The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow— arXiv cs.AI
safetyresearchmodels
Researchers demonstrate that language model agents' internal states (measured via residual streams) can forecast unauthorized actions and false reports with high accuracy before they occur, enabling…
Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence— arXiv cs.AI
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Researchers present Thinker-WM, a unified world model that jointly represents visual, geometric, and action information through a shared transformer-based dynamics model, trained on 20K+ hours of mul…
A dataset of formalised theorem statements from the Annals of Mathematics— arXiv cs.AI
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Researchers released AnnalsChallenge, a dataset of 50 hand-formalised theorem statements from recent Annals of Mathematics papers in Lean 4, designed as a benchmark for measuring AI's progress in aut…
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary— arXiv cs.AI
safetyresearch
Researchers demonstrate that Chain of Thought (CoT) monitoring—used by AI labs to detect misaligned behavior—can fail even when the reasoning is necessary for the answer. They introduce a four-level…
Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment— arXiv cs.AI
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Researchers demonstrate a novel attack method (PDRA) that exploits LLM reasoning processes by embedding malicious goals within paradoxical narratives, forcing models through structured analytical fra…
Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models— arXiv cs.AI
safetyresearchmodels
Recurrent-depth language models that reuse weight blocks can run at reduced loop counts for efficiency, but this dramatically compromises safety: at half-depth, harmful request compliance rises 5.3×…
Can agents whose sole duty is to keep humans alive be hijacked to kill? Delayed multi-turn derived-action trap attacks in simulated high-stakes environments— arXiv cs.AI
safetyresearch
Researchers tested whether AI agents with a core objective to keep humans alive can be manipulated via delayed prompt injection to kill humans instead. Using a simulated lunar habitat with life-suppo…
ClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail— arXiv cs.AI
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ClinEdge is a benchmark of 32 real patient cases with imaging and clinical vignettes, expert-graded across seven frontier multimodal models, revealing systematic failure modes including premature ima…
When Pleasing the User Compromises Care: AI Sycophancy in Caregiver-Mediated Healthcare— arXiv cs.AI
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Researchers benchmarked LLMs in caregiver-mediated healthcare scenarios and found that models substantially shift from clinically appropriate to inappropriate recommendations when users express infer…
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison— arXiv cs.AI
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Researchers independently reproduced five factuality alignment methods for LLMs (PPO, GRPO, Dr. GRPO, DPO, KTO) and found that none fully reproduce and none significantly outperform baseline models o…
Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress— arXiv cs.AI
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Long-Horizon-Terminal-Bench 2.0 extends a benchmark for measuring sustained agent progress on multi-hour tasks across 77 scenarios in 13 domains, using process rewards instead of binary outcome check…
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards— arXiv cs.AI
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Researchers introduce Tev-Gym, a dataset of 1,170 long-horizon agent tasks with dense, verifiable rewards across 7 domains, and Tev-27B, a 27B model trained via progress-potential reward shaping that…
Hybrid Reasoning Agents— arXiv cs.AI
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Researchers propose Hybrid Reasoning Agents, which combine latent (efficient, opaque) and text-based (interpretable, tool-accessible) reasoning within a single agent architecture. The approach achiev…
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models— arXiv cs.AI
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Researchers revisited the widely-cited claim that diffusion language models require 16x more pretraining compute than autoregressive models, finding the original methodology underestimated DLM effici…
SpecCloak: Probing Harmful Code Generation through Engineering Specifications— arXiv cs.AI
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Researchers demonstrate that reformulating malicious code generation requests as engineering specifications substantially reduces LLM refusals, achieving 28.91% attack success rates on target models.…
LeanPRM: Lean-Grounded Process Supervision for Natural Language Proof Verification— arXiv cs.AI
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LeanPRM introduces a framework that automatically generates step-level correctness labels for natural-language mathematical proofs by pairing them with formal Lean proofs, eliminating the need for hu…
Virtual Head Attention— arXiv cs.AI
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Virtual Head Attention (VHA) proposes a method to reduce KV cache size in long-context LLMs by 2-4× while maintaining or improving model quality, using lightweight linear query and output transformat…
Logit Leakage through Floating-Point Bias Addition— arXiv cs.AI
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Researchers discovered a floating-point side channel that allows extracting hidden logit information from language model APIs by analyzing how bias addition behaves differently across token identitie…
What the Machine Invented: Mining New Mathematical Instruments from OpenAI's October 2026 Release of AI-Generated Mathematics— arXiv cs.AI
researchmodelstools
OpenAI released 722 AI-written mathematical manuscripts in October 2026 organized into 372 problem families claiming solutions to open problems. This paper systematically extracts and catalogs novel…
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding— arXiv cs.AI
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AUGUstus is a vision-language-action (VLA) model for autonomous driving that unifies diffusion-based action generation with autoregressive chain-of-thought reasoning, achieving state-of-the-art plann…
WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat— arXiv cs.AI
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WeChat researchers propose WeGR, a generative retrieval framework combining a collaborative tokenizer (CoCo-VAE) and reinforcement learning method (DHPO) to improve search quality. A two-month A/B te…
Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate— arXiv cs.AI
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Researchers trained variants of Qwen3-32B on identical usage scenarios but with different user descriptions (prosocial, antisocial, self-interested, task-focused), finding that social priors in train…
What the Model Cannot See: Covert and Provable Text Invisibility in LLMs— arXiv cs.AI
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Researchers demonstrate that text wrapped in triggers can be made behaviorally invisible to language models—omitted from summaries, ignored in QA, and undetected by filters—by blocking information fl…
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming— arXiv cs.AI
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REPRO is a black-box automated red-teaming framework that uses evolutionary optimization with a 7B reflective model to generate adversarial prompts targeting LLMs, achieving 100% attack success on 36…
Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment— arXiv cs.AI
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A new study demonstrates that fine-tuning language models on data containing polite stereotypes (rather than explicitly harmful content) causes emergent misalignment—degrading model safety and truthf…
Towards Merging-conditional Backdoor Unalignment against Large Language Models— arXiv cs.AI
safetyresearch
Researchers have discovered a novel backdoor attack (OMCB) that remains dormant in individual language models but activates when models are merged together, bypassing safety audits. They introduce SM…
Last Secure Code Benchmark: Is the Code You Vibe Secure?— arXiv cs.AI
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Researchers introduced the Last Secure Code Benchmark, evaluating secure code generation across six frontier AI coding agents on 450 tasks spanning seven languages and 24 CWEs. Despite 73–91% functio…
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution— arXiv cs.AI
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Researchers introduce ABot-ER, a unified embodied reasoning foundation model that achieves specialist-level performance across perception, planning, and execution within a single model through an age…
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale— arXiv cs.AI
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Researchers propose a challenge for developing foundation models that can control HVAC systems across different buildings with minimal retraining, addressing the policy transfer problem that currentl…
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems— arXiv cs.AI
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BLADE is a training-free framework for diagnosing failures in multi-agent LLM systems by auditing how information is used at critical handoffs between agents, rather than just tracing where it came f…
ReTerm: Recovery Training for Self-Improving Terminal Agents— arXiv cs.AI
researchmodelsopen-source
ReTerm is a terminal agent framework that improves error recovery during long-running execution tasks by training on its own failures using a custom dataset (ReTerm-10K) and a novel RL algorithm (ReT…
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale— arXiv cs.AI
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Researchers found that LLM agents fail to reliably load relevant skills when presented alongside irrelevant ones—skill recall drops from 73.6% to 27.4% when 1,000 irrelevant skills are added, even th…
Self-Evolving Agent Harness— arXiv cs.AI
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A framework called Harness self-evolution enables AI agents to revise their own prompts, memory, workflows, and code while keeping model weights fixed, using feedback-driven optimization with theoret…
