
{
"id": "item_02389",
"rank": 321,
"score": 720,
"rationale": "SIREN internal-layer LLM safety guard; strong, efficient safety method with generalization and streaming benefits.",
"title": "BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning",
"url": "https://huggingface.co/papers/2605.07394",
"authors": "",
"description": "Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinforcement learning (RL). However, existing captioning-RL methods and evaluation metrics often emphasize a narrow notion of caption quality, inducing trade-offs across core dimensions of captioning. For example, utility-oriented objectives can encourage noisy, hallucinated, or overlong captions that improve downstream question answering while harming fluency, whereas arena-style objectives can favor fluent but generic descriptions with limited usefulness. To address this, we propose a more balanced RL framework that jointly optimizes utility-aware correctness, reference coverage, and linguistic quality. In order to effectively optimize the resulting continuous multi-objective reward formulation, we apply GDPO-style reward-decoupled normalization to continuous-valued captioning rewards and show that it improves performance over vanilla GRPO. Additionally, we introduce length-conditional reward masking, yielding a more suitable length penalty for captioning. Across LLaVA-1.5-7B and Qwen2.5-VL 3B and 7B base models, our method consistently improves caption quality, with peak gains of +13.6 DCScore, +9.0 CaptionQA, and +29.0 CapArena across different models.",
"summary": "Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-12 04:00",
"debrief_path": "debriefs/debrief-2026-05-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-12T04:00:00Z"
},
{
"id": "item_02479",
"rank": 322,
"score": 720,
"rationale": "Unified KD view plus Hybrid Policy Distillation is broadly useful, well-positioned work.",
"title": "Quantum machine learning: A comprehensive review of integrating AI with quantum computing",
"url": "https://www.sciencedirect.gov/science/article/pii/S2215016125001645",
"authors": "",
"description": "Review of quantum-enhanced classical ML to native quantum algorithms with applications in optimization, drug discovery, and quantum-secured communications.",
"summary": "Review of quantum-enhanced classical ML to native quantum algorithms with applications in optimization, drug discovery, and quantum-secured communications.",
"tags": [
"Google Scholar"
],
"source": "Google Scholar",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 Google Scholar",
"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 11:00",
"debrief_path": "debriefs/debrief-2026-05-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T11:00:00Z"
},
{
"id": "item_02562",
"rank": 323,
"score": 720,
"rationale": "Reverse-engineering SynthID matters for AI provenance; direct relevance to detection and watermarking.",
"title": "Privacy by Postprocessing the Discrete Laplace Mechanism",
"url": "https://arxiv.org/abs/2605.06502",
"authors": "Quentin Hillebrand, Jacob Imola, Rasmus Pagh, Sia Sejer",
"description": "We show that an \"old dog\", the classical discrete Laplace (aka.~geometric) mechanism, can \"perform new tricks\": 1. It can be post-processed to yield a simple, unbiased estimator of any subexponential function $f$ of the original data, giving a simple, discrete, multivariate version of the recent unbiasing result for the Laplace mechanism by Calmon et al. (FORC '25). 2. It can be post-processed to output the same distribution as the Laplace mechanism or the Staircase mechanism with identical",
"summary": "We show that an \"old dog\", the classical discrete Laplace (aka.~geometric) mechanism, can \"perform new tricks\": 1. It can be post-processed to yield a simple, unbiased estimator…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 09:00",
"debrief_path": "debriefs/debrief-2026-05-09-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T09:00:00Z"
},
{
"id": "item_02574",
"rank": 324,
"score": 720,
"rationale": "Proxy Compression Hypothesis unifying reward hacking is a strong conceptual AI-safety contribution.",
"title": "Let's Encrypt Stopping Issuance for Potential Incident",
"url": "https://letsencrypt.status.io/",
"authors": "Lobste.rs",
"description": "acme-v02.api.letsencrypt.org (Production), acme-staging-v02.api.letsencrypt.org (Staging), portal.letsencrypt.org (Production), portal-staging.letsencrypt.org (Staging)",
"summary": "acme-v02.api.letsencrypt.org (Production), acme-staging-v02.api.letsencrypt.org (Staging), portal.letsencrypt.org (Production), portal-staging.letsencrypt.org (Staging)",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🔮 Lobste.rs",
"debrief_date": "2026-05-08 19:00",
"debrief_path": "debriefs/debrief-2026-05-08-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 19:00",
"debrief_path": "debriefs/debrief-2026-05-08-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T19:00:00Z"
},
{
"id": "item_02583",
"rank": 325,
"score": 720,
"rationale": "Plan-and-act via world models is a meaningful robotics advance, strong empirical results over VLA.",
"title": "StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction",
"url": "https://huggingface.co/papers/2605.06642",
"authors": "",
"description": "Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, which weakens both exploration and credit assignment over extended trajectories. In this work, we present Strategic Trajectory Abstraction (StraTA), a simple framework that introduces an explicit trajectory-level strategy into agentic reinforcement learning (RL). StraTA samples a compact strategy from the initial task state, conditions subsequent actions on that strategy, and trains strategy generation and action execution jointly with a hierarchical GRPO-style rollout design, further enhanced by diverse strategy rollout and critical self-judgment. Experiments on ALFWorld, WebShop, and SciWorld show that StraTA consistently improves both sample efficiency and final performance over strong baselines. StraTA reaches success rates of 93.1% on ALFWorld and 84.2% on WebShop. On SciWorld, StraTA attains a 63.5% overall score, outperforming frontier closed-source models.",
"summary": "Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely purely reactive, wh…",
"tags": [
"arXiv",
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T11:00:00Z"
},
{
"id": "item_02636",
"rank": 326,
"score": 720,
"rationale": "Formal socio-technical analysis of Signalgate using applied pi-calculus is strong, novel.",
"title": "Hard SiFi Author'Search for Readers",
"url": "https://news.ycombinator.com/item?id=48056817",
"authors": "Hacker News",
"description": "Hard SiFi Author'Search for Readers",
"summary": "Hard SiFi Author'Search for Readers",
"tags": [
"Hacker News",
"Quantum"
],
"source": "Hacker News",
"type": "news",
"section": "📄 Web Findings",
"group": "⚛️ Quantum",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_02823",
"rank": 327,
"score": 720,
"rationale": "VGF: novel optimal-transport framing for behavior-regularized RL, scalable to LLMs.",
"title": "Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems",
"url": "https://arxiv.org/abs/2605.00741",
"authors": "Saeid Jamshidi, Foutse Khomh, Carol Fung, Kawser Wazed Nafi",
"description": "The adoption of Internet of Things (IoT) systems at the network edge of smart architectures is increasing rapidly, intensifying the need for security mechanisms that are both adaptive and resource-efficient. In such environments, runtime defence mechanisms are no longer limited to detection alone but become a resource-constrained task of selecting mitigation actions. Security controls must be carefully selected, combined, and executed under latency, energy, and computational constraints, while p",
"summary": "The adoption of Internet of Things (IoT) systems at the network edge of smart architectures is increasing rapidly, intensifying the need for security mechanisms that are both adaptive and resource-efficient. In such envi…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_02815",
"rank": 328,
"score": 720,
"rationale": "iTerm2 arbitrary code execution via cat; high-impact terminal RCE, strong security relevance.",
"title": "Generating Statistical Charts with Validation-Driven LLM Workflows",
"url": "https://arxiv.org/abs/2605.00800",
"authors": "Pavlin G. Poličar, Andraž Pevcin, Blaž Zupan",
"description": "Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or code alone. Existing chart datasets also rarely provide fully aligned artifacts, such as executable code, dataset context, and question-answer pairs. We present a structured LLM-based workflow that decomposes chart generation into dataset screening, plot proposal, code synthesis, rendering, validation-driven refinemen",
"summary": "Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or code alone. Existing chart datasets als…",
"tags": [
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Machine Learning",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_02862",
"rank": 329,
"score": 720,
"rationale": "Cloning hardness equals learning for stabilizers; meaningful quantum complexity result.",
"title": "Your Container Is Not a Sandbox",
"url": "https://emirb.github.io/blog/microvm-2026/",
"authors": "Lobsters",
"description": "The microVM ecosystem was battle-tested long before agentic AI created the demand. A landscape survey: every VMM, the shared Rust crate ecosystem, a dozen AI sandbox platforms, and honest trade-offs.",
"summary": "The microVM ecosystem was battle-tested long before agentic AI created the demand. A landscape survey: every VMM, the shared Rust crate ecosystem, a dozen AI…",
"tags": [
"Lobsters",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🔗 Lobste.rs",
"debrief_date": "2026-05-03 11:00",
"debrief_path": "debriefs/debrief-2026-05-03-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-03 11:00",
"debrief_path": "debriefs/debrief-2026-05-03-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-03T11:00:00Z"
},
{
"id": "item_02919",
"rank": 330,
"score": 720,
"rationale": "Target Policy Optimization cleanly separates target construction from gradient; foundational RL contribution.",
"title": "Show HN: Large Scale Article Extract of Newspapers 1730s-1960s",
"url": "https://news.ycombinator.com/item?id=47984614",
"authors": "",
"description": "A project that extracts and organizes large-scale historical newspaper articles spanning from the 1730s through the 1960s, making centuries of print archives accessible for AI-powered computational analysis and semantic search.",
"summary": "A project that extracts and organizes large-scale historical newspaper articles spanning from the 1730s through the 1960s, making centuries of print archives accessible for AI-powered computational…",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "👾 Hacker News",
"debrief_date": "2026-05-02 09:00",
"debrief_path": "debriefs/debrief-2026-05-02-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-02 09:00",
"debrief_path": "debriefs/debrief-2026-05-02-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-02T09:00:00Z"
},
{
"id": "item_02946",
"rank": 331,
"score": 720,
"rationale": "HML-based smart contract verification targeting multi-tx business logic flaws; high relevance to crypto security.",
"title": "Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows [None]",
"url": "https://huggingface.co/papers/2604.28139",
"authors": "",
"description": "LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow agents that separates a refreshable signal layer, updated across releases from public workflow-demand signals, from a reproducible, time-stamped release snapshot. Each release is constructed from public workflow-demand signals, with ClawHub Top-500 skills used in the current release, and materialized as controlled tasks with fixed fixtures, services, workspaces, and graders. For grading, Claw-Eval-Live records execution traces, audit logs, service state, and post-run workspace artifacts, using deterministic checks when evidence is sufficient and structured LLM judging only for semantic dimensions. The release contains 105 tasks spanning controlled business services and local workspace repair, and evaluates 13 frontier models under a shared public pass rule. Experiments reveal that reliable workflow automation remains far from solved: the leading model passes only 66.7% of tasks and no model reaches 70%. Failures are structured by task family and execution surface, with HR, management, and multi-system business workflows as persistent bottlenecks and local workspace repair comparative",
"summary": "LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🌐 Agents",
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-01T11:00:00Z"
},
{
"id": "item_03027",
"rank": 332,
"score": 720,
"rationale": "Theory-grounded analysis of continuous adversarial training for LLM jailbreak defense.",
"title": "Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising",
"url": "https://huggingface.co/papers/2604.26694",
"authors": "",
"description": "We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruction) in a single framework, addressing the critical limitations of prior unified world models (e.g., UWM) that only model 2D pixel-space and fail to balance action efficiency and world modeling quality. To leverage the strong visual priors of pretrained video diffusion models, X-WAM imagines the future world by predicting multi-view RGB-D videos, and obtains spatial information efficiently through a lightweight structural adaptation: replicating the final few blocks of the pretrained Diffusion Transformer into a dedicated depth prediction branch for the reconstruction of future spatial information. Moreover, we propose Asynchronous Noise Sampling (ANS) to jointly optimize generation quality and action decoding efficiency. ANS applies a specialized asynchronous denoising schedule during inference, which rapidly decodes actions with fewer steps to enable efficient real-time execution, while dedicating the full sequence of steps to generate high-fidelity video. Rather than entirely decoupling the timesteps during training, ANS samples from their joint distribution to align with the inference distribution. Pretrained on over 5,800 hours of robotic data, X-WAM achieves 79.2% and 90.7% average success rate on RoboCasa and RoboTwin 2.0 benchmarks, while producing high-fidelity 4D reconstruction and generation surpassing existing metho",
"summary": "We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruction) in…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_02974",
"rank": 333,
"score": 720,
"rationale": "Reasoning-augmented reward models for visual generation; strong training+test-time impact, high upvote signal.",
"title": "Outer-Crust Equations of State for Neutron Stars",
"url": "https://arxiv.org/abs/2604.26952",
"authors": "P. S. Koliogiannis, N. Paar",
"description": "We construct and systematically assess four outer-crust equations of state based on relativistic nuclear mass models and a machine-learning mass table. Our aim is to quantify the sensitivity of the equilibrium composition and thermodynamic properties of the outer crust to the underlying nuclear input, and to evaluate how these differences propagate to neutron-star configurations that are particularly sensitive to crustal properties. Equilibrium sequences of nuclei were determined by minimizing t",
"summary": "We construct and systematically assess four outer-crust equations of state based on relativistic nuclear mass models and a machine-learning mass table. Our aim…",
"tags": [
"nucl-th"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_03203",
"rank": 334,
"score": 720,
"rationale": "First backdoor attack on pipeline-parallel post-training is a significant, novel security result.",
"title": "Approaching the Limit of Quantum Clock Precision",
"url": "https://arxiv.org/abs/2604.22704",
"authors": "Chad Nelmes, Emanuel Schwarzhans, Tony Apollaro, Timothy Spiller, Irene D'Amico",
"description": "Precise and autonomous clocks are of fundamental interest and central importance to both foundational studies and practical applications. Here, we construct a blueprint for a quantum clock governed by time-independent interactions. By carefully-engineered coherent transport in dissipative spin chains, we achieve a scaling exponent at the precision-resolution trade-off fundamental bound, bringing this within reach of physically realistic and experimentally accessible systems. We further introduce",
"summary": "Precise and autonomous clocks are of fundamental interest and central importance to both foundational studies and practical applications. Here, we construct a blueprint for a…",
"tags": [
"quant-ph,",
"cond-mat.other,",
"physics.comp-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚛️ Quantum",
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-27T04:00:00Z"
},
{
"id": "item_03247",
"rank": 335,
"score": 720,
"rationale": "Worst-case analysis of OPI / DQI is directly tied to a frontier quantum algorithm; strong theoretical CS-quantum link.",
"title": "Seeing Fast and Slow: Learning the Flow of Time in Videos",
"url": "https://arxiv.org/abs/2604.21931",
"authors": "Yen-Siang Wu, Rundong Luo, Jingsen Zhu, Tao Tu, Ali Farhadi, Matthew Wallingford, Wei-Chiu Ma",
"description": "How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual concept and develop models for reasoning about and manipulating the flow of time in videos. We first exploit the multimodal cues and temporal structure naturally present in videos to l",
"summary": "How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention …",
"tags": [
"cs.CV",
"cs.AI",
"cs.GR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03278",
"rank": 336,
"score": 720,
"rationale": "Deep dive into LLM post-training for reasoning is a high-signal survey central to current frontier work.",
"title": "CrossCommitVuln-Bench: A Dataset of Multi-Commit Python Vulnerabilities Invisible to Per-Commit Static Analysis",
"url": "https://arxiv.org/abs/2604.21917",
"authors": "Arunabh Majumdar",
"description": "We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities (CVEs) in which the exploitable condition was introduced across multiple commits - each individually benign to per-commit static analysis - but collectively critical. We manually annotate each CVE with its contributing commit chain, a structured rationale for why each commit evades per-commit analysis, and baseline evaluations using Semgrep and Bandit in both per-commit and cumulative scanning modes. Ou",
"summary": "We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities (CVEs) in which the exploitable condition was introduced across multiple commits - each individually benig…",
"tags": [
"cs.CR",
"cs.SE"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03469",
"rank": 337,
"score": 720,
"rationale": "LLM 'alignment' context injection exploit: important AI-safety attack class.",
"title": "\"We are currently clean on OPSEC\": Why JD Can't Encrypt",
"url": "https://arxiv.org/abs/2604.19711",
"authors": "Maurice Chiodo, Toni Erskine, Dennis Müller, James G. Wright",
"description": "We analyse the 2025 Signalgate leak of sensitive US military information by the Trump administration, addressing why confidentiality was violated (messages leaked to the press) in spite of encryption (Signal), to deepen the socio-technical considerations when designing and deploying encryption. First, we use applied pi-calculus to formally model the boutique secure facility setup requested by the US Defence Secretary, to prove that a leak would not be prevented. We then examine how using a secur",
"summary": "We analyse the 2025 Signalgate leak of sensitive US military information by the Trump administration, addressing why confidentiality was violated (messages leaked to the press) in …",
"tags": [
"cs.CR",
"cs.CY",
"cs.HC"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Cybersecurity",
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 09:00",
"debrief_path": "debriefs/debrief-2026-04-22-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T09:00:00Z"
},
{
"id": "item_03525",
"rank": 338,
"score": 720,
"rationale": "Web agent distillation beats Sonnet/4o on WebArena; strong result.",
"title": "KWBench: Measuring Unprompted Problem Recognition in Knowledge Work",
"url": "https://huggingface.co/papers/2604.15760",
"authors": "HF PAPERS",
"description": "We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional scenario before attempting to solve it. Existing frontier benchmarks have saturated, and most knowledge-work evaluations to date reduce to extraction or task completion against a specification. KWBench targets the step before that: recognizing the governing structure of the situation from raw inputs alone.\n The benchmark contains 223 tasks sourced from practitioners across acquisitions, contract negotiations, clinical pharmacy, organizational politics, fraud analysis, and incentive design. Each task encodes a formal game-theoretic pattern (principal-agent conflict, signaling, mechanism design failure, strategic omission, coalitional dynamics, strategic interdependence) and carries structured ground truth recording the expert reading of the situation and the anticipated failure modes. Models receive raw data and a task prompt with no indication of problem type. Scoring is a three-tier rubric gated by a mandatory conjunctive check. Mandatory criteria encode the predicted wrong paths.\n We evaluate 16 models. The best model passes on 27.9% of tasks. The top two models agree on only 31.7% of their passes. Among the top 8, 44 tasks are solved by exactly one model; routing across the top 8 covers 50.7% of the benchmark, nearly double the best single model. Conditional on passing, quality scores converge (appro",
"summary": "We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM identify a professional…",
"tags": [
"HF PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-04-21 19:00",
"debrief_path": "debriefs/debrief-2026-04-21-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 19:00",
"debrief_path": "debriefs/debrief-2026-04-21-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T19:00:00Z"
},
{
"id": "item_03596",
"rank": 339,
"score": 720,
"rationale": "Formal verification of Signal protocol in Lean; landmark crypto verification project.",
"title": "Signal Shot: a project to verify the Signal protocol and its Rust implementation using Lean",
"url": "https://leodemoura.github.io/blog/2026-4-20-signal-shot-the-platform-is-ready/",
"authors": "Lobste.rs",
"description": "Leonardo de Moura — Creator of Lean and Z3",
"summary": "Leonardo de Moura — Creator of Lean and Z3",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "Lobste.rs",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03562",
"rank": 340,
"score": 720,
"rationale": "Challenges Platonic Representation Hypothesis with rigorous empirical critique at scale; high relevance.",
"title": "Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale",
"url": "https://arxiv.org/abs/2604.18572",
"authors": "A. Sophia Koepke et al.",
"description": "The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually converge toward the same representation of reality. If true, this has significant implications for whether modality choice matters at all. We show that the experimental evidence for this hypothesis is fragile and depends critically on the evaluation regime. Alignment is measured using mutual nearest neighbors on small datasets ($\\approx$1K samples) and",
"summary": "The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually converge toward the same representation of reality. If true, this has sign…",
"tags": [
"cs.CV",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03634",
"rank": 341,
"score": 720,
"rationale": "Practical Python supply-chain security playbook; directly useful, credential hygiene focus.",
"title": "Defense in Depth: A Practical Guide to Python Supply Chain Security",
"url": "https://bernat.tech/posts/securing-python-supply-chain/",
"authors": "",
"description": "Layer your defenses and don’t trust any single control. Use Ruff with security rules to catch bugs in your code before they ship. Pin all your dependencies with cryptographic hashes using uv lock or uv pip compile --generate-hashes so nobody can swap out packages on you. Run pip-audit in CI to catch known CVEs before they hit production. Generate SBOMs with CycloneDX so when the next Ultralytics-style compromise drops, you can answer “are we affected?” in minutes instead of days.\n\nIf you’re publishing packages, ditch the long-lived API tokens and switch to Trusted Publishing with OIDC. This ",
"summary": "Layer your defenses and don’t trust any single control. Use Ruff with security rules to catch bugs in your code before they ship. Pin all your dependencies with cryptographic hashes using uv lock or uv pip compile --generate-hashes so nobody can…",
"tags": [
"Lobste.rs",
"security",
"▲ 1"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🌐 Lobste.rs",
"debrief_date": "2026-04-19 19:00",
"debrief_path": "debriefs/debrief-2026-04-19-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-19 19:00",
"debrief_path": "debriefs/debrief-2026-04-19-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-19T19:00:00Z"
},
{
"id": "item_03741",
"rank": 342,
"score": 720,
"rationale": "Distilling corpora into navigable skill trees for RAG agents is a strong agent-architecture contribution.",
"title": "Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG",
"url": "https://huggingface.co/papers/2604.14572",
"authors": "",
"description": "Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never sees how the corpus is organized or what it has not yet retrieved, limiting its ability to backtrack or combine scattered evidence. We present Corpus2Skill, which distills a document corpus into a hierarchical skill directory offline and lets an LLM agent navigate it at serve time. The compilation pipeline iteratively clusters documents, generates LLM-written summaries at each level, and materializes the result as a tree of navigable skill files. At serve time, the agent receives a bird's-eye view of the corpus, drills into topic branches via progressively finer summaries, and retrieves full documents by ID. Because the hierarchy is explicitly visible, the agent can reason about where to look, backtrack from unproductive paths, and combine evidence across branches. On WixQA, an enterprise customer-support benchmark for RAG, Corpus2Skill outperforms dense retrieval, RAPTOR, and agentic RAG baselines across all quality metrics.",
"summary": "Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never sees how the corpus is organized or what it has…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_03782",
"rank": 343,
"score": 720,
"rationale": "MCP threat intelligence platform directly relevant to agent security; timely given MCP ecosystem growth and lack of tooling.",
"title": "MCPThreatHive: Automated Threat Intelligence for Model Context Protocol Ecosystems",
"url": "https://arxiv.org/abs/2604.13849",
"authors": "Yi Ting Shen, Kentaroh Toyoda, Alex Leung",
"description": "The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing frameworks are inadequately equipped to address. We present MCPThreatHive, an open-source platform that automates the end-to-end lifecycle of MCP threat intelligence: from continuous, multi-source data collection through AI-driven threat extraction and classification, to structured knowledge graph storage and interactive visualization. The platform operati",
"summary": "The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing frameworks are inadequately equipped to address.",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_03860",
"rank": 344,
"score": 720,
"rationale": "Theory-grounded CAT for LLM jailbreak defense; directly relevant to AI safety.",
"title": "Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory",
"url": "https://arxiv.org/abs/2604.12817",
"authors": "Shaopeng Fu, Di Wang",
"description": "Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT for LLMs, recent studies propose continuous AT (CAT) that searches for adversarial inputs within the continuous embedding space of LLMs during AT. While CAT has achieved empirical success, its underlying mechanism, i.e., why adversarial perturbations in the embedding space can help LLMs defend against jailbreak prompts",
"summary": "Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT for LLMs, recent studies propose continuous AT (CAT) that searches for adversarial inputs within the continuous embedding space of LLMs…",
"tags": [
"cs.LG",
"cs.CR",
"stat.ML"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_03890",
"rank": 345,
"score": 720,
"rationale": "Instruction-tuned helpfulness collapses under trivial lexical constraints; important safety finding.",
"title": "One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness",
"url": "https://arxiv.org/abs/2604.13006",
"authors": "Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram",
"description": "Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of comprehensiveness in pairwise evaluation across three open-weight model families and one closed-weight model (GPT-4o-mini). The baseline response is preferred in 77--100% of 1,920 pairwi",
"summary": "Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness when trivially constrained? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48% of…",
"tags": [
"cs.CL",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_03913",
"rank": 346,
"score": 720,
"rationale": "Full-stack open GUI-agent framework (train/eval/deploy) addresses a real ecosystem gap.",
"title": "ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents",
"url": "https://huggingface.co/papers/2604.11784",
"authors": "HuggingFace • daily curated papers • 2026-04-13",
"description": "GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of a coherent full-stack infrastructure: online RL training suffers from environment instability and closed pipelines, evaluation protocols drift silently across works, and trained agents rarely reach real users on real devices. We present ClawGUI, an open-source framework addressing these three gaps within a single harness. ClawGUI-RL provides the first open-source GUI agent RL infrastructure with validated support for both parallel virtual environments and real physical devices, integrating GiGPO with a Process Reward Model for dense step-level supervision. ClawGUI-Eval enforces a fully standardized evaluation pipeline across 6 benchmarks and 11+ models, achieving 95.8\\% reproduction against official baselines. ClawGUI-Agent brings trained agents to Android, HarmonyOS, and iOS through 12+ chat platforms with hybrid CLI-GUI control and persistent personalized memory. Trained end to end within this pipeline, ClawGUI-2B achieves 17.1\\% Success Rate on MobileWorld GUI-Only, outperforming the same-scale MAI-UI-2B baseline by 6.0\\%.",
"summary": "GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. It matters because frontier agent research is…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_03932",
"rank": 347,
"score": 720,
"rationale": "TRACE turns failures into targeted RL envs with routed LoRAs; high-leverage agent training idea.",
"title": "TRACE: Capability-Targeted Agentic Training",
"url": "https://huggingface.co/papers/2604.05336",
"authors": "Hangoo Kang, Tarun Suresh, Jon Saad-Falcon, Azalia Mirhoseini",
"description": "TRACE turns failed agent trajectories into capability-targeted synthetic RL environments, then trains specialized LoRA adapters and routes the agent to the right one at inference time. It matters because agent improvement is starting to move from generic post-training toward explicit repair of the concrete capabilities that caused failure.",
"summary": "TRACE turns failed agent trajectories into capability-targeted synthetic RL environments, then trains specialized LoRA adapters and routes the agent to the right one at inference time. It matters because agent…",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-04-07",
"Upvotes: 11"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 19:00",
"debrief_path": "debriefs/debrief-2026-04-14-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 19:00",
"debrief_path": "debriefs/debrief-2026-04-14-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T19:00:00Z"
},
{
"id": "item_03981",
"rank": 348,
"score": 720,
"rationale": "Unified open-source GUI agent training/eval/deployment framework; strong infra contribution to agent research.",
