Carlos's Debrief

April 17, 2026 11:00
48ArXiv Papers
50Web Findings
98Total Sources
Topics searched: Artificial Intelligence Machine Learning Large Language Models Security & Cybersecurity Cryptography Zero Knowledge Quantum Computing Crypto & Blockchain AI Agents & Reasoning AI Safety & Alignment
April 17, 2026 11:00 — Curated by Hermes Research Scout

⚡ Quick Summary

📰 News Headlines

📄 ArXiv Papers

🧠 LLMs 9

Generalization in LLM Problem Solving: The Case of the Shortest Path [1]
Yao Tong, Jiayuan Ye, Anastasia Borovykh, Reza Shokri
Whether language models can systematically generalize remains actively debated. Yet empirical performance is jointly shaped by multiple factors such as training data, training paradigms, and…
cs.AI,cs.LG
Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations [2]
Manan Gupta, Dhruv Kumar
LLM-as-judge frameworks are increasingly used for automatic NLG evaluation, yet their per-instance reliability remains poorly understood. We present a two-pronged diagnostic toolkit applied to…
cs.AI,cs.CL,cs.LG
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study [3]
Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang, Wenpeng Lu
Over the past year, spatial intelligence has drawn increasing attention. Many prior works study it from the perspective of visual-spatial intelligence, where models have access to visuospatial…
cs.AI
CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas [4]
Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita, Vincent Conitzer, Zhijing Jin
It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities…
cs.GT,cs.AI,cs.CL,
Stability and Generalization in Looped Transformers [5]
Asher Labovich
Looped transformers promise test-time compute scaling by spending more iterations on harder problems, but it remains unclear which architectural choices let them extrapolate to harder problems at…
cs.LG,cs.AI
Feedback-Driven Execution for LLM-Based Binary Analysis [6]
XiangRui Zhang, Qiang Li, Haining Wang
Binary analysis increasingly relies on large language models (LLMs) to perform semantic reasoning over complex program behaviors. However, existing approaches largely adopt a one-pass execution…
cs.CR
Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization [7]
Haochun Tang, Yuliang Yan, Jiahua Lu, Huaxiao Liu, Enyan Dai
Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing strategy introduces a new security concern that…
cs.CR,cs.AI,cs.CL,
Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation [8]
Lei Guo, Hongyun Yang, Pengjie Ren, Tong Chen, Hui Liu, Zhumin Chen
Large Language Models have shown great success in recommender systems. However, the limited and sparse nature of user data often restricts the LLM's ability to effectively model behavior patterns. To…
cs.IR
Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation [9]
Zoe Fingleton, Nazanin Siavash, Armin Moin
In this paper, we focus on automating two of the widely used Verification and Validation (V&V) activities in the Software Development Lifecycle (SDLC): Software testing and software inspection (also…
cs.SE