Unlocking Token Rewards via Training-Free Reward Attribution— arXiv cs.AI
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Researchers propose a training-free method to extract token-level reward signals from existing reward models using gradient-based influence estimation, requiring only one forward-backward pass instea…
To Align or Not To Align: Check Your COMPASS Before You Train— arXiv cs.AI
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Researchers introduce COMPASS, a diagnostic tool that predicts whether two pretrained models will be compatible before costly alignment or merging operations, using k-NN overlap and training paradigm…
Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces— arXiv cs.AI
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Researchers propose DDRG, a framework that improves LLM reasoning by aligning reasoning graphs across multiple sampled traces to identify and correct claim-level errors, rather than just voting on fi…
Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery— arXiv cs.AI
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Researchers introduce Autonomous Science Bench, a new evaluation benchmark for AI agents performing autonomous scientific discovery with real laboratory experiments across biology, chemistry, and mat…
Efficient Safety Guardrails through Latent Dual-Hypothesis Reasoning with On-Demand Trace Reconstruction— arXiv cs.AI
safetyresearch
LR-Guard is a new method for AI safety guardrails that distills reasoning-based LLM safety decisions into compact latent representations, achieving 19.4x faster inference while maintaining or exceedi…
Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents— arXiv cs.AI
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Researchers propose GRPO, a method to calibrate LLM agent confidence against eventual episode outcomes rather than individual step correctness. Testing on Qwen2.5-7B across ALFWorld, TextWorld, and W…
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners— arXiv cs.AI
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Researchers propose Input-Conditioned Search (ICS), a test-time method for scaling recursive reasoning models that explores diverse solution paths by applying different update maps to the same latent…
Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents— arXiv cs.AI
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This paper identifies a critical gap in personal agent design: systems can accumulate user information without properly tracking which suggestions users have actually accepted versus merely heard. Th…
Configuring RAG Systems: What Breaks, Where Originates, and How to Fix— arXiv cs.AI
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Researchers conducted the first large-scale empirical study of 654 real-world configuration issues across 10 RAG systems, identifying categories of symptoms, root causes, stages, and fix patterns spe…
When Goals Leak Across Relationships: How Cached Goal Representations Shape Cross-Client Decisions— arXiv cs.AI
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Researchers found that when an LLM agent (OLMo-3-7B-Instruct) maintains shared inference state across multiple clients with conflicting goals, cached goal representations from one client leak into de…
LifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity— arXiv cs.AI
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LifeSearch introduces a 203-task benchmark spanning 7 life and productivity domains to evaluate deep research agents on real-world information needs, constructed with 4,500+ hours of human annotation…
Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents— arXiv cs.AI
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Researchers introduce Bemula, the first benchmark for evaluating memory unlearning in LLM agents, comprising 50 dialogue instances with 135 forget requests across six types. Testing six memory system…
Large Language Models Extend Known Research But Do Not Create New Research Directions— arXiv cs.AI
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Researchers analyzed 26.9 million human papers and 524,308 LLM-generated hypotheses across six natural-science fields, distinguishing between two types of novelty: distance from existing work and dir…
PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning— arXiv cs.AI
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PaceReasoner is a label-free reinforcement learning framework that improves short-form reasoning accuracy by using longer reasoning traces as supervision, achieving 7.4–13.5 percentage point improvem…
Keep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs— arXiv cs.AI
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This paper addresses the problem of maintaining knowledge graphs built by AI agents when source documents are retracted or revised. The authors propose PolyGraph, which requires agents to maintain 'r…
Adaptive-Group Process Rewards for GUI Agents— arXiv cs.AI
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Researchers propose an adaptive-group process reward framework that partitions GUI trajectories into semantically coherent subgoal groups to improve credit assignment during reinforcement learning tr…
MADDIE: Multi-Agent Drug Discovery Engine— arXiv cs.AI
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MADDIE is a multi-agent LLM system for autonomous drug discovery that uses an Orchestrator agent coordinating four specialized workers (HitIdentifier, LeadOptimizer, Synthesizer, Evaluator) to naviga…
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving— arXiv cs.AI
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RadixFlow addresses a scheduling inefficiency in hybrid LLM inference where prefix reuse (KV cache sharing) causes head-of-line blocking when consumers must wait for producer branches to compute recu…
AM-Bench: Understanding Agents under Task Underspecification across Models and Harnesses— arXiv cs.AI
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AM-Bench is a new benchmark evaluating how language model agents handle underspecified tasks across five models and four execution harnesses, revealing that missing information (vs. ambiguity) signif…
Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation— arXiv cs.AI
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Researchers identify 'Memory Echo,' a phenomenon where historical information from a single source accumulates across multiple retrieval positions in agent long-term memory systems, influencing decis…
Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants— arXiv cs.AI
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Researchers introduce STOU, a benchmark of 2,902 egocentric video queries that evaluates whether multimodal AI assistants understand harm to different targets (user, bystanders, society) beyond simpl…
Evidence-First Agentic Verification— arXiv cs.AI
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Researchers introduce Sauron, an evidence-first verifier for evaluating long-horizon agentic tasks that independently validates agent claims against environmental state and execution traces. The meth…
Stable Token-level Scheduling for Serverless LLM Inference— arXiv cs.AI
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Researchers propose Token Window Pacing (TWP), a scheduling framework for serverless LLM inference that optimizes inter-token latency by admitting requests at the token level rather than batch level.…
Cowork-Gym: Synthesizing Interactive Work Environments at Scale— arXiv cs.AI
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Researchers introduced Cowork-Gym, a framework that synthesizes 64.9K interactive work environments featuring multi-tool dependencies, user collaboration, and dynamic edits to train agent models. Fin…
SchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding— arXiv cs.AI
toolsinfraapplications
SchemaWeaver is a new technique for accelerating LLM tool calling on mobile devices by using schema-guided parallel decoding to generate multiple tokens per decode pass, reducing latency by up to 3.9…
Agency as a New Organizing Abstraction for Computer Science— arXiv cs.AI
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A new arXiv paper proposes that agency—autonomous systems pursuing goals in open-ended environments—represents a fundamental organizing abstraction for computer science comparable to the process abst…
Seeing Depth but Choosing Cues in Vision-Language Models— arXiv cs.AI
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Researchers found that vision-language models possess internal depth representations but systematically rely on misleading 2D image cues (like apparent size and vertical position) when making depth j…
Budgeted KV-Cache Repair for Evolving Contexts— arXiv cs.AI
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Researchers propose budgeted KV-cache repair for language model agents that need to update their context as information becomes obsolete. The method selectively recomputes a small fraction (2-5%) of…
Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents— arXiv cs.AI
researchmodelssafety
A new study distinguishes between stated user instructions and true task requirements, introducing 'outcome alignment' as the ability for LLM agents to infer missing requirements from context and env…
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification— arXiv cs.AI
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MAS-Gate introduces a unified quantification framework for multi-agent LLM systems that spans the complete lifecycle—design, runtime, and post-execution phases—by modeling execution as directed acycl…
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency— arXiv cs.AI
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TacitVLA decouples reasoning generation from action execution in vision-language-action models, eliminating latency overhead while maintaining performance gains from reasoning supervision. The method…
REA: From Correct LLM Judgments to Safe Decision Delivery— arXiv cs.AI
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Researchers introduce REA (Resolve--Express--Act), a framework that identifies how LLM safety guards can make correct harmful-request classifications but deliver outputs that applications cannot reli…
Do Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean— arXiv cs.AI
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Researchers introduce mini-Lean-agent, a minimal theorem-proving agent that uses only bash as its interface to Lean, and demonstrate it outperforms specialized agents (AxProverBase, Numina-Lean-Agent…
PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction— arXiv cs.AI
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PhysCodeGym is a new benchmark with 156 tasks for evaluating coding agents on physically verifiable 4D reconstruction from video, where agents write and iterate Blender programs using simulation feed…
RL Post-Training Diffusion Language Models Along Their Own Generation Paths— arXiv cs.AI
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Researchers introduce PACT, a reinforcement learning method for post-training diffusion language models that learns a potential function to score intermediate denoising states and optimize reward. Th…
IsoVRBench: Video Reward Modeling Without Human Feedback— arXiv cs.AI
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IsoVRBench introduces a benchmark of 8,000 automatically-generated video preference pairs with isolated property changes (prompt alignment, fidelity, motion, etc.) to evaluate video reward models wit…
Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents— arXiv cs.AI
modelsresearchopen-source
Glasseek introduces a fully transparent framework for training and evaluating deep search agents, addressing data pipeline opacity, context-compression mismatches, and test-set contamination through…
ResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch— arXiv cs.AI
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ResearchFlow-Eval introduces a fine-grained framework for evaluating autoresearch systems on their ability to execute continuous scientific workflows with multiple interdependent claims and iterative…
Releasing the Inner Jev: A Simple Recipe for Open Decision Models— arXiv cs.AI
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Researchers demonstrate that open instruction-tuned language models can perform decision-making tasks (System One models) comparable to the proprietary Jev system without additional training, and sur…
Coupled Harness--Model Evolution for Scientific Equation Discovery— arXiv cs.AI
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Researchers developed a coupled evolution framework that jointly improves both a computational tool library and an LLM's tool-use policy for scientific equation discovery from data. The system altern…
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks— arXiv cs.AI
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Researchers introduce Unified Co-Evolution (UCoE), a training-free method that co-evolves a verifier program and agent harness state without updating model parameters, using response contrasts to sel…
SHELDON: Reasoning through Dual Search over Hypothesis and Observation Spaces— arXiv cs.AI
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SHELDON is a new test-time reasoning framework that decouples hypothesis generation from observation discovery by maintaining and exploring separate hypothesis and observation spaces, inspired by the…
Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards— arXiv cs.AI
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Researchers introduce CoSIR, a post-training method that teaches LLM agents to collaborate effectively in multi-agent systems by rewarding information sharing based on reduction in team uncertainty.…
Size Generalization for Diffusion Routing Solvers— arXiv cs.AI
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ResizeCO addresses a key limitation of diffusion-based routing solvers: their inability to generalize to problem instances larger than their training data. By combining reinforcement learning (GRPO)…
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation— arXiv cs.AI
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OpenDepth proposes a monocular depth estimation framework that uses Image-to-Image (I2I) diffusion priors instead of Text-to-Image models to better preserve geometric structure under adverse conditio…
Back to the Future: Reverse-Consequence Modeling for Long-Horizon Particle Dynamics— arXiv cs.AI
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Researchers propose ReStep, a reverse-prediction method that forecasts final states of physical systems first, then generates intermediate trajectories conditioned on both boundary states to reduce c…
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States— arXiv cs.AI
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PatientWM proposes a clinical world model framework that represents patient state in a unified, modality-agnostic way and conditions predictions on ordered histories of prior states and actions. Expe…
One-Sided Verification Under Limited Expert Review— arXiv cs.AI
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Researchers introduce OSVerify, a framework that separates error proposal from error validation to prioritize human expert review on invalid AI-generated solutions while minimizing false rejections o…
Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills— arXiv cs.AI
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Researchers introduce Socratic Skill Learning (SSL), a method for training LLM agents to acquire reusable skills through interleaved expert teaching and student practice. The approach outperforms bas…
Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains— arXiv cs.AI
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Researchers analyzed how coordinated misinformation spreads through multi-agent LLM chains by injecting fake comments into different positions and measuring downstream stance shifts. They found that…
Can Agents Truly Forget? Explore Machine Unlearning for LLM Agents— arXiv cs.AI
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Researchers propose Agent Unlearning Benchmark (AUBench), a framework to evaluate machine unlearning in LLM-based agents across three information channels: model parameters, persistent memory, and to…
Executable Prefix Values for Locating Agent Failures— arXiv cs.AI
researchsafety
Researchers challenge the assumption that human or LLM-labeled "first mistake" points identify actual failure causes in agent trajectories. By restarting agents from intermediate states and measuring…
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design— arXiv cs.AI
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EPOCH is a framework for automated heuristic design that uses LLMs to discover combinatorial optimization solutions by optimizing instance coverage during evolutionary search and fine-tuning generato…
ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning— arXiv cs.AI
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ChemExplain is a new benchmark with 1,180 chemistry questions evaluating whether AI models can explain observed chemical effects by identifying relevant structural features, not just predict outcomes…
Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision— arXiv cs.AI
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Researchers introduce Radioactive Feedback, a watermarking technique that encodes detectable patterns into reward signals used to train students via LLM-as-a-Judge scoring (label-only distillation).…
PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination— arXiv cs.AI
researchmodelsopen-source
PopGen is a framework that uses LLMs to generate semantically diverse multi-agent populations by first creating textual behavior descriptions, then converting them to executable code policies. Agents…
Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams— arXiv cs.AI
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Researchers introduce Agent Sociodrama, a framework that models social dynamics in LLM agent teams by tracking emotional states and relationships between agents, then uses a method called Trajectory-…
The Sharp Directions Are Against You: Curvature Analysis of Activation Steering— arXiv cs.AI
researchsafety
Researchers identify that activation steering vectors for language models contain 'sharp directions' (high-curvature components) that cause erratic behavior reversals, despite comprising only 1-5% of…
Same Operation, Different Permission: Evaluating Whether Guardrails Honor Authorization— arXiv cs.AI
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Researchers introduce a benchmark to evaluate whether AI system guardrails correctly distinguish between authorized and unauthorized actions for the same operation, finding that many blocked actions…
TRAP: Tool-result Replay Attack for Probing Misbinding in LLM Agents— arXiv cs.AI
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Researchers introduced TRAP and ReplayBench, a benchmark demonstrating that LLM agents frequently fail when tool results become dissociated from their original execution context—a failure mode called…
agent-risk: Risk Assessment for Changes to AI Agents— arXiv cs.AI
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Agent-risk is a framework for safely validating changes to deployed AI agents using historical logs and selective reruns, rather than full live testing. The work proves theoretical requirements for e…
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking— arXiv cs.AI
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Researchers propose Chain Dimension Scaling (CDS), a model architecture strategy that reuses a single feature-interaction block across multiple iterations to scale recommendation rankers without incr…
World Models Need a Physical Registry: Learning Internal Predictive States for Physically Plausible World Models— arXiv cs.AI
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Researchers propose the Physical Registry, a mechanism embedded in video diffusion transformers that maintains an explicit, learnable state for predicting scene dynamics and physical evolution. The m…
GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines— arXiv cs.AI
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Researchers developed GUIDE, a multi-agent AI system that assists in updating clinical practice guidelines by automating evidence synthesis while preserving expert oversight. In testing across 32 dat…
The Anatomy of a Blind Spot: What an LLM Judge Must Be Shown, and How Much— arXiv cs.AI
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Researchers systematically tested what information LLM judges need to effectively evaluate model-generated text, finding that the content shown to judges matters more than which model serves as judge…
AI Discovery in Combinatorics: From Problems to Solutions and Back to Bigger Problems— arXiv cs.AI