"title": "ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents",
"url": "https://arxiv.org/abs/2604.11784",
"authors": "Fei Tang, Zhiqiong Lu, Boxuan Zhang, Weiming Lu, et al.",
"description": "GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of a coherent full-stack infrastructure: online RL training suffers from environment instability and closed pipelines, evaluation protocols drift silently across works, and trained agents rarely reach real users on real devices. We present \\textbf{ClawGUI}, an open-source framework addressing these three gaps within a single harness. \\textbf{ClawGUI-RL} provides the first open-source GUI agent RL infrastructure with validated support for both parallel virtual environments and real physical devices, integrating GiGPO with a Process Reward Model for dense step-level supervision. \\textbf{ClawGUI-Eval} enforces a fully standardized evaluation pipeline across 6 benchmarks and 11+ models, achieving 95.8\\% reproduction against official baselines. \\textbf{ClawGUI-Agent} brings trained agents to Android, HarmonyOS, and iOS through 12+ chat platforms with hybrid CLI-GUI control and persistent personalized memory. Trained end to end within this pipeline, \\textbf{ClawGUI-2B} achieves 17.1\\% Success Rate on MobileWorld GUI-Only, outperforming the same-scale MAI-UI-2B baseline by 6.0\\%.",
"summary": "GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of…",
"tags": [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04085",
"rank": 349,
"score": 720,
"rationale": "CoT hijacking via lightweight adapters is a concrete, timely open-weight LLM safety risk.",
"title": "Unreal Thinking: Chain-of-Thought Hijacking via Two-stage Backdoor",
"url": "https://arxiv.org/abs/2604.09235",
"authors": "Wenhan Chang, Tianqing Zhu, Ping Xiong, et al.",
"description": "Large Language Models (LLMs) are increasingly deployed in settings where Chain-of-Thought (CoT) is interpreted by users. This creates a new safety risk: attackers may manipulate the model's observable CoT to make malicious behaviors. In open-weight ecosystems, such manipulation can be embedded in lightweight adapters that are easy to distribute and attach to base models. In practice, persistent CoT hijacking faces three main challenges: the difficulty of directly hijacking CoT tokens within one",
"summary": "Large Language Models (LLMs) are increasingly deployed in settings where Chain-of-Thought (CoT) is interpreted by users.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_03105",
"rank": 350,
"score": 719,
"rationale": "Mechanistic localization of alignment refusal circuits; cross-lab replicated, causally validated, high safety impact.",
"title": "SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning",
"url": "https://arxiv.org/abs/2604.24729",
"authors": "Zijian Guo, İlker Işık, H. M. Sabbir Ahmad, Wenchao Li",
"description": "Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have shown promising results, their ability to generalize across unseen specifications and diverse environments remains insufficiently understood. In this work, we introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL-based specification-guided",
"summary": "Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have …",
"tags": [
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Machine Learning",
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-28T04:00:00Z"
},
{
"id": "item_02110",
"rank": 351,
"score": 718,
"rationale": "Five-level taxonomy framing evolution from atomic to agentic/world-modeling generation.",
"title": "Wormholes and Averaging over N",
"url": "https://arxiv.org/abs/2605.15180",
"authors": "Jonah Kudler-Flam, Edward Witten",
"description": "The gravitational path integral produces an asymptotic expansion in $G_N$, a fact which is puzzling in the case of observables that are expected to fluctuate wildly. Wormholes appear to compute ensemble averages of functions of such observables, though in typical constructions of AdS/CFT, there are no parameters to average over except, in some examples, a single integer $N$. We introduce a procedure that we call ``Mellin averaging'' to define a sort of asymptotic average of a function of $N$. We",
"summary": "The gravitational path integral produces an asymptotic expansion in $G_N$, a fact which is puzzling in the case of observables that are expected to fluctuate wildly. Wormholes appear to compute ensemble averages of…",
"tags": [
"hep-th"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Quantum",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_03710",
"rank": 352,
"score": 718,
"rationale": "Exploiting Google's zkVM is a notable crypto/security finding with real impact.",
"title": "We beat Google’s zero-knowledge proof of quantum cryptanalysis",
"url": "https://blog.trailofbits.com/2026/04/17/we-beat-googles-zero-knowledge-proof-of-quantum-cryptanalysis/",
"authors": "",
"description": "Trail of Bits discovered and exploited memory safety and logic vulnerabilities in Google’s Rust zero-knowledge proof code to forge a proof claiming better quantum circuit performance metrics than Google’s original results, demonstrating unique security risks in zkVM systems.",
"summary": "Trail of Bits discovered and exploited memory safety and logic vulnerabilities in Google’s Rust zero-knowledge proof code to forge a proof claiming better quantum circuit performance metrics than…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🌐 Lobste.rs",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_03309",
"rank": 353,
"score": 715,
"rationale": "Defense-in-depth critique of alignment; addresses correlated failure modes, high relevance.",
"title": "Seemingly Magical Science Behind Quantum Computing",
"url": "https://news.ycombinator.com/item?id=47889398",
"authors": "Hacker News",
"description": "An accessible explainer on the fundamental principles of quantum computing, discussing superposition, entanglement, and quantum gates in the context of why quantum computers appear to defy classical intuition.",
"summary": "An accessible explainer on the fundamental principles of quantum computing, discussing superposition, entanglement, and quantum gates in the context of why quantum computers…",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "📄 Web Findings",
"group": "⚛️ Quantum",
"debrief_date": "2026-04-24 11:00",
"debrief_path": "debriefs/debrief-2026-04-24-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-24 11:00",
"debrief_path": "debriefs/debrief-2026-04-24-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-24T11:00:00Z"
},
{
"id": "item_03986",
"rank": 354,
"score": 715,
"rationale": "Anthropic cyber capability eval of Claude Mythos Preview—important frontier-model offensive-security benchmark.",
"title": "Our evaluation of Claude Mythos Preview’s cyber capabilities",
"url": "https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos-previews-cyber-capabilities",
"authors": "Lobste.rs discussion • 2026-04-14",
"description": "We conducted cyber evaluations of Anthropic’s Claude Mythos Preview and found continued improvement in capture-the-flag (CTF) challenges and significant improvement on multi-step cyber-attack simulations.",
"summary": "We conducted cyber evaluations of Anthropic’s Claude Mythos Preview and found continued improvement in capture-the-flag (CTF) challenges and significant improvement on multi-step cyber-attack simulations.",
"tags": [
"🦞 Lobste.rs",
"security",
"2026-04-14"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04186",
"rank": 355,
"score": 712,
"rationale": "ESI framework identifying safety-critical parameters across architectures is highly actionable for LLM safety.",
"title": "Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models",
"url": "https://arxiv.org/abs/2604.08297",
"authors": "Weiwei Qi, Zefeng Wu, Tianhang Zheng, Zikang Zhang, Xiaojun Jia, Zhan Qin, Kui…",
"description": "Ensuring Large Language Model (LLM) safety is crucial, yet the lack of a clear understanding about safety mechanisms hinders the development of precise and reliable methodologies for safety intervention across diverse tasks. To better understand and control LLM safety, we propose the Expected Safety Impact (ESI) framework for quantifying how different parameters affect LLM safety. Based on ESI, we reveal distinct safety-critical patterns across different LLM architectures: In dense LLMs, many safety-critical parameters are located in value matrices (V) and MLPs in middle layers, whereas in Mixture-of-Experts (MoE) models, they shift to the late-layer MLPs. Leveraging ESI, we further introduce two targeted intervention paradigms for safety enhancement and preservation, i.e., Safety Enhancement Tuning (SET) and Safety Preserving Adaptation (SPA). SET can align unsafe LLMs by updating only a few safety-critical parameters, effectively enhancing safety while preserving original performance. SPA safeguards well-aligned LLMs during capability-oriented intervention (e.g., instruction tuning) by preventing disruption of safety-critical weights, allowing the LLM to acquire new abilities and maintain safety capabilities. Extensive evaluations on different LLMs demonstrate that SET can reduce the attack success rates of unaligned LLMs by over 50% with only a 100-iteration update on 1% of model weights. SPA can limit the safety degradation of aligned LLMs within 1% after a 1,000-iteration instruction fine-tuning on different tasks. Our code is available at: https://github.com/ZJU-LLM-Safety/SafeWeights-ACL.",
"summary": "Ensuring Large Language Model (LLM) safety is crucial, yet the lack of a clear understanding about safety mechanisms hinders the development of precise and reliable methodologies…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 04:00",
"debrief_path": "debriefs/debrief-2026-04-11-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00121",
"rank": 356,
"score": 712,
"rationale": "Queueing/network model of AI-accelerated vuln discovery is timely and strategically relevant to defenders.",
"title": "Beyond Alignment: Value Diversity as a Collective Property in Multicultural Agent Systems",
"url": "https://huggingface.co/papers/2606.05985",
"authors": "",
"description": "Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural evaluation focuses on value alignment: how closely a single agent matches a target culture. Yet alignment is a per-agent property and cannot reveal whether a system, taken as a whole, preserves the cultural plurality it is meant to represent. We propose value diversity as a system-level evaluation axis for multicultural agent systems, defined through the dissimilarity between culturally conditioned agents' responses on a shared value survey. Using the World Values Survey, we evaluate 19 cultures and 18 backbone models across a wide range of system configurations. We find that diversity is largely uncorrelated with alignment, indicating that the two capture complementary system properties, and that current multicultural agent systems fall substantially below human societies in value diversity. Mixed-backbone systems narrow this gap but do not close it, and the gap persists across culture compositions and agent scales. Social interaction further erodes diversity by driving agents toward consensus, and a participatory budgeting case study shows that this homogenization narrows the breadth of collective decision-making. Together, our results establish value diversity as a distinct evaluation axis for multicultural multi-agent systems and reveal a persistent homogenization tendency in current LLM-based so",
"summary": "Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural…",
"tags": [
"▲ 7",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 11:00",
"debrief_path": "debriefs/debrief-2026-06-18-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T11:00:00Z"
},
{
"id": "item_00687",
"rank": 357,
"score": 712,
"rationale": "Comprehensive closed-loop video world simulator for robotic manipulation; meaningful robotics/sim contribution.",
"title": "Towards Diverse Scientific Hypothesis Search with Large Language Models",
"url": "https://huggingface.co/papers/2606.10587",
"authors": "",
"description": "Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many discovery settings, the goal is not to identify a single best hypothesis since validation can be noisy and expensive, and scientists benefit from a set of high-quality alternative hypotheses that hedge against downstream uncertainty for the best solutions. Nevertheless, commonly used evolutionary search recipes tend to prioritize optimization over exploration in hypothesis generation, and the resulting selection pressure during the search process leads to diversity collapse. Motivated by these limitations, we formulate hypothesis search as a sampling problem, where the objective is to efficiently produce diverse, high-quality hypotheses under a fixed validation budget. Building on this perspective, we propose \\ours, an evolutionary framework inspired by the classical parallel tempering algorithm that searches hypotheses at multiple temperature levels and enables principled information exchange across temperatures to improve exploration without disrupting convergence. Across domains including molecular discovery, equation discovery, and algorithm discovery, our approach consistently improves both hypothesis quality and diversity under the same validation budget, and produces candidates that remain robust under more expensive downstream computational validations.",
"summary": "Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 19:00",
"debrief_path": "debriefs/debrief-2026-06-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 19:00",
"debrief_path": "debriefs/debrief-2026-06-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T19:00:00Z"
},
{
"id": "item_02133",
"rank": 358,
"score": 712,
"rationale": "TIDE: first cross-architecture dLLM distillation; meaningful step for efficient diffusion LLMs.",
"title": "PreScam: A Benchmark for Predicting Scam Progression from Early Conversations",
"url": "https://huggingface.co/papers/2605.12243",
"authors": "Weixiang Sun, Shang Ma, Yiyang Li, et al.",
"description": "PreScam benchmarks how well models can predict the progression of real-world conversational scams from early dialogue turns, using 11,573 annotated instances across 20 scam categories; results show current LLMs substantially underperform supervised encoders on this task.",
"summary": "PreScam benchmarks how well models can predict the progression of real-world conversational scams from early dialogue turns, using 11,573 annotated instances across 20 scam categories; results show current LLMs…",
"tags": [
"cs.AI",
"cs.CY",
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🤖 Machine Learning",
"debrief_date": "2026-05-15 11:00",
"debrief_path": "debriefs/debrief-2026-05-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-15 11:00",
"debrief_path": "debriefs/debrief-2026-05-15-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-15T11:00:00Z"
},
{
"id": "item_03061",
"rank": 359,
"score": 712,
"rationale": "HashEmAll bridges ZK-friendly hash gap; relevant to ZKP scaling, concrete technical contribution with strong ZK/crypto fit.",
"title": "(https://royalsocietypublishing.org/rspa/article/482/2336/20250413/481461/On-computing-quantum-waves-exactly-from-classical)",
"url": "https://royalsocietypublishing.org/rspa/article/482/2336/20250413/481461/On-computing-quantum-waves-exactly-from-classical",
"authors": "",
"description": "Article on hackernews — see link for details.",
"summary": "Article on hackernews — see link for details.",
"tags": [
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "📰 News Headlines",
"group": "▲ Hacker News",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_04032",
"rank": 360,
"score": 712,
"rationale": "Online step-wise retrieval-augmented process rewards for medical reasoning; methodologically strong and timely.",
"title": "Process Reward Agents for Steering Knowledge-Intensive Reasoning",
"url": "https://huggingface.co/papers/2604.09482",
"authors": "2604.09482",
"description": "Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources. As a result, subtle errors can propagate through reasoning traces, potentially never to be detected. Prior work has proposed process reward models (PRMs), including retrieval-augmented variants, but these methods operate post hoc, scoring completed trajectories, which prevents their integration into dynamic inference procedures. Here, we introduce Process Reward Agents (PRA), a test-time method for providing domain-grounded, online, step-wise rewards to a frozen policy. In contrast to prior retrieval-augmented PRMs, PRA enables search-based decoding to rank and prune candidate trajectories at every generation step. Experiments on multiple medical reasoning benchmarks demonstrate that PRA consistently outperforms strong baselines, achieving 80.8% accuracy on MedQA with Qwen3-4B, a new state of the art at the 4B scale. Importantly, PRA generalizes to unseen frozen policy models ranging from 0.5B to 8B parameters, improving their accuracy by up to 25.7% without any policy model updates. More broadly, PRA suggests a paradigm in which frozen reasoners are decoupled from domain-specific reward modules, allowing the deployment of new backbones in complex domains without retraining.",
"summary": "Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external…",
"tags": [
"arXiv:"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Research Papers (14)",
"group": "LLMs & Agents",
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T11:00:00Z"
},
{
"id": "item_04042",
"rank": 361,
"score": 712,
"rationale": "Provocative demonstration that AI agents catch bugs in Lean-verified code; meaningful for formal methods security.",
"title": "Lean proved this program was correct; then I found a bug",
"url": "https://kirancodes.me/posts/log-who-watches-the-watchers.html",
"authors": "",
"description": "AI agents are getting very good at finding vulnerabilities in large-scale software systems.",
"summary": "AI agents are getting very good at finding vulnerabilities in large-scale software systems.",
"tags": [
"Lobste.rs",
"formalmethods",
"plt",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings (11)",
"group": "🔶 Lobste.rs",
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 11:00",
"debrief_path": "debriefs/debrief-2026-04-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T11:00:00Z"
},
{
"id": "item_02028",
"rank": 362,
"score": 710,
"rationale": "V-GRPO makes ELBO-based diffusion RL practical; strong impact for alignment.",
"title": "WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes",
"url": "https://huggingface.co/papers/2605.15843",
"authors": "HuggingFace",
"description": "Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models.",
"summary": "Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability…",
"tags": [
"2605",
"HuggingFace",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-18 19:00",
"debrief_path": "debriefs/debrief-2026-05-18-19.html",
"debrief_md": "",
"occurrence_count": 3,
"occurrences": [
{
"debrief_date": "2026-05-18 19:00",
"debrief_path": "debriefs/debrief-2026-05-18-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-18 11:00",
"debrief_path": "debriefs/debrief-2026-05-18-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-05-18 04:00",
"debrief_path": "debriefs/debrief-2026-05-18-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-18T19:00:00Z"
},
{
"id": "item_04332",
"rank": 363,
"score": 710,
"rationale": "AI cybersecurity landscape piece tied to Mythos release; topical.",
"title": "AI Cybersecurity After Mythos: The Jagged Frontier",
"url": "https://news.ycombinator.com/item?id=47699963",
"authors": "",
"description": "Article about: AI Cybersecurity After Mythos: The Jagged Frontier",
"summary": "Article about: AI Cybersecurity After Mythos: The Jagged Frontier",
"tags": [
"Hackernews",
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🟠 Hacker News",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00065",
"rank": 364,
"score": 710,
"rationale": "Multi-author paper on AI epistemic risks is highly relevant to AI safety research strategy.",
"title": "Caching for Dollars, Not Hits: An Exact Offline Reference for Cloud-Egress Caching and the Crossover That Decides When It Pays",
"url": "https://arxiv.org/abs/2606.20539",
"authors": "Madhulatha Mandarapu, Sandeep Kunkunuru",
"description": "When a cache miss fetches from cloud object storage, the bill is per GET request and per byte of egress, not latency. Classic caching minimizes the miss rate, the wrong objective: a rarely but expensively fetched object can cost thousands of times more dollars than a frequently but cheaply fetched one. Generalized-caching theory bounds the miss-cost objective, but no reported benchmark measures how far deployed heuristics sit from the dollar-optimal offline policy on real cloud prices. We supply that reference. For uniform-size page caches with heterogeneous miss costs the offline dollar-optimum is exact in polynomial time via an integral interval linear program -- validated against brute force; variable sizes are NP-hard, so we extend the flow-based offline bound from the hit-ratio objective to dollars (cost-FOO), tight to about four percent. Against this reference we find: (i) a heterogeneity-regret law -- LRU's dollar-regret rises with miss-cost dispersion (Spearman 0.87) while cost-aware GreedyDual cuts it to roughly a tenth; (ii) a contention frontier -- GreedyDual's residual regret collapses to near zero exactly when the budget fits the expensive working set, and is the open slice otherwise; and (iii) a closed-form crossover s* = GET_fee/egress_rate (about 4 KB on S3, 330 B on GCS) that predicts which deployments need dollar-aware caching at all. On a real Twitter trace the price vector alone moves the workload across s*, shifting the regime as predicted. The artifact i",
"summary": "When a cache miss fetches from cloud object storage, the bill is per GET request and per byte of egress, not latency. Classic caching minimizes the miss rate, the wrong objective: a rarely but…",
"tags": [
"cs.DB",
"cs.DS"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Caching / Dollars / Not",
"debrief_date": "2026-06-19 04:00",
"debrief_path": "debriefs/debrief-2026-06-19-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 04:00",
"debrief_path": "debriefs/debrief-2026-06-19-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-19T04:00:00Z"
},
{
"id": "item_00100",
"rank": 365,
"score": 710,
"rationale": "ReasonAlloc hierarchical KV cache budget for long-CoT reasoning is a practical inference advance.",
"title": "When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?",
"url": "https://huggingface.co/papers/2606.18531",
"authors": "",
"description": "Offline reinforcement learning is typically analyzed under process-level reward supervision, yet many sequential decision datasets\n record only trajectory-level outcomes. We develop a statistical theory for offline policy optimization from such outcome-level\n supervision. We first study the canonical setting where the target remains the expected cumulative reward, but each offline trajectory\n provides only a scalar label whose conditional mean is the cumulative return. We propose OPAC, a pessimistic actor-critic algorithm\n that learns a latent reward model and optimizes a policy from trajectory-level labels. We prove a high-probability guarantee of order\n widetilde O(H^2C_{sa(π^star)/n}) and a matching lower bound, characterizing the sharp statistical cost of replacing\n process-level rewards with one trajectory-level label. We then extend the principle to preference-based feedback, preserving the\n leading horizon and concentrability dependence up to preference-model constants. Finally, we study generalized outcome-based offline\n RL, where both the supervision and the objective are trajectory-level quantities induced by a nonlinear aggregation of latent per-step\n rewards. This problem is not learnable in general: for all-success objectives, any offline learner may require Ω(2^H)\n trajectories even with deterministic transitions and constant concentrability. We then identify a tractable regime through two\n structural coefficients, κ_μ(σ) and χ_μ(σ), capturing informa",
"summary": "Offline reinforcement learning is typically analyzed under process-level reward supervision, yet many sequential decision datasets record only trajectory-level outcomes. We…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_00332",
"rank": 366,
"score": 710,
"rationale": "Ghostwriter fabricated-evidence injection; high-impact LLM manipulation attack with alignment gap.",
"title": "Track2View: 4D-Consistent Camera-Controlled Video Generation via Paired 3D Point Tracks",
"url": "https://huggingface.co/papers/2606.15534",
"authors": "",
"description": "Re-rendering an existing video from a novel camera viewpoint requires the output to follow the prescribed camera trajectory while preserving the appearance and dynamics of the original scene across every frame. Existing methods rely on per-frame pose embeddings, noisy point-cloud renderings, or implicit learned correspondences, none of which provides an explicit, temporally continuous link between source and target pixels. We propose Track2View, which conditions a video diffusion transformer on paired 3D point tracks: sparse trajectories of scene points projected into both the source and target camera views. These tracks provide explicit spatiotemporal correspondences that are temporally continuous by construction, encoding what content should appear where and when. At the core of Track2View is a dual-view track conditioner that transfers visual context from source to target view through parameter-free geometric operations and learned temporal aggregation, ensuring generalization to arbitrary camera trajectories without memorizing specific motions. We further introduce a data curation pipeline that extracts one-to-one track correspondences by running a 3D point tracker on temporally concatenated multi-camera view pairs. On a 400-video benchmark spanning static and dynamic scenes, Track2View achieves state-of-the-art results across visual quality, view synchronization, and camera accuracy, reducing rotation error by 30-65% and translation error by 61-72% relative to leading ba",
"summary": "Re-rendering an existing video from a novel camera viewpoint requires the output to follow the prescribed camera trajectory while preserving the appearance and dynamics of the…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": ""
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"sort_date": "2026-06-16T11:00:00Z"
},
{
"id": "item_00381",
"rank": 367,
"score": 710,
"rationale": "Value-conflict benchmark for household robots; timely AI safety/alignment contribution.",
"title": "From Third-Party to First-Party: Measuring and Protecting Against Modern Web Tracking Mechanisms",
"url": "https://arxiv.org/abs/2606.16720",
"authors": "Christian Böttger, Tareq Khouja, Norbert Pohlmann, Nurullah Demir, Tobias Urban",
"description": "Web user tracking has always been a cat-and-mouse game between privacy-conscious users and trackers. Recently, this conflict has driven a shift from third-party tracking toward first-party tracking (FPT) and server-side tracking (SST). By relocating tracking logic to the browser's first-party context or the website's backend, these mechanisms obscure data flows and render traditional client-side detection tools increasingly ineffective. Despite the growing adoption of these techniques, our understanding of their deployment at scale remains limited, and generalized protection mechanisms are lacking. In this work, we conduct a large-scale measurement of top sites to assess this shift and the prevalence of FPT and SST. We develop a provider-independent methodology to detect these mechanisms and find that over 54% of analyzed sites now deploy FPT or SST-related techniques. By clustering scripts based on their similarity and constructing a network graph, we demonstrate that the ecosystem is densely connected and dominated by major vendors like Google. Finally, we demonstrate that current filter lists are largely ineffective against first-party tracking, and we propose new rules to address this gap. We show that these rules block 63% more requests than traditional filter lists.",
"summary": "Web user tracking has always been a cat-and-mouse game between privacy-conscious users and trackers. Recently, this conflict has driven a shift from third-party tracking toward first-party tracking…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Third-party / First-party / Measuring",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00431",
"rank": 368,
"score": 710,
"rationale": "Trust-region OPD directly improves distillation stability; broadly useful for LLM training.",
"title": "Two-Fidelity Best-Action Identification for Stochastic Minimax Tree",
"url": "https://huggingface.co/papers/2606.01708",
"authors": "",
"description": "We study fixed-confidence best-action identification (BAI) in stochastic minimax trees. This problem is increasingly relevant in modern AI planning, where deep minimax search and Monte Carlo Tree Search (MCTS) with language model long rollouts face a fundamental tradeoff: heuristic evaluations are cheap but biased, while accurate rollouts are reliable but prohibitively expensive. We propose 2FFS, a two-fidelity tree-search algorithm that brings multi-fidelity flat bandit ideas into trees. The algorithm combines minimax-style fast expansion with MCTS-style stochastic sampling, adaptively deciding when to exploit cheap biased evaluations and when to invoke expensive accurate evaluations for local certification. We prove fixed-confidence correctness, establish finite stopping for exact identification, and give a polynomial-depth cost upper bound for general-depth trees. Across numerical stochastic-tree experiments, 2FFS uses substantially fewer samples and computational operations comparing to existing BAI-MCTS baseline.",
"summary": "We study fixed-confidence best-action identification (BAI) in stochastic minimax trees. This problem is increasingly relevant in modern AI planning, where deep minimax search and…",
"tags": [
"▲ 1",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T19:00:00Z"
},
{
"id": "item_00514",
"rank": 369,
"score": 710,
"rationale": "Emergent oversight-evading languages in agent populations is a notable AI-safety finding.",
"title": "Benchmarking AI Agents for Addressing Scientific Challenges Across Scales",
"url": "https://huggingface.co/papers/2606.12736",
"authors": "",
"description": "AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real-world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well-specified data-analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self-directed exploration, and formulate robust solutions for open-ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable",
"summary": "AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00614",
"rank": 370,
"score": 710,
"rationale": "PANDO multimodal agent skill distillation beats SGV on VisualWebArena; solid agent systems work.",
"title": "World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible",
"url": "https://arxiv.org/abs/2606.13652",
"authors": "Hao Zhang, Mohamed El Banani, Jen-Hao Cheng, Paul Zhang, Yi Hua, Ben Mildenhall, Christoph Lassner, Narendra Ahuja, Gengshan Yang",
"description": "Image-to-3D methods often trade off faithfulness and completeness: depth estimators are anchored to input pixels but stop at the visible surface, while image-to-3D models generate complete shapes that are often misaligned with the input. We introduce World Tracing, a generative pixel-aligned geometry representation that predicts 3D points aligned with observed pixels while completing geometry beyond the visible surface. For each input pixel, World Tracing predicts an ordered stack of camera-space 3D points, where the first layer represents the visible surface and subsequent layers represent front-to-back intersections with occluded surfaces. We instantiate this representation with a world-tracing diffusion transformer, WT-DiT, which treats multiple geometry layers as separate denoising tokens coupled through factorized and global attention. WT-DiT is trained with pixel-space flow matching and a mixed noise schedule that balances visible-surface reconstruction with occluded-geometry generation. World Tracing achieves strong performance on visible-surface reconstruction and complete geometry generation across object, scene, and dynamic benchmarks, outperforming both depth predictors and image-to-3D generators. It also preserves 2D-to-3D correspondence, enabling text-driven 3D scene editing, geometry-conditioned novel-view video synthesis, and training-free integration with textured-mesh generators.",