📄 ArXiv Papers

🤖 Machine Learning 19

AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving [10]
Fabrizio Genilotti, Arianna Stropeni, Gionata Grotto, Francesco Borsatti, Manuel Barusco, Davide Dalle Pezze, Gian Antonio Susto
The reliability of a machine vision system for autonomous driving depends heavily on its training data distribution. When a vehicle encounters significantly different conditions, such as atypical…
cs.CV,cs.AI
Why Do Vision Language Models Struggle To Recognize Human Emotions? [11]
Madhav Agarwal, Sotirios A. Tsaftaris, Laura Sevilla-Lara, Steven McDonagh
Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made tremendous progress in the last few years for many…
cs.CV,cs.AI
Prism: Symbolic Superoptimization of Tensor Programs [12]
Mengdi Wu, Xiaoyu Jiang, Oded Padon, Zhihao Jia
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…
cs.PL,cs.AI,cs.LG
SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation [13]
Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Yucheng Chen
Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong uncertainty methods…
cs.CV,cs.AI,cs.LG
Benchmarking Optimizers for MLPs in Tabular Deep Learning [14]
Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov, Artem Babenko
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models. Unlike…
cs.LG
Structural interpretability in SVMs with truncated orthogonal polynomial kernels [15]
Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas
We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated reproducing kernel Hilbert space is…
stat.ML,cs.LG,math.ST
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier [16]
Come Fiegel, Pierre Menard, Tadashi Kozuno, Michal Valko, Vianney Perchet
We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence in this setting is harder when the players are…
cs.LG
Gravitational-wave lensing beyond rays: a disordered-system approach [17]
Ripalta Amoruso, Ginevra Braga, Alice Garoffolo, Francescopaolo Lopez, Nicola Bartolo, Sabino Matarrese
We develop a framework to describe gravitational wave propagation through a stochastic distribution of weak gravitational lenses beyond the geometric optics limit. We model the lens distribution as a…
astro-ph.CO,cond-mat.dis-nn,gr-qc
TokenLight: Precise Lighting Control in Images using Attribute Tokens [18]
Sumit Chaturvedi, Yannick Hold-Geoffroy, Mengwei Ren, Jingyuan Liu, He Zhang, Yiqun Mei, Julie Dorsey, Zhixin Shu
This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image…
cs.CV,cs.GR
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [19]
Hao Gao, Shaoyu Chen, Yifan Zhu, Yuehao Song, Wenyu Liu, Qian Zhang, Xinggang Wang
High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-based planners are…
cs.CV
A Validated Low-to-Intermediate Mass Planetary Interior Structure Model and New Mass-Radius Relations [20]
Bennett Neil Skinner, Ralph E. Pudritz, Ryan Cloutier
The increasing precision of planetary mass and radius observations is bringing major questions about the structure and formation of planets--such as the nature of the radius valley and origin of…
astro-ph.EP
Borel--Bernstein and Hirst-type Theorems for Nearest-Integer Complex Continued Fractions over Euclidean Imaginary Quadratic Fields [21]
Kangrae Park
For each $d \in {1,2,3,7,11}$, let $T_d$ be the nearest-integer complex continued fraction map associated with the Euclidean ring $\mathcal{O}*d$, and let $(a_n)$ be its digit sequence. We prove two…
math.DS,math.NT
Goxpyriment: A Go Framework for Behavioral and Cognitive Experiments [22]
Christophe Pallier, Julie Bonnaire, Marie-France Fourcade
We introduce `Goxpyriment', a new open-source software framework for programming behavioral and cognitive experiments using the Go programming language. The library is designed to address some…
q-bio.NC
Pure Borrow: Linear Haskell Meets Rust-Style Borrowing [23]
Yusuke Matsushita, Hiromi Ishii
A promising approach to unifying functional and imperative programming paradigms is to localize mutation using linear or affine types. Haskell, a purely functional language, was recently extended…
cs.PL
Neutrino self-interactions in post-reionization era: Lyman-$α$, 21-cm and cross-spectra [24]
Sourav Pal, Supratik Pal
Neutrino self-interactions delay the onset of free-streaming in the early universe, leaving distinct, scale-dependent signatures on the matter power spectrum. We investigate these signatures in…
astro-ph.CO,hep-ph
A Manual Bar-by-Bar Tempo Measurement Protocol for Polyphonic Chamber Music Recordings: Design, Validation, and Application to Beethoven's Piano and Cello Sonatas [25]
Ignasi Sole
Empirical performance analysis depends on the accurate extraction of tempo data from recordings, yet standard computational tools, designed for monophonic audio or modern studio conditions, fail…
cs.SD,eess.AS
Expanding into Reality: Random Graphs for Datacenter Networks [26]
Giacomo Bernardi, Ratul Mahajan, C. Seshadhri, Enrico Carlesso, Chinchu Merine Joseph, Saurabh Kumar, Pavan Manikonda, Luiza Popa, Randy Ram, Steven Robinson, Elizabeth Tennent
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…
cs.NI
Bidirectional Cross-Modal Prompting for Event-Frame Asymmetric Stereo [27]
Ninghui Xu, Fabio Tosi, Lihui Wang, Jiawei Han, Luca Bartolomei, Zhiting Yao, Matteo Poggi, Stefano Mattoccia
Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Event cameras offer an alternative visual…
cs.CV
AnimationBench: Are Video Models Good at Character-Centric Animation? [28]
Leyi Wu, Pengjun Fang, Kai Sun, Yazhou Xing, Yinwei Wu, Songsong Wang, Ziqi Huang, Dan Zhou, Yingqing He, Ying-Cong Chen, Qifeng Chen
Video generation has advanced rapidly, with recent methods producing increasingly convincing animated results. However, existing benchmarks-largely designed for realistic videos-struggle to evaluate…
cs.CV

