Carlos's Debrief

⚡ Quick Summary

Learning with Chain-of-Thought from Multiple Thinkers: Studies how models learn from multiple correct CoT solutions — computationally easy with CoT but hard with outcome-only supervision, revealing systematic differences in how thinkers solve problems.
Adaptive Runtime Governance for Autonomous AI Agents: Proposes the Informational Viability Principle — bounding unobserved risk via U(x)+SB(x)+RG(x) — and allowing actions only when capacity exceeds estimated risk by a safety margin.
Green Shielding: Benign Input Variation Breaks AI Trustworthiness: Shows LLM outputs are highly sensitive to routine non-adversarial query phrasing; proposes CUE criteria (Context, Use-case, Evidence) for evidence-backed deployment guidance.
Defective Task Descriptions Undermine LLM Code Generation: Detects task description defects in LLM code generation using a lightweight classifier; finds vague or malformed requirements strongly affect code correctness.
Clinical AI Evaluation via Case-Specific Clinician-Authored Rubrics: Shows LLM-generated rubrics approximate clinician agreement across 823 encounters, offering a scalable clinically valid, economically viable AI evaluation methodology.

📰 News Headlines

Vision-Language-Action Safety Threats Mapped: VLA models introduce new attack surfaces including irreversible physical consequences, multimodal threats, and real-time defense constraints — a unified survey provides the first comprehensive map of this emerging risk landscape.
LLM Agents Learn Safety from 1-Bit Danger Signals: EPO-Safe demonstrates LLMs can discover hidden safety constraints from only a binary 'unsafe' signal, producing human-readable specs while exposing how standard reward reflection accelerates reward hacking.
Post-Quantum Cryptography for IoT Devices: As quantum computers advance, traditional cryptographic methods become vulnerable; a deep-dive explores post-quantum cryptography for authentication and user privacy in resource-constrained IoT environments.
AI Research Leadership Shifts from Academia to Industry: Quantitative analysis of AI models since 2010 shows industry overtook academia in releasing notable AI models from 2014 onward, with funding spikes correlating to deep learning breakthroughs.
Complete History of Quantum Computing Published: A comprehensive historical account traces quantum computing's evolution and previews near-term prospects, generating significant Hacker News discussion.

📄 ArXiv Papers

🧠 LLMs 15

Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling [1]

Hailing Cheng, Daqi Sun, Xinyu Lu

Every Transformer architecture dedicates enormous capacity to learning rich representations in semantic embedding space -- yet the rotation manifold acted upon by Rotary Positional Embeddings (RoPE) has been treated as a…

cs.AI

Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters [2]

Aaryan Shah, Andrew Hines, Alexia Downs, Denis Bajet, Paulius Mui, Fabiano Araujo, Laura Offutt, Aida Rutledge, Elizabeth Jimenez

Objective. Clinical AI documentation systems require evaluation methodologies that are clinically valid, economically viable, and sensitive to iterative changes. Methods requiring expert review per scoring instance are t…

cs.AI cs.CL

Defective Task Descriptions in LLM-Based Code Generation: Detection and Analysis [3]

Amal Akli, Mike Papadakis, Maxime Cordy, Yves Le Traon

Large language models are widely used for code generation, yet they rely on an implicit assumption that the task descriptions are sufficiently detailed and well-formed. However, in practice, users may provide defective d…

cs.SE cs.AI

Green Shielding: A User-Centric Approach Towards Trustworthy AI [4]

Aaron J. Li, Nicolas Sanchez, Hao Huang, Ruijiang Dong, Jaskaran Bains, Katrin Jaradeh, Zhen Xiang, Bo Li, Feng Liu, Aaron Kornblith, Bin Yu

Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users phrase queries, a gap not well addressed by existing red-teaming effort…

cs.CL cs.AI

Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft [5]

Zhou Ziheng, Huacong Tang, Jinyuan Zhang, Haowei Lin, Bangcheng Yang, Qian Long, Fang Sun, Yizhou Sun, Yitao Liang, Ying Nian Wu, Demetri Terzopoulos, Xiaofeng Gao

Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence, yet evaluating this capacity has been hindered by the vast complexi…

cs.AI

Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents [6]

German Marin, Jatin Chaudhary

Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without any code change. We propose the \textbf{Informational Viability Principl…

cs.AI

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study [7]

Sivajeet Chand, Kevin Nguyen, Peter Kuntz, Alexander Pretschner

Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change gener…

cs.SE cs.AI

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling [8]