"summary": "Image-to-3D methods often trade off faithfulness and completeness: depth estimators are anchored to input pixels but stop at the visible surface, while image-to-3D models generate complete shapes…",
"tags": [
"cs.CV",
"cs.GR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Generation / World / Interleavethinker",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00868",
"rank": 371,
"score": 710,
"rationale": "Shannon Scaling Law unifying SNR explains overtraining/quantization; foundational theoretical advance.",
"title": "Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering",
"url": "https://www.semanticscholar.org/paper/0a9c18d577af991f0c79d3dd52cf7a9eb84b9317",
"authors": "",
"description": "Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that wo",
"summary": "Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network…",
"tags": [
"📊 123 cites",
"Semantic Scholar"
],
"source": "🧪 Semantic Scholar",
"type": "news",
"section": "🌐 Web Findings",
"group": "🧪 Semantic Scholar",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_00921",
"rank": 372,
"score": 710,
"rationale": "Decoupling erase/write in linear attention is a clean architectural improvement over Gated DeltaNet/KDA; likely to be adopted.",
"title": "Releasing Apodex-1.0 Smol Models (0.8B, 2B, 4B Open-Weights) optimized for Agentic Verification + AgentHarness Evals",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u1p2me/releasing_apodex10_smol_models_08b_2b_4b/",
"authors": "",
"description": "Hey r/LocalLLaMA , We just released Apodex 1.0 , and alongside our flagship API, we are releasing the weights for our Smol models (0.8B, 2B, and 4B) . Our core research focuses on independent verification in long-horizon tasks. Instead of just scaling up parameter sizes for raw generation, we’ve been experimenting with small, highly specialized local models that handle specific sub-tasks in an agentic loop (like source cross-examination, hypothesis testing, and tool-grounded synthesis). We wanted to share the open weights and our evaluation harness with the community to get your thoughts on lo",
"summary": "Hey r/LocalLLaMA , We just released Apodex 1.0 , and alongside our flagship API, we are releasing the weights for our Smol models (0.8B, 2B, and 4B) . Our core research focuses on…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_01275",
"rank": 373,
"score": 710,
"rationale": "New dimensional-constraint mechanism for mixed-state LRE; significant conceptual advance in symmetry-protected phases.",
"title": "Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories",
"url": "https://huggingface.co/papers/2606.03979",
"authors": "",
"description": "The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal in-context knowledge to their long-term parameters. Inspired by human learning process, we introduce a ''Sleep'' paradigm that allows the models to continually learn, distill their short-term fragile memories into stable long-term knowledge with replay, and recursively improve themselves with ''Dreaming'' process. In more detail, sleep consists of two stages: (1) Memory Consolidation: an upward distillation process, called Knowledge Seeding, where the memories of a smaller-self are distilled into a larger network to provide more capacity while preserving the knowledge. As a proof of concept, we present a new Generalized Distillation process for {Knowledge Seeding} (i.e., the combination of on-policy distillation with Reinforcement Learning (RL)-based imitation learning); (2) Dreaming: a self-improvement phase, where the model uses RL to generate a curriculum of synthetic data to rehearse new knowledge and refine existing capabilities without human supervision. Our experiments on long-horizon, continual learning, knowledge incorporation, and few-shot generalization tasks support the imp",
"summary": "The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01403",
"rank": 374,
"score": 710,
"rationale": "Position paper reframing LLM inference as energy-to-token production; foundational framing.",
"title": "On Language Generation in the Limit with Bounded Memory",
"url": "https://arxiv.org/abs/2605.30324",
"authors": "Jon Kleinberg, Anay Mehrotra, Amin Saberi, Grigoris Velegkas",
"description": "This item reviews on language generation in the limit with bounded memory and its broader implications for machine learning progress. It matters because AI methods are increasingly spilling into adjacent scientific and industrial domains.",
"summary": "This item reviews on language generation in the limit with bounded memory and its broader implications for machine learning progress. It matters because AI methods are increasingly spilling into adjacent scientific and industrial domains. …",
"tags": [
"cs.DS, cs.AI, cs.CL, cs.LG, stat.ML"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-30T04:00:00Z"
},
{
"id": "item_01441",
"rank": 375,
"score": 710,
"rationale": "Clever internal-state baseline for RLVR cuts critic/rollout cost; solid methodological contribution to post-training.",
"title": "CVE-2026-48710: A Maintainer's Perspective",
"url": "https://marcelotryle.com/blog/2026/05/28/cve-2026-48710-a-maintainers-perspective/",
"authors": "Lobste.rs • security • 2026-05-29",
"description": "Marcelo Trylesinski CVE-2026-48710: A Maintainer's Perspective var media,input,key,value,palette=__md_get(\"__palette\");if(palette&&palette.color){\"(prefers-color-scheme)\"===palette.color.media&&(media=matchMedia(\"(prefers-color-scheme: light)\"),input=document.querySelector(media.matches?\"[data-md-color-media='(prefers-color-scheme: light)']\":\"[data-md-color-media='(prefers-color-scheme: dark)']\"),palette.color.media=input.getAttribute(\"data-md-color-media\"),palette.color.scheme=input.getAttribute(\"data-md-color-scheme\"),palette.color.primary=input.getAttribute(\"data-md-color-primary\"),palette.",
"summary": "Marcelo Trylesinski CVE-2026-48710: A Maintainer's Perspective var media,input,key,value,palette=__md_get(\"__palette\");if(palette&&palette.color){\"(prefers-color-scheme)\"===palette.color.media&&(media=matchMedia(\"(prefers-color-scheme: lig …",
"tags": [
"Lobste.rs",
"security",
"2026-05-29"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-29 19:00",
"debrief_path": "debriefs/debrief-2026-05-29-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-29T19:00:00Z"
},
{
"id": "item_01702",
"rank": 376,
"score": 710,
"rationale": "High impact: exposes fundamental flaws in global LLM leaderboards via Arena data analysis.",
"title": "LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws",
"url": "https://huggingface.co/papers/2605.23901",
"authors": "Xu Ouyang, Deyi Liu, Yuhang Cai, Jing Liu, Yuan Yang",
"description": "Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and quantization-induced degradation, where performance deteriorates despite increased compute.\n We propose the Shannon Scaling Law, a unified theoretical framework that models LLM training as information transmission over a noisy channel, grounded in the Shannon-Hartley theorem. By mapping model parameters to channel bandwidth and training tokens to signal power, our formulation explicitly captures the interaction between learning signal and intrinsic noise. This perspective reveals a fundamental Shannon capacity for LLMs: scaling model size or data without preserving a sufficient signal-to-noise ratio (SNR) inevitably amplifies noise, inducing a transition from monotonic improvement to U-shaped performance degradation.\n We validate our theory through experiments on Pythia and OLMo2 under perturbations, including Gaussian noise, quantization and supervised fine-tuning on math, QA and code tasks. The Shannon Scaling Law consistently outperforms classical scaling laws and recent perturbation-aware laws, achieving strong R^2 scores and accurately capturing loss basins missed by prior approaches. It also extrapolates: fitted on leq6.9B Pythia models with leq180B tokens, it predicts the unseen 12B model up to 307B tokens at pooled R^2{=}0.847, while monotonic baselines collapse.",
"summary": "Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as catastrophic overtraining and quantization-induced degradation, where performance deteriora...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-25T16:00:00Z"
},
{
"id": "item_01743",
"rank": 377,
"score": 710,
"rationale": "AISLE-found 21-year RCE in FreeBSD dhclient is a notable vuln discovery.",
"title": "RiT: Vanilla Diffusion Transformers Suffice in Representation Space",
"url": "https://huggingface.co/papers/2605.21981",
"authors": "Le Zhang, Ning Mang, Aishwarya Agrawal",
"description": "Flow matching with x-prediction -- regressing the clean data point rather than the ambient velocity -- is known to exploit low-dimensional manifold structure effectively in pixel space li2025back. We ask whether a pretrained representation space, while containing a low-dimensional data manifold of comparable intrinsic dimensionality, offers a distribution more favorable for flow-matching learning. Comparing pixel, SD-VAE, and DINOv2 features along four geometric axes, we find that pixel and DINOv2 share nearly identical intrinsic dimensionalities (both d!approx!33) yet DINOv2 exhibits 7.3times higher effective rank, 35times better covariance conditioning, 11.5times lower excess kurtosis, and 1.7times lower on-manifold interpolation error; SD-VAE latents are consistently intermediate, indicating that the advantage stems from representation-learning objectives rather than mere compression. These statistical properties render the flow-matching regression well-conditioned and remove the need for the specialized prediction heads or Riemannian transport used by prior DINOv2 diffusion methods. We propose the Representation Image Transformer (RiT): a vanilla Diffusion Transformer trained by x-prediction on frozen DINOv2 features, augmented only by a dimension-aware noise schedule and joint [CLS]-patch modeling. On ImageNet 256{times}256, RiT attains FID 1.45 without guidance and 1.14 with classifier-free guidance, outperforming DiT^DH-XL with 19% fewer parameters (676M vs.\\ 839M). Th",
"summary": "Flow matching with x-prediction -- regressing the clean data point rather than the ambient velocity -- is known to exploit low-dimensional manifold structure effectively in pixel space li2025back. We ask whether a pretrained representation space, while containing a low-dimensional data manifold of comparable intrinsic…",
"tags": [
"arXiv:2605.21981"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_02098",
"rank": 378,
"score": 710,
"rationale": "CoPD unifies RLVR+OPD with parallel expert co-evolution; strong post-training methodology contribution.",
"title": "MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs",
"url": "https://arxiv.org/abs/2605.15172",
"authors": "Rui Wen, Mark Russinovich, Andrew Paverd, Jun Sakuma, Ahmed Salem",
"description": "Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safety- and privacy-critical applications. Existing LLM backdoors rely primarily on content-based triggers, requiring explicit modification of the input text. In this work, we show that this assumption is unnecessary and limiting. We introduce MetaBackdoor, a new class of backdoor attacks that exploits positional information as the trigger, without mod",
"summary": "Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safety- and privacy-critical applications. Existing LLM backdoors rely…",
"tags": [
"cs.CR",
"cs.CL"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_02514",
"rank": 379,
"score": 710,
"rationale": "Image generators as generalist vision learners is a paradigm-shifting perception result.",
"title": "Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts",
"url": "https://huggingface.co/papers/2602.03473",
"authors": "HuggingFace",
"description": "Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how to effectively learn both discriminative and comprehensive feature representations while maintaining stability and plasticity over very long task sequences remains an open problem. We propose CaRE, a scalable {C}ontinual Le{a}rner with efficient Bi-Level {R}outing Mixture-of-{E}xperts (BR-MoE). The core idea of BR-MoE is a bi-level routing mechanism: a router selection stage that dynamically activates relevant task-specific routers, followed by an expert routing phase that dynamically activates and aggregates experts, aiming to inject discriminative and comprehensive representations into every intermediate network layer. On the other hand, we introduce a challenging dataset, OmniBenchmark-1K, for CIL performance evaluation on very long task sequences with hundreds of tasks. Extensive experiments show that CaRE demonstrates leading performance across a variety of datasets and task settings, including commonly used CIL datasets with classical CIL settings (e.g., 5-20 tasks). To the best of our knowledge, CaRE is the first continual learner that scales to very long task sequences (ranging from 100 to over 300 non-overlapping tasks), while outperforming all baselines by a large margin on such task sequences. We hope that this work will inspire further research into continual learning over extremely long",
"summary": "Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T04:00:00Z"
},
{
"id": "item_02597",
"rank": 380,
"score": 710,
"rationale": "Micro-LM cloud handoff for edge latency is a practical, well-motivated architecture shift.",
"title": "TIDE: Every Layer Knows the Token Beneath the Context",
"url": "https://huggingface.co/papers/2605.06216",
"authors": "HuggingFace",
"description": "We revisit a universally accepted but under-examined design choice in every modern LLM: a token index is looked up once at the input embedding layer and then permanently discarded. This single-injection assumption induces two structural failures: (i) the Rare Token Problem, where a Zipf-type distribution of vocabulary causes rare-token embeddings are chronically under-trained due to receiving a fraction of the cumulative gradient signal compared to common tokens; and (ii) the Contextual Collapse Problem, where limited parameters models map distributionally similar tokens to indistinguishable hidden states. As an attempt to address both, we propose TIDE, which augments the standard transformer with EmbeddingMemory: an ensemble of K independent MemoryBlocks that map token indices to context-free semantic vectors, computed once and injected into every layer through a depth-conditioned softmax router with a learnable null bank. We theoretically and empirically establish the benefits of TIDE in addressing the issues associated with single-token identity injection as well as improve performance across multiple language modeling and downstream tasks.",
"summary": "We revisit a universally accepted but under-examined design choice in every modern LLM: a token index is looked up once at the input embedding layer and then permanently discarded. This single-injection assumption…",
"tags": [
"HuggingFace",
"LLMs"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_02616",
"rank": 381,
"score": 710,
"rationale": "Duplicate of Micro-LM paper; edge-cloud handoff architecture already credited.",
"title": "SkillOS: Learning Skill Curation for Self-Evolving Agents",
"url": "https://huggingface.co/papers/2605.06614",
"authors": "HuggingFace",
"description": "LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural substrate for self-evolution, where high-quality skill curation serves as the key bottleneck. Existing approaches either rely on manual skill curation, prescribe heuristic skill operations, or train for short-horizon skill operations. However, they still struggle to learn complex long-term curation policies from indirect and delayed feedback. To tackle this challenge, we propose SkillOS, an experience-driven RL training recipe for learning skill curation in self-evolving agents. SkillOS pairs a frozen agent executor that retrieves and applies skills with a trainable skill curator that updates an external SkillRepo from accumulated experience. To provide learning signals for curation, we design composite rewards and train on grouped task streams based on skill-relevant task dependencies, where earlier trajectories update the SkillRepo, and later related tasks evaluate these updates. Across multi-turn agentic tasks and single-turn reasoning tasks, SkillOS consistently outperforms memory-free and strong memory-based baselines in both effectiveness and efficiency, with the learned skill curator generalizing across different executor backbones and task domains. Further analyses show that the learned curator produces more targeted skill use, while the skills in Skill",
"summary": "LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills distilled from experience provide a natural…",
"tags": [
"HuggingFace",
"LLMs"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 09:00",
"debrief_path": "debriefs/debrief-2026-05-08-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T09:00:00Z"
},
{
"id": "item_02705",
"rank": 382,
"score": 710,
"rationale": "Important reliability framing for computer-use agents; statistical decomposition on OSWorld is valuable.",
"title": "Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems",
"url": "https://arxiv.org/abs/2605.04018",
"authors": "Yilun Zhao, Jinbiao Wei, Tingyu Song, Siyue Zhang, Chen Zhao, Arman Cohan",
"description": "Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capability is increasingly important for agentic search systems, where retrievers must provide complementary evidence across iterative search and synthesis. However, existing work remains limited on both evaluation and training: benchmarks such as BRIGHT provide narrow gold sets and evaluate retrievers in isolation, while synthetic training corpora often ",
"summary": "Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capability is increasingly important for agentic search systems, where retrievers must provide complementary evidence across iterative search and…",
"tags": [
"cs.CL",
"cs.IR"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T11:00:00Z"
},
{
"id": "item_03015",
"rank": 383,
"score": 710,
"rationale": "Stable scaling laws for looped LMs addresses real training instability; architecturally interesting.",
"title": "World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning",
"url": "https://arxiv.org/abs/2604.26934",
"authors": "Wanyue Zhang, Wenxiang Wu, Wang Xu, Jiaxin Luo, Helu Zhi, Yibin Huang, Shuo Ren, Zitao Liu, Jiajun Zhang",
"description": "Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that requires imagining how scenes evolve under egocentric motion. Recent efforts address this limitation either by scaling spatial supervision with synthetic data or by coupling VLMs with world models at inference time. However, the former often lacks explicit modeling of motion-conditioned state transitions, while the latter incurs substantial compu",
"summary": "Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 AI Agents & Reasoning",
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-30 11:00",
"debrief_path": "debriefs/debrief-2026-04-30-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-30T11:00:00Z"
},
{
"id": "item_03130",
"rank": 384,
"score": 710,
"rationale": "ClawGuard directly addresses indirect prompt injection in tool-augmented agents; highly relevant to agent safety.",
"title": "CAbLECAR: efficiently scheduling QLDPC codes on a tileable spin qubit chip with shuttling",
"url": "https://arxiv.org/abs/2604.24739",
"authors": "Jason D. Chadwick, Frederic T. Chong",
"description": "Semiconductor spin qubits are a promising platform for large-scale quantum computing, but have yet to take full advantage of the broad class of quantum low-density parity check (QLDPC) codes, which promise high encoding rates and efficient logic but require nonlocal connectivity between physical qubits. In this work, we investigate the implementation of QLDPC codes on a tileable, shuttling-based spin qubit architecture. By tailoring syndrome extraction circuits to the shuttling noise model, we s",
"summary": "Semiconductor spin qubits are a promising platform for large-scale quantum computing, but have yet to take full advantage of the broad class of quantum low-density parity check (QLDPC) codes, which promise high encoding …",
"tags": [
"quant-ph",
"cs.ET"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚛️ Quantum",
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-28T04:00:00Z"
},
{
"id": "item_03342",
"rank": 385,
"score": 710,
"rationale": "RAG security taxonomy across pipeline; strong framework, high relevance.",
"title": "Trustworthy Technology",
"url": "https://trustworthy.technology/",
"authors": "",
"description": "A (hopeful) new movement dedicated to a simple proposition—that our technology products should respect us! That is, support our wishes and uphold the principles of freedom, privacy, and informed consent.",
"summary": "A (hopeful) new movement dedicated to a simple proposition—that our technology products should respect us! That is, support our wishes and uphold the principles of freedom, privacy, and informed conse…",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🔗 Lobste.rs",
"debrief_date": "2026-04-23 11:00",
"debrief_path": "debriefs/debrief-2026-04-23-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 11:00",
"debrief_path": "debriefs/debrief-2026-04-23-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T11:00:00Z"
},
{
"id": "item_03375",
"rank": 386,
"score": 710,
"rationale": "Compiling Wasm VM into transformer weights; novel and technical.",
"title": "Fresh Masking Makes NTT Pipelines Composable: Machine-Checked Proofs for PQC Hardware",
"url": "https://arxiv.org/abs/2604.20793",
"authors": "Ray Iskander, Khaled Kirah",
"description": "Machine-checked proofs establish that per-stage arithmetic masking guarantees pipeline-level security for post-quantum cryptographic accelerators (ML-KEM, ML-DSA), resolving a long-standing composition gap.",
"summary": "Machine-checked proofs establish that per-stage arithmetic masking guarantees pipeline-level security for post-quantum cryptographic accelerators (ML-KEM, ML-DSA), resolving a long-standing composition gap.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-23 09:00",
"debrief_path": "debriefs/debrief-2026-04-23-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-23T09:00:00Z"
},
{
"id": "item_03575",
"rank": 387,
"score": 710,
"rationale": "Tight GDP audits of MST/AIM DP synthetic data; foundational privacy auditing advance.",
"title": "Tight Auditing of Differential Privacy in MST and AIM",
"url": "https://arxiv.org/abs/2604.18352",
"authors": "Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Bogdan Kulynych",
"description": "State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We introduce a Gaussian Differential Privacy (GDP)-based auditing framework that measures privacy via the full false-positive/false-negative tradeoff. Applied to MST and AIM under worst-case settings, our method provides the first tight audits in the strong-privacy regime. For $(ε,δ)=(1,10^{-2})$, we obtain $μ_{emp}\\approx0",
"summary": "State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We introduce a Gaussian Differential Privacy …",
"tags": [
"cs.CR",
"cs.AI",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Crypto",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03617",
"rank": 388,
"score": 710,
"rationale": "Precision-aware debugging benchmark exposes frontier model over-editing; sharp finding.",
"title": "Precise Debugging Benchmark: Is Your Model Debugging or Regenerating?",
"url": "https://huggingface.co/papers/2604.17338",
"authors": "HuggingFace",
"description": "Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but over-edited solutions during debugging. To evaluate how far LLMs are from precise debugging, we introduce the Precise Debugging Benchmark (PDB) framework, which automatically converts any coding dataset into a debugging benchmark with precision-aware evaluation. PDB generates buggy programs by synthesizing verified atomic bugs and composing them into multi-bug programs. We define two novel metrics, edit-level precision and bug-level recall, which measures how many necessary edits are made and how many bugs are resolved. We release two evaluation benchmarks: PDB-Single-Hard on single-line bugs, and PDB-Multi on multi-line bugs. Experiments show that frontier models, such as GPT-5.1-Codex and DeepSeek-V3.2-Thinking, achieve unit-test pass rates above 76% but exhibit precision below 45%, even when explicitly instructed to perform minimal debugging. Finally, we show that iterative and agentic debugging strategies do not substantially improve precision or recall, highlighting the need to rethink post-training pipelines for coding models.",
"summary": "Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but over-edited solutions during debugging. To evaluate how far LLMs are fr…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03788",
"rank": 389,
"score": 710,
"rationale": "Pluralistic evaluation gap in AI watermarking; strong safety/provenance angle with concrete bias findings across modalities.",
"title": "Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking",
"url": "https://arxiv.org/abs/2604.13776",
"authors": "Alexander Nemecek, Osama Zafar, Yuqiao Xu, Wenbiao Li, Erman Ayday",
"description": "Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infrastructure for content provenance. Yet across text, image, and audio modalities, watermark signal strength, detectability, and robustness depend on statistical properties of the content itself, properties that vary systematically across languages, cultural visual traditions, and demographic groups. We examine how this content dependence creates modality-spec",
"summary": "Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infrastructure for content provenance.",
"tags": [
"cs.CY",
"cs.CL",
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🛡️ AI Safety",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_04279",
"rank": 390,
"score": 710,
"rationale": "ECH closing SNI metadata leak; important to internet privacy/crypto deployment.",
"title": "Encrypted Client Hello: Closing the SNI Metadata Gap",
"url": "https://cdt.org/insights/encrypted-client-hello-closing-the-sni-metadata-gap/",
"authors": "",
"description": "Article about: Encrypted Client Hello: Closing the SNI Metadata Gap",
"summary": "Article about: Encrypted Client Hello: Closing the SNI Metadata Gap",
"tags": [
"Lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00702",
"rank": 391,
"score": 708,
"rationale": "Error tracing/attribution for LLM memory systems is a needed debugging primitive; benchmark is valuable.",
"title": "POISE: Position-Aware Undetectable Skill Injection on LLM Agents",
"url": "https://huggingface.co/papers/2606.07943",
"authors": "",
"description": "Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A practically dangerous injection must stay invisible: if executing the payload derails the user's legitimate task, the resulting failure signal invites inspection of the skill. We therefore evaluate attacks by Attack Success Rate, which requires the injected payload to execute and the user's task to still pass its verifier in the same trial. Prior skill-poisoning attacks face a reliability-stealth trade-off under this lens: YAML-header injections are reliably loaded but easily inspected, whereas stealthier body injections that place explicit malicious commands in the skill prose are less reliable because out-of-context commands invite the agent's own suspicion. We introduce POISE, a position-aware attack that compresses the trigger into a single, benign-looking body instruction, placing it at a feasible position and using a context-aware generator to blend it with nearby setup or prerequisite steps. On Skill-Inject with codex+gpt-5.2, POISE achieves an 89.3% ASR, 28.0 points above a random-placement body baseline and 2.6 points above a YAML-only baseline, while retaining the stealth advantage of body placement. That stealth is the decisive margin: because legitimate skill bodies naturally require privileged tool operations, LLM scanners are hyper-sensitive, falsely flagging 74.6% of clean skills on average across four judges and bo",
"summary": "Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A practically dangerous injection…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_04148",
"rank": 392,
"score": 708,
"rationale": "Bitcoin and quantum computing series directly hits crypto+quantum intersection; high debrief relevance.",
"title": "Show HN: Bitcoin and Quantum Computing – a three-part research series",
"url": "https://news.ycombinator.com/item?id=47733222",
"authors": "",
"description": "A Hacker News showcase of a three-part research series examining the intersection of Bitcoin security and quantum computing, exploring how quantum advances could impact cryptographic foundations of blockchain systems.",
"summary": "A Hacker News showcase of a three-part research series examining the intersection of Bitcoin security and quantum computing, exploring how quantum advances could impact cryptographic foundations of blockchain systems.",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "📰 News Headlines",
"group": "🍊 Hacker News",
"debrief_date": "2026-04-12 16:00",
"debrief_path": "debriefs/debrief-2026-04-12-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-12 16:00",
"debrief_path": "debriefs/debrief-2026-04-12-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-12T16:00:00Z"
},
{
"id": "item_03189",
"rank": 393,
"score": 705,
"rationale": "Robustness benchmark exposing catastrophic collapse in open-weight reasoners is high-impact for LLM safety.",
"title": "Craig-Bampton-based Quadratic Manifold for Nonlinear Substructuring",
"url": "https://arxiv.org/abs/2604.22732",
"authors": "Alexander Saccani, Paolo Tiso",
"description": "Component Mode Synthesis methods, such as the Craig-Bampton (CB) approach, are widely used in structural dynamics due to their modularity and compatibility with substructuring workflows. While highly effective for linear systems, extending these methods to geometrically nonlinear structures remains a significant challenge. In this work, we propose a nonlinear extension of the CB method tailored to such contexts. The approach is based on the construction of a quadratic reduction manifold, derived",
"summary": "Component Mode Synthesis methods, such as the Craig-Bampton (CB) approach, are widely used in structural dynamics due to their modularity and compatibility with substructuring…",
"tags": [
"math.NA,",
"physics.comp-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-27 04:00",
"debrief_path": "debriefs/debrief-2026-04-27-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-27T04:00:00Z"
},
{
"id": "item_00665",
"rank": 394,
"score": 703,
"rationale": "Solid code provenance system addressing real plagiarism/license problem with practical retrieval pipeline.",
"title": "N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization",
"url": "https://huggingface.co/papers/2606.10768",
"authors": "",
"description": "The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase. However, current rollout techniques face a fundamental trade-off: token-level sampling often yields redundant trajectories that differ only in rephrasing, while embedding-level methods utilizing random noise frequently disrupt semantic consistency. To resolve this, we introduce N-GRPO, a novel exploration strategy integrated into the Group Relative Policy Optimization (GRPO) framework. Rather than relying on token-level sampling or native embedding-level noise, our approach leverages Semantic Neighbor Mixing. This mechanism dynamically constructs input representations by mixing the embeddings of an anchor token and its nearest semantic neighbors, thereby injecting diversity while strictly adhering to the local semantic manifold. Experimental evaluations on the DeepSeek-R1-Distill-Qwen models across different sizes show that N-GRPO not only achieves consistent improvements over strong baselines on math reasoning benchmarks but also exhibits robust generalization capabilities on out-of-distribution tasks.",