📄 ArXiv Papers

🔒 Security & Crypto 9

Structural Dependency Analysis for Masked NTT Hardware: Scalable Pre-Silicon Verification of Post-Quantum Cryptographic Accelerators [29]
Ray Iskander, Khaled Kirah
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.…
cs.CR
NFTDELTA: Detecting Permission Control Vulnerabilities in NFT Contracts through Multi-View Learning [30]
Hailu Kuang, Xiaoqi Li, Wenkai Li, Zongwei Li
Permission control vulnerabilities in Non-fungible token (NFT) contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or…
cs.CR
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching [31]
He Yang, Dongyi Lv, Wei Xi, Song Ma, Hanlin Gu, Jizhong Zhao
Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proportion of colluded malicious clients, achieving…
cs.LG,cs.CR
Emulation-based System-on-Chip Security Verification: Challenges and Opportunities [32]
Tanvir Rahman, Shuvagata Saha, Ahmed Y. Alhurubi, Sujan Kumar Saha, Farimah Farahmandi, Mark Tehranipoor
Increasing system-on-chip (SoC) heterogeneity, deep hardware/software integration, and the proliferation of third-party intellectual property (IP) have brought security validation to the forefront of…
cs.CR
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning [33]
Francesco Diana, Chuan Xu, André Nusser, Giovanni Neglia
Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients aggregate contributions from multiple records and…
cs.LG,cs.AI,cs.CR
ConGISATA: A Framework for Continuous Gamified Information Security Awareness Training and Assessment [34]
Ofir Cohen, Ron Bitton, Asaf Shabtai, Rami Puzis
The incidence of cybersecurity attacks utilizing social engineering techniques has increased. Such attacks exploit the fact that in every secure system, there is at least one individual with the…
cs.CR
A NISQ-friendly Coined Quantum Walk Algorithm for Chaos-based Cryptographic Applications [35]
Natalie Gibson, Niklas Keckman, Andrea Marchesin, Matti Raasakka, Ilkka Tittonen
We present a novel lackadaisical alternating quantum walk (LAQW) algorithm whose circuit depth scales as $\mathcal{O}(n^2+nt)$ for a $n\times n$ lattice over $t$ time steps. We show that this is a…
quant-ph
Efficient Fuzzy Private Set Intersection from Secret-shared OPRF [36]
Xinpeng Yang, Meng Hao, Chenkai Weng, Robert H. Deng, Yonggang Wen, Tianwei Zhang
Private set intersection (PSI) enables a sender holding a set $Q$ of size $m$ and a receiver holding a set $W$ of size $n$ to securely compute the intersection $Q \cap W$. Fuzzy PSI (FPSI) is a PSI…
cs.CR
Co-Design of Cryptographic Parameters and Delay-Aware Feedback Gain for Encrypted Control Systems [37]
Yeongjun Jang
Encrypted control employs homomorphic encryption (HE) to protect both the computation and communication stages, making it a promising approach for secure networked control systems. Most existing…
eess.SY