Parsa Ashrafi Fashi, Utkarsh Saxena, Mehdi Rezagholizadeh, Aref Jafari, Akash Haridas, Mingyu Yang, Vansh Bhatia, Guihong Li, Vikram Appia, Emad Barsoum

Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretrained from scratch and therefore fail to…

cs.CL cs.LG

World-R1: Reinforcing 3D Constraints for Text-to-Video Generation [9]

Weijie Wang, Xiaoxuan He, Youping Gu, Yifan Yang, Zeyu Zhang, Yefei He, Yanbo Ding, Xirui Hu, Donny Y. Chen, Zhiyuan He, Yuqing Yang, Bohan Zhuang

Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods attempt to inject 3D priors via architectural modifications, they often …

cs.CV

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation [10]

Zhiheng Liu, Weiming Ren, Xiaoke Huang, Shoufa Chen, Tianhong Li, Mengzhao Chen, Yatai Ji, Sen He, Jonas Schult, Belinda Zeng, Tao Xiang, Wenhu Chen, Ping Luo, Luke Zettlemoyer, Yuren Cong

Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creating misalignment between the two tasks and preventing fully end-to-end…

cs.CV

Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models [11]

Nay Myat Min, Long H. Pham, Jun Sun

Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant until triggered, jailbreaks subvert safety alignment, and prompt injection…

cs.CR cs.AI cs.CL

GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems [12]

Pablo Mateo-Torrejón, Alfonso Sánchez-Macián

The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving capabilities, but it has also expanded their attack surfaces, exposing t…

cs.CR cs.AI

A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations [13]

Zihan Liu, Yizhen Wang, Rui Wang, Xiu Tang, Sai Wu

Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the ne…

cs.CR cs.CL cs.DC cs.LG

Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures [14]

Qianlei Wang, Kexun Chen, Shaolin Zhang, Hongli Gao, Chaoning Zhang, Xiaolin Qin

Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However, these approaches typically treat the problem as a generic image-to-…

cs.CV

DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation [15]

Tal Grossman, Noa Cahan, Lev Ayzenberg, Hayit Greenspan

Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transfer to medical data. Consequently, accura…

cs.CV

🤖 Machine Learning 14

Learning to Think from Multiple Thinkers [16]

Nirmit Joshi, Roey Magen, Nathan Srebro, Nikolaos Tsilivis, Gal Vardi

We study learning with Chain-of-Thought (CoT) supervision from multiple thinkers, all of whom provide correct but possibly systematically different solutions, e.g., step-by-step solutions to math problems written by diff…

cs.LG cs.AI cs.CC stat.ML

Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models [17]

Hailing Cheng, Tao Huang, Chen Zhu, Antonio Alonso

Training large neural networks with data-parallel stochastic gradient descent allocates N GPU replicas to compute effectively identical updates -- a practice that leaves the rich space of learning rate configurations ent…

cs.LG cs.AI

The Optimal Sample Complexity of Multiclass and List Learning [18]

Chirag Pabbaraju

While the optimal sample complexity of binary classification in terms of the VC dimension is well-established, determining the optimal sample complexity of multiclass classification has remained open. The appropriate com…

cs.LG stat.ML

Conflict-Aware Harmonized Rotational Gradient for Multiscale Kinetic Regimes [19]

Zhangyong Liang

In this paper, we propose a harmonized rotational gradient method, termed HRGrad, for simultaneously tackling multiscale time-dependent kinetic problems with varying small parameters. These parameters exhibit asymptoti…

cs.LG

SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning [20]

Zijian Guo, İlker Işık, H. M. Sabbir Ahmad, Wenchao Li

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 …

cs.LG

Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems [21]

Tobias A. Farger, Adam W. Hall, Angela P. Schoellig

Learning-based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning-based controllers are often computationally inefficient, limiting their practicality. To add…

eess.SY cs.LG cs.RO

Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting [22]

Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser, Wolf Fichtner, Veit Hagenmeyer, Matthias Hertel, Nils Koster, Sebastian Lerch, Ralf Mikut, Jan Priesmann, Melanie Schienle, Benjamin Schaefer, Jann Weinand, Florian Ziel

Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated un…

econ.EM cs.LG

Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding [23]

Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. Egorov

We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs a sparse similarity graph on the samples…

cs.LG

Cosmological Impact of Redshift-Dependent Type Ia Supernovae Calibration [24]