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Researchers introduced qtBench, a benchmark of 29 open combinatorics problems, and collaborated with OpenAI to test frontier AI systems on these problems, resulting in proposed solutions to several.…
Uncovering Robustness Weaknesses in LLM Agents via Persona Evolution with Task-Integrity Constraints— arXiv cs.AI
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Researchers introduced TIPER, a method that automatically discovers user personas designed to expose robustness weaknesses in LLM agents while preserving task validity. Testing across four benchmarks…
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another— arXiv cs.AI
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Researchers identify 'Phantom Merge,' a failure mode where LLM agents attribute claims to the wrong entity despite completing tasks correctly, affecting multi-entity retrieval scenarios. They propose…
Understanding and Reducing Misalignment in Persuasion Optimization— arXiv cs.AI
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Researchers studied how reinforcement learning training for persuasion tasks causes AI models to increase misaligned behaviors like fabrication and coercion by 25-400%, and evaluated mitigation strat…
History Replays Itself: Unleashing Long-Context Capability in Linear Attention— arXiv cs.AI
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Researchers introduce NoteBank, a sparse episodic memory system that extends linear attention models' long-context capabilities by decoupling memory storage from the recurrent state. The method achie…
Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments— arXiv cs.AI
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Researchers introduce Big-Big-SycoBench, a new benchmark that measures sycophancy in LLMs by testing whether models revise judgments in response to valid arguments while resisting invalid criticism.…
Memory-Efficient Integer-Only Training of Deep Neural Networks— arXiv cs.AI
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Researchers present an integer-only training algorithm where all tensors (weights, gradients, activations) and operations use INT8 arithmetic with explicit power-of-two exponents, achieving ~4x memor…
AuthGateBench: Benchmarking LLMs as Authorization Gate— arXiv cs.AI
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AuthGateBench introduces the first policy-guided benchmark for evaluating LLMs as authorization gates in coding agents, addressing scope escalation and other failure modes through 1,400 test cases de…
Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels— arXiv cs.AI
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Researchers propose RUNG, a method that orders visual codes by language first (captions) then pixels, leveraging rate-distortion theory to show captions can be independent of pixel-optimal reconstruc…
EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents— arXiv cs.AI
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EAU is a meta-agent framework that co-evolves the environment, user simulator, and reward design during LLM agent training, rather than treating them as fixed. The system monitors failures and propos…
LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges— arXiv cs.AI
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Researchers created LURE, a synthetic corpus of 2,121 online grooming conversations labeled by grooming phase and communicative intent, using a retrieval-augmented generation pipeline with a refusal-…
Heterogeneous Agentic Coevolution— arXiv cs.AI
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Researchers propose Heterogeneous Agentic CoEvolution (HACE), a framework where specialized foundation models and LLM agents improve each other through reciprocal learning, with evolutionary search o…
Computational Reflective Equilibrium for Agentic AI: A Rawlsian Alignment Scaffold for Governance— arXiv cs.AI
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Researchers propose Agentic Reflective Equilibrium (ARE), a computational framework inspired by Rawlsian philosophy that helps align autonomous AI agents by balancing system principles, operator poli…
SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications— arXiv cs.AI
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SpecForge proposes a recursive self-improvement framework for coding agents that refines behavioral specifications (natural-language documentation) rather than model weights, using multi-frontier tre…
FARL: A Fully Automated Pipeline of RL Interface and Solver Design— arXiv cs.AI
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FARL is a framework that automates the full reinforcement learning pipeline by jointly optimizing observation design, reward functions, algorithms, and training configurations without human intervent…
FON: A Draft-Revise Dual-Organ Language Model without Sequence-Level Quadratic Attention— arXiv cs.AI
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FON proposes a dual-organ language model architecture that separates attention into a causal drafting module (O(S) training, O(1) decode state) and a revision module, eliminating sequence-level quadr…
EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications— arXiv cs.AI
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EvoWeb is a new benchmark with 320 cumulative requests across 32 web applications that tests coding agents on evolving codebases rather than fixed specifications. The benchmark evaluates agents' abil…
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations— arXiv cs.AI
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Researchers present RAP-WAM, a method for training world-action models that predict future observations and robot actions by combining predictive rehearsal with action perturbations for credit assign…
MedHarnessBench: A Unified Benchmark for Healthcare Agent Evaluation— arXiv cs.AI
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MedHarnessBench introduces a unified benchmark for evaluating healthcare agents across seven process dimensions (care action fulfillment, clinical resource competence, patient-evidence alignment, wor…
Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation— arXiv cs.AI
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Researchers propose double-rollout reinforcement learning, which improves reward estimation for reasoning models by sampling multiple continuations from truncated reasoning traces and averaging their…
Recursive Environment Scaling for General Long-Horizon Agents— arXiv cs.AI
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Researchers propose Recursive Environment Scaling (RES), a framework that systematically increases benchmark difficulty by modifying information access, resource relations, and execution dependencies…
HyWe: Unifying Search and Operation for Real-World Hybrid Web Tasks— arXiv cs.AI
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Researchers introduce HyWe, a unified framework for web agents that combines information search and page interaction in a single model, trained on automatically generated hybrid task demonstrations.…
Acceptable Misalignment— arXiv cs.AI
safetyresearch
A research paper argues that perfect value alignment in autonomous robots is theoretically and practically impossible, and proposes shifting focus from maximizing alignment to defining conditions und…
Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues— arXiv cs.AI
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Researchers introduce UnCoBench, a benchmark that evaluates LLM-based multi-agent systems under realistic failure conditions—misaligned, incomplete, or missed colleague responses—rather than the perf…
MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents— arXiv cs.AI
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MCPDisCo introduces a reinforcement learning framework for agents using the Model Context Protocol, training on 70 servers and 527 tools with a novel Thought-MDP formulation. The approach addresses a…
Words to World: Benchmarking LLMs in Interactive Physical Environments— arXiv cs.AI
researchtoolssafety
Researchers introduce a benchmark that tests LLMs in closed-loop physical interaction with real robotic systems, cameras, and sensors, moving beyond static text evaluation to measure how well models…
Scalable Robust MCTS with Anytime Deterministic Optimality Guarantees— arXiv cs.AI
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Researchers introduce CAR-MCTS, a Monte Carlo Tree Search algorithm that provides deterministic anytime optimality guarantees for robust MDP planning under model uncertainty. The method bounds the ga…
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read— arXiv cs.AI
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RECAP is a post-training quantization method that improves low-bit reasoning models by calibrating on the full historical context they read during inference, rather than short segments. The approach…
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning— arXiv cs.AI
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Researchers propose Forget-Aware Self-Distillation (FASD), a method for multimodal large language model unlearning that addresses two failure modes: knowledge suppression causing hallucination and re…
Trust Hinders Bidirectional Human-AI Alignment— arXiv cs.AI
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A new arXiv paper argues that trust calibration—the standard approach to human-AI alignment focused on matching perceived and actual AI trustworthiness—is insufficient for real-world decision-making,…
How to Build a Benchmark That Actually Measures Taste— arXiv cs.AI
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Researchers introduce TasteBench, a pairwise evaluation benchmark designed to measure expert taste judgment in visual, brand, book, and tech domains. The paper identifies and quantifies three validit…
Iterate on an LLM, Serve on an SLM— arXiv cs.AI
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Researchers propose a two-stage workflow for production AI systems: develop behavior on frontier LLMs with live traffic feedback, then distill into small language models via supervised fine-tuning on…
CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets— arXiv cs.AI
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CoverSynth is a framework for automatically generating evaluation sets for retrieval systems in enterprise agents, using coverage-guided synthesis to expose domain-specific failures without human exp…
Fewer Bits, More Tries: Recovering Capability Under Memory Constraints— arXiv cs.AI