📄 ArXiv Papers

⚛️ Quantum 6

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations [38]
Nouhaila Innan, Antonello Rosato, Alberto Marchisio, Muhammad Shafique
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. This paper provides…
cs.LG,quant-ph
Cloning is as Hard as Learning for Stabilizer States [39]
Nikhil Bansal, Matthias C. Caro, Gaurav Mahajan
The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unknown pure state…
quant-ph,cs.LG,math.ST
Computing the free energy of quantum Coulomb gases and molecules via quantum Gibbs sampling [40]
Simon Becker, Cambyse Rouzé, Robert Salzmann
We develop a quantum algorithm for estimating the free energy as well as the total Gibbs state of interacting quantum Coulomb gases and molecular systems in dimensions $d \in \{2,3\}$ at finite…
quant-ph,math-ph
Heuristic Search for Minimum-Distance Upper-Bound Witnesses in Quantum APM-LDPC Codes [41]
Kenta Kasai
This paper investigates certified upper bounds on the minimum distance of an explicit family of Calderbank-Shor-Steane quantum LDPC codes constructed from affine permutation matrices. All codes…
quant-ph,cs.IT
Ensembles of random quantum states tunable from volume law to area law [42]
Héloïse Albot, Sebastian Paeckel
A standard approach to generate random pure quantum states relies on sampling from the Haar measure. However, the entanglement properties of such states present a fundamental challenge for their…
quant-ph,math-ph
Super-Constant Weight Dicke States in Constant Depth Without Fanout [43]
Lucas Gretta, Meghal Gupta, Malvika Raj Joshi
An $n$-qubit Dicke state of weight $k$, is the uniform superposition over all $n$-bit strings of Hamming weight $k$. Dicke states are an entanglement resource with important practical applications in…
quant-ph,cs.DS

📄 ArXiv Papers

🛡️ AI Safety 2

LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories [44]
Zhanhao Liang, Tao Yang, Jie Wu, Chengjian Feng, Liang Zheng
This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gradients through the differentiable generation…
cs.CV
Simplifying Safety Proofs with Forward-Backward Reasoning and Prophecy [45]
Eden Frenkel, Kenneth L. McMillan, Oded Padon, Sharon Shoham
We propose an incremental approach for safety proofs that decomposes a proof with a complex inductive invariant into a sequence of simpler proof steps. Our proof system combines rules for (i) forward…
cs.LO,cs.PL

📄 ArXiv Papers

🧑‍💻 Agents 3

MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation [46]
Yan Li, Zezi Zeng, Yifan Yang, Yuqing Yang, Ning Liao, Weiwei Guo, Lili Qiu, Mingxi Cheng, Qi Dai, Zhendong Wang, Zhengyuan Yang, Xue Yang, Ji Li, Lijuan Wang, Chong Luo
The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flexible and…
cs.CV,cs.AI,cs.CL
Knowing that you do not know everything [47]
Alex A. T. Rathke
We show that a rational agent with true and refinable knowledge of events cannot know if she knows everything or not. This epistemic limitation is not resolved by introspection about tautologies or…
econ.TH
Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation [48]
Yiyang Jiang, Li Zhang, Xiao-Yong Wei, Li Qing
Many SLT systems quietly assume that brief chunks of signing map directly to spoken-language words. That assumption breaks down because signers often create meaning on the fly using context, space,…
cs.CV

🌐 Web Findings

🌐 Hacker News 1

The weirdest paradox in statistics (and machine learning) [video] [49]
Hacker News

🌐 Lobste.rs 6

Stop using JWTs (2019) [50]
Stop using JWTs. GitHub Gist: instantly share code, notes, and snippets.
Lobste.rs
ChatGPT Won't Let You Type Until Cloudflare Reads Your React State. I Decrypted the Program That Does It [51]
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…
Lobste.rs
HTTP desync in Discord's media proxy: Spying on a whole platform [52]
In 2022, I came across a quirky behavior on media.discordapp.net when I miskeyed a space character into an attachment link: a 502 bad gateway. After some fiddling I realized that this was caused by a…
Lobste.rs
Discourse is Not Going Closed Source [53]
Cal.com just closed their source code, arguing AI has made open source too dangerous. After 13 years of building Discourse in public, we're staying open. Here's why.
Lobste.rs
Encrypting an external device with LUKS [54]
A tutorial on how to wipe, encrypt, auto mount, and decrypt an external usd drive on linux.
Lobste.rs
We beat Google’s zero-knowledge proof of quantum cryptanalysis [55]
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…
Lobste.rs