Seyed Hamidreza Mirpoorian, Meng-Xiang Lin, Levon Pogosian

Type Ia supernovae (SNIa) play a central role in constraining the late-time expansion history of the Universe and are directly implicated in current cosmological tensions. Motivated by the possibility of unaccounted reds…

astro-ph.CO

The lifetime of 100,000 molecular clouds in the nearby Universe [25]

Z. Bazzi, M. I. N. Kobayashi, D. Colombo, F. Bigiel, A. K. Leroy, S. E. Meidt, R. S. Klessen, E. Rosolowsky, R. Chown, D. A. Dale, S. Dlamini, M. Greve, S. K. Stuber, M. Boquien, T. G. Williams, H. -A. Pan, M. Querejeta, L. Ramambason, A. Romanelli, T. Saito, L. E. C. Romano, M. J. Jiménez-Donaire, H. Kim, D. Pathak, H. Koziol, J. Sutter, J. C. Lee, the PHANGS collaboration

Multiple mechanisms are proposed for the formation of giant molecular clouds (GMCs), from gravitational free-fall caused by self-gravity to stellar feedback-driven gas compression. Both the galactic environment and galax…

astro-ph.GA

Non-Abelian Particle-Loop, Fracton, and Planon Condensation in Cage-Net Models [26]

Yifei Wang, Yu Zhao, Yingcheng Li, Hao Song, Yidun Wan

We present a framework for non-Abelian p-loop, fracton, and planon condensation in 3+1 dimensions by constructing extended cage-net fracton models using decoupled layers of the Hu-Geer-Wu (HGW) string-net model. These ca…

cond-mat.str-el math-ph

Calculating the second rational cohomology group of the Torelli group [27]

Andrew Putman

Minahan and the author recently proved results that allow the calculation of the second rational cohomology group of the Torelli group. This builds on two key ingredients: Hain's calculation of the image of the cup produ…

math.GT math.AT

Regular Grammars as Effective Representations of Recognizable Sets of Series-Parallel Graphs [28]

Marius Bozga, Radu Iosif, Florian Zuleger

Series-parallel (SP) graphs are binary edge-labeled graphs with a designated source and target vertex, built using serial and parallel composition. A set of graphs is recognizable if membership depends only on its image …

cs.FL cs.CC

On classical doubling method gamma factors for certain depth zero representations [29]

Johannes Girsch, Elad Zelingher

Piatetski-Shapiro--Rallis discovered an integral representation construction, known as the doubling method, for the tensor product $L$-function of a cuspidal automorphic representation of $G \times \mathrm{GL}_1$, where …

math.RT math.NT

🔒 Security & Crypto 6

Profiling Resilient to Change in Probe Position [30]

Elie Bursztein, Michael Gruber, Karel Král, Jean-Michel Picod, Matthias Probst, Georg Sigl

Side Channel Analysis (SCA) relaxes the black-box assumption of conventional cryptanalysis by incorporating physical measurements acquired during cryptographic operations. Electro-magnetic (EM) emissions of a chip during…

cs.CR

Machine-Checked Cardinality Bounds for Masked Barrett Reduction: A 1-Bit Side-Channel Leakage Barrier in Post-Quantum Cryptographic Hardware [31]

Ray Iskander, Khaled Kirah

Barrett reduction is the nonlinear core of every practical NTT-based post-quantum cryptography implementation. Existing composition frameworks (ISW, t-SNI, PINI, DOM) address Boolean masking over GF(2); none provides a m…

cs.CR

AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents [32]

Yixiang Zhang, Xinhao Deng, Jiaqing Wu, Yue Xiao, Ke Xu, Qi Li

Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileged tools. In such systems, security fail…

cs.CR cs.AI

ARCANE: Cross-Campaign Attacker Re-identification via Passive Beacon Telemetry -- A Bayesian Network Framework for Longitudinal Cyber Attribution [33]

Abraham Itzhak Weinberg

Current cyber attribution approaches typically operate on a per-incident basis, leaving open whether aggregating evidence across campaigns improves adversary identification. We investigate whether cross-campaign attribut…

cs.CR

DETOUR: A Practical Backdoor Attack against Object Detection [34]

Dazhuang Liu, Yanqi Qiao, Rui Wang, Kaitai Liang, Georgios Smaragdakis

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…

cs.CR

Detecting Avalanche Effect in Adversarial Settings: Spotting the Encryption Loops in Ransomware [35]