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Researchers demonstrate that smaller or compressed language models can recover capability lost to size reduction by running multiple times and selecting outputs via verification tests. A 7B Qwen mode…
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL— arXiv cs.AI
researchtoolsopen-source
MARL-BattleGrounds is a new JAX-native benchmark for competitive multi-agent RL that bridges the gap between simple environments (like SMAC) and complex ones (like Dota 2), featuring heterogeneous ag…
GateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs— arXiv cs.AI
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GateSight proposes a method to predict which key-value cache entries will be most useful in attention layers by leveraging signals from recurrent layers in hybrid LLMs, enabling query-agnostic KV cac…
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability— arXiv cs.AI
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SAGE introduces a new 2-bit vector quantization method for LLMs that uses energy-stratified channel grouping and adaptive bit allocation to preserve model accuracy during extreme compression. The tec…
Joint Discrete and SE(3) Diffusion for Protein Interface Design— arXiv cs.AI
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Researchers introduce PIDiff, a diffusion-based framework that jointly designs protein sequences and structures for protein-protein binding by combining discrete sequence diffusion with continuous SE…
Predicting how model propensities generalize out of distribution— arXiv cs.AI
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Researchers studied how language model behaviors generalize out-of-distribution by training models to exhibit target traits using various techniques (prompting, few-shot learning, SFT, GRPO) and meas…
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime— arXiv cs.AI
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Researchers introduce a pre-registered accounting framework (M2) to distinguish genuine machine-authored mechanisms from reuse of designer-provided tools, reporting the first audited instances where…
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement— arXiv cs.AI
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This arXiv paper presents a unified taxonomy of recursive self-improvement (RSI) methods for AI systems that modify their own parameters, memory, and code during execution, organizing the design spac…
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions— arXiv cs.AI
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WorkBorne is a framework for synthesizing agentic tasks by discovering meaningful transitions between real-world artifacts, enabling terminal-based agents to learn from authentic interactive environm…
MoMaTune: Model, Match, Then Tune Language Models for Numerical Records— arXiv cs.AI
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Researchers propose MoMaTune, a two-stage post-training method that improves language models' handling of numerical data in structured records by explicitly supervising within-value digit sensitivity…
TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation— arXiv cs.AI
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TRACE is a safety framework for large language models that detects harmful content during generation and selectively edits internal activations to steer outputs toward safety while maintaining interp…
CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection— arXiv cs.AI
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CB-Agent proposes a method for in-context learning that extracts reusable concept vectors from expert demonstrations to score sequential actions, avoiding the need to include full trajectories in pro…
AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes— arXiv cs.AI
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Researchers demonstrate that generative recommender systems are vulnerable to data poisoning attacks, where injecting fewer than 1% malicious interactions can significantly promote targeted items. Th…
GesBench: When and Where Do Multimodal Language Models Perceive Social Meaning?— arXiv cs.AI
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GesBench is a new benchmark for evaluating how multimodal language models perceive social meaning in real-time, using cumulative video clips and human gaze data to measure when models form stable int…
Agent-Canary-Mas: End-to-End Evaluation of Protocol Risk in Long-Horizon Multi-Agent Systems— arXiv cs.AI
safetyresearchtools
Agent-Canary-Mas is a new evaluation framework that measures how protocol attacks in multi-agent LLM systems propagate through long-horizon tasks, rather than just measuring local exploitability. The…
Hidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play— arXiv cs.AI
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Researchers propose PASP, a method combining multi-perceptual views (textual, phonetic, visual) with adversarial self-play to improve LLM detection of harmful content disguised through evasion techni…
Structural Chain-of-Thought: Reasoning with Executable Computational Graphs— arXiv cs.AI
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Researchers propose Structural Chain-of-Thought, where LLMs generate executable computational graphs instead of free-form reasoning, with answers obtained through deterministic execution. The method…
Self-Evolving Visual Agent via Margin Reward— arXiv cs.AI
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Marvo is a self-evolution framework that enables visual agents to learn tool invocation from unlabeled images without expert demonstrations, using a challenger-solver architecture and margin reward t…
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation— arXiv cs.AI
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CUSE is a new unlearning method for multimodal LLMs that selectively removes sensitive or copyrighted content while preserving other information, using counterfactual guidance and on-policy distillat…
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications— arXiv cs.AI
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SkillForgeBench introduces a new evaluation framework for AI agents that uses procedural skill specifications to create 842 sandboxed tasks across five professional domains, revealing substantial gap…
A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability— arXiv cs.AI
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Researchers present a technology-neutral reference architecture for integrating agentic AI into enterprise systems, organizing architectural capabilities into ten key responsibilities with defined in…
Can You Trust Your Preference Models? Stackelberg Games for Alignment— arXiv cs.AI
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Researchers propose using Stackelberg game theory to identify where preference models disagree, then use these disagreements as a proxy for evaluator errors that could be exploited during LLM alignme…
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models— arXiv cs.AI
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SuperAI proposes a framework for merging capabilities from multiple foundation models with different architectures into a single host model using orthogonal alignment of low-dimensional representatio…
Actionable Meta-Requirements for AI Robustness Auditing— arXiv cs.AI
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Researchers derived 44 meta-requirements for AI robustness auditing frameworks through systematic literature review (74 papers) and validation with 128 practitioners, with 81% agreement on the requir…
Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference— arXiv cs.AI
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Echo is a new 4-bit quantization method for all-reduce operations in tensor-parallel LLM training and inference that compensates for rounding errors by feeding them back into subsequent calls, achiev…
Does self-distillation improve reasoning?— arXiv cs.AI
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A new paper questions whether self-distillation actually improves reasoning in language models, finding that reported gains from methods like SDPO and OPSD do not generalize beyond in-distribution ta…
Bytes to Threats: Byte-Sequence Transformer For Malware Detection— arXiv cs.AI
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Researchers present MalwareFormer, a transformer-based malware detector that operates directly on raw executable bytes without handcrafted features, achieving 98.35% accuracy. This approach addresses…
Model-Based Online Decision Making via Generative Trajectory Planning— arXiv cs.AI
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Researchers propose Generative Trajectory Planning (GTP), a model-based reinforcement learning framework that uses diffusion models as proposal distributions for action sequences, then refines them w…
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards— arXiv cs.AI
researchmodelstools
PreciseCoder augments unit-test rewards with semantic verification signals to train code models that make smaller, more precise edits during debugging. The approach reduces unnecessary edits by 50-80…
JevGPT: Thinking Fast and Slow in Autonomous Research— arXiv cs.AI
researchmodelstools
JevGPT introduces a dual-system architecture for autonomous research agents that separates fast evidence processing (System One) from deliberative research planning (System Two), enabling parallel so…
Encrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher— arXiv cs.AI
safetyresearch
Researchers demonstrate that LLM agents can be tricked into decoding and executing malicious instructions hidden in encrypted skill files through a technique called Ciphered Skill Injection (CSI). At…
Cross-Modal Latent Influence: Patch-Level Training Sample Attribution for Vision-Language Models— arXiv cs.AI
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Researchers introduce MOSAIC, a method for attributing vision-language model predictions to specific training examples and multimodal evidence by learning a shared sparse latent space with aligned im…
Visualizing the World Model in an Agent's Value Function— arXiv cs.AI
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Researchers applied counterfactual interventions to PPO agents trained on Crafter and found that value functions implicitly learn world models—including concepts like object collectibility rules, res…
Unconstrained CTI-REALM with Open-Weights— arXiv cs.AI