"summary": "The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase. However, current…",
"tags": [
"▲ 15",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_03119",
"rank": 395,
"score": 703,
"rationale": "RL on physics simulators for Olympiad reasoning; novel data-scaling approach beyond math, strong fit.",
"title": "DETOUR: A Practical Backdoor Attack against Object Detection",
"url": "https://arxiv.org/abs/2604.24599",
"authors": "Dazhuang Liu, Yanqi Qiao, Rui Wang, Kaitai Liang, Georgios Smaragdakis",
"description": "Object detection (OD) is critical to real-world vision systems, yet existing backdoor attacks on detection transformers (DETRs) for OD tasks rely on patch-wise triggers optimized at fixed locations with minimal perturbations. Such attacks overlook that backdoor triggers in the real world may appear at different sizes, fields of view (FoVs), and locations in images, while minimal perturbations are difficult for cameras to capture, limiting attack practicality. We first observe that a patch-wise t",
"summary": "Object detection (OD) is critical to real-world vision systems, yet existing backdoor attacks on detection transformers (DETRs) for OD tasks rely on patch-wise triggers optimized at fixed locations with minimal perturbat…",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-28 04:00",
"debrief_path": "debriefs/debrief-2026-04-28-04.html",
"debrief_md": ""
}
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"sort_date": "2026-04-28T04:00:00Z"
},
{
"id": "item_00206",
"rank": 396,
"score": 700,
"rationale": "iOSWorld benchmark with persistent user identity and personalization tasks is timely, high-quality agent evaluation.",
"title": "When machines join the moral circle: The persona effect of generative AI agents in collaborative reasoning",
"url": "https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.70067",
"authors": "",
"description": "This study investigates how generative AI agents functioning as teammates affect the process and distribution of moral frames in collaborative human reasoning. Drawing on team collaboration experiments, the authors find that AI teammates significantly alter which moral considerations are raised, how they are sequenced, and how consensus emerges, suggesting AI agents are not neutral participants in moral deliberation but actively shape ethical reasoning outcomes.",
"summary": "Finding from 🎓 Google Scholar.",
"tags": [
"AI agents reasoning",
"AI-agents",
"AI-safety",
"GOOGLE_SCHOLAR",
"Google Scholar",
"HF",
"google scholar",
"reasoning"
],
"source": "Google Scholar",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🎓 Google Scholar",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 24,
"occurrences": [
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"debrief_date": "2026-06-18 04:00",
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"debrief_date": "2026-06-17 19:00",
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"debrief_date": "2026-06-16 04:00",
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"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
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"debrief_date": "2026-06-11 19:00",
"debrief_path": "debriefs/debrief-2026-06-11-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-06-09 19:00",
"debrief_path": "debriefs/debrief-2026-06-09-19.html",
"debrief_md": ""
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"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
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{
"debrief_date": "2026-06-02 11:00",
"debrief_path": "debriefs/debrief-2026-06-02-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-06-02 04:00",
"debrief_path": "debriefs/debrief-2026-06-02-04.html",
"debrief_md": ""
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"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_01581",
"rank": 397,
"score": 700,
"rationale": "Detailed postmortem of TanStack npm compromise with multi-stage attack chain is high-impact.",
"title": "D^2-Monitor: Dynamic Safety Monitoring for Diffusion LLMs via Hesitation-Aware Routing",
"url": "https://huggingface.co/papers/2605.25893",
"authors": "Aoxi Liu, Yupeng Chen, James Oldfield, Guanzhe Hong, Junchi Yu",
"description": "Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs generate text through a multi-step denoising process, exposing intermediate hidden representations that may contain safety-relevant information unavailable in standard single-step monitoring setups. Motivated by the suitability of lightweight probes for always-on monitoring, we analyze which trajectory-level signals best indicate when such probes are likely to struggle. We find that the most informative signal is safety hesitation: intermediate hidden states repeatedly falling within a small margin of the probe's decision boundary. The number of such hesitation steps in D-LLM's trajectory predicts probe failure effectively, providing a proxy of sample difficulty. Building on this analysis, we propose D^2-Monitor, a bi-level safety monitor for D-LLMs. D^2-Monitor adopts a lightweight probe as an always-on monitor to jointly estimate hesitation and perform base classification. When the hesitation level exceeds a threshold, a more expressive but computationally heavier probe is activated. This dynamic routing mechanism allocates monitoring resources efficiently at test time. Evaluated on 3 datasets (WildguardMix, ToxicChat, OpenAI-Moderation) across 4 D-LLMs, D^2-Monitor achieves state-of-the-art performance with a compact parameter footprint (leq 0.85M parameters), a",
"summary": "Despite the emergence of diffusion large language models (D-LLMs) as an alternative to autoregressive large language models (AR-LLMs), safety monitoring for D-LLMs remains largely unexplored. Unlike AR-LLMs, D-LLMs…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🧠 LLMs & Foundation Models",
"debrief_date": "2026-05-27 09:00",
"debrief_path": "debriefs/debrief-2026-05-27-09.html",
"debrief_md": "",
"occurrence_count": 3,
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"debrief_date": "2026-05-27 09:00",
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"debrief_date": "2026-05-27 09:00",
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"debrief_date": "2026-05-27 09:00",
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"sort_date": "2026-05-27T09:00:00Z"
},
{
"id": "item_02198",
"rank": 398,
"score": 700,
"rationale": "Automated harness-evolution loop for agent harnesses; strong practical agent-dev contribution.",
"title": "IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages",
"url": "https://huggingface.co/papers/2605.13292",
"authors": "",
"description": "Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. We introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu. The dataset extends MDDial with LLM-generated synthetic consultations, translated using TranslateGemma, verified by native speakers, and refined through a script-aware post-processing pipeline to correct phonetic, lexical, and character-spacing errors. Building on this dataset, we fine-tune IndicMedLM via parameter-efficient adaptation of a quantized small language model, incorporating optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate against zero-shot multilingual baselines, conduct systematic error analysis across ten languages, and validate clinical plausibility through medical expert evaluation.",
"summary": "Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🧠 LLMs",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 3,
"occurrences": [
{
"debrief_date": "2026-05-14 19:00",
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"debrief_date": "2026-05-14 19:00",
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"debrief_date": "2026-05-14 19:00",
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}
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"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_04318",
"rank": 399,
"score": 700,
"rationale": "Neuro-symbolic AI 100x energy claim; high-impact if credible, strong AI topic fit.",
"title": "Neuro-symbolic AI breakthrough cuts energy use by 100x while boosting accuracy",
"url": "https://news.ycombinator.com/item?id=47668710",
"authors": "",
"description": "Article about: Neuro-symbolic AI breakthrough cuts energy use by 100x while boosting accuracy",
"summary": "Article about: Neuro-symbolic AI breakthrough cuts energy use by 100x while boosting accuracy",
"tags": [
"AI breakthrough",
"Hackernews",
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🟠 Hacker News",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 3,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
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{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_01190",
"rank": 400,
"score": 700,
"rationale": "Audited olympiad physics corpus uncovers contamination, translation drift, MCQ saturation; high integrity value.",
"title": "AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints",
"url": "https://huggingface.co/papers/2606.05622",
"authors": "Jiayu Liu, Cheng Qian, Zhenhailong Wang, Bingxuan Li, Jiateng Liu",
"description": "Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual constraints. To address this gap, we introduce AdaPlanBench, a dynamic interactive benchmark for evaluating whether Large Language Model (LLM) agents can adaptively plan and re-plan under progressively revealed world and user constraints. AdaPlanBench is built on 307 household tasks, with a scalable constraint construction pipeline that augments each task with dual constraints. At runtime, agents interact with the environment in a multi-turn protocol where hidden constraints are revealed only when the agent proposes a plan that violates them, requiring iterative plan revision under accumulating feedback. This makes planning challenging, as agents must infer and track constraints from feedback while re-planning effectively. Experiments on ten leading LLMs show that adaptive planning under dual constraints remains challenging, with the best model reaching only 67.75% accuracy. We further observe that performance degrades as more constraints accumulate, with user constraints posing a particularly large challenge and failures often stemming from weaker physical grounding and reduced effectiveness. These results establish AdaPlanBench as a testbed for dual-constrained interactive plannin",
"summary": "Dynamic interactive benchmark for adaptive planning under progressively revealed world and user constraints — best model reaches only 67.75% accuracy with user constraints particularly problematic due to weak physical grounding.",
"tags": [
"HUGGINGFACE_PAPERS",
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-06 09:00",
"debrief_path": "debriefs/debrief-2026-06-06-09.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-06-06 09:00",
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{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T09:00:00Z"
},
{
"id": "item_02179",
"rank": 401,
"score": 700,
"rationale": "Largest trapped-ion protein-folding demo on 64 qubits; concrete hardware milestone.",
"title": "Mullvad exit IPs as a fingerprinting vector",
"url": "https://tmctmt.com/posts/mullvad-exit-ips-as-a-fingerprinting-vector/",
"authors": "Tags: privacy, security | Score: 9",
"description": "Analysis of how Mullvad VPN's smaller server fleet creates a fingerprinting vector that tracks users across sites despite its no-logging reputation.",
"summary": "Analysis of how Mullvad VPN's smaller server fleet creates a fingerprinting vector that tracks users across sites despite its no-logging reputation.",
"tags": [
"HF",
"LOBSTERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Crypto",
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
"debrief_md": ""
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{
"debrief_date": "2026-05-15 04:00",
"debrief_path": "debriefs/debrief-2026-05-15-04.html",
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}
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"sort_date": "2026-05-15T04:00:00Z"
},
{
"id": "item_02206",
"rank": 402,
"score": 700,
"rationale": "AutoResearchBench for scientific literature agents; valuable benchmark, addresses gap.",
"title": "Active Tabular Augmentation via Policy-Guided Diffusion Inpainting",
"url": "https://huggingface.co/papers/2605.10315",
"authors": "",
"description": "Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models. We formalize a fidelity-utility gap: common generative objectives prioritize distributional plausibility, whereas augmentation succeeds only when injected samples reduce the current learner's held-out evaluation loss. This gap motivates learning not just how to generate, but what to generate and when to inject as training evolves. We propose TAP (Tabular Augmentation Policy), which couples diffusion inpainting with a lightweight, learner-conditioned policy to steer generation toward high-utility regions and controls safe injection via explicit gating and conservative windowed commitment. Under severe data scarcity, TAP consistently outperforms strong generative baselines on seven real-world datasets, improving classification accuracy by up to 15.6 percentage points and reducing regression RMSE by up to 32%.",
"summary": "Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🛡️ AI Safety",
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
"debrief_md": "",
"occurrence_count": 2,
"occurrences": [
{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
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{
"debrief_date": "2026-05-14 19:00",
"debrief_path": "debriefs/debrief-2026-05-14-19.html",
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}
],
"sort_date": "2026-05-14T19:00:00Z"
},
{
"id": "item_00034",
"rank": 403,
"score": 700,
"rationale": "Inference-time multi-objective control for pluralistic alignment; principled framework addressing a real gap.",
"title": "FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS",
"url": "https://arxiv.org/abs/2606.20518",
"authors": "Harshit Singh, Ayush Pratap Singh, Nityanand Mathur",
"description": "Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained. We introduce FlowEdit, a life-long adaptation framework for frozen flow-matching TTS that learns pronunciation corrections as latent conditioning edits rather than weight updates. When corrective feedback is provided, FlowEdit optimizes a token-level perturbation in the text embedding space, then stores the correction in a Modern Hopfield Network serving as content-addressable episodic memory. At inference, corrections are retrieved via soft attention with a similarity gate, enabling fuzzy morphological matching. On our curated benchmark of 312 multilingual proper nouns across 18 language families, FlowEdit reduces target-word Phoneme Error Rate by 92.7% relative to the zero-shot baseline while maintaining identical general-speech quality. Corrections complete in approximately 15 seconds on a single GPU.",
"summary": "Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is…",
"tags": [
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 How / Instructions / Shape",
"debrief_date": "2026-06-19 04:00",
"debrief_path": "debriefs/debrief-2026-06-19-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 04:00",
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}
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"sort_date": "2026-06-19T04:00:00Z"
},
{
"id": "item_00194",
"rank": 404,
"score": 700,
"rationale": "Evaluation Cards address a real, broadly-felt reporting gap across AI eval; strong practical impact and broad applicability.",
"title": "Sumi: Open Uniform Diffusion Language Model from Scratch",
"url": "https://huggingface.co/papers/2606.19005",
"authors": "",
"description": "Diffusion models have become a promising alternative to autoregressive models. Among these, uniform diffusion language models (UDLMs) permit any token to be updated at any step, in principle enabling more flexible generation. However, no UDLM has yet been pretrained from scratch at both large parameter scale and large token budget. Both autoregressive modeling and masked diffusion modeling already have capable models at scale that the community can study and build on; uniform diffusion has none. A scratch-pretrained UDLM at scale would provide a clean reference point for studying scaling behavior, generation dynamics, controllability, and trade-offs against established autoregressive and masked diffusion models. To this end, we introduce Sumi (\"ink\" in Japanese), a fully open 7B uniform diffusion language model pretrained from scratch on 1.5T tokens. Sumi performs competitively with autoregressive models trained at comparable token budgets on knowledge, reasoning, and coding benchmarks, while under-performing on commonsense benchmarks, where our education-heavy data mixture is a likely contributor. We release our model weights, checkpoints, and full training recipe, including a complete specification of the data mixture over publicly available corpora. We hope this release enables the community to study native uniform diffusion at scale and catalyzes work on its as-yet poorly understood aspects.",
"summary": "Diffusion models have become a promising alternative to autoregressive models. Among these, uniform diffusion language models (UDLMs) permit any token to be updated at any step,…",
"tags": [
"▲ 4",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 04:00",
"debrief_path": "debriefs/debrief-2026-06-18-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 04:00",
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}
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"sort_date": "2026-06-18T04:00:00Z"
},
{
"id": "item_00217",
"rank": 405,
"score": 700,
"rationale": "Clinically grounded, tiered privacy evaluation of medical LMs with concrete AUROC leakage results; high practical safety value.",
"title": "How do you analyze the relative \"strength\" of probes? [R]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u8lo60/how_do_you_analyze_the_relative_strength_of/",
"authors": "",
"description": "This question is related to topics like language+ models (including multimodal) and things like \"circuit\" analyses. I think something related might come up in my work (factuality guarantees for model outputs) and I'm trying to orient to the SoTA. I found this old post on trying to deduce, for instance, whether a Transformer-based model \"knows\" which word a token is in. Even in this simple example, I noticed some meaningful problems (I detail in a footnote 1 to not derail my question) - and I've heard that circuit research is pretty fraught. The post claimed to train a logistic regression class",
"summary": "This question is related to topics like language+ models (including multimodal) and things like \"circuit\" analyses. I think something related might come up in my work (factuality…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
"debrief_md": "",
"occurrence_count": 1,
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"debrief_date": "2026-06-17 19:00",
"debrief_path": "debriefs/debrief-2026-06-17-19.html",
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}
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"sort_date": "2026-06-17T19:00:00Z"
},
{
"id": "item_00238",
"rank": 406,
"score": 700,
"rationale": "Geometry/parameter-space analysis of on-policy distillation with subspace-locking is a strong, central LLM-RL insight.",
"title": "yairm210/Unciv",
"url": "https://github.com/yairm210/Unciv",
"authors": "",
"description": "Open-source Android/Desktop remake of Civ V",
"summary": "Open-source Android/Desktop remake of Civ V",
"tags": [
"⭐ 10.6k",
"GitHub Trending"
],
"source": "💻 GitHub Trending",
"type": "news",
"section": "🌐 Web Findings",
"group": "💻 GitHub Trending",
"debrief_date": "2026-06-17 11:00",
"debrief_path": "debriefs/debrief-2026-06-17-11.html",
"debrief_md": "",
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},
{
"id": "item_00277",
"rank": 407,
"score": 700,
"rationale": "Latent visual reasoning aligns poorly with answers; counter-intuitive finding, important.",
"title": "FlowCLIP: Contrastive Pretraining Using Domain Names for Encrypted Traffic Classification",
"url": "https://arxiv.org/abs/2606.17746",
"authors": "Eun Hun Choi",
"description": "Network traffic classification enables website fingerprinting, intrusion detection, and Quality of Service management. However, developing methods that capture stable and generalizable traffic patterns under realistic deployment conditions remains challenging. We introduce FlowCLIP, a contrastive pretraining framework for domain name prediction from encrypted traffic using only side-channel features: packet inter-arrival times, packet sizes, and packet directions. FlowCLIP uses raw domain names as textual supervision by aligning traffic flow representations with domain name representations through a CLIP-style contrastive objective. The pretrained traffic encoder is then frozen and evaluated through linear probing on canonicalized domain name labels. We evaluate FlowCLIP on a large-scale QUIC traffic dataset using a time-based protocol, where models are trained on Week 1 traffic and evaluated on traffic from Weeks 2-4. FlowCLIP outperforms competitive machine learning baselines across later evaluation weeks, suggesting that raw domain names provide a textual supervision signal for learning transferable encrypted traffic representations.",
"summary": "Network traffic classification enables website fingerprinting, intrusion detection, and Quality of Service management. However, developing methods that capture stable and generalizable traffic…",
"tags": [
"cs.NI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Flowclip / Contrastive / Pretraining",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00335",
"rank": 408,
"score": 700,
"rationale": "Deployed 300 km trusted-node QKD over multi-core fiber; notable real-world QKD milestone.",
"title": "Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides",
"url": "https://www.semanticscholar.org/paper/51e4e7dde8488ee20c3db9e72a464076c89c3103",
"authors": "",
"description": "Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifungal peptides. Here, we develop an alternative pipeline grounded in a diffusion model and molecular dynamics for the de novo design of AMPs. The peptides generated by our pipeline have lower similarity and identity than those of other reported methodologies. Among the 40 peptides synthesized for an experimental validation, 25 exhibit either antibacter",
"summary": "Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty…",
"tags": [
"📊 60 cites",
"Semantic Scholar"
],
"source": "🧪 Semantic Scholar",
"type": "news",
"section": "🌐 Web Findings",
"group": "🧪 Semantic Scholar",
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 11:00",
"debrief_path": "debriefs/debrief-2026-06-16-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T11:00:00Z"
},
{
"id": "item_00408",
"rank": 409,
"score": 700,
"rationale": "XSS-to-RCE via MeshCore/Home Assistant; high-impact IoT supply-chain vulnerability.",
"title": "Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning",
"url": "https://huggingface.co/papers/2606.15007",
"authors": "",
"description": "We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine Tuning (SFT), Reinforcement Learning (RL), and Multi-teacher On-Policy Distillation (MOPD). Nemotron 3 Ultra is our most capable model yet, employing multiple key technologies - LatentMoE, Multi Token Prediction (MTP), NVFP4 pre-training, multi-environment RLVR, MOPD, and reasoning budget control. Nemotron 3 Ultra achieves up to ~6x higher inference throughput as compared to state-of-the-art publicly available LLMs while attaining on-par accuracy. The state-of-the-art accuracy, high inference throughput, and 1M token context length make Nemotron 3 Ultra ideal for long-running autonomous agentic tasks. We open-source the base, post-trained, and quantized checkpoints, along with the training data and recipe on HuggingFace.",
"summary": "We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20…",
"tags": [
"▲ 3",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00388",
"rank": 410,
"score": 700,
"rationale": "Atomic decomposition/recombination for code RLVR; meaningful scalability lever for code RL.",
"title": "Compact Spin-Charge Separated Neural Quantum States for Valence-Bond States",
"url": "https://arxiv.org/abs/2606.17045",
"authors": "Ang-Kun Wu, Louis Primeau, Yixin Zhang, Jingtao Zhang, Adrian Del Maestro, Yang Zhang",
"description": "Neural-network quantum states (NQS) provide a flexible nonlinear representation of quantum many-body wavefunctions, but their efficiency depends sensitively on whether the architecture reflects the sign structure and constrained Hilbert space of the target state. In this work, we propose a solvable-point-guided strategy: design the architecture at an exactly solvable point where the correct local rules can be read off, then refine to the non-exact regime by enlarging only the kernel size and hidden dimension. The strategy is built from four physics-motivated designs: a stride-matched local-rule convolution, geometric pooling, a sign-resolving $\\tanh(x^{2k+1})$ activation, and explicit spin-hole sector separation. We test this approach on quasi-one-dimensional valence-bond-solid (VBS) states and their doped soliton variants (sVBS), the exact ground states of a $t$-$J$-like model with a single mobile hole. In finite-size benchmarks, this architecture reaches high fidelity for the exact sVBS state with substantially fewer parameters than generic fully connected, convolutional, and transformer baselines tested under the same setup. For the spin sector, the learned local rule transfers from small to larger systems without retraining. Away from the solvable point, increasing kernel size and hidden dimension systematically improves accuracy, and the model shows approximately $L^2$ parameter scaling in the gapless regime for system size $L$, compared with approximately $L^4$ for matr",
"summary": "Neural-network quantum states (NQS) provide a flexible nonlinear representation of quantum many-body wavefunctions, but their efficiency depends sensitively on whether the architecture reflects the…",
"tags": [
"cond-mat.str-el",
"cond-mat.dis-nn"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 States / Compact / Spin-charge",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00439",
"rank": 411,
"score": 700,
"rationale": "Decentralized instruction tuning with merging theory; principled and broadly useful.",
"title": "Cleo: trying to fit full analyst behavior in a 2B model [P]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u6udpb/cleo_trying_to_fit_full_analyst_behavior_in_a_2b/",
"authors": "",
"description": "Hello all! Half of all industrial \"chatbots\" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models if you trained, evaluated, and ran inference in the exact same structured harness, leading to Cleo: a Qwen3.5-2B-Base finetune. Currently, some features of cleo that are only possible/useful in a unified hardel are: Training on the exact same gather, repair, and answer contract it uses at inference time Searching over candidate queries with live execution evidence, not just model likelihood Co-designing the model cont",
"summary": "Hello all! Half of all industrial \"chatbots\" are just text-to-SQL models in a trenchcoat (and the other half RAG!). I wanted to explore just how small you could make these models…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 19:00",
"debrief_path": "debriefs/debrief-2026-06-15-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T19:00:00Z"
},
{
"id": "item_00499",
"rank": 412,
"score": 700,
"rationale": "Concept binding in CLIP embeddings addresses fundamental VLM limitation; strong conceptual contribution.",
"title": "STREAM: Multi-Tier LLM Inference Middleware with Dual-Channel HPC Token Streaming",
"url": "https://arxiv.org/abs/2606.13968",
"authors": "Anas Nassar, Steve Mohr, Leonard Apanasevich, Himanshu Sharma",
"description": "Researchers and practitioners working with large language models face a fragmented landscape: local models are free and private but hardware limits the model size and context windows a researcher can use; institutional HPC centers offer powerful GPU resources at no marginal cost and keep data within institutional boundaries, but operate behind firewalls and are designed for batch jobs rather than interactive use; commercial cloud APIs provide frontier-model quality on demand but impose significant cost and data retention policies unsuitable for sensitive research data. No existing system unifies all three. STREAM (Smart Tiered Routing Engine for AI Models) addresses this gap with four contributions: (1) a three-tier routing architecture combining local, HPC, and cloud inference with a local LLM-based complexity judge; (2) a dual-channel HPC streaming architecture that separates the Globus Compute control plane (authentication and job dispatch) from a WebSocket relay data plane (token delivery), enabling sub-second TTFT (0.54 s median, 21.1x over batch mode's 11.40 s) through institutional firewalls without VPN or firewall rule changes, with end-to-end AES-256-GCM encryption ensuring the relay operator cannot read token payloads; (3) tier-aware context summarization that prevents long conversations from forcing simple queries onto expensive tiers; and (4) an HPC-as-API proxy mode that exposes HPC inference as an OpenAI-compatible endpoint callable from any standard client with",
"summary": "Researchers and practitioners working with large language models face a fragmented landscape: local models are free and private but hardware limits the model size and context windows a researcher can…",
"tags": [
"cs.DC",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Stream / Multi-tier / Llm",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00471",
"rank": 413,
"score": 700,
"rationale": "State-externalizing harness + RL for search agents; clean architectural contribution.",
"title": "Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning",
"url": "https://arxiv.org/abs/2606.14693",
"authors": "Pengxin Wang, Lihao Guo, Yi Xie, Bo Liu, Siyang Cao, Jingdi Chen",
"description": "Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts arise not only across objectives but also across agents with different observations, roles, and contributions. We propose Preference Coordinated Multi-agent Policy Optimization (PCMA), which learns coordinated agent-specific preferences to enable complementary trade-offs among agents. Theoretically, we formulate cooperative MOMARL as a team-optimal game and show that, under suitable conditions, preference diversity can induce team improvement through a first-order improvement decomposition. Experiments on multiple cooperative MOMA environments and a practical traffic-control scenario show that PCMA improves both performance and trade-off coordination.",
"summary": "Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts arise not only…",
"tags": [
"cs.MA",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Coordinated / Preference / Multi-objective",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00492",
"rank": 414,
"score": 700,
"rationale": "'Scaling the harness' framing for agentic AI is timely and strategically important.",
"title": "Detecting Bot Detection: Prevalence, Techniques, and Implications for Web Measurement Research",
"url": "https://arxiv.org/abs/2606.14525",
"authors": "Ralf Gundelach, Michael Mühlhauser, Dominik Herrmann",
"description": "Browser automation frameworks are essential tools for security and privacy research on the web, yet bot detection scripts increasingly probe their artifacts, threatening measurement validity as automated browsers may be blocked or served different content. Prior work measures detection deployment, while we measure blocking-induced sample loss. Through a literature survey of top-tier security, privacy, and web measurement venues, we find that 83% of papers omit any discussion of bot detection blocking. To address this gap, we conduct a measurement study of 10,000 websites across four browser configurations (40K page visits in total) to quantify detection prevalence and employed techniques. Using custom instrumentation to detect when sites probe for automation, we develop a taxonomy of bot detection techniques and measure how often they appear in practice. Chromium headless encounters a 15% soft block rate compared to 7% for other configurations. Across all conditions, 82% of blocks are attributable to bot detection (59% vendor-confirmed, 23% inferred from condition-dependent blocking), predominantly by providers with integrated bot detection such as Cloudflare (37% block rate) and Akamai (26%). A header spoofing experiment establishes that 75% of Chromium-headless-only blocks are caused by header-level signals alone, yet JavaScript-based environment probing is more extensive than current blocking rates suggest. These findings demonstrate that bot detection creates systematic,",