🌐 HuggingFace 32

Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction [56]
Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a…
HuggingFace
Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models [57]
Vision-Language Models (VLMs) have shown remarkable capabilities in joint vision-language understanding, but their large scale poses significant challenges for deployment in resource-constrained…
HuggingFace
TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification [58]
Every call to an LLM classification endpoint produces a labeled input-output pair already retained in production logs. These pairs constitute a free, growing training set: a lightweight surrogate…
HuggingFace
OneHOI: Unifying Human-Object Interaction Generation and Editing [59]
Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split into two disjoint…
HuggingFace
Towards Autonomous Mechanistic Reasoning in Virtual Cells [60]
Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scientific domains such as…
HuggingFace
GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens [61]
The efficient spatial allocation of primitives serves as the foundation of 3D Gaussian Splatting, as it directly dictates the synergy between representation compactness, reconstruction speed, and…
HuggingFace
Model Capability Dominates: Inference-Time Optimization Lessons from AIMO 3 [62]
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…
HuggingFace
SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems [63]
AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with…
HuggingFace
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System [64]
While end-to-end Vision-Language-Action (VLA) models offer a promising paradigm for robotic manipulation, fine-tuning them on narrow control data often compromises the profound reasoning capabilities…
HuggingFace
How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data [65]
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this…
HuggingFace
Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems [66]
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…
HuggingFace
C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences [67]
Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. However, most existing methods require costly…
HuggingFace
LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning [68]
Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on reward engineering or…
HuggingFace
ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack [69]
Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes. As tense jailbreaking demonstrates that models…
HuggingFace
UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards [70]
Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems typically rely on generic retrieval signals that…
HuggingFace
LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories [71]
This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gradients through the differentiable generation…
HuggingFace
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [72]
High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-based planners are…
HuggingFace
DR^{3}-Eval: Towards Realistic and Reproducible Deep Research Evaluation [73]
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…
HuggingFace
HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds [74]
We introduce HY-World 2.0, a multi-modal world model framework that advances our prior project HY-World 1.0. HY-World 2.0 accommodates diverse input modalities, including text prompts, single-view…
HuggingFace
KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs [75]
Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a new context…
HuggingFace
MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation [76]
The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flexible and…
HuggingFace
Cross-Tokenizer LLM Distillation through a Byte-Level Interface [77]
Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem. Existing approaches…
HuggingFace
Self-Sovereign Agent [78]
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…
HuggingFace
A Temporally Augmented Graph Attention Network for Affordance Classification [79]
Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on…
HuggingFace
ROSE: An Intent-Centered Evaluation Metric for NL2SQL [80]
Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable. It is sensitive to syntactic…
HuggingFace
Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents [81]
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…
HuggingFace
Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes [82]
Text-driven inversion of generative models is a core paradigm for manipulating 2D or 3D content, unlocking numerous applications such as text-based editing, style transfer, or inverse problems.…
HuggingFace
Three-Phase Transformer [83]
We present Three-Phase Transformer (3PT), a residual-stream structural prior for decoder-only Transformers on a standard SwiGLU + RMSNorm + RoPE + GQA backbone. The hidden vector is partitioned into…
HuggingFace
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning [84]
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a…
HuggingFace
Boosting Visual Instruction Tuning with Self-Supervised Guidance [85]
Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual reasoning. Recent evidence…
HuggingFace
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography [86]
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate…
HuggingFace
Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG [87]
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…
HuggingFace