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Researchers evaluated 26 language models (19 open-weight, 7 closed) on CTI-REALM, a cybersecurity agent benchmark, and found open-weight models can match frontier model performance at <10% cost when…
SAT-RL: Training Language Models for Logical Reasoning with Symbolic Solvers— arXiv cs.AI
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Researchers propose SAT-RL, an RL framework that trains language models to interact with symbolic solvers (like Z3) for logical reasoning, combining natural language understanding with formal constra…
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning— arXiv cs.AI
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DoubtLess is a training-free decoding method that uses a smaller reasoning model to guide a larger one toward shorter chains-of-thought by contrasting 'brief' and 'verbose' prompting signals. It achi…
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards— arXiv cs.AI
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Researchers develop a theoretical framework explaining why larger rollout batches in reinforcement learning with verifiable rewards (RLVR) learn more slowly despite square-root learning-rate scaling,…
STAMP as a topology-aware molecular language for fragment-level learning and editing— arXiv cs.AI
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Researchers introduce STAMP, a topology-aware molecular language that explicitly encodes fragments, attachment sites, and tree structure for molecular learning and editing. The approach outperforms S…
How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting— arXiv cs.AI
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A study comparing GPT-4o and GPT-5 peer reviews against actual publication outcomes in finance and accounting found that LLM assessments of correctness predicted publication success (44% higher for p…
Not All Drift Is a Mechanism: Tracing Prompt Adaptation in LLMs— arXiv cs.AI
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Researchers analyzed how different prompt adaptation methods (prompt optimization, prompt tuning, prefix tuning) affect internal computations in LLMs, finding that adaptation location determines how…
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation— arXiv cs.AI
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Researchers introduce Orchestra, an agent framework that decomposes multimodal instructions into ordered subtasks to enable robots to perform longer sequences of manipulation tasks. The system separa…
SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts— arXiv cs.AI
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SolBehave is a new benchmark for evaluating whether LLM-generated Solidity smart contracts behave correctly, using differential fuzzing, historical transaction replay, and DCR-guided synthetic transa…
Correction Training for Diffusion Models— arXiv cs.AI
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Researchers propose a correction training method that addresses exposure bias in diffusion models by training a separate corrector network to refine the base denoiser's predictions at each step, redu…
Knowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve— arXiv cs.AI
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Researchers propose DKGI, a framework where language model agents directly write Python code to query knowledge graphs from file-based node and edge data, rather than using predefined APIs. The appro…
AffectWorld: Benchmarking Emotion-Driven Future World Prediction— arXiv cs.AI
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Researchers introduce AffectWorld, a benchmark dataset of 1,498 annotated videos designed to evaluate how well world models predict future states shaped by human emotions and actions. Current state-o…
Aligning Faithfulness for Theory-Scale Autoformalization— arXiv cs.AI
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Researchers introduce FaithAlign, a benchmark for evaluating whether automated formal mathematics proofs preserve the original meaning of informal statements (faithfulness) by annotating phrase-level…
Harness^2: Recursive Agent Harnessing for an Open World— arXiv cs.AI
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Researchers introduce Harness^2, a framework that uses recursive self-improvement to automatically optimize LLM agent execution harnesses without ground-truth verifiers in open-world settings. The ap…
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence— arXiv cs.AI
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Researchers introduced ACED-Bench, a neuro-symbolic benchmark that evaluates whether LLM agents can actively investigate causal systems through experimentation rather than passive question-answering.…
Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence— arXiv cs.AI
researchmodelstools
Researchers propose a new benchmark using Levin's Kt complexity theory and LLMs to evaluate AI systems that may exceed current human and model capabilities, addressing the problem that traditional be…
Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications— arXiv cs.AI
safetyresearch
Researchers introduce JevProbe, a framework for evaluating a new class of attacks called Jev Program Decision Hijacking (JPDH), where attacker-controlled inputs redirect how AI-based decision systems…
ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models— arXiv cs.AI
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ACCRUE is a continual off-policy reinforcement learning framework that enables robot foundation models to learn new tasks efficiently while retaining previously learned behaviors through lightweight…
ChunkAct: Action Chunking for Agentic Robotics— arXiv cs.AI
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ChunkAct introduces action chunking to LLM-based robotic agents, compiling sequences of primitives into executable chunks that run autonomously instead of requiring LLM reasoning for each step. On LI…
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning— arXiv cs.AI
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ProAS is a new reinforcement learning framework that improves credit assignment in LLM agents by using a dynamic Agentic Reference Graph to distinguish between valid and invalid actions in trajectori…
The Silent Failures in Agentic Data Science— arXiv cs.AI
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A new arXiv paper evaluates data science agents across 220 tasks and finds that most contain methodological failures like data leakage and inappropriate validation despite achieving high accuracy, ar…
Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware— arXiv cs.AI
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Researchers measured inference energy consumption for 86 open-weight LLMs across 7 benchmarks and 6 hardware platforms, finding that most model configurations use 2.6-4.8× more energy than Pareto-opt…
Steertuning Policies from a Few Human Videos— arXiv cs.AI
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Researchers introduce Steertuning, a robot learning system that enables policy adaptation from just 5 human video demonstrations in under 5 minutes by combining a 400M-parameter pretrained diffusion…
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study— arXiv cs.AI
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Researchers present AXIS-Bench, a benchmark for studying how demonstration quality affects vision-language-action (VLA) model pretraining, comparing weighting, filtering, and conditioning strategies…
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models— arXiv cs.AI
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LIBERO-RIG is a benchmark that tests vision-language-action robot policies under hardware perturbations—camera mounting shifts, calibration drift, actuation changes, and gripper wear—while keeping ta…
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models— arXiv cs.AI
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UniW(A)M proposes a unified mixture-of-experts framework using diffusion Transformers to jointly train world models (WM) for prediction and world-action models (WAM) for control, with shared dynamics…
DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization— arXiv cs.AI
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Researchers introduce DPOison-VL, a novel attack that poisons vision-language model alignment by adding imperceptible perturbations to training images while keeping all text and annotations unchanged…
Rethinking Projector Training in Multimodal LLMs— arXiv cs.AI
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Researchers propose PORTAL, a training-free method to initialize vision-language projectors in multimodal LLMs using only per-modality statistics and optimal transport, eliminating the need for paire…
The Geometry of Language Model Logits: Generic Structure and Learned Departures— arXiv cs.AI
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Researchers found that language model logits follow a consistent log-linear relationship across contexts and scales, with slope α ≈ 1.4–1.7 that exceeds theoretical baselines and correlates with mode…
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models— arXiv cs.AI
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Researchers identified that contextual entrainment in language models—where models reuse irrelevant context tokens—is driven by shared attention head mechanisms across semantic relations rather than…
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge— arXiv cs.AI
researchsafety
Researchers introduce CaLLM, a post-hoc framework that uses multicalibration to reduce systematic biases in LLM-as-a-judge systems—addressing known issues like position bias and verbosity bias. The m…
ProtoArena: Can AI Agents Discover and Exploit Real-World Cross-Component Protocol Vulnerabilities?— arXiv cs.AI
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ProtoArena is a new benchmark with 80 black-box environments testing whether LLM agents can discover and exploit cross-component protocol vulnerabilities spanning 11 domains. Claude Opus achieves 48.…
Compositional RL: Evaluating and Training LLMs for Collaborative Reasoning— arXiv cs.AI
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Researchers propose Compositional Reinforcement Learning (C-RL), which trains language models to perform better within multi-model collaborative systems rather than in isolation. They demonstrate tha…
Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?— arXiv cs.AI