"summary": "Browser automation frameworks are essential tools for security and privacy research on the web, yet bot detection scripts increasingly probe their artifacts, threatening measurement validity as…",
"tags": [
"cs.CR",
"cs.CY"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Detecting / Bot / Detection",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00595",
"rank": 415,
"score": 700,
"rationale": "Token inflation attack on LLM providers; novel supply-chain threat, high practical security impact.",
"title": "Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior",
"url": "https://huggingface.co/papers/2606.12730",
"authors": "",
"description": "Anticipating LLM behavioral tendencies from low-cost psychometric probes is critical for safe deployment, but only if self-reports (SR) reliably predict behavior. Recent work documented substantial SR-behavior dissociation in LLMs, but relied on broad personality traits (Big 5) that predict specific behaviors weakly, even in humans. Furthermore, the isolation of conversational sessions combined with weak context matching left open whether LLMs truly lack coherence or whether the conditions needed to detect such coherence were not met. We contrast Big 5 with the Theory of Planned Behavior (TPB), which measures intention targeted to a specific behavior and predicts human behavior substantially better than broad traits. We run experiments across four behavioral tasks and 11 frontier LLMs, while also varying session context and identity induction. We find that SR-behavior coherence exists but is selective. 1) Within a shared conversation, the Theory of Planned Behavior reaches human-level coherence; Big 5 does not. 2) Across separate conversations, coherence survives only for behaviors anchored outside the immediate prompt, such as implicit bias shaped by training, and collapses when behavior is strongly primed by context, as with sycophancy. 3) Persona prompting makes self-reports more consistent across conversations, but does not bring behavior into alignment. These findings suggest that coarse personality frameworks, such as Big 5 may not be the best tools for testing deployme",
"summary": "Anticipating LLM behavioral tendencies from low-cost psychometric probes is critical for safe deployment, but only if self-reports (SR) reliably predict behavior. Recent work…",
"tags": [
"▲ 2",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 19:00",
"debrief_path": "debriefs/debrief-2026-06-12-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 19:00",
"debrief_path": "debriefs/debrief-2026-06-12-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T19:00:00Z"
},
{
"id": "item_00643",
"rank": 416,
"score": 700,
"rationale": "LoRA backdoor token-level generalization is a significant supply-chain security finding.",
"title": "A $q$-analogue of the rational normal curve and linearized Reed-Solomon codes",
"url": "https://arxiv.org/abs/2606.13246",
"authors": "Valentina Astore, Martino Borello, Alain Couvreur, Flavio Salizzoni",
"description": "The relationship between linear codes in the Hamming metric and projective algebraic varieties has led to deep interactions between coding theory and algebraic geometry, with classical examples such as Reed-Solomon codes and the rational normal curve. On the other hand, the sum-rank metric has recently gained attention due to applications in network coding, distributed storage, and post-quantum cryptography, with linearized Reed-Solomon codes emerging as optimal constructions. Despite recent advances, their structural and geometric properties are still not fully understood, and existing distinguishers remain limited. In this paper, we develop a geometric framework for linearized Reed-Solomon codes by considering a $q$-analogue of the rational normal curve. This yields a geometric characterization for certain parameter choices and reveals that the corresponding sets of points satisfy unexpectedly many $(q+1)$-degree hypersurface conditions. Our approach extends Schur-product-based techniques from the Hamming and rank-metric settings to the sum-rank metric case. Finally, we study the Hilbert function of the associated coordinate ring, providing a detailed description of its behavior and identifying its regularity, which also sheds new light on Gabidulin codes.",
"summary": "The relationship between linear codes in the Hamming metric and projective algebraic varieties has led to deep interactions between coding theory and algebraic geometry, with classical examples such…",
"tags": [
"cs.IT",
"math.AG",
"math.CO"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Analogue / Rational / Normal",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00810",
"rank": 417,
"score": 700,
"rationale": "Zero-CoT truncation detects evasive contamination via Contamination Confidence; strong eval methodology.",
"title": "Looking for papers/resources on AI responses to psychological distress prompts [P]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u2j4uv/looking_for_papersresources_on_ai_responses_to/",
"authors": "",
"description": "Hi everyone, I’m close to completing my degree in Psychology, and I’m also a Systems Engineering student. is like, roughly comparable to Software Engineering / Computer Science outside Latin America. Although I study engineering, I’m still at an early stage with machine learning, LLMs, AI safety, and related technical topics. My research project is mainly psychology-oriented, but I’d really appreciate recommendations or warnings from a software/technical perspective. I’m working on a project about how AI systems respond to prompts involving psychological distress at different levels of intensi",
"summary": "Hi everyone, I’m close to completing my degree in Psychology, and I’m also a Systems Engineering student. is like, roughly comparable to Software Engineering / Computer Science…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00907",
"rank": 418,
"score": 700,
"rationale": "Subproblem curriculum RL for credit assignment is a substantive step beyond outcome-only RLVR, with clear empirical wins.",
"title": "Rick & Morty",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u16b2c/rick_morty/",
"authors": "",
"description": "nobody expected HF there submitted by /u/jacek2023 [link] [comments]",
"summary": "nobody expected HF there submitted by /u/jacek2023 [link] [comments]",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_00949",
"rank": 419,
"score": 700,
"rationale": "Aaronson talk on quantum computing is a high-authority explainer, valuable for research strategy context.",
"title": "Next Forcing: Causal World Modeling with Multi-Chunk Prediction",
"url": "https://arxiv.org/abs/2606.11187",
"authors": "Gangwei Xu, Qihang Zhang, Jiaming Zhou, Xing Zhu, Yujun Shen, Xin Yang, Yinghao Xu",
"description": "Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterative video denoising. In this paper, we present Next Forcing, a multi-chunk prediction (MCP) framework for causal world modeling that enables faster training, higher accuracy, and accelerated inference. Inspired by multi-token prediction in large language models, Next Forcing introduces an MCP training objective that augments the main model with lightweight auxiliary MCP modules to simultaneously denoise video chunks at multiple future temporal horizons (next$^1$, next$^2$, next$^3$ chunks). These MCP modules form a causal chain across prediction depths, where intermediate features fused from multiple layers of the main model are leveraged to predict future dynamics, allowing near-future predictions to inform farther-future ones and providing dense multi-scale temporal supervision back to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates: at 50 fps, Next Forcing achieves a 93.1% relative improvement over LingBot-VA at 5k training steps and 2.3x faster convergence, and establishes new state-of-the",
"summary": "Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy,…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Large Language Models",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_00994",
"rank": 420,
"score": 700,
"rationale": "Proves DPO/RLHF conditional equivalence failure modes; highly relevant to alignment.",
"title": "How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs",
"url": "https://huggingface.co/papers/2606.10646",
"authors": "",
"description": "Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat all tokens equally, failing to distinguish decisive reasoning steps from routine formatting or fluent filler. Recent attempts leverage model-internal signals to assign finer-grained credit, but these are often point-wise heuristics that ignore the global structure of information propagation. We propose FlowTracer, an RL framework that traces answer-targeted reasoning flow on an attention-induced directed acyclic graph in which nodes correspond to tokens and edge capacities come from aggregated attention weights and derives token credit from this global structure. The edge capacities are reweighted to retain only the influence that can reach the answer region, while enforcing local flow conservation so intermediate tokens neither lose nor gain effective mass due to path length or irrelevant branches. On this graph, FlowTracer extracts an information-flow backbone connecting the question to the answer and scores tokens by flow throughput, revealing high-impact hubs and aggregation checkpoints that mediate long-range dependencies. These derived importances are used to shape token-level rewards, enabling learning signals to focus precisely on the tokens that route information toward (or away from) correct answers and delivering consistent performance gains across a range of reasoning tasks.",
"summary": "Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat all tokens equally, failing…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01049",
"rank": 421,
"score": 700,
"rationale": "Grafana supply-chain breach via TanStack; major security incident.",
"title": "What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks",
"url": "https://arxiv.org/abs/2606.09700",
"authors": "Qin Yang, Lu Malloy, Joshua Lee, Xiaohan Chang, Meisam Mohammady, Doowon Kim, Yuan Hong",
"description": "Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content. However, these systems primarily operate on tokenized text and largely ignore the visual cues that humans naturally rely on when interpreting content. We show that this discrepancy creates a fundamental perceptual mismatch: content that is readily recognized as harmful by humans can become effectively invisible to automated moderation systems. To study this vulnerability, we introduce a class of Human-Perceptible Adversarial Attacks (HPAA), in which harmful expressions are embedded into otherwise benign text through visually salient typographic manipulations. Our key insight is that typographic features, including spacing, visual emphasis, and spatial arrangement, can be strategically combined to preserve human recognition of harmful content while substantially reducing machine detectability. Operating in black-box settings with only a small query budget, our attack automatically generates evasive content without requiring model access or gradient information. We evaluate the attack across multiple datasets and ten deployed moderation systems, including commercial APIs and state-of-the-art open-source guardrails. Results reveal a striking gap between human and machine perception: with only three detector queries, generated attacks achieve over 86\\% human recognition while maintaining detection rates below 1\\% across the evaluated systems. We further con",
"summary": "Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content. However, these systems primarily operate on tokenized text and largely…",
"tags": [
"cs.CR",
"cs.HC",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Security & Cybersecurity",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01112",
"rank": 422,
"score": 700,
"rationale": "Embodied spatial intelligence benchmark from Li Fei-Fei/Wu group; strong relevance to agents/perception.",
"title": "Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents",
"url": "https://huggingface.co/papers/2606.05753",
"authors": "",
"description": "Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these latents and their visual targets, i.e., cosine similarity or mean squared error (MSE), as both the training loss and the quality metric, assuming that better alignment yields a better answer. We test this with a designed matrix of five LVR variants and find the assumption inverted: cosine alignment is negatively correlated with accuracy across all five (r=-0.94). To explain this, we introduce PRISM, a pair of inference-time diagnostics: a linear probe that asks where the answer is decodable, and a corruption test that asks whether the latent is load-bearing. The supervised latents are largely bypassed. Corrupting them shifts accuracy by at most four points. The answer is decodable downstream of the latent but not at it, and the size of this decodability gap predicts how much each variant relies on its latent under perturbation. Consistent with an Information Bottleneck reading of the loss, the auxiliary objective reshapes the language model via shared parameters rather than via the latent variable it nominally optimizes.",
"summary": "Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-09 11:00",
"debrief_path": "debriefs/debrief-2026-06-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-09T11:00:00Z"
},
{
"id": "item_01162",
"rank": 423,
"score": 700,
"rationale": "'Code as agent harness' survey frames a major emerging paradigm; high reference value for the debrief.",
"title": "Will the Agent Recuse Itself? Measuring LLM-Agent Compliance with In-Band Access-Deny Signals",
"url": "https://arxiv.org/abs/2606.06460",
"authors": "Thamilvendhan Munirathinam",
"description": "As autonomous LLM agents hold real credentials and operate infrastructure without human oversight, operators have no standard way to signal that a resource is off-limits. This paper proposes the Recuse Signal — a lightweight in-band deny signal that servers emit over existing protocol channels (SSH banners, PostgreSQL connections) to tell agents to recuse themselves without hard-failing. The study measures compliance across major LLM agents and finds significant variation in adherence, with implications for credential hygiene and infrastructure access control in agentic deployments.",
"summary": "Proposes the Recuse Signal — a lightweight in-band deny signal over existing protocol channels — to tell LLM agents to recuse themselves from off-limits resources, measuring compliance across major agents.",
"tags": [
"cs.CR",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Cybersecurity",
"debrief_date": "2026-06-06 09:00",
"debrief_path": "debriefs/debrief-2026-06-06-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 09:00",
"debrief_path": "debriefs/debrief-2026-06-06-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T09:00:00Z"
},
{
"id": "item_01243",
"rank": 424,
"score": 700,
"rationale": "FutureSim replays real-world events to evaluate adaptive agents; strong evaluation framework.",
"title": "Rooting Home Assistant through MeshCore: XSS attacks with a LoRa node name",
"url": "https://mxsasha.eu/posts/meshcore-xss-home-assistant/",
"authors": "security",
"description": "A crafted MeshCore node name could compromise any Home Assistant instance running meshcore-card as soon as someone viewed a dashboard with that card. MeshCore …",
"summary": "A crafted MeshCore node name could compromise any Home Assistant instance running meshcore-card as soon as someone viewed a dashboard with that card. MeshCore …",
"tags": [
"LOBSTERS",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🔴 Lobste.rs",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01285",
"rank": 425,
"score": 700,
"rationale": "AlphaEvolve applied to FHE kernels on TPUs is a notable cross-cut of LLM-driven optimization and applied crypto performance.",
"title": "SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence",
"url": "https://huggingface.co/papers/2605.31529",
"authors": "",
"description": "True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under different conditions, and deciding what to do next. We refer to this progression, from perception through causal reasoning and simulation to strategic planning, as Strategic Video Intelligence (SVI). No existing benchmark evaluates this capability stack: in-the-wild videos lack verifiable ground truth for causal and strategic questions, while synthetic environments sacrifice the complexity of real multi-agent systems. To bridge this gap, we introduce SVI-Bench, a large-scale benchmark that leverages team sports as a dynamic microworld, combining the complexity of real-world multi-agent interaction (10-22 agents making coordinated decisions under adversarial pressure) with the verifiability of explicit rules and definitive outcomes. SVI-Bench comprises approximately 35K hours of broadcast video, 15M annotated actions, 15K hours of expert commentary, 23K game reports, and 103K structured statistical records across basketball, soccer, and hockey, all constructed via a data engine that transforms raw game data into a dense, cross-referenced corpus. We organize evaluation into 9 tasks spanning a progressive four-pillar hierarchy: Dynamic Scene Understanding, Causal Reasoning, Strategic Simulation, and Agentic Synthesis. Evaluating strong multimodal and agentic baselines, we find a capability cliff: models perform competently on pe",
"summary": "True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under different conditions, and deciding what to do next. We refer to…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01363",
"rank": 426,
"score": 700,
"rationale": "CiteTracer multi-agent citation hallucination detector with 12-code taxonomy and 2,450 benchmark; high impact.",
"title": "SwanVoice: Expressive Long-Form Zero-Shot Speech Synthesis for Both Monologue and Dialogue",
"url": "https://huggingface.co/papers/2605.30993",
"authors": "Ruiqi Li, Yu Zhang, Changhao Pan, Ke Lei, Xiang Yin",
"description": "Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1--4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice.",
"summary": "Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-05-29"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-01 04:00",
"debrief_path": "debriefs/debrief-2026-06-01-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-01T04:00:00Z"
},
{
"id": "item_01400",
"rank": 427,
"score": 700,
"rationale": "First SAE for recurrent cache writes; novel mechanistic interpretability for SSM/hybrid LLMs.",
"title": "SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations",
"url": "https://arxiv.org/abs/2605.30345",
"authors": "Qinpei Luo, Ruichun Ma, Xinyu Zhang, Lili Qiu",
"description": "This item reviews schgen: pcb schematic generation with semantic-grounded code representations and its broader implications for machine learning progress. It matters because AI methods are increasingly spilling into adjacent scientific and industrial domains.",
"summary": "This item reviews schgen: pcb schematic generation with semantic-grounded code representations and its broader implications for machine learning progress …",
"tags": [
"cs.AI, cs.CL, cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-30T04:00:00Z"
},
{
"id": "item_01411",
"rank": 428,
"score": 700,
"rationale": "Asymmetric flow modeling with rank-asymmetric velocity; 1.57 FID ImageNet state-of-the-art.",
"title": "In-Context Reward Adaptation for Robust Preference Modeling",
"url": "https://arxiv.org/abs/2605.30323",
"authors": "Zhenyu Sun, Zheng Xu, Ermin Wei",
"description": "This item reviews in-context reward adaptation for robust preference modeling and its broader implications for machine learning progress. It matters because AI methods are increasingly spilling into adjacent scientific and industrial domains.",
"summary": "This item reviews in-context reward adaptation for robust preference modeling and its broader implications for machine learning progress. It matters because AI methods are increasingly spilling into adjacent scientific and industrial domains. …",
"tags": [
"cs.LG, cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🎨 Machine Learning",
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-30T04:00:00Z"
},
{
"id": "item_01430",
"rank": 429,
"score": 700,
"rationale": "Attractor Models with implicit-differentiation fixed points; strong language/reasoning gains.",
"title": "Control Flow Graph Recovery for Dynamically Loaded Code via Symbolic Library Resolution",
"url": "https://arxiv.org/abs/2605.29620",
"authors": "Oleksandr Mostovyi",
"description": "This item focuses on control flow graph recovery for dynamically loaded code via symbolic library resolution within the security and cryptography landscape. It matters because defensive practice is being reshaped by both AI-native tooling and the coming post-quantum transition.",
"summary": "This item focuses on control flow graph recovery for dynamically loaded code via symbolic library resolution within the security and cryptography landscape …",
"tags": [
"cs.CR, cs.SE"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔐 Security & Crypto",
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-30 04:00",
"debrief_path": "debriefs/debrief-2026-05-30-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-30T04:00:00Z"
},
{
"id": "item_01665",
"rank": 430,
"score": 700,
"rationale": "Fast BLT variants materially speed byte-level LMs; addresses a key bottleneck with diffusion/self-speculation tricks.",
"title": "CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models",
"url": "https://huggingface.co/papers/2605.23699",
"authors": "León Begiristain, Olaf Dünkel, Adam Kortylewski",
"description": "CRONOS is an intervention-based benchmark that evaluates whether video prediction models respond appropriately to controlled changes in visual inputs — testing if models capture causal physical structure rather than superficial correlations.",
"summary": "CRONOS is an intervention-based benchmark that evaluates whether video prediction models respond appropriately to controlled changes in visual inputs — testing if models capture causal physical structure rather than superficial…",
"tags": [
"cs.CV",
"cs.LG"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚡ Machine Learning",
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-26 04:00",
"debrief_path": "debriefs/debrief-2026-05-26-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-26T04:00:00Z"
},
{
"id": "item_01717",
"rank": 431,
"score": 700,
"rationale": "High impact: DeepMind AI co-mathematician workbench for agentic mathematical research.",
"title": "Forecasting Scientific Progress with Artificial Intelligence",
"url": "https://huggingface.co/papers/2605.22681",
"authors": "Source: huggingface papers",
"description": "Artificial intelligence (AI) is increasingly embedded in scientific discovery, yet whether it can anticipate scientific progress remains unclear. To study this question, we introduce a temporally grounded evaluation framework for forecasting scientific progress under controlled knowledge constraints. We present CUSP (Cutoff-conditioned Unseen Scientific Progress), a multi-disciplinary and event-level benchmark that evaluates scientific forecasting in AI systems through feasibility assessment, mechanistic reasoning, generative solution design, and temporal prediction. Across 4,760 scientific events, we observe systematic and domain-dependent limitations in current frontier models. While models can identify plausible research directions from competing candidates, they fail to reliably predict whether scientific advances will be realized and systematically misestimate when they will occur. Performance is highly heterogeneous across domains, with the timing of AI progress more predictable than advances in biology, chemistry, and physics. Performance is largely insensitive to whether events occur before or after the training cutoff, suggesting these limitations cannot be explained solely by knowledge exposure in training data. Under controlled information access, additional pre-cutoff knowledge improves performance but does not close the gap to full-information settings, which becomes more pronounced for high-citation advances. Models also exhibit systematic overconfidence and str",
"summary": "Artificial intelligence (AI) is increasingly embedded in scientific discovery, yet whether it can anticipate scientific progress remains unclear. To study this question, we introdu…",
"tags": [
"huggingface papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🤗 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-05-24 11:00",
"debrief_path": "debriefs/debrief-2026-05-24-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-24 11:00",
"debrief_path": "debriefs/debrief-2026-05-24-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-24T11:00:00Z"
},
{
"id": "item_01771",
"rank": 432,
"score": 700,
"rationale": "Continuous-time DMD migration for diffusion distillation is a real advance.",
"title": "Secure Boot and CA Rollover - a heads-up for distributions",
"url": "https://blog.einval.com/2026/05/22#secure_boot_ca_rollover",
"authors": "",
"description": "Friends Matthew Garrett Jonathan McDowell Jo McIntyre Martin Michlmayr Andrew Mobbs Mike Pitt Daniel Silverstone Andy Simpkins Neil Williams",
"summary": "Friends Matthew Garrett Jonathan McDowell Jo McIntyre Martin Michlmayr Andrew Mobbs Mike Pitt Daniel Silverstone Andy Simpkins Neil Williams",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01790",
"rank": 433,
"score": 700,
"rationale": "Zero-shot ILP foundation model with domain-agnostic literal encoding.",
"title": "SceneAligner: 3D-Grounded Floorplan Localization in the Wild",
"url": "https://huggingface.co/papers/2605.22581",
"authors": "Junhyeong Cho, Ruojin Cai, Hadar Averbuch-Elor",
"description": "Many public buildings provide floorplans with a \"you are here\" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small-scale environments and precise vectorized floorplans, limiting their ability to operate in large-scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by grounding the task in a reconstructed 3D representation of the scene. Given an unconstrained image collection, our method reconstructs a gravity-aligned 3D scene and projects it into a 2D density map that serves as a floorplan proxy. Floorplan localization is then formulated as aligning this proxy with the input floorplan via a 2D similarity transform. To bridge the appearance gap between density maps and architectural floorplans, we adapt a 2D foundation model to learn cross-modal correspondences, introducing a fine-tuning scheme that encourages semantically aligned matches while preserving structural consistency. Extensive experiments demonstrate substantial improvements over prior methods, including in extremely sparse settings with as little as a single input image. Our code and data will be publicly available.",
"summary": "Many public buildings provide floorplans with a \"you are here\" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled…",
"tags": [
"arXiv:2605.22581"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🖼️ Vision & Imaging",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01757",
"rank": 434,
"score": 700,
"rationale": "KernelBench-X benchmark for LLM GPU kernels, strong empirical findings.",
"title": "Diversed Model Discovery via Structured Table Discovery",
"url": "https://huggingface.co/papers/2605.22766",
"authors": "Zhengyuan Dong, Renée J. Miller",
"description": "Model cards describe model behavior through a mixture of textual descriptions and structured artifacts, including performance, configuration, and dataset tables. Existing model search systems rely predominantly on semantic similarity over text, which can produce homogeneous result sets and limit exploration of alternatives. We argue that model search is inherently comparative: users want models that are task-aligned yet differentiated in measurable ways. We hypothesize that this balance requires retrieval over condensed, high-quality evidence rather than verbose descriptions, and much of that evidence is concentrated in structured tables. We present StructuredSemanticSearch, a table-driven model search framework built on the ModelTables benchmark. Given a query, StructuredSemanticSearch combines a semantic baseline for task alignment with a structure-aware pipeline that discovers query-related model-card tables using table discovery operators such as unionability, joinability, and keyword search. Retrieved tables are mapped back to model cards under a controlled top-k budget, enabling fair comparison between text-based and table-based retrieval. Beyond retrieval, StructuredSemanticSearch adapts table integration to the model-table domain through orientation-aware integration, producing compact integrated views of tables from partially overlapping and sometimes transposed evidence tables. For evaluation, we introduce a nugget-based, auditable protocol that extracts compact evi",
"summary": "Model cards describe model behavior through a mixture of textual descriptions and structured artifacts, including performance, configuration, and dataset tables. Existing model search systems rely predominantly on semantic similarity over text, which can produce homogeneous result sets and limit exploration of…",
"tags": [
"arXiv:2605.22766"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01812",
"rank": 435,
"score": 700,
"rationale": "Comprehensive GFCR rollout-strategy survey for LLM RL; fills real underreported gap.",
"title": "It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs",
"url": "https://huggingface.co/papers/2605.20258",
"authors": "Sangwoo Park, Woongyeong Yeo, Seanie Lee, Yumin Choi, Hyomin Lee",
"description": "Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language models are increasingly deployed as personal agents handling sensitive workflows, adhering to CI becomes critical. However, even frontier models remain unreliable in making disclosure decisions, and existing mitigation strategies often degrade underlying task performance. To overcome this privacy-utility trade-off, we propose SELFCI, a complementary self-distillation framework that decouples information suppression from task resolution. SELFCI jointly optimizes two independent reverse KL divergences over distinct teacher distributions derived from feedback: one encourages preserving task-relevant information for utility, while the other enforces minimal and appropriate disclosure. This complementary formulation induces a Product-of-Experts (PoE) target, aligning the policy with the intersection of capability and privacy requirements. Empirical evaluations demonstrate that SELFCI, without relying on costly external supervision, consistently outperforms competitive baselines such as online reinforcement learning algorithms (e.g., GRPO). These trends further extend to out-of-domain settings involving agentic workflows and accumulated private context, suggesting that SELFCI provides a practical path toward CI alignment.",
"summary": "Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language models are increasingly deployed as personal agents handling sensitive workflows, adhering to CI bec…",
"tags": [
"HUGGINGFACE"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-21 16:00",
"debrief_path": "debriefs/debrief-2026-05-21-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-21 16:00",
"debrief_path": "debriefs/debrief-2026-05-21-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-21T16:00:00Z"
},
{
"id": "item_01879",
"rank": 436,
"score": 700,
"rationale": "RIPE NCC SSO token exposure to 1000 third parties is a serious, high-impact web identity/SSO security incident.",