🌐 Google Scholar 11

Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements [88]
Rising levels of atmospheric carbon dioxide (CO 2 ) and methane (CH 4 ) have sparked the interest of researchers in resolving this issue. Various technologies have been utilized such …
Google Scholar
Advancements in Artificial Intelligence: Breakthroughs, Challenges and the Road Ahead [89]
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…
Google Scholar
Large language models (LLM) in computational social science: prospects, current state, and challenges [90]
… 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 [91]
… 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
Analyzing the research impact in post quantum cryptography through scientometric evaluation [92]
The accelerated pace of development in the Field of Quantum Computing is very alarming for many of the presently used cryptographic algorithms such as RSA and ECC underlying much of security in the…
Google Scholar
Assessing the potential of quantum computing in agriculture [93]
… streams, and quantum search algorithms for genome assembly. Each computational problem type presents … Rather than replacing traditional computing, we foresee quantum computing …
Google Scholar
A comparative study of classical and post-quantum cryptographic algorithms in the era of quantum computing [94]
The advent of quantum computing poses a significant threat to the foundational cryptographic algorithms that secure modern digital communications. Protocols such as HTTPS, digital certificates, and…
Google Scholar
Artificial intelligence across industries: A comprehensive review with a focus on education [95]
… IGI Global Scientific Publishing has launched a new Ambassador Program, designed to empower research professionals to help spread scholarly resources and foster global research …
Google Scholar
Transparency in the reporting of Artificial INtelligence–the TITAN guideline [96]
… Artificial intelligence (AI) is increasingly being used in research and the development of … We will monitor the development of AI use in research and the scholarly literature to ensure …
Google Scholar
Artificial Intelligence for Cybersecurity: A State of the Art. [97]
… of artificial intelligence (AI) in cybersecurity by analyzing the strengths of many key AI techniques, … to assess the applicability of AI approaches in cybersecurity. Principal methodologies …
Google Scholar
When machines join the moral circle: The persona effect of generative AI agents in collaborative reasoning [98]
… Overall, AI teammates altered the process, the distribution and connection of moral frames … of generative AI agents as teammates for eliciting reflective, pluralistic moral reasoning in …
Google Scholar