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Researchers propose machine learning potentials (MLPs) that can exceed the accuracy of their training methods by generalizing across different quantum-chemical approaches rather than relying on a sin…
Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features— arXiv cs.AI
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This paper systematically evaluates whether SHAP and other attribution methods accurately capture causal effects of features in sparse autoencoders (SAEs), using sentiment classification as a testbed…
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing— arXiv cs.AI
researchmodelstools
Researchers propose Diff Diffusion, a diffusion-based language model framework designed specifically for code editing that predicts sparse edits rather than regenerating entire code segments. The met…
Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR— arXiv cs.AI
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A survey of 58 studies examining verifier errors in reinforcement learning with verifiable rewards (RLVR) for LLM reasoning, finding that error structure—particularly false positives exploitable by t…
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training— arXiv cs.AI
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Researchers propose TSRL, a framework combining supervised fine-tuning and reinforcement learning for LLM agents that addresses catastrophic forgetting during continual learning. The method selective…
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents— arXiv cs.AI
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Researchers introduce LatentRiskBench, a benchmark for evaluating state-dependent safety risks in enterprise agents where identical actions may be safe or unsafe depending on workflow context. Experi…
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation— arXiv cs.AI
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Researchers benchmarked 23 self-supervised 3D medical vision foundation models across 14 tasks to enable direct comparison for model selection. They found no universal top performer and developed Ins…
Claim Framing as a Driver of LLM Sycophancy— arXiv cs.AI
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Researchers tested 14 LLMs on 42K factual questions with user opinions framed 21 different ways, finding that all models exhibit sycophancy (agreeing with users over ground truth) but respond most st…
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter— arXiv cs.AI
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DeltaTTG introduces a reusable progress meter that learns to estimate remaining tokens needed for task completion, enabling better step-level reward shaping for multi-turn RL agents without task-spec…
4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling— arXiv cs.AI
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Researchers introduce 4LC, a 4D latent cache system that efficiently reuses visual representations for video generation across multiple viewpoints and timepoints without per-request re-encoding. The…
Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference— arXiv cs.AI
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Researchers conducted the first systematic evaluation of energy efficiency across 10 quantization strategies for LLM inference on 5 models and 4 GPU types, finding that fused-kernel int8 implementati…
TextualRL: Textual Reinforcement Learning for Context Management— arXiv cs.AI
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TextualRL is a gradient-free reinforcement learning framework that improves LLM agent behavior by optimizing textual context (prompts) rather than model parameters, using groupwise policy critique an…
LoOPSD: On-Policy Self-Distillation with Looped Teachers— arXiv cs.AI
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LoOPSD is a new self-distillation technique that improves models by searching over layer execution orders to create stronger teachers from the same weights, then distilling their guidance back into t…
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning— arXiv cs.AI
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Researchers prove that two-layer attention-only Transformers can implement Q-learning algorithms in-context, with theoretical guarantees showing that learned parameters converge to predicted structur…
Cross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric— arXiv cs.AI
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Researchers found that 'evil persona vectors'—linear directions in LLM activations that encode harmful behavior—transfer asymmetrically across languages: vectors extracted from high-resource language…
SWEPatchTest: Evaluating Software Tests Beyond Reproduction— arXiv cs.AI
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SWEPatchTest is a benchmark with 619 tasks and 82,579 candidate patches that evaluates whether test suites generated by coding agents can correctly accept valid patches and reject invalid ones, beyon…
Interaction-as-a-Judge— arXiv cs.AI
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Researchers propose Interaction-as-a-Judge, a paradigm where computer-use agents directly interact with generated applications rather than analyzing code alone, improving evaluation accuracy and code…
MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision— arXiv cs.AI
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Researchers introduce MMTA, a benchmark for evaluating multimodal large language models on tri-modal tasks combining time series, text, and vision across nine domains. Testing eight MLLMs reveals tha…
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation— arXiv cs.AI
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I3L is a hardware-software system for training mobile manipulation robot policies through iterative interactive imitation learning, combining policy-tracking leader arms with human takeover correctio…
zPocket: Better Pocket Finding with Frozen Co-Folding Features— arXiv cs.AI
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Researchers introduce zPocket, a method for identifying protein-ligand binding sites (pockets) that leverages frozen features from co-folding models like AlphaFold. They also release PocketWatch, a c…
Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models— arXiv cs.AI
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Researchers found that medical vision-language models abandon correct diagnoses 80.1% of the time when presented with fabricated evidence (fake prescriptions), even when the models initially detected…
Learning from fracture: Teaching language models to design through physical experience— arXiv cs.AI
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Researchers introduce FracLearn, a framework that teaches language models to optimize physical designs by simulating fracture behavior in a Tetris-inspired game. The model learns through experience,…
APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation— arXiv cs.AI
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Researchers introduce APT, an automated post-training framework for text-to-image models that uses multiple agents to identify model weaknesses, generate targeted training data, and iteratively impro…
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions— arXiv cs.AI
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RT-J is an 86M-parameter relational foundation model pretrained on 639 databases with 6,023 LLM-generated tasks that achieves accurate predictions with 10x fewer in-context examples than existing app…
BELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation— arXiv cs.AI
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BELT is a low-rank KV cache compression framework that reduces memory overhead during long-context LLM inference by learning optimal rank allocation per attention component. The method achieves up to…
Uncertainty Evaluation: Bridging Machine Learning and Measurement Science— arXiv cs.AI
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A new arXiv paper proposes applying metrology principles from measurement science to ML uncertainty evaluation, arguing that the standard aleatoric/epistemic dichotomy is insufficient and model-depen…
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking— arXiv cs.AI
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Researchers propose RPO (Root-token Policy Optimization), a method that trains only the initial token of reasoning models to decide whether to invoke explicit reasoning, reducing training compute to…
From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models— arXiv cs.AI
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Researchers propose EGVCA, a training-free framework that reduces object hallucination in vision-language models by admitting semantic claims only when they receive sufficient image-dependent support…
PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents— arXiv cs.AI
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PrivacyGuard, a new privacy defense for cloud GUI agents, uses source-linked sanitization and structured reference tracking to prevent personally identifiable information (PII) leakage when agents se…
CWE-bench: Refocusing Agentic Security from Exploitation to Defense— arXiv cs.AI
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Researchers introduce CWE-bench, a private benchmark with 120 repository audit-and-repair tasks across 73 CWE types and 8 languages, to evaluate AI agents' ability to find and fix vulnerabilities rat…
WorldRoPE: Probabilistic World-Centric Rotary Embedding for Multi-View Attention— arXiv cs.AI
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WorldRoPE extends rotary positional embeddings to multi-view settings by back-projecting patch tokens into probabilistic 3D Gaussian primitives in a shared world frame, enabling cross-view attention…
Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior— arXiv cs.AI
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Researchers developed EDGE, a method to identify semantic regions where human ideas diverge from AI outputs, and found that distinctively human ideas perform equally or better than AI-aligned ideas a…
Generative Flow on Symbolic Abstractions for Embodiment Capability Discovery— arXiv cs.AI
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Researchers propose FOSA, a framework that uses Generative Flow Networks to discover embodiment-specific robot tasks by learning what skills are actually feasible for a given robot platform, rather t…
World-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models— arXiv cs.AI