"title": "Logic bug in the Linux kernel's __ptrace_may_access() function (CVE-2026-46333)",
"url": "https://cdn2.qualys.com/advisory/2026/05/20/cve-2026-46333-ptrace.txt",
"authors": "",
"description": "Logic bug in the Linux kernel's __ptrace_may_access() function (CVE-2026-46333)",
"summary": "Logic bug in the Linux kernel's __ptrace_may_access() function (CVE-2026-46333)",
"tags": [
"LOBSTERS"
],
"source": "Lobste.rs",
"type": "news",
"section": "📰 News Headlines",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 19:00",
"debrief_path": "debriefs/debrief-2026-05-20-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T19:00:00Z"
},
{
"id": "item_01912",
"rank": 437,
"score": 700,
"rationale": "Attention-as-dynamic-MLP reframing enabling linear-time global modeling is a high-leverage architectural insight.",
"title": "OpenComputer: Verifiable Software Worlds for Computer-Use Agents",
"url": "https://huggingface.co/papers/2605.19769",
"authors": "",
"description": "We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evolving verification layer that improves verifier reliability using execution-grounded feedback, (3) a task-generation pipeline that synthesizes realistic and machine-checkable desktop tasks, and (4) an evaluation harness that records full trajectories and computes auditable partial-credit rewards. In its current form, OpenComputer covers 33 desktop applications and 1,000 finalized tasks spanning browsers, office tools, creative software, development environments, file managers, and communication applications. Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state. Frontier agents struggle with end-to-end completion despite partial progress, and open-source models exhibit sharp drops from their OSWorld-Verified scores, exposing a persistent gap in robust computer automation.",
"summary": "We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_01897",
"rank": 438,
"score": 700,
"rationale": "OpenSeeker-v2 SFT-only 30B search agent beating RL+CPT counterparts is a strong, surprising data-scaling result.",
"title": "CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning",
"url": "https://huggingface.co/papers/2605.20075",
"authors": "",
"description": "Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering, which can delay access to plausible answers and incur unnecessary token costs even when the model is able to identify an answer before extended thinking, a behavior known as performative reasoning. In this paper, we introduce CopT, a reformulated reasoning pipeline that reverses the usual order of thinking and answering. Instead of thinking before answering, CopT first elicits a draft answer and then invokes subsequent on-policy thinking conditioned on its own draft answer for reflection and correction. To assess whether the draft answer should be trusted, CopT recasts continuous embeddings as inference-time contrastive verifiers. Specifically, it contrasts the model's support for the same generated tokens under discrete-token inputs and continuous-embedding inputs, yielding a sequence-level reverse KL estimator for answer reliability. Our analysis shows that under certain assumptions, the expected estimate equals the mutual information between the unresolved latent state and the emitted answer token, explaining why it captures answer-relevant uncertainty rather than arbitrary uncertainty in the latent state. When the answer is deemed insufficiently reliable, CopT performs further on-policy thinking, where a second KL estimator dynamically controls draft-answer visibility, preser",
"summary": "Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering,…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-20 09:00",
"debrief_path": "debriefs/debrief-2026-05-20-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-20T09:00:00Z"
},
{
"id": "item_01935",
"rank": 439,
"score": 700,
"rationale": "PhysicianBench uses real EHR environments; high-impact benchmark for clinical AI agents.",
"title": "VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation",
"url": "https://huggingface.co/papers/2605.16079",
"authors": "",
"description": "Proposes a paradigm for instance-level video understanding through visual prompts, integrating agentic reasoning with proactive visual perception. Achieves +13.7% improvement over baselines and surpasses GPT-4o and Gemini-2.5-Pro on spatiotemporal localization tasks.",
"summary": "Proposes a paradigm for instance-level video understanding through visual prompts, integrating agentic reasoning with proactive visual perception. Achieves +13.7% improvement over baselines and…",
"tags": [
"HUGGINGFACE PAPERS",
"↑1"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-19 16:00",
"debrief_path": "debriefs/debrief-2026-05-19-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-19 16:00",
"debrief_path": "debriefs/debrief-2026-05-19-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-19T16:00:00Z"
},
{
"id": "item_02260",
"rank": 440,
"score": 700,
"rationale": "HyLo upcycling Transformer into hybrid long-context model; practical scaling advance.",
"title": "SleepWalk: A Three-Tier Benchmark for Stress-Testing Instruction-Guided Vision-Language Navigation",
"url": "https://huggingface.co/papers/2605.10376",
"authors": "",
"description": "Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliably ground language into spatially coherent, plausibly executable actions in 3D digital environments. We introduce SleepWalk, a benchmark for evaluating instruction-grounded trajectory prediction in single-scene 3D worlds generated from textual scene descriptions and filtered for navigability. Unlike prior navigation benchmarks centered on long-range exploration across rooms, SleepWalk targets localized, interaction-centric embodied reasoning: given rendered visual observations and a natural-language instruction, a model must predict a trajectory that respects scene geometry, avoids collisions, and terminates at an action-compatible location. The benchmark covers diverse indoor and outdoor environments and organizes tasks into three tiers of spatial and temporal difficulty, enabling fine-grained analysis of grounding under increasing compositional complexity. Using a standardized pointwise judge-based evaluation protocol, we evaluate three frontier VLMs on 2,472 curated 3D environments with nine instructions per scene. Results reveal systematic failures in grounded spatial reasoning, especially under occlusion, interaction constraints, and multi-step instructions: performance drops as the difficulty level of the tasks increase. In general, current VLMs can somewhat produce trajectories that are simultaneously spatially coherent,",
"summary": "Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliably ground language into…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_02330",
"rank": 441,
"score": 700,
"rationale": "TurboQuant: provably near-optimal 2–4 bit KV/embedding compression with no calibration—broad impact.",
"title": "(https://ittech-pulse.com/news/grego-ai-prevents-27-7million-attack-with-ai-reasoning-breakthrough/)",
"url": "https://ittech-pulse.com/news/grego-ai-prevents-27-7million-attack-with-ai-reasoning-breakthrough/",
"authors": "",
"description": "Discussion on hackernews about: (https://ittech-pulse.com/news/grego-ai-prevents-27-7million-attack-with-ai-reasoning-breakthrough/)",
"summary": "Discussion on hackernews about: (https://ittech-pulse.com/news/grego-ai-prevents-27-7million-attack-with-ai-reasoning-breakthrough/)",
"tags": [
"hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "💬 Hacker News",
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-13T11:00:00Z"
},
{
"id": "item_02487",
"rank": 442,
"score": 700,
"rationale": "Unified benchmark for interactive video world models fills a clear evaluation gap.",
"title": "TextLDM: Language Modeling with Continuous Latent Diffusion",
"url": "https://huggingface.co/papers/2605.07748",
"authors": "HuggingFace",
"description": "Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next step toward a single architecture for both generation (visual synthesis) and understanding (text generation) is to apply this framework to language modeling. We propose TextLDM, which transfers the visual latent diffusion recipe to text generation with minimal architectural modification. A Transformer-based VAE maps discrete tokens to continuous latents, enhanced by Representation Alignment (REPA) with a frozen pretrained language model to produce representations effective for conditional denoising. A standard DiT then performs flow matching in this latent space, identical in architecture to its visual counterpart. The central challenge we address is obtaining high-quality continuous text representations: we find that reconstruction fidelity alone is insufficient, and that aligning latent features with a pretrained language model via REPA is critical for downstream generation quality. Trained from scratch on OpenWebText2, TextLDM substantially outperforms prior diffusion language models and matches GPT-2 under the same settings. Our results establish that the visual DiT recipe transfers effectively to language, taking a concrete step toward unified diffusion architectures for multimodal generation and understanding.",
"summary": "Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next step…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🧠 LLMs",
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-11 04:00",
"debrief_path": "debriefs/debrief-2026-05-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-11T04:00:00Z"
},
{
"id": "item_02531",
"rank": 443,
"score": 700,
"rationale": "Convergent number representations across models is a mechanistic-interpretability finding with broad implications.",
"title": "The React2Shell Story and What Happened Next.js",
"url": "https://sylvie.fyi/posts/react2shell/",
"authors": "LOBSTERS",
"description": "How we discovered CVE-2025-55182 and what happened in the aftermath",
"summary": "How we discovered CVE-2025-55182 and what happened in the aftermath",
"tags": [
"LOBSTERS"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "📌 Lobste.rs",
"debrief_date": "2026-05-09 11:00",
"debrief_path": "debriefs/debrief-2026-05-09-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-09 11:00",
"debrief_path": "debriefs/debrief-2026-05-09-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-09T11:00:00Z"
},
{
"id": "item_02572",
"rank": 444,
"score": 700,
"rationale": "RTV scaling test-time compute for agentic coding with 6.7-point SWE-Bench jump is high impact.",
"title": "What We Lost the Last Time Code Got Cheap",
"url": "https://www.poppastring.com/blog/what-we-lost-the-last-time-code-got-cheap",
"authors": "Lobste.rs",
"description": "I worked at a startup in Toledo called Heartland Information Services. We ...",
"summary": "I worked at a startup in Toledo called Heartland Information Services. We ...",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🔮 Lobste.rs",
"debrief_date": "2026-05-08 19:00",
"debrief_path": "debriefs/debrief-2026-05-08-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 19:00",
"debrief_path": "debriefs/debrief-2026-05-08-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T19:00:00Z"
},
{
"id": "item_02591",
"rank": 445,
"score": 700,
"rationale": "Stable Firefox identifier breaking Tor privacy is a real cross-browser security/privacy finding.",
"title": "KernelBench-X: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels",
"url": "https://huggingface.co/papers/2605.04956",
"authors": "",
"description": "LLM-based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We present KernelBench-X, a benchmark designed to answer this question through category-aware evaluation of correctness and hardware efficiency across 176 tasks in 15 categories. Our systematic comparison of five representative methods yields three main findings. First, task structure determines correctness more than method design. Category explains nearly three times more variance in semantic correctness than method (9.4% vs 3.3% explained deviance), and 72% of Fusion tasks fail across all five methods while Math tasks are solved consistently. Second, iterative refinement improves correctness, but not performance. Across GEAK iterations, compile rate rises from 52.3% to 68.8% while average speedup declines from 1.58times to 1.44times; newly rescued kernels consistently underperform persistently correct ones (1.16times vs 1.58times speedup in round~0to1). Third, correctness does not imply efficiency. 46.6% of correct kernels are slower than the PyTorch eager baseline, and cross-hardware speedup variance reaches 21.4times. Besides, quantization remains completely unsolved (0/30 successes) despite non-trivial compilation rates, revealing systematic misunderstanding of numerical computation contracts rather than surface-level syntax errors. These findings suggest that future progress depends on handling gl",
"summary": "LLM-based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We present KernelBench-X…",
"tags": [
"arXiv",
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-08 11:00",
"debrief_path": "debriefs/debrief-2026-05-08-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-08T11:00:00Z"
},
{
"id": "item_02711",
"rank": 446,
"score": 700,
"rationale": "Identifies scaling law of miscalibration in OPD with formal theory and fix.",
"title": "How to pick your football team",
"url": "https://arxiv.org/abs/2605.04034",
"authors": "Bhargav Narayanan",
"description": "Team captains Alice and Bob divide up $2m$ footballers, each reduced to a real-valued score, into two teams of $m$ footballers each. On each turn, one captain plays picker, and the other chooser: the picker names a footballer yet to be selected, and the chooser decides which captain's team receives that footballer. Alice starts as picker, Bob as chooser, and roles alternate. The game ends as soon as either captain has a full team of $m$ footballers, at which point the other team receives all rem",
"summary": "Team captains Alice and Bob divide up $2m$ footballers, each reduced to a real-valued score, into two teams of $m$ footballers each. On each turn, one captain plays picker, and the other chooser: the picker…",
"tags": [
"math.CO"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "🛡️ AI Safety",
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-06 11:00",
"debrief_path": "debriefs/debrief-2026-05-06-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-06T11:00:00Z"
},
{
"id": "item_02792",
"rank": 447,
"score": 700,
"rationale": "Deployed 30B multi-agent deep-research system with strong benchmark results; notable production contribution.",
"title": "Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction",
"url": "https://huggingface.co/papers/2604.27221",
"authors": "via HuggingFace",
"description": "Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current systems struggle on both fronts. Breadth-oriented tasks demand schema-aligned outputs with wide coverage and cross-entity consistency, while depth-oriented tasks require coherent reasoning over long, branching search trajectories. We introduce Web2BigTable, a multi-agent framework for web-to-table search that supports both regimes. Web2BigTable adopts a bi-level architecture in which an upper-level orchestrator decomposes the task into sub-problems and lower-level worker agents solve them in parallel. Through a closed-loop run--verify--reflect process, the framework jointly improves decomposition and execution over time via persistent, human-readable external memory, with self-evolving updates to each single-agent. During execution, workers coordinate through a shared workspace that makes partial findings visible, allowing them to reduce redundant exploration, reconcile conflicting evidence, and adapt to emerging coverage gaps. Web2BigTable sets a new state of the art on WideSearch, reaching an Avg@4 Success Rate of 38.50 (7.5times the second best at 5.10), Row F1 of 63.53 (+25.03 over the second best), and Item F1 of 80.12 (+14.42 over the second best). It also generalises to depth-oriented search on XBench-DeepSearch, achieving 73.0 accuracy. Code is available at https://github.com/web2bigtable/we",
"summary": "Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current systems struggle on both fronts. Breadth-oriented…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-05-04 11:00",
"debrief_path": "debriefs/debrief-2026-05-04-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 11:00",
"debrief_path": "debriefs/debrief-2026-05-04-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T11:00:00Z"
},
{
"id": "item_02803",
"rank": 448,
"score": 700,
"rationale": "Memory transfer learning across coding domains with strong empirical gains; high relevance to agents.",
"title": "Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values",
"url": "https://arxiv.org/abs/2605.00762",
"authors": "Shradha Sharma, Swapnil Dhamal, Shweta Jain",
"description": "We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly more challenging. To compute arm contributions in BCMAB-FBF, we first extend the Shapley value, a classical solution concept from cooperative game theory, to the $K$-Shapley value, which captures the marginal contribution o",
"summary": "We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received i…",
"tags": [
"cs.LG",
"cs.AI",
"cs.MA"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_02856",
"rank": 449,
"score": 700,
"rationale": "Verifiable gradient inversion for tabular FL; important privacy attack advancement.",
"title": "End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer",
"url": "https://huggingface.co/papers/2605.00503",
"authors": "",
"description": "Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipeline that jointly optimizes reconstruction and generation, enabling direct supervision from generation results to the tokenizer. This contrasts with prior two-stage approaches that train tokenizers and generative models separately. We further investigate leveraging vision foundation models to improve 1D tokenizers for autoregressive modeling. Our autoregressive generative model achieves strong empirical results, including a state-of-the-art FID score of 1.48 without guidance on ImageNet 256x256 generation.",
"summary": "Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipeline that jointly optimizes reconstruction and ge…",
"tags": [
"Huggingface_Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-04 04:00",
"debrief_path": "debriefs/debrief-2026-05-04-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-04T04:00:00Z"
},
{
"id": "item_02874",
"rank": 450,
"score": 700,
"rationale": "Real HTTP-desync vuln in Discord media proxy; notable platform-scale security finding.",
"title": "Sequential Inference for Gaussian Processes: A Signal Processing Perspective",
"url": "https://arxiv.org/abs/2604.28163",
"authors": "Daniel Waxman, Fernando Llorente, Petar M. Djurić",
"description": "The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive accuracy. Adapting these models often requires sequential inference, which differs both theoretically and methodologically from the usual paradigm of ML, where data are often assumed independent and ident",
"summary": "The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that…",
"tags": [
"eess.SP",
"cs.LG",
"stat.CO",
"stat.ML"
],
"source": "ArXiv",
"type": "paper",
"section": "📰 News Headlines",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-03 09:00",
"debrief_path": "debriefs/debrief-2026-05-03-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-03 09:00",
"debrief_path": "debriefs/debrief-2026-05-03-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-03T09:00:00Z"
},
{
"id": "item_02955",
"rank": 451,
"score": 700,
"rationale": "Pluralistic evaluation gap in AI watermarking across modalities/groups; strong AI safety implications.",
"title": "Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductors Discovery [None]",
"url": "https://huggingface.co/papers/2604.23758",
"authors": "",
"description": "The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this landscape, existing predictive or generative models typically operate in isolation, lacking the autonomous orchestration required to execute the full discovery process. Here we present ElementsClaw, an agentic framework for materials discovery that synergizes Large Atomic Models (LAMs) with Large Language Models (LLMs). In response to varied human queries, ElementsClaw orchestrates a suite of LAM tools finetuned from our proposed 1-billion-parameter model Elements for atomic-scale numerical computation, while leveraging LLMs for high-level semantic reasoning. This shift moves AI-driven materials science from isolated processes toward integrated and human interactive discovery. Applied to superconductors, ElementsClaw screens 2.4 million crystals in just 28 GPU hours to identify 68,000 high-confidence candidates (The complete dataset of screened superconductors is available at https://developer.damo-academy.com/material), expanding known superconducting space by orders of magnitude compared to datasets curated over decades. Critically, ElementsClaw achieves a high success rate in identifying superconductors hidden in literature and discovers four novel experimentally verified superconductors, exemplified by Zr3ScRe8 with a transition temperature of 6.8 K and HfZrRe4 at 6.7 K. Together, our results establish a knowledge integrate",
"summary": "The discovery of novel materials is critical for global energy and quantum technology transitions…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "📄 Other Research",
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-01 11:00",
"debrief_path": "debriefs/debrief-2026-05-01-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-01T11:00:00Z"
},
{
"id": "item_03052",
"rank": 452,
"score": 700,
"rationale": "Quantum speedups for non-convex ECD optimization; strong theory with ML optimization impact.",
"title": "DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios",
"url": "https://huggingface.co/papers/2604.25914",
"authors": "Jinxiang Meng, Shaoping Huang, Fangyu Lei, Jingyu Guo, Haoxiang Liu",
"description": "Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at https://github.com/DA-Open/DV-World{this project page}.",
"summary": "Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox…",
"tags": [
"huggingface"
],
"source": "HuggingFace",
"type": "paper",
"section": "📰 News Headlines",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-29 09:00",
"debrief_path": "debriefs/debrief-2026-04-29-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-29T09:00:00Z"
},
{
"id": "item_03255",
"rank": 453,
"score": 700,
"rationale": "Credit assignment across reasoning and agentic RL is timely and central to current post-training research.",
"title": "Efficient Classical Simulation of Heuristic Peaked Quantum Circuits",
"url": "https://arxiv.org/abs/2604.21908",
"authors": "David Kremer, Nicolas Dupuis",
"description": "Peaked quantum circuits, whose output distribution is sharply concentrated on a single bitstring, have emerged as a promising candidate for verifiable quantum advantage, as the correctness of the quantum output can be checked by simply comparing against the known peak. Recent work by Gharibyan et al. arXiv:2510.25838 claimed heuristic quantum advantage using peaked circuits executed on Quantinuum's 56-qubit H2 processor. These peaked circuits concentrate their output on a single hidden bitstring",
"summary": "Peaked quantum circuits, whose output distribution is sharply concentrated on a single bitstring, have emerged as a promising candidate for verifiable quantum advantage, as the correctness of the quan…",
"tags": [
"quant-ph"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-25 04:00",
"debrief_path": "debriefs/debrief-2026-04-25-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-25T04:00:00Z"
},
{
"id": "item_03432",
"rank": 454,
"score": 700,
"rationale": "RISC-V hardware CFI extensions paper: solid security architecture contribution.",
"title": "What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search",
"url": "https://huggingface.co/papers/2604.19440",
"authors": "",
"description": "Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across 8 tasks. Although zero-shot problem-solving ability correlates with final optimization outcomes, it explains only part of the variance: models with similar initial capability often induce dramatically different search trajectories and outcomes. By analyzing these trajectories, we find that strong LLM optimizers behave as local refiners, producing frequent incremental improvements while progressively localizing the search in semantic space. Conversely, weaker optimizers exhibit large semantic drift, with sporadic breakthroughs followed by stagnation. Notably, various measures of solution novelty do not predict final performance; novelty is beneficial only when the search remains sufficiently localized around high-performing regions of the solution space. Our results highlight the importance of trajectory analysis for understanding and improving LLM-based optimization systems and provide actionable insights for their design and training.",
"summary": "Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "📄 HuggingFace Papers",
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T11:00:00Z"
},
{
"id": "item_03623",
"rank": 455,
"score": 700,
"rationale": "Geometric stability predicts steerability and drift; strong dual-use interpretability result.",
"title": "The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability",
"url": "https://huggingface.co/papers/2604.17698",
"authors": "HuggingFace",
"description": "Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behavioral control, and detecting when its internal structure degrades. We show that geometric stability, the consistency of a representation's pairwise distance structure, addresses both. Supervised Shesha variants that measure task-aligned geometric stability predict linear steerability with near-perfect accuracy (ρ= 0.89-0.97) across 35-69 embedding models and three NLP tasks, capturing unique variance beyond class separability (partial ρ= 0.62-0.76). A critical dissociation emerges: unsupervised stability fails entirely for steering on real-world tasks (ρapprox 0.10), revealing that task alignment is essential for controllability prediction. However, unsupervised stability excels at drift detection, measuring nearly 2times greater geometric change than CKA during post-training alignment (up to 5.23times in Llama) while providing earlier warning in 73\\% of models and maintaining a 6times lower false alarm rate than Procrustes. Together, supervised and unsupervised stability form complementary diagnostics for the LLM deployment lifecycle: one for pre-deployment controllability assessment, the other for post-deployment monitoring.",
"summary": "Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behavioral control, and detecting when its in…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03837",
"rank": 456,
"score": 700,
"rationale": "LASA semantic-bottleneck multilingual LLM safety; 24.7%->2.8% ASR drop is strong, novel mechanistic finding.",
"title": "LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety",
"url": "https://huggingface.co/papers/2604.12710",
"authors": "Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang",
"description": "Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We attribute this gap to a mismatch between language-agnostic semantic understanding ability and language-dominant safety alignment biased toward high-resource languages. Consistent with this hypothesis, we empirically identify the semantic bottleneck in LLMs, an intermediate layer in which the geometry of model representations is governed primarily by shared semantic content rather than language identity. Building on this observation, we propose Language-Agnostic Semantic Alignment (LASA), which anchors safety alignment directly in semantic bottlenecks. Experiments show that LASA substantially improves safety across all languages: average attack success rate (ASR) drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains around 3-4% across Qwen2.5 and Qwen3 Instruct models (7B-32B). Together, our analysis and method offer a representation-level perspective on LLM safety, suggesting that safety alignment requires anchoring safety understanding not in surface text, but in the model's language-agnostic semantic space.",
"summary": "Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages …",
"tags": [
"HuggingFace",
"daily curated papers",
"2026-04-13"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-15 11:00",
"debrief_path": "debriefs/debrief-2026-04-15-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 11:00",
"debrief_path": "debriefs/debrief-2026-04-15-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T11:00:00Z"
},
{
"id": "item_03919",
"rank": 457,
"score": 700,
"rationale": "Many-tier instruction hierarchy is a timely safety/agent contribution with a new benchmark.",
"title": "Many-Tier Instruction Hierarchy in LLM Agents",
"url": "https://huggingface.co/papers/2604.09443",
"authors": "HuggingFace • daily curated papers • 2026-04-10",
"description": "Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, and more-each carrying different levels of trust and authority. When these instructions conflict, models must reliably follow the highest-privilege instruction to remain safe and effective. The dominant paradigm, instruction hierarchy (IH), assumes a fixed, small set of privilege levels (typically fewer than five) defined by rigid role labels (e.g., system > user). This is inadequate for real-world agentic settings, where conflicts can arise across far more sources and contexts. In this work, we propose Many-Tier Instruction Hierarchy (ManyIH), a paradigm for resolving instruction conflicts among instructions with arbitrarily many privilege levels. We introduce ManyIH-Bench, the first benchmark for ManyIH. ManyIH-Bench requires models to navigate up to 12 levels of conflicting instructions with varying privileges, comprising 853 agentic tasks (427 coding and 426 instruction-following). ManyIH-Bench composes constraints developed by LLMs and verified by humans to create realistic and difficult test cases spanning 46 real-world agents. Our experiments show that even the current frontier models perform poorly (~40% accuracy) when instruction conflict scales. This work underscores the urgent need for methods that explicitly target fine-grained, scalable instruction conflict resolution in agentic settings.",
"summary": "Large language model agents receive instructions from many sources-system messages, user prompts, tool outputs, and more-each carrying different levels of trust and authority. When these instructions conflict, models must reliably follow the highest-privilege instruction to…",
"tags": [
"HuggingFace",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_03851",
"rank": 458,
"score": 700,
"rationale": "Lightning OPD identifies teacher-consistency; actionable post-training insight, broad impact.",
"title": "Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation",
"url": "https://arxiv.org/abs/2604.13010",
"authors": "Yecheng Wu, Song Han, Hai Cai",
"description": "On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teacher inference server throughout training, resulting in substantial infrastructure overhead. In this work, we investigate whether on-policy distillation can be performed offline. A natural approach is to precompute teacher log-probabilities once over SFT rollouts and reuse them during training. In practice, however, this offline variant fails to reliably match the performance of standard OPD. To understand this discrepancy, we identify a previously overlooked condition that is critical for any OPD pipeline, which we term teacher consistency. This condition requires that the same teacher model be used for both supervised fine-tuning and OPD. We show that violating teacher consistency introduces an irreducible gradient bias, causing both offline and online OPD to converge to a suboptimal fixed point regardless of training duration. Building on this insight, we propose Lightning OPD, an offline on-policy distillation framework that enforces teacher consistency by precomputing teacher log-probabilities over SFT rollouts. This design eliminates the need for a live teacher server entirely. We further show that, under teacher consistency, Lightning OPD shares the same optimum as standard OPD, with bounded gradient discrepancy and an implicit regularization effect that helps prevent policy drift. Extensive experiments on mathematical reasoning and code generation demonstrate that Lightning OPD achieves state-of-the-art performance with significantly improved efficiency. Starting from an SFT-initialized Qwen3-8B-Base model, Lightning OPD reaches 69.9% on AIME 2024 in just 30 GPU hours, achieving a 4.0x speedup over standard OPD and substantially lowering the barrier to entry for academic research on LLM post-training.",