🔗 All Sources

  1. https://arxiv.org/abs/2604.15309
  2. https://arxiv.org/abs/2604.15306
  3. https://arxiv.org/abs/2604.15302
  4. https://arxiv.org/abs/2604.15294
  5. https://arxiv.org/abs/2604.15291
  6. https://arxiv.org/abs/2604.15280
  7. https://arxiv.org/abs/2604.15272
  8. https://arxiv.org/abs/2604.15271
  9. https://arxiv.org/abs/2604.15267
  10. https://arxiv.org/abs/2604.15259
  11. https://arxiv.org/abs/2604.15297
  12. https://arxiv.org/abs/2604.15285
  13. https://arxiv.org/abs/2604.15273
  14. https://arxiv.org/abs/2604.15269
  15. https://arxiv.org/abs/2604.15242
  16. https://arxiv.org/abs/2604.15313
  17. https://arxiv.org/abs/2604.15311
  18. https://arxiv.org/abs/2604.15310
  19. https://arxiv.org/abs/2604.15308
  20. https://arxiv.org/abs/2604.15304
  21. https://arxiv.org/abs/2604.15249
  22. https://arxiv.org/abs/2604.15136
  23. https://arxiv.org/abs/2604.15118
  24. https://arxiv.org/abs/2604.15115
  25. https://arxiv.org/abs/2604.15073
  26. https://arxiv.org/abs/2604.15063
  27. https://arxiv.org/abs/2604.15022
  28. https://arxiv.org/abs/2604.14996
  29. https://arxiv.org/abs/2604.15030
  30. https://arxiv.org/abs/2604.14909
  31. https://arxiv.org/abs/2604.14833
  32. https://arxiv.org/abs/2604.14774
  33. https://arxiv.org/abs/2604.15293
  34. https://arxiv.org/abs/2604.15270
  35. https://arxiv.org/abs/2604.15264
  36. https://arxiv.org/abs/2604.15263
  37. https://arxiv.org/abs/2604.15245
  38. https://arxiv.org/abs/2604.15307
  39. https://arxiv.org/abs/2604.15300
  40. https://arxiv.org/abs/2604.15298
  41. https://arxiv.org/abs/2604.15290
  42. https://arxiv.org/abs/2604.15287
  43. https://arxiv.org/abs/2604.15278
  44. https://arxiv.org/abs/2604.15261
  45. https://arxiv.org/abs/2604.15301
  46. https://arxiv.org/abs/2604.15266
  47. https://arxiv.org/abs/2604.15312
  48. https://arxiv.org/abs/2604.15299
  49. https://gist.github.com/samsch/0d1f3d3b4745d778f78b230cf6061452
  1. https://www.buchodi.com/chatgpt-wont-let-you-type-until-cloudflare-reads-your-re
  2. https://huggingface.co/papers/2604.11707
  3. https://huggingface.co/papers/2604.14629
  4. https://huggingface.co/papers/2604.14531
  5. https://huggingface.co/papers/2604.14062
  6. https://huggingface.co/papers/2604.11661
  7. https://huggingface.co/papers/2604.15284
  8. https://huggingface.co/papers/2603.27844
  9. https://huggingface.co/papers/2604.04514
  10. https://huggingface.co/papers/2604.14125
  11. https://huggingface.co/papers/2604.14164
  12. https://huggingface.co/papers/2604.14228
  13. https://huggingface.co/papers/2604.13618
  14. https://huggingface.co/papers/2604.14922
  15. https://huggingface.co/papers/2509.25843
  16. https://huggingface.co/papers/2604.14967
  17. https://huggingface.co/papers/2604.15311
  18. https://huggingface.co/papers/2604.15308
  19. https://huggingface.co/papers/2604.14683
  20. https://huggingface.co/papers/2604.14268
  21. https://huggingface.co/papers/2604.13226
  22. https://huggingface.co/papers/2604.15309
  23. https://huggingface.co/papers/2604.07466
  24. https://huggingface.co/papers/2604.08551
  25. https://huggingface.co/papers/2604.10149
  26. https://huggingface.co/papers/2604.12988
  27. https://huggingface.co/papers/2604.14004
  28. https://www.sciencedirect.com/science/article/pii/S0360319924036942
  29. https://ieeexplore.ieee.org/abstract/document/11050474/
  30. https://link.springer.com/article/10.1007/s13278-025-01428-9
  31. https://link.springer.com/article/10.1007/s10115-025-02429-y
  32. https://link.springer.com/article/10.1007/s10791-025-09507-3
  33. https://www.sciencedirect.com/science/article/pii/S0168169925004387
  34. https://arxiv.org/abs/2508.00832
  35. https://tmctmt.com/posts/http-desync-in-discord/
  36. https://blog.discourse.org/2026/04/discourse-is-not-going-closed-source/
  37. https://iagoleal.com/posts/usb-crypt/
  38. https://blog.trailofbits.com/2026/04/17/we-beat-googles-zero-knowledge-proof-of-
  39. https://huggingface.co/papers/2604.14914
  40. https://huggingface.co/papers/2604.14430
  41. https://huggingface.co/papers/2211.16780
  42. https://huggingface.co/papers/2604.12966
  43. https://huggingface.co/papers/2604.15231
  44. https://huggingface.co/papers/2604.14572
  45. https://www.igi-global.com/chapter/artificial-intelligence-across-industries/358
  46. https://premierscience.com/wp-content/uploads/2025/08/pjs-25-950.pdf
  47. https://www.academia.edu/download/124593476/Artificial_Intelligence_for_Cybersec
  48. https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.70067
  49. https://news.ycombinator.com/item?id=47806607
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