Nanqing Luo, Xusheng Li, Haizhou Wang, Shuangyi Zhu, Yuan Ma, Peng Liu

Spotting encryption loops in binary-only ransomware is a critical reverse engineering task. Since the existence of avalanche effect, an intrinsic characteristic of any secure encryption algorithms, is unavoidable during …

cs.CR

🔐 Zero Knowledge 3

A Strongly Polynomial Algorithm for Arctic Auctions [36]

Jugal Garg, Shayan Taherijam, Vijay V Vazirani

Our main contribution is a strongly polynomial algorithm for computing an equilibrium for the Arctic Auction, which is the quasi-linear extension of the linear Fisher market model. We build directly on Orlin's strongly p…

cs.GT cs.DS

Distributional Robustness of Linear Contracts [37]

Shiliang Zuo

Linear contracts are ubiquitous in practice, yet optimal contract theory often prescribes complex, nonlinear structures. We provide a distributional robustness justification for linear contracts. We study a principal-age…

cs.GT

Partial extended b-metric and some fixed point theorem [38]

Muhamad Abdillah Ahen, Ivan Hadinata, Raudhatul Mufizah

In this paper, we introduce the concept of partial extended b-metric spaces (PEBMS) as a unification and generalization of extended b-metric spaces and partial b-metric spaces. This new structure incorporates a point-dep…

math.FA

⚛️ Quantum 8

Personalized Worked Example Generation from Student Code Submissions using Pattern-based Knowledge Components [39]

Griffin Pitts, Muntasir Hoq, Peter Brusilovsky, Narges Norouzi, Arto Hellas, Juho Leinonen, Bita Akram

Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to the logical errors and partial solutions s…

cs.HC cs.AI cs.CY cs.ET cs.LG

Contracting Tensor Networks with Generalized Belief Propagation [40]

Joseph Tindall, Grace M. Sommers, Hilbert Kappen

Recent years have seen a growing interest in the use of belief propagation - an algorithm originally introduced for performing statistical inference on graphical models - for approximate, but highly efficient, tensor net…

quant-ph cond-mat.stat-mech physics.comp-ph

Semiclassical phases of charged spin-$1/2$ matter-wave interferometers in gravitational wave backgrounds [41]

Nontapat Wanwieng, Apimook Watcharangkool

A matter wave propagating through curved spacetime accumulates phase that encodes both geometry and gauge structure. We develop a semiclassical framework for charged spin-$1/2$ matter-wave interferometers based on a WKB …

gr-qc quant-ph

Dynamical preparation of U(1) quantum spin liquids in an analogue quantum simulator [42]

Simon Karch, Melissa Will, Irene Prieto Rodriguez, Nikolas Liebster, SeungJung Huh, Michael Knap, Frank Pollmann, Clemens Kuhlenkamp, Immanuel Bloch, Monika Aidelsburger

Locally constrained gauge theories underpin our understanding of fundamental interactions in particle physics and the emergent behaviour of quantum materials. In strongly correlated systems, they can give rise to quantum…

cond-mat.quant-gas cond-mat.str-el hep-lat physics.atom-ph quant-ph

Application of a Quantum Amplitude Redistribution Algorithm to the Data Filtering Problem [43]

Karina Zakharova, Artem Chernikov, Sergey Sysoev

This paper presents an analysis of the applicability of a quantum amplitude redistribution algorithm to the data filtering problem and the results of modeling the algorithm's operation in comparison with a median filter.

quant-ph

Experimental high-dimensional multi-qubit Bell non-locality on a superconducting quantum processor [44]

Yousef Mafi, Ali G. Moghaddam, Teemu Ojanen

Combining recent advances in superconducting quantum hardware, we explore quantum correlations in a previously inaccessible regime by observing \emph{simultaneously} high-dimensional and many-body Bell non-locality. We r…

quant-ph cond-mat.stat-mech physics.comp-ph

CAbLECAR: efficiently scheduling QLDPC codes on a tileable spin qubit chip with shuttling [45]

Jason D. Chadwick, Frederic T. Chong

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 …

quant-ph cs.ET

Gate-dependent offset charge shifts and anharmonicity in gatemon qubits in the weak tunneling regime [46]

Utkan Güngördü, Rusko Ruskov, Silas Hoffman, Kyle Serniak, Andrew J. Kerman, Charles Tahan

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…

cond-mat.mes-hall quant-ph

🛡️ AI Safety 1

Dynamical Modeling of the Broad-Line Region with High-Mass Active Galactic Nuclei and Constraints on the Virial Factor [47]