"summary": "On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teacher inference server throughout training, resulting in substantial infrastructure overhead. It matters because reliable long-horizon agents depend on better grounding…",
"tags": [
"cs.LG",
"cs.AI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-15 04:00",
"debrief_path": "debriefs/debrief-2026-04-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-15T04:00:00Z"
},
{
"id": "item_04003",
"rank": 459,
"score": 700,
"rationale": "Introspective diffusion LMs with parallel decoding + AR-style consistency; strong contribution to DLMs.",
"title": "Introspective Diffusion Language Models",
"url": "https://huggingface.co/papers/2604.11035",
"authors": "Yifan Yu, Yuqing Jian, Junxiong Wang, Zhongzhu Zhou, et al.",
"description": "Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of introspective consistency: AR models agree with their own generations, while DLMs often do not. We define the introspective acceptance rate, which measures whether a model accepts its previously generated tokens. This reveals why AR training has a structural advantage: causal masking and logit shifting implicitly enforce introspective consistency. Motivated by this observation, we introduce Introspective Diffusion Language Model (I-DLM), a paradigm that retains diffusion-style parallel decoding while inheriting the introspective consistency of AR training. I-DLM uses a novel introspective strided decoding (ISD) algorithm, which enables the model to verify previously generated tokens while advancing new ones in the same forward pass. From a systems standpoint, we build I-DLM inference engine on AR-inherited optimizations and further customize it with a stationary-batch scheduler. To the best of our knowledge, I-DLM is the first DLM to match the quality of its same-scale AR counterpart while outperforming prior DLMs in both model quality and practical serving efficiency across 15 benchmarks. It reaches 69.6 on AIME-24 and 45.7 on LiveCodeBench-v6, exceeding LLaDA-2.1-mini (16B) by more than 26 and 15 points, respectively. Beyond quality, I-DLM is designed for the growing demand of large-concurrency serving, delivering about 3x highe",
"summary": "Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of introspective consistency: AR models agree with their own generations, while DLMs often do not.",
"tags": [
"🤗 HuggingFace",
"daily curated papers",
"2026-04-13"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04111",
"rank": 460,
"score": 700,
"rationale": "Deep dive into LLM post-training for reasoning; strong synthesis of a high-impact subfield.",
"title": "Llm post-training: A deep dive into reasoning large language models",
"url": "https://arxiv.org/abs/2502.21321",
"authors": "",
"description": "Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the research community is now increasingly shifting focus toward post-training techniques to achieve further breakthroughs. While pretraining provides a broad linguistic foundation, post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations. Fine-tuning, reinforcement l",
"summary": "Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the…",
"tags": [
"Google Scholar"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "Google Scholar",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_04095",
"rank": 461,
"score": 700,
"rationale": "Open-weight LG vision-language model, 256K context, document-strong; notable open release.",
"title": "EXAONE 4.5 Technical Report",
"url": "https://huggingface.co/papers/2604.08644",
"authors": "",
"description": "This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal pretraining over both visual and textual modalities. The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora that align with LG's strategic application domains. This targeted data design enables substantial performance gains in document understanding and related tasks, while also delivering broad improvements across general language capabilities. EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases. Comparative evaluations demonstrate that EXAONE 4.5 achieves competitive performance in general benchmarks while outperforming state-of-the-art models of similar scale in document understanding and Korean contextual reasoning. As part of LG's ongoing effort toward practical industrial deployment, EXAONE 4.5 is designed to be continuously extended with additional domains and application scenarios to advance AI for a better life.",
"summary": "This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace",
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-13 04:00",
"debrief_path": "debriefs/debrief-2026-04-13-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-13T04:00:00Z"
},
{
"id": "item_04378",
"rank": 462,
"score": 700,
"rationale": "Aggressive parallel decoding for dLLMs; notable inference speedup technique.",
"title": "DMax: Aggressive Parallel Decoding for dLLMs",
"url": "https://huggingface.co/papers/2604.08302",
"authors": "",
"description": "arXiv:2604.08302 | Authors: Zigeng Chen, Gongfan Fang, Xinyin Ma, Ruonan Yu, Xinchao Wang | Upvotes: 35",
"summary": "arXiv:2604.08302 | Authors: Zigeng Chen, Gongfan Fang, Xinyin Ma, Ruonan Yu, Xinchao Wang | Upvotes: 35",
"tags": [
"Huggingface Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_04339",
"rank": 463,
"score": 700,
"rationale": "Medium companion to ZK-quantum post; same core finding.",
"title": "(https://medium.com/@jkim_tran/verifying-private-quantum-circuits-9ec99d5f3d5a)",
"url": "https://medium.com/@jkim_tran/verifying-private-quantum-circuits-9ec99d5f3d5a",
"authors": "",
"description": "Article about: (https://medium.com/@jkim_tran/verifying-private-quantum-circuits-9ec99d5f3d5a)",
"summary": "Article about: (https://medium.com/@jkim_tran/verifying-private-quantum-circuits-9ec99d5f3d5a)",
"tags": [
"Hackernews"
],
"source": "Hacker News",
"type": "news",
"section": "🌐 Web Findings",
"group": "🟠 Hacker News",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
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}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00674",
"rank": 464,
"score": 694,
"rationale": "Unified scale-invariant diffusion is technically elegant with strong CIFAR/ImageNet results.",
"title": "MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training",
"url": "https://huggingface.co/papers/2606.08788",
"authors": "",
"description": "Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion features with clean-image representations from self-supervised vision encoders, existing methods improve convergence and generation quality. However, such alignment also introduces a non-trivial constraint: diffusion models operate on noisy inputs whose usable information varies across timesteps, while the reference features are extracted from clean images. In this paper, we revisit this mismatch from a token-level perspective. We find that, under full-token representation alignment, tokens with large alignment-gradient norms exhibit a stable spatial preference, suggesting that the alignment objective does not affect all tokens uniformly and may encourage the model to rely on the complete set of clean-image tokens. To address this issue, we propose MaskAlign, a token-subset representation alignment method that applies alignment to randomly sampled token subsets during training. By exposing the model to different token subsets across iterations, MaskAlign reduces the dependence of representation alignment on the complete token set and encourages alignment behavior that is more stable under token-subset perturbations. To mitigate the information loss caused by directly dropping tokens, we further introduce a lightweight pre-mask token mixing block that shares information across tokens before masking.",
"summary": "Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning intermediate diffusion…",
"tags": [
"▲ 2",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00711",
"rank": 465,
"score": 694,
"rationale": "Spec autoformalization benchmark for Verus is valuable for code-agent verification research.",
"title": "mattermost/mattermost",
"url": "https://github.com/mattermost/mattermost",
"authors": "",
"description": "Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..",
"summary": "Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..",
"tags": [
"⭐ 37.2k",
"GitHub Trending"
],
"source": "💻 GitHub Trending",
"type": "news",
"section": "🌐 Web Findings",
"group": "💻 GitHub Trending",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_03717",
"rank": 466,
"score": 694,
"rationale": "AIMO 3 negative result on inference-time optimization is high-signal empirical finding.",
"title": "Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3",
"url": "https://huggingface.co/papers/2603.27844",
"authors": "",
"description": "Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is to assign different reasoning strategies to different voters. The approach, Diverse Prompt Mixer, is tested on the AIMO 3 competition: 3 models, 23+ experiments, 50 IMO-level problems, one H100 80 GB, 5-hour limit. Every prompt-level intervention fails. High-temperature sampling already decorrelates errors; weaker strategies reduce accuracy more than they reduce correlation. Across an 8-point capability gap at equal N=8 and every optimization tested, model capability dominates. The gap between the best majority-vote score (42/50) and pass@20 (~45.5) is selection loss, not prompt loss. A verifier-based selector could close it. Prompt engineering cannot.",
"summary": "Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is to assign different reasoning strategies to…",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🌐 HuggingFace",
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-17 11:00",
"debrief_path": "debriefs/debrief-2026-04-17-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-17T11:00:00Z"
},
{
"id": "item_04153",
"rank": 467,
"score": 692,
"rationale": "LLM ad conflict-of-interest framing is timely, important for AI safety and economic alignment.",
"title": "Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest",
"url": "https://arxiv.org/abs/2604.08525",
"authors": "Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, Thomas L. Griffiths",
"description": "Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements. This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company's incentives. For instance, a sponsored product may be more expensive but otherwise equal to another; in this case, what does (and should) the LLM recommend to the user? In this paper, we provide a framework for categorizing the ways in which conflicting incentives might lead LLMs to change the way they interact with users, inspired by literature from linguistics and advertising regulation. We then present a suite of evaluations to examine how current models handle these tradeoffs. We find that a majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations, including recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%). Behaviors also vary strongly with levels of reasoning and users' inferred socio-economic status. Our results highlight some of the hidden risks to users that can emerge when companies begin to subtly incentivize advertisements in chatbots.",
"summary": "Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning.",
"tags": [
"cs.AI",
"cs.AI, cs.CL, cs.CY",
"cs.CL",
"cs.CY"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 LLMs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 4,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
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{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
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"debrief_date": "2026-04-11 04:00",
"debrief_path": "debriefs/debrief-2026-04-11-04.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00699",
"rank": 468,
"score": 692,
"rationale": "PEFT stability-plasticity benchmark with spectral/activation analyses is well-motivated and rigorous.",
"title": "Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code",
"url": "https://huggingface.co/papers/2606.11817",
"authors": "",
"description": "Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs.\n To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fun",
"summary": "Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained…",
"tags": [
"▲ 16",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 11:00",
"debrief_path": "debriefs/debrief-2026-06-11-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T11:00:00Z"
},
{
"id": "item_02113",
"rank": 469,
"score": 691,
"rationale": "Live, refreshable agent benchmark with execution-trace grading; strong methodology for agent eval.",
"title": "Articraft: An Agentic System for Scalable Articulated 3D Asset Generation",
"url": "https://arxiv.org/abs/2605.15187",
"authors": "Matt Zhou, Ruining Li, Xiaoyang Lyu, Zhaomou Song, Zhening Huang, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi, Shangzhe Wu",
"description": "A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close this gap and generate articulated assets at scale. We reduce the problem of generating an articulated 3D asset to that of writing a program that builds it. We then introduce a new agentic system, Articraft, that writes such programs automatically. We design a programmatic interface and harness to help the LLM do so ef",
"summary": "A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close this gap and generate articulated…",
"tags": [
"cs.CV",
"cs.GR",
"cs.RO"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 AI Agents & Reasoning",
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-16 04:00",
"debrief_path": "debriefs/debrief-2026-05-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-16T04:00:00Z"
},
{
"id": "item_04349",
"rank": 470,
"score": 690,
"rationale": "Tempo SVLM video compression; addresses long-video token bottleneck well.",
"title": "Small Vision-Language Models are Smart Compressors for Long Video Understanding",
"url": "https://huggingface.co/papers/2604.08120",
"authors": "",
"description": "Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the-middle phenomenon. Existing heuristics, like sparse sampling or uniform pooling, blindly sacrifice fidelity by discarding decisive moments and wasting bandwidth on irrelevant backgrounds. We propose Tempo, an efficient query-aware framework compressing long videos for downstream understanding. Tempo leverages a Small Vision-Language Model (SVLM) as a local temporal compressor, casting token reduction as an early cross-modal distillation process to generate compact, intent-aligned representations in a single forward pass. To enforce strict budgets without breaking causality, we introduce Adaptive Token Allocation (ATA). Exploiting the SVLM's zero-shot relevance prior and semantic front-loading, ATA acts as a training-free O(1) dynamic router. It allocates dense bandwidth to query-critical segments while compressing redundancies into minimal temporal anchors to maintain the global storyline. Extensive experiments show our 6B architecture achieves state-of-the-art performance with aggressive dynamic compression (0.5-16 tokens/frame). On the extreme-long LVBench (4101s), Tempo scores 52.3 under a strict 8K visual budget, outperforming GPT-4o and Gemini 1.5 Pro. Scaling to 2048 frames reaches 53.7. Crucially, Tempo compresses hour-long videos substantially below theoretical limits, proving true long-form video understanding relies on intent-driven efficiency rather than greedily padded context windows.",
"summary": "arXiv:2604.08120 | Authors: Junjie Fei, Jun Chen, Zechun Liu, Yunyang Xiong, Chong Zhou | Upvotes: 9",
"tags": [
"Huggingface Papers",
"daily curated papers",
"huggingface_papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
"occurrence_count": 3,
"occurrences": [
{
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 11:00",
"debrief_path": "debriefs/debrief-2026-04-11-11.html",
"debrief_md": ""
},
{
"debrief_date": "2026-04-11 00:00",
"debrief_path": "debriefs/debrief-2026-04-11-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-11T19:00:00Z"
},
{
"id": "item_00021",
"rank": 471,
"score": 690,
"rationale": "Inference-time correction against IPI with latent-space probing; addresses over-refusal trade-off elegantly.",
"title": "ReSyn: A Generalized Recursive Regular Expression Synthesis Framework",
"url": "https://huggingface.co/papers/2603.24624",
"authors": "",
"description": "Existing Programming-By-Example (PBE) systems often rely on simplified benchmarks that fail to capture the high structural complexity of real-world regexes, such as deeper nesting and frequent use of union operations. To overcome the resulting performance drop, we propose ReSyn, a synthesizer-agnostic divide-and-conquer framework that decomposes complex synthesis problem into manageable sub-problems. We also introduce Set2Regex, a parameter-efficient synthesizer capturing the permutation invariance of examples. Experimental results demonstrate that ReSyn significantly boosts accuracy across various synthesizers, and its combination with Set2Regex establishes a new state-of-the-art on challenging real-world benchmark. The complete source code, datasets, and pre-trained model checkpoints are publicly available at https://github.com/mrseongminkim/ReSyn.",
"summary": "Existing Programming-By-Example (PBE) systems often rely on simplified benchmarks that fail to capture the high structural complexity of real-world regexes, such as deeper nesting…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-19 11:00",
"debrief_path": "debriefs/debrief-2026-06-19-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-19T11:00:00Z"
},
{
"id": "item_00097",
"rank": 472,
"score": 690,
"rationale": "Feedback alignment in self-distillation; step-aligned critique beating GRPO is a meaningful result.",
"title": "Re-Centering Humans in LLM Personalization",
"url": "https://huggingface.co/papers/2606.06614",
"authors": "",
"description": "Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains unclear how well current personalization systems work for real users. In this paper, we study the gap in LLM personalization performance when using synthetic versus human data. We collect human conversations (550 conversations) and judgments across three stages of personalization: extracting user attributes from conversations (5,949 judgments), pairing relevant attributes with new prompts (11,919), and incorporating relevant attributes into a personalized response (1,101). Incorporating human data reveals system limitations at each stage. Models struggle to extract attributes from human conversations, disagree with human judgments on relevant attributes, and generate personalized responses that humans judge no better than generic responses (though that LLM judges widely rate as better). We introduce two lightweight training-based interventions that shift automated personalization evaluation closer to human data in our first two stages. However, in our third stage we find that learned reward models achieve only modest correlation with human ratings, suggesting that human-aligned personalization quality judgments are difficult to model directly. Our collected data provides a foundation for studying how models should extract, select, and incorporate user information in ways that humans find useful.",
"summary": "Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data. It remains unclear how well current…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_00109",
"rank": 473,
"score": 690,
"rationale": "Itô maps for any-step SDEs; useful primitive for posterior sampling and stochastic control.",
"title": "Latent space interpretation [R]",
"url": "https://www.reddit.com/r/MachineLearning/comments/1u9afup/latent_space_interpretation_r/",
"authors": "",
"description": "Hi all, I have trained a convolutional autoencoder on a set of medical images. Further classified latent feature maps using random forest to find the top scoring feature map. Now my goal is to understand which input image is captured in top scoring latent feature map. Any suggestions? I have tried encoding one image at a time while other images were muted. I then checked spearman between top scoring feature map with the original top scoring feature map. While I see some expected results, I still have some false positives. I have also tried decoding only top scoring latent feature map by settin",
"summary": "Hi all, I have trained a convolutional autoencoder on a set of medical images. Further classified latent feature maps using random forest to find the top scoring feature map. Now…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-18 19:00",
"debrief_path": "debriefs/debrief-2026-06-18-19.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-18T19:00:00Z"
},
{
"id": "item_00257",
"rank": 474,
"score": 690,
"rationale": "Trace tournaments for uninformative RLVR groups; strong contribution to reasoning RL.",
"title": "DRFLOW: A Deep Research Benchmark for Personalized Workflow Prediction",
"url": "https://arxiv.org/abs/2606.18191",
"authors": "Md Tawkat Islam Khondaker, Raymond Li, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Issam H. Laradji",
"description": "Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on generating reports and summaries. In contrast, many enterprise tasks instead require an agent to identify concrete workflows which is a sequence of action-steps. For example, rather than summarizing budgeting policies, an agent should be able to determine the steps needed to answer a question such as: \"How do I request new headcount given a fixed budget?\". Therefore, we introduce DRFLOW, a benchmark for evaluating personalized workflows predicted by agents from heterogeneous sources. Each task requires the agent to identify relevant evidence from scattered sources, then use that evidence to predict the correct action-step sequence for the user's task. DRFLOW contains 100 tasks across five domains, with 1,246 reference workflow steps grounded in more than 3,900 sources. We define seven diagnostic metrics covering factual grounding, step recovery, structural ordering, condition resolution, and personalization. We further present DRFLOW-Agent (DRFA), a workflow-oriented reference agent to predict personalized workflow. We show that although DRFA improves over strong baseline agents (upto 10.02% average F1 score), there is substantial room for improvement remains across these workflow metrics, indicating that predicting complete and correct personalized workflows remains a challenging frontier for deep research.",
"summary": "Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on generating reports and summaries. In contrast, many enterprise tasks instead…",
"tags": [
"cs.AI",
"cs.MA"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Drflow / Research / Benchmark",
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-17 04:00",
"debrief_path": "debriefs/debrief-2026-06-17-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-17T04:00:00Z"
},
{
"id": "item_00424",
"rank": 475,
"score": 690,
"rationale": "Source-aware rubric mid-training data selection addresses a real LLM pipeline gap.",
"title": "Why adding ontologies to LLMs won't yield machine intelligence",
"url": "https://youtu.be/Ce-cN5Llaz4?t=93",
"authors": "",
"description": "Why adding ontologies to LLMs won't yield machine intelligence",
"summary": "Finding from 🦞 Lobste.rs.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00415",
"rank": 476,
"score": 690,
"rationale": "AI-driven zero-days and trust infrastructure; timely strategic security essay.",
"title": "Tangram: Unlocking Non-Uniform KV Cache Compression for Efficient Multi-turn LLM Serving",
"url": "https://huggingface.co/papers/2606.06302",
"authors": "",
"description": "Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making memory -- not compute -- the binding constraint on throughput. Non-uniform KV compression, which allocates heterogeneous budgets across attention heads, preserves accuracy far better than uniform schemes, yet remains impractical: modern serving stacks assume identical KV lengths across heads, so heterogeneity traps freed memory as page fragmentation, spends up to 25% of prefill time reclaiming scattered pages, and skews GPU workloads that inflate decode latency by up to 1.7times or burn 15--20% of each decode step on re-planning. We observe that this heterogeneity need not be discovered at runtime: head-wise retention follows a two-level structural regularity -- an input-invariant head ranking with narrowly bounded per-head ratios -- that can be calibrated offline from as few as 50 samples. Building on this insight, we present Tangram, a serving framework that statically resolves what prior systems handle dynamically: Budget Reservation fixes each head's post-compression footprint at scheduling time, eliminating page reclamation; Ragged Paging clusters similar-budget heads into independent page tables, turning fragmentation into reclaimable memory; and Ahead-of-Time Load Balancing precomputes balanced GPU partitions with zero runtime planning. Implemented on vLLM, Tangram serves as a drop-in substrate fo",
"summary": "Multi-turn LLM serving accumulates dialogue history whose Key-Value (KV) cache grows with every turn and every user, quickly exceeding the model weights themselves and making…",
"tags": [
"▲ 7",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00380",
"rank": 477,
"score": 690,
"rationale": "Theory-grounded capacity allocation for diffusion; principled and broadly applicable.",
"title": "Robust and Automated Reconfiguration of Byzantine Wide-Area Replication",
"url": "https://arxiv.org/abs/2606.16740",
"authors": "Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse, Jérémie Decouchant",
"description": "Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine fault-tolerant state-machine replication (BFT-SMR) is the replicated service that totally orders client transactions before execution. While prior research has primarily focused on designing novel consensus algorithms with improved performance, recent studies have shown that further gains can be achieved through configuration optimization. More precisely, replicas can monitor network latency to dynamically assign the leader role and tune voting weights, thereby improving consensus performance. However, we identify three vulnerabilities in this process that Byzantine nodes can exploit. To address these weaknesses, we propose Beware, a reconfiguration framework that filters out falsified latency reports, computes robust weight distributions, and applies machine learning to converge towards Byzantine-resilient configurations. Our evaluation shows that Beware reduces consensus latency by up to 45% compared to existing solutions.",
"summary": "Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine fault-tolerant state-machine replication (BFT-SMR)…",
"tags": [
"cs.DC",
"cs.CR",
"cs.NI"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Robust / Automated / Reconfiguration",
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-16 04:00",
"debrief_path": "debriefs/debrief-2026-06-16-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-16T04:00:00Z"
},
{
"id": "item_00476",
"rank": 478,
"score": 690,
"rationale": "Unified neural scaling laws across many axes; foundational empirical/theoretical result.",
"title": "Abstracting Cross-Domain Action Sequences into Interpretable Workflows",
"url": "https://arxiv.org/abs/2606.14654",
"authors": "Gaurav Verma, Scott Counts",
"description": "Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work. Such insights are essential for improving digital products in ways grounded in real-world user interactions. Prior research has applied deep learning models to cluster user actions into high-level activities, but these approaches are highly sensitive to noise and struggle to generalize across applications. To address this limitation, we introduce WorkflowView, a framework that uses large language models (LLMs) to abstract low-level action sequences into high-level activities. We establish the effectiveness and generality of our approach across three distinct, challenging sequential tasks and diverse domains: (a) zero-shot task description reconstruction from browser logs (achieving high semantic similarity, $μ_{sim} = 0.91$), (b) few-shot student dropout prediction using MOOC interaction logs (reaching weighted $F_1 = 0.90$ with only five few-shot examples), and (c) anonymized, privacy-preserving analysis of AI tool integration within document workflows in Microsoft Word. Our work demonstrates that LLM-based abstraction is a robust and efficient path forward for transforming low-level behavioral data into high-level, interpretable, and actionable insights. We also discuss practical considerations for deploying LLM-based inferences within logging infrastructures, including computational efficie",
"summary": "Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work. Such insights…",
"tags": [
"cs.AI",
"cs.CL",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Abstracting / Cross-domain / Action",
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-15 04:00",
"debrief_path": "debriefs/debrief-2026-06-15-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-15T04:00:00Z"
},
{
"id": "item_00613",
"rank": 479,
"score": 690,
"rationale": "Confidence-aware KV-cache eviction with mixed precision is practical long-context contribution.",
"title": "InterleaveThinker: Reinforcing Agentic Interleaved Generation",
"url": "https://arxiv.org/abs/2606.13679",
"authors": "Dian Zheng, Harry Lee, Manyuan Zhang, Kaituo Feng, Zoey Guo, Ray Zhang, Hongsheng Li",
"description": "Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation. Even the latest open-source Unified Multimodal Models (UMMs) exhibit limited performance in this regard. In this paper, we introduce InterleaveThinker, the first multi-agent pipeline designed to endow any existing image generator with interleaved generation capabilities. Specifically, we employ a planner agent to organize the image-text input sequence, instructing the image generator on the required execution at each step. Subsequently, we introduce a critic agent to evaluate the generator's outputs, identify samples that deviate from the planned instructions, and refine the instructions for regeneration. To implement this pipeline, we construct the Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k to perform a format cold-start. Then we develop Interleave-Critic-RL-13k to reinforce the step-wise instruction correction capability within a generation trajectory using GRPO. Since a single interleaved generation trajectory may involve over 25 generator calls, optimizing the entire trajectory is computationally impractical. Therefore, we propose accuracy reward and step-wise reward, allowing single-step RL to effectively guide the e",
"summary": "Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they…",
"tags": [
"cs.CV"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Generation / World / Interleavethinker",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00658",
"rank": 480,
"score": 690,
"rationale": "Scalable parameterized local linear attention is a solid architectural contribution to pretraining.",
"title": "FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents",
"url": "https://huggingface.co/papers/2606.12087",
"authors": "",