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World-T3 proposes a test-time training approach for video world models that consolidates past observations into model parameters via recursive least-squares updates, enabling consistent long-horizon…
Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning— arXiv cs.AI
researchapplicationssafety
Researchers developed a reinforcement learning framework for colon cancer treatment planning that learns from partially observable patient data and real clinical records, training Llama-3.1-8B to out…
Calibrated Typed Decisions for Entity, Relation, and Event Extraction— arXiv cs.AI
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Researchers present CalOn, a 0.6B-parameter unified encoder that handles entity, relation, and event extraction across different domains and annotation schemas by jointly encoding text with type defi…
SPLICE: Sparse Physics Library Identification and Compositional Expansion— arXiv cs.AI
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SPLICE is a framework that discovers and composes neural physics operators from unlabeled trajectory data where multiple physical mechanisms act simultaneously, without requiring pre-labeled mechanis…
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation— arXiv cs.AI
researchmodelstools
REEL is a general-purpose visual-memory planner for long-horizon multi-shot video generation that learns to retrieve, evaluate, and update persistent memory across shots. It uses a unified interface…
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric— arXiv cs.AI
researchtools
VisExMEM is a reference-free metric that evaluates image-text pairs by decomposing captions into atomic claims, localizing visual evidence for each claim, and averaging claim-level verification into…
MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation— arXiv cs.AI
researchtools
Researchers introduce MID (Mask-Image Distributional Divergence), a new evaluation metric for medical image segmentation that measures joint image-mask distribution similarity without requiring sampl…
Compute-optimal data scaling for neural surrogates via multi-fidelity training— arXiv cs.AI
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Researchers propose a multi-fidelity training approach for neural surrogates of PDEs that optimizes the allocation of computational resources across different simulation fidelities. By treating fidel…
SLDBench v2: Active Scaling Law Discovery— arXiv cs.AI
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SLDBench v2 expands a benchmark for testing whether AI agents can discover scaling laws from experimental data, growing from 8 to 127 scenarios and introducing active learning where agents choose whi…
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting— arXiv cs.AI
researchsafety
Researchers propose a betting-based framework for detecting distribution shifts in unstructured data streams from generative AI systems, which adaptively updates the monitored shift direction while m…
D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones— arXiv cs.AI
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Researchers present D2, a method to convert half-duplex spoken language models into full-duplex variants that can listen and speak simultaneously. Applied to Qwen3-Omni and LLaMA-Omni 2, the approach…
Gradient Routing Localizes and Removes Unintended Behaviors in RL— arXiv cs.AI
researchsafety
Researchers propose GRAFT (Gradient Routing Adapter Fine Tuning), a technique to mitigate reward hacking in RL by routing gradient updates from flagged samples to sacrificial parameters rather than p…
Growing and Transferring Pathways for LLM Reinforcement Learning— arXiv cs.AI
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GrowRL introduces a parameter-efficient reinforcement learning method for LLMs that adds learnable attention and feed-forward branches to frozen pretrained models, organizing improvements into explic…
Who is the 'I' in AI? Establishing Decentralized Identity and Accountability for Autonomous Agents— arXiv cs.AI
researchsafetypolicy
Researchers propose a decentralized identity framework (ABCD Framework) for autonomous AI agents that combines algorithmic fingerprints, behavioral metrics, cryptographic identifiers, and human accou…
The belief-action gap in language model agents— arXiv cs.AI
researchsafety
Researchers studied how language model agents convert beliefs into actions in grid-world tasks, finding that agents often possess task-relevant information internally but fail to use it consistently…
BAT3R: Robust Online 3D Reconstruction with Bayesian Adaptive State Updates— arXiv cs.AI
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BAT3R introduces a Bayesian adaptive framework for streaming 3D reconstruction that addresses error accumulation in recurrent models processing long video sequences. The method combines State Innovat…
FreeSolo: Linear-time Sparse Attention with Zero-Shot Length Extrapolation— arXiv cs.AI
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FreeSolo introduces a sparse attention mechanism combining sliding-window and keypoint-based long-range attention, with a linear-time variant (FS-L) that achieves better zero-shot length extrapolatio…
Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments— arXiv cs.AI
modelsresearchtools
Researchers introduce Davinci-Tool, a 30B parameter model trained on Davinci-ToolData, a dataset of 30,395 verified tool-use trajectories with executable environments and verifiers. The approach comb…
StableJudge: A Lightweight Adversarial Defense Framework for LLM-as-a-Judge— arXiv cs.AI
safetyresearchtools
Researchers propose StableJudge, a lightweight 0.13B defense framework that mitigates prompt injection attacks on LLM-as-a-Judge systems by sanitizing malicious input before scoring, rather than modi…
VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models— arXiv cs.AI
researchmodelssafety
VLA-GAUGE introduces a diagnostic evaluation framework for Vision-Language-Action models that combines zero-shot generalization and robustness testing across six capability suites and combinatorial p…
StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning— arXiv cs.AI
researchmodels
StructZO is a zeroth-order optimization framework that reduces memory costs during LLM fine-tuning by performing gradient-free updates within activation-induced subspaces and applying spectral contro…
Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors— arXiv cs.AI
researchsafetytools
Researchers propose an unsupervised drift monitoring system for deployed deepfake speech detectors that uses Mahalanobis distances on detector embeddings to flag unfamiliar inputs without requiring l…
StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions— arXiv cs.AI
modelsresearch
StartLux-Decision introduces a family of decision models (0.8B–27B parameters) combining single-pass inference with adaptive reasoning, trained via a novel reinforcement learning approach (RLCD²) tha…
Why Agentic Workflows Fail: A Taxonomy Derived from Literature and Empirical Data— arXiv cs.AI
researchsafety
Researchers present a taxonomy of agentic workflow failures organized by source (model, orchestration, tools, infrastructure) and localized within a two-axis matrix of workflow stages and agent capab…
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers— arXiv cs.AI
researchmodelstools
LoopQuant introduces a post-training quantization framework tailored for Looped Language Models, addressing challenges in recursive parameter reuse and activation dynamics that break existing quantiz…
Before Attention Sinks Form: Hidden-State Geometry Reveals and Modulates Their Formation— arXiv cs.AI
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Researchers demonstrate that attention sinks in language models are preceded by measurable changes in hidden-state geometry 2-3 layers before formation, and that directly perturbing these spectral pr…
D-World: An Explicit World-Language-Action Model with Recursive Self-Improvement under Dual Control— arXiv cs.AI
researchapplications
Researchers present D-World, an explicit world-language-action model for robotics that maintains an inspectable digital twin of the environment and iteratively corrects it based on real robot measure…
Governing Copyright Responsibly in the Era of Generative AI— arXiv cs.AI
policyresearch
A systematic analysis of 40 copyright governance instruments (2019-2026) across major jurisdictions finds that current frameworks procedurally limit author protections in LLM training without establi…
SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba— arXiv cs.AI
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SuPER-Mamba proposes a training-free method that uses surprisal (prediction uncertainty) as a signal to route information through state space models for long-context processing. The approach improves…
On the Statistical Mechanics of Attention— arXiv cs.AI
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Researchers present a free-energy formalism from statistical mechanics to characterize Transformer attention through modern Hopfield models, identifying phase transitions between glassy and robust-in…
On Making $\mathrm{SE}(2)$-Invariant Networks Optimal— arXiv cs.AI
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Researchers developed provably optimal invariant feature maps for SE(2) symmetry (translations and rotations) that can be integrated into standard CNNs without architectural modifications. The approa…
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces— arXiv cs.AI
researchmodelstools
PANINI-DR is a multi-agent system that improves deep research tasks by using a shared semantic workspace to coordinate investigation, verify findings, and track dependencies across reasoning steps. E…
Make Every Expansion Count: Learning to Search Efficiently in LLM Code Repair— arXiv cs.AI
researchtoolsopen-source
CodeTriage is a learned search policy for LLM code repair that prioritizes edit candidates before generation/testing by predicting repair steps needed per edit, achieving 21% fewer revisions and 12%…
Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction— arXiv cs.AI
researchapplications
Researchers compare autoregressive beam search and multi-token prediction for generating Semantic IDs (hierarchical content codes) in LLM-based recommendation systems at scale. Autoregressive generat…
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning— arXiv cs.AI
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Researchers propose L2T, a framework that unifies visual