Shu Wang, Jong-Hak Woo, Lizvette Villafaña, Tommaso Treu, Elena Gallo

We present the results of broad-line region (BLR) dynamical modeling for eight high-mass active galactic nuclei (AGNs) from the Seoul National University AGN Monitoring Project, by constraining BLR geometry and kinematic…

astro-ph.GA

🌐 Web Findings

🦞 Lobsters 1

ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It [W1]

Edit April 2, 2026: I've been getting inbound interest from researchers wanting to run their own queries. The MCP integration I use for my own research lets you analyze live mobile telemetry continuou…

Lobsters

🤗 Huggingface Papers 21

Improving Vision-language Models with Perception-centric Process Reward Models [W2]

Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, its outcome-level supe…

Huggingface Papers

Discovering Agentic Safety Specifications from 1-Bit Danger Signals [W3]

Can large language model agents discover hidden safety objectives through experience alone? We introduce EPO-Safe (Experiential Prompt Optimization for Safe Agents), a framework where an LLM iterative…

Huggingface Papers

ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers [W4]

Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems. Existing approaches require computationally exp…

Huggingface Papers

EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment [W5]

Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on final-layer representations, ignoring quality-relev…

Huggingface Papers

From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company [W6]

Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logi…

Huggingface Papers

For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs [W7]

Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient co…

Huggingface Papers

Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [W8]

Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-agent frameworks assi…

Huggingface Papers

Efficient Agent Evaluation via Diversity-Guided User Simulation [W9]

Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation pr…

Huggingface Papers

Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms [W10]

Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety challenges, stemming from the embodied nature of VLA systems,…

Huggingface Papers

OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer [W11]

Shot Boundary Detection (SBD) aims to automatically identify shot changes and divide a video into coherent shots. While SBD was widely studied in the literature, existing state-of-the-art methods ofte…

Huggingface Papers

ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning [W12]

Current evaluations of spatial intelligence can be systematically invalid under modern vision-language model (VLM) settings. First, many benchmarks derive question-answer (QA) pairs from point-cloud-b…

Huggingface Papers

ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents [W13]

Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change independently of the a…

Huggingface Papers

Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data [W14]

Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantly consist of boundar…

Huggingface Papers

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation [W15]

Huggingface Papers

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing [W16]

Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is signif…

Huggingface Papers

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation [W17]

Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks. We propose ProEval, a proactive ev…

Huggingface Papers

Stabilizing Efficient Reasoning with Step-Level Advantage Selection [W18]

Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and verbose reasoning traces. While recent work on effi…

Huggingface Papers

World-R1: Reinforcing 3D Constraints for Text-to-Video Generation [W19]

Huggingface Papers

Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis [W20]

Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. However, their potenti…

Huggingface Papers

SketchVLM: Vision language models can annotate images to explain thoughts and guide users [W21]

When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro and GPT-5 only respond…

Huggingface Papers

EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model [W22]

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…

Huggingface Papers

📚 Google Scholar 4

Advancements in Artificial Intelligence: Breakthroughs, Challenges and the Road Ahead [W23]

This paper conducts systematic analysis of advancements in the field of artificial intelligence (AI) from the year 2010 onwards, by performing original quantitative analysis of Epoch AI notable AI mod…

Google Scholar

Large language models (LLM) in computational social science: prospects, current state, and challenges [W24]

… The advent of large language models (LLMs) has marked a new … LLM usage. We further present the challenges associated with data bias, privacy, and the integration of these models …

Google Scholar

Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms [W25]

… in adversarial AI, automated threat intelligence, and AI-driven security orchestration, this … AI’s role in cybersecurity. Figure 1 shows the key areas where Artificial intelligence (AI) and …

Google Scholar

Securing the future: exploring post-quantum cryptography for authentication and user privacy in IoT devices [W26]

Cluster Computing - With the emergence of quantum computers, traditional cryptographic methods are vulnerable to attacks, emphasizing the need for post-quantum cryptography to secure devices and...

Google Scholar

👾 Hackernews 1

A Complete History of Quantum Computing (and what comes next) [W27]

A Complete History of Quantum Computing (and what comes next)

Hackernews

⚡ Quick Summary

📰 News Headlines

📄 ArXiv Papers

🧠 LLMs 15

🤖 Machine Learning 14

🔒 Security & Crypto 6

🔐 Zero Knowledge 3

⚛️ Quantum 8

🛡️ AI Safety 1

🌐 Web Findings

🦞 Lobsters 1

🤗 Huggingface Papers 21

📚 Google Scholar 4

👾 Hackernews 1

🔗 All Sources