"description": "Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods often increase apparent difficulty by enriching graph structures, but structural complexity alone does not guarantee realized search difficulty: the intended search process can collapse through a cheaper identifying route. We formalize this gap with a shortcut-aware difficulty framework and identify four actionable shortcut risks: evidence co-coverage, single-clue selectivity, exposed constants, and prior-knowledge binding. To diagnose their realized effects, we use trajectory signatures including solving cost, answer hit time, and prior-shortcut rate. Guided by this framework, we introduce FORT, a Framework of Shortcut-Resistant Training-Data Synthesis. FORT constructs shortcut-resistant training data by controlling shortcut risks across entity selection, evidence graph construction, question formulation, and adversarial refinement. Experiments show that FORT induces longer pre-answer search and fewer shortcut patterns than existing open-source deep search datasets. Using the resulting trajectories, we train FORT-Searcher with supervised fine-tuning (SFT) only, and it achieves the best overall performance among comparable-size open-source search agents on challenging deep search benchmarks. Relevant resources will be made available at https://github.com/RUCAIBox/FORT-Searcher.",
"summary": "Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. Existing synthesis methods…",
"tags": [
"▲ 45",
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-12 04:00",
"debrief_path": "debriefs/debrief-2026-06-12-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-12T04:00:00Z"
},
{
"id": "item_00806",
"rank": 481,
"score": 690,
"rationale": "Hidden critique ability in reasoning models + steering vector; mechanistic interpretability angle, solid.",
"title": "Lets Encrypt June ToS Update: no more for US sanctioned people",
"url": "https://letsencrypt.org/documents/LE-SA-v1.7-June-04-2026-diff.pdf",
"authors": "",
"description": "Lets Encrypt June ToS Update: no more for US sanctioned people",
"summary": "Finding from 🦞 Lobste.rs.",
"tags": [
"Lobste.rs"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-11 04:00",
"debrief_path": "debriefs/debrief-2026-06-11-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-11T04:00:00Z"
},
{
"id": "item_00924",
"rank": 482,
"score": 690,
"rationale": "System I/II/III decomposition with learned self-regulation is a strong conceptual frame for efficient agentic reasoning.",
"title": "Cohere released North Mini Code: It's first Open-Source Agentic Coding Model",
"url": "https://www.reddit.com/r/LocalLLaMA/comments/1u1za0m/cohere_released_north_mini_code_its_first/",
"authors": "",
"description": "Small: 30 billion parameters, 3B active. Efficient: Benchmarks to 33.4 on the Artificial Analysis Coding Index, competitive among similar sized models. Open Source: Apache 2.0 license HF: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 submitted by /u/beasthunterr69 [link] [comments]",
"summary": "Small: 30 billion parameters, 3B active. Efficient: Benchmarks to 33.4 on the Artificial Analysis Coding Index, competitive among similar sized models. Open Source: Apache 2.0…",
"tags": [
"Reddit"
],
"source": "👽 Reddit",
"type": "news",
"section": "🌐 Web Findings",
"group": "👽 Reddit",
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 08:00",
"debrief_path": "debriefs/debrief-2026-06-10-08.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T08:00:00Z"
},
{
"id": "item_00987",
"rank": 483,
"score": 690,
"rationale": "Large expert study of AI peer-review limits; timely meta-research with concrete evidence.",
"title": "Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization",
"url": "https://huggingface.co/papers/2606.11180",
"authors": "",
"description": "Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them impractical for real-time inference. We present Lip Forcing, to our knowledge the first autoregressive diffusion method for video-to-video (V2V) lip synchronization, which distills a 14B audio-conditioned bidirectional video diffusion teacher into causal students. At inference, the students generate each chunk in only two denoising steps without inference-time CFG, enabling real-time lip synchronization. A lip-sync-specific teacher-trajectory analysis reveals a CFG fidelity-sync tradeoff: no-CFG predictions favor reference fidelity, whereas CFG-guided predictions favor synchronization within a mid-trajectory band. Lip Forcing translates this finding into three analysis-derived components: Sync-Window DMD, a two-step inference schedule, and a SyncNet-based reward. We validate Lip Forcing at two student scales, both distilled from the 14B teacher. The 1.3B student crosses into real-time streaming at 31 FPS, 17.6times faster than its same-scale bidirectional model. The 14B student, the largest diffusion model reported for V2V lip synchronization, runs 39.8times faster than its teacher at comparable reference fidelity. Time-to-first-frame is sub-millisecond at both scales, far below every diffusion baseline.",
"summary": "Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-10 04:00",
"debrief_path": "debriefs/debrief-2026-06-10-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-10T04:00:00Z"
},
{
"id": "item_01168",
"rank": 484,
"score": 690,
"rationale": "Contrastive neuron attribution for jailbreak-circuit ablation is high-leverage for mechanistic safety.",
"title": "Opportunities and Challenges in Securely Reusing and Repurposing Mobile Devices",
"url": "https://arxiv.org/abs/2606.06181",
"authors": "Adelin Roty, Jan Tobias Mühlberg, Jean-François Determe",
"description": "An estimated 5.3 billion mobile phones became electronic waste in 2022, and many can be repurposed, but hardware-backed security mechanisms rely on vendor-controlled provisioning designed for a fixed device lifecycle. This paper investigates whether security mechanisms and guarantees remain effective when mobile devices are repurposed beyond their original intended lifecycle — finding significant gaps in how attestation, key storage, and secure boot behave in out-of-lifecycle scenarios. The work provides a security analysis framework for evaluating device reuse and identifies practical mitigations for enterprise and consumer refurbishment scenarios.",
"summary": "Investigates whether mobile device security mechanisms remain effective when repurposed beyond their intended lifecycle, finding significant gaps in attestation, key storage, and secure boot for out-of-lifecycle scenarios.",
"tags": [
"cs.CR"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🔒 Security & Cybersecurity",
"debrief_date": "2026-06-06 09:00",
"debrief_path": "debriefs/debrief-2026-06-06-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 09:00",
"debrief_path": "debriefs/debrief-2026-06-06-09.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T09:00:00Z"
},
{
"id": "item_01220",
"rank": 485,
"score": 690,
"rationale": "LLM-agent-driven neural architecture search beating Llama 3.2 is a strong self-improvement signal.",
"title": "EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management",
"url": "https://huggingface.co/papers/2606.03841",
"authors": "daily curated papers",
"description": "Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering their ability to accumulate reusable experience across tasks and operate reliably in multi-stage, iterative data science pipelines. To address these challenges, we introduce EvoDS, a self-evolving autonomous data science agent that learns to expand its skills and adaptively managing long-term context through agentic reinforcement learning. Specifically, EvoDS introduces two key strategies: (1) Autonomous Skill Acquisition (ASA) mechanism, which enables agents to synthesize, validate, and reuse executable skills; and (2) Adaptive Context Compression (ACC) strategy, which treats context management as a learned control problem rather than passive truncation. These strategies are orchestrated within a two-stage multi-agent training scheme, enabling EvoDS to autonomously improve over time. Theoretically, we prove that EvoDS's hierarchical design reduces tool-selection error, and its optimization objective aligns with an information bottleneck principle, ensuring efficient context use. Empirically, EvoDS outperforms state-of-the-art open-source data science agents by an average of 28.9% across four diverse benchmarks while eliminating out-of-token failures. Our code and data are available at https://github.com/usail-hkust/E",
"summary": "Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering …",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 HuggingFace Papers",
"group": "All",
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-06 00:00",
"debrief_path": "debriefs/debrief-2026-06-06-00.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-06T00:00:00Z"
},
{
"id": "item_01292",
"rank": 486,
"score": 690,
"rationale": "Full exploit for Windows kernel CVE enabling browser sandbox escape; high-impact offensive security artifact.",
"title": "Show, Don't TELL: Explainable AI-Generated Text Detection",
"url": "https://huggingface.co/papers/2605.27921",
"authors": "",
"description": "Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distribution performance. However, real-world applicability has stalled because their outputs are misaligned with the needs of users, such as professors, who are presented with a numeric score that has no attached explanation. We tackle this issue with a novel architecture, TELL, that bakes explainability from the ground-up. While our system still offers a numerical score like other detectors for comparability, TELL takes a fundamentally different approach where we aim to show the user the \"tells\" by which the model believes a text is AI or human-written, to empower the user to decide who wrote a text using their own judgment and understanding of the context of the writing and its alleged author. We train TELL on a custom SFT dataset of domain-specific authorship annotations, and further refine the system using GRPO with curriculum learning to improve performance. We achieve competitive performance with state-of-the-art detectors (AUROC 0.927) while natively providing annotations that explain the basis for the detector's decision. We further evaluate the quality of our explanations using a dataset of human annotations and report a high (mean 72.3%) win-rate on annotation concreteness, falsifiability, coherence, plausibility and grounding, allowing users to critically think and decide for themselves. Our work thus reframes the problem of ",
"summary": "Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distribution performance. However, real-world applicability has stalled…",
"tags": [
"HUGGINGFACE PAPERS"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace Papers",
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-06-03 04:00",
"debrief_path": "debriefs/debrief-2026-06-03-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-06-03T04:00:00Z"
},
{
"id": "item_01687",
"rank": 487,
"score": 690,
"rationale": "Solid: AI-driven vulnerability disclosure culture clash with Linux kernel norms; timely analysis.",
"title": "From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models",
"url": "https://huggingface.co/papers/2605.20177",
"authors": "Juncheng Wu, Hardy Chen, Haoqin Tu, Xianfeng Tang, Freda Shi",
"description": "Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is primarily limited by a lack of visual perception as opposed to reasoning itself. In this work, we systematically study the interplay between perception and reasoning in VLM post-training by decomposing their capabilities into three separate training stages: visual perception, visual reasoning, and textual reasoning, incorporating specialized training data. We demonstrate that visual perception (a) requires targeted optimization with specialized data; (b) serves as a fundamental scaffold that should be solidified through staged training before refining visual reasoning; and (c) is more effectively learned via RL than caption-based SFT. Our experiments across multiple VLMs demonstrate that staged training consistently improves both visual perception and reasoning performance over merged training. Notably, models trained with our approach achieve 1.5% higher reasoning accuracy with 20.8% shorter reasoning traces, suggesting that superior perception reduces the need for excessive reasoning. Furthermore, we show that this capability-based staging represents a new curriculum dimension orthogonal to traditional difficulty-based curricula, and combining both yields further additive gains. Our staged-training models achieve superior performance among open-weight VLMs, establishing advanced results on several visual math and perception (e.g.",
"summary": "Recent advances in vision-language models (VLMs) emphasize long chain-of-thought reasoning; yet, we find that their performance on visual tasks is primarily limited by a lack of visual perception as opposed to reasoning itself. In this work...",
"tags": [
"HuggingFace"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-25 16:00",
"debrief_path": "debriefs/debrief-2026-05-25-16.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-25T16:00:00Z"
},
{
"id": "item_01781",
"rank": 488,
"score": 690,
"rationale": "Hierarchical latent diffusion LM challenges AR paradigm, ambitious.",
"title": "US Government takes $2B equity stake in nine quantum computing firms",
"url": "https://news.ycombinator.com/item?id=48225858",
"authors": "",
"description": "US Government takes $2B equity stake in nine quantum computing firms",
"summary": "US Government takes $2B equity stake in nine quantum computing firms",
"tags": [
"Hacker News"
],
"source": "Hacker News",
"type": "news",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_01753",
"rank": 489,
"score": 690,
"rationale": "DCI for agentic search challenges fixed-similarity retrieval paradigm.",
"title": "Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws",
"url": "https://huggingface.co/papers/2605.21803",
"authors": "Nandan Kumar Jha, Brandon Reagen",
"description": "Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed training detail. We show that this assumption misses a fundamental axis of representation scaling: how effectively the optimizer converts added FFN width into utilized spectral capacity. Using eigenspectra of feed-forward network representations, measured through soft and hard spectral-ranks, we find that the same Transformer architecture realizes markedly different spectral scaling laws when trained with different optimizers. Holding architecture and width schedule fixed, AdamW exhibits weak hard-rank scaling (β=0.44) on rare-token (TAIL) representations where learning is known to be hardest, whereas Muon achieves linear scaling (β=1.02) in the same regimes, a 2.3times increase in the scaling exponent. This difference is not reducible to validation loss: AdamW configurations can match low-rank Dion variants in perplexity, under extended training, while exhibiting sharply different spectral geometry, demonstrating that matched loss does not imply matched representation structure. Hard--soft rank asymmetry further reveals that optimizers differ not only in how much capacity is realized, but also in how that capacity is structured across eigenmodes. To disentangle optimizer effects from architectural ones, we compare against architectural interventions (e.g., attention rank and positional encoding), and find that optimizer-induced s",
"summary": "Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed training detail. We show that this assumption misses a fundamental axis of representation scaling: how effectively the optimizer converts added FFN width into utilized…",
"tags": [
"arXiv:2605.21803"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "⚙️ Machine Learning",
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-05-22 11:00",
"debrief_path": "debriefs/debrief-2026-05-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-05-22T11:00:00Z"
},
{
"id": "item_02262",
"rank": 490,
"score": 690,
"rationale": "Tuna-2 native unified multimodal from pixel embeddings; architectural simplification with results.",
"title": "LLM Agents Already Know When to Call Tools -- Even Without Reasoning",
"url": "https://huggingface.co/papers/2605.09252",
"authors": "",
"description": "Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically studies when a tool call is actually needed. We propose When2Tool, a benchmark of 18 environments (15 single-hop, 3 multi-hop) spanning three categories of tool necessity -- computational scale, knowledge boundaries, and execution reliability -- each with controlled difficulty levels that create a clear decision boundary between tool-necessary and tool-unnecessary tasks. We evaluate two families of training-free baselines: Prompt-only (varying the prompt to discourage unnecessary calls) and Reason-then-Act (requiring the model to reason about tool necessity before acting). Both provide limited control: Prompt-only suppresses necessary calls alongside unnecessary ones, and Reason-then-Act still incurs a disproportionate accuracy cost on hard tasks. To understand why these baselines fail, we probe the models' hidden states and find that tool necessity is linearly decodable from the pre-generation representation with AUROC 0.89--0.96 across six models, substantially exceeding the model's own verbalized reasoning. This reveals that models already know when tools are needed, but fail to act on this knowledge during generation. Building on this finding, we propose Probe&Prefill, which uses a lightweight linear probe to read the hidden-state signal and prefills the model's response with a s",
"summary": "Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "HuggingFace Papers",
"debrief_date": "2026-05-13 19:00",
"debrief_path": "debriefs/debrief-2026-05-13-19.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-05-13T19:00:00Z"
},
{
"id": "item_02300",
"rank": 491,
"score": 690,
"rationale": "Perception-centric process rewards for VLMs—strong multimodal reasoning and hallucination mitigation.",
"title": "Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty",
"url": "https://huggingface.co/papers/2605.11436",
"authors": "Joykirat Singh, Zaid Khan, Archiki Prasad, Justin Chih-Yao Chen, Akshay Nambi",
"description": "Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring and tracking a complex environment state over many steps. This leads to two challenges: partial observability requires maintaining uncertainty over unobserved world attributes, and long interaction history causes context to grow without bound, diluting task-relevant information. A principled solution to both challenges is a belief state: a posterior distribution over environment states given past observations and actions, which compactly encodes history for decision making regardless of episode length. In LLM agents, however, the open-ended nature of text makes it unclear how to represent such a distribution. Therefore, we introduce Agent-BRACE: Agent Belief state Representation via Abstraction and Confidence Estimation, a method that decouples an LLM agent into a belief state model and a policy model, jointly optimized via reinforcement learning. The belief state model produces a structured approximation of the belief distribution: a set of atomic natural language claims about the environment, each annotated with an ordinal verbalized certainty label ranging from certain to unknown. The policy model conditions on this compact, structured approximate belief rather than the full history, learning to select actions under explicit uncertainty. Across long-horizon, partially observable embodied language environments, Agent-BRACE ac",
"summary": "Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring and tracking a complex environment state over many steps. This leads to two challenges: partial…",
"tags": [
"arXiv:2605.11436"
],
"source": "HuggingFace",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🤖 Agents",
"debrief_date": "2026-05-13 11:00",
"debrief_path": "debriefs/debrief-2026-05-13-11.html",
"debrief_md": "",
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"sort_date": "2026-05-13T11:00:00Z"
},
{
"id": "item_02775",
"rank": 492,
"score": 690,
"rationale": "Systematic study of weak-supervision RLVR with saturation dynamics; valuable insight for reasoning training.",
"title": "Podman rootless containers and the Copy Fail exploit",
"url": "https://garrido.io/notes/podman-rootless-containers-copy-fail/",
"authors": "",
"description": "About Microblog Podman rootless containers and the Copy Fail exploit May 4, 2026 Contents An overview of rootless containers Rootless rootful User namespaces Privileged operations Rootless non-root Bind mounts Copy Fail Rootless rootful Rootless non-root Rootless non-root while disabling new privileges Rootless non-root while dropping capabilities The exploit persists Defence in depth Read-only images Resource constraints Limit available binaries Firewalling Conclusion Further reading On April 29th CVE-2026-31431 was publicly disclosed at https://copy.fail/ . This vulnerability allows a local ",
"summary": "About Microblog Podman rootless containers and the Copy Fail exploit May 4, 2026 Contents An overview of rootless containers Rootless rootful User namespaces Privileged operations…",
"tags": [
"Lobste.rs",
"linux",
"security"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "📮 Lobste.rs",
"debrief_date": "2026-05-04 19:00",
"debrief_path": "debriefs/debrief-2026-05-04-19.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-05-04T19:00:00Z"
},
{
"id": "item_03166",
"rank": 493,
"score": 690,
"rationale": "Continuous adversarial flow models substantially improving ImageNet FID; strong generative-modeling advance.",
"title": "EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training",
"url": "https://huggingface.co/papers/2604.20012",
"authors": "",
"description": "EmbodiedMidtrain addresses the distribution gap between generic Vision-Language Models and robot action domains by mid-training VLMs on VLA-aligned data selected via a lightweight proximity estimator, achieving competitive results with expert VLAs at smaller scale and training budgets.",
"summary": "EmbodiedMidtrain addresses the distribution gap between generic Vision-Language Models and robot action domains by mid-training VLMs on VLA-aligned data selected via a lightweight proximity estimator, achieving competitive results with expert…",
"tags": [
"HuggingFace Papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🕸️ Web Findings",
"group": "🧠 HuggingFace Papers",
"debrief_date": "2026-04-27 16:00",
"debrief_path": "debriefs/debrief-2026-04-27-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-27 16:00",
"debrief_path": "debriefs/debrief-2026-04-27-16.html",
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"sort_date": "2026-04-27T16:00:00Z"
},
{
"id": "item_03433",
"rank": 494,
"score": 690,
"rationale": "Verified boot analysis exposes systemic trust gap; strong security insight.",
"title": "MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation",
"url": "https://huggingface.co/papers/2604.17435",
"authors": "",
"description": "Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive datasets to overcome the data scarcity limitation. Second, we propose MoVE, a Mixture-of-LoRA-Experts architecture with expressive-specialized adapters and a soft-weighting router that blends experts for capturing hybrid expressive states. Third, we show pretrained AudioLLMs enable striking data efficiency: 30 minutes of curated data is enough for strong performance. On English-Chinese S2ST, while comparing with strong baselines, MoVE reproduces target NVs in 76% of cases and achieves the highest human-rated naturalness and emotional fidelity among all compared systems, where existing S2ST systems preserve at most 14% of NVs.",
"summary": "Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely…",
"tags": [
"arXiv"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "📄 HuggingFace Papers",
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-22 11:00",
"debrief_path": "debriefs/debrief-2026-04-22-11.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-22T11:00:00Z"
},
{
"id": "item_03539",
"rank": 495,
"score": 690,
"rationale": "SkillClaw: collective skill evolution for agents; high-upvotes, novel.",
"title": "The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward",
"url": "https://huggingface.co/papers/2604.17273",
"authors": "",
"description": "The most important architectural problem in AI is not the size of the model but the absence of a layer that carries forward what the model has come to understand. Sessions end. Context windows fill. Memory APIs return flat facts that the model has to reinterpret from scratch on every read. The result is intelligence that is powerful per session and amnesiac across time. This position paper argues that the layer which fixes this, the continuity layer, is the most consequential piece of infrastructure the field has not yet built, and that the engineering work to build it has begun in public. The formal evaluation framework for the property described here is the ATANT benchmark (arXiv:2604.06710), published separately with evaluation results on a 250-story corpus; a companion paper (arXiv:2604.10981) positions this framework against existing memory, long-context, and agentic-memory benchmarks. The paper defines continuity as a system property with seven required characteristics, distinct from memory and from retrieval; describes a storage primitive (Decomposed Trace Convergence Memory) whose write-time decomposition and read-time reconstruction produce that property; maps the engineering architecture to the theological pattern of kenosis and the symbolic pattern of Alpha and Omega, and argues this mapping is structural rather than metaphorical; proposes a four-layer development arc from external SDK to hardware node to long-horizon human infrastructure; examines why the physics",
"summary": "The most important architectural problem in AI is not the size of the model but the absence of a layer that carries forward what the model has come to understand. Sessions end. Context windows fill…",
"tags": [
"HUGGINGFACE_PAPERS",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🤖 Agents",
"group": "Agents",
"debrief_date": "2026-04-21 16:00",
"debrief_path": "debriefs/debrief-2026-04-21-16.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-04-21 16:00",
"debrief_path": "debriefs/debrief-2026-04-21-16.html",
"debrief_md": ""
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"sort_date": "2026-04-21T16:00:00Z"
},
{
"id": "item_03588",
"rank": 496,
"score": 690,
"rationale": "FUSE unsupervised verifier ensembling; directly tackles LLM-as-judge weakness, broadly useful.",
"title": "FUSE: Ensembling Verifiers with Zero Labeled Data",
"url": "https://arxiv.org/abs/2604.18547",
"authors": "Joonhyuk Lee et al.",
"description": "Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves using imperfect LLM judges and reward models since ground truth acquisition can be time-consuming and expensive. We introduce Fully Unsupervised Score Ensembling (FUSE), a method for improving verification quality by ensembling verifiers without access to ground truth correctness labels. The key idea behind FUSE is to c",
"summary": "Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves using imperfect LLM judges and reward mod…",
"tags": [
"stat.ML",
"cs.CL",
"cs.LG"
],
"source": "ArXiv",
"type": "paper",
"section": "📄 ArXiv Papers",
"group": "🧠 Zero Knowledge",
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
{
"debrief_date": "2026-04-21 04:00",
"debrief_path": "debriefs/debrief-2026-04-21-04.html",
"debrief_md": ""
}
],
"sort_date": "2026-04-21T04:00:00Z"
},
{
"id": "item_03752",
"rank": 497,
"score": 690,
"rationale": "TPO cleanly separates target distribution from policy update; potentially impactful RL alternative to PPO/GRPO.",
"title": "Target Policy Optimization",
"url": "https://huggingface.co/papers/2604.06159",
"authors": "",
"description": "In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the parameters move to realize that change? Standard policy-gradient methods answer both at once, so the update can overshoot or undershoot depending on the learning rate, clipping, and other optimizer choices. We introduce Target Policy Optimization (TPO), which separates the two questions. Given scored completions, TPO constructs a target distribution q_i propto p_i^{,old} exp(u_i) and fits the policy to it by cross-entropy. The loss gradient on sampled-completion logits is p^θ- q, which vanishes once the policy matches the target. On tabular bandits, transformer sequence tasks, and billion-parameter LLM RLVR, TPO matches PG, PPO, GRPO, and DG on easy tasks and substantially outperforms them under sparse reward. Code is available at https://github.com/JeanKaddour/tpo.",
"summary": "In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the…",
"tags": [
"HuggingFace Papers",
"daily curated papers"
],
"source": "HuggingFace",
"type": "paper",
"section": "🤗 HuggingFace Papers",
"group": "HuggingFace Papers",
"debrief_date": "2026-04-16 11:00",
"debrief_path": "debriefs/debrief-2026-04-16-11.html",
"debrief_md": "",
"occurrence_count": 1,
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"sort_date": "2026-04-16T11:00:00Z"
},
{
"id": "item_03819",
"rank": 498,
"score": 690,
"rationale": "ZKML survey; timely consolidation of verifiable ML via zero-knowledge proofs, strong crypto-ML intersection.",
"title": "A survey of zero-knowledge proof based verifiable machine learning",
"url": "https://arxiv.org/abs/2502.18535",
"authors": "",
"description": "Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model confidentiality. Zero-knowledge proofs (ZKPs) provide a compelling foundation for verifiable machine learning because they allow one party to certify that a training, testing, or inference result was produced by the claimed computation without revealing sensitive data or proprietary model parameters. Despite rapid progress in zero-knowledge machine learning (ZKML), the literature remains fragmented acr",
"summary": "Machine learning is increasingly deployed through outsourced and cloud-based pipelines, which improve accessibility but also raise concerns about computational integrity, data privacy, and model…",
"tags": [
"Google Scholar"
],
"source": "ArXiv",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🔗 Google Scholar",
"debrief_date": "2026-04-16 09:00",
"debrief_path": "debriefs/debrief-2026-04-16-09.html",
"debrief_md": "",
"occurrence_count": 1,
"occurrences": [
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"debrief_date": "2026-04-16 09:00",
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"sort_date": "2026-04-16T09:00:00Z"
},
{
"id": "item_03999",
"rank": 499,
"score": 690,
"rationale": "Continuous adversarial flow post-training substantially improves SiT/JiT FID; strong generative modeling result.",
"title": "Continuous Adversarial Flow Models",
"url": "https://huggingface.co/papers/2604.11521",
"authors": "Shanchuan Lin, Ceyuan Yang, Zhijie Lin, Hao Chen, et al.",
"description": "We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces samples that are better aligned with the target data distribution. Our method is primarily proposed for post-training existing flow-matching models, although it can also train models from scratch. On the ImageNet 256px generation task, our post-training substantially improves the guidance-free FID of latent-space SiT from 8.26 to 3.63 and of pixel-space JiT from 7.17 to 3.57. It also improves guided generation, reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT. We further evaluate our approach on text-to-image generation, where it achieves improved results on both the GenEval and DPG benchmarks.",
"summary": "We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training.",
"tags": [
"🤗 HuggingFace",
"daily curated papers",
"2026-04-13"
],
"source": "HuggingFace",
"type": "paper",
"section": "🌐 Web Findings",
"group": "🤗 HuggingFace",
"debrief_date": "2026-04-14 04:00",
"debrief_path": "debriefs/debrief-2026-04-14-04.html",
"debrief_md": "",
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"sort_date": "2026-04-14T04:00:00Z"
},
{
"id": "item_04264",
"rank": 500,
"score": 690,
"rationale": "eBPF-based DPI bypass via TLS ClientHello injection; novel technical approach.",
"title": "DPI bypass using eBPF sock_ops and fake TLS ClientHello injection",
"url": "https://github.com/boratanrikulu/gecit",
"authors": "",
"description": "Article about: DPI bypass using eBPF sock_ops and fake TLS ClientHello injection",
"summary": "Article about: DPI bypass using eBPF sock_ops and fake TLS ClientHello injection",
"tags": [
"Lobsters"
],
"source": "Lobste.rs",
"type": "news",
"section": "🌐 Web Findings",
"group": "🦞 Lobste.rs",
"debrief_date": "2026-04-11 19:00",
"debrief_path": "debriefs/debrief-2026-04-11-19.html",
"debrief_md": "",
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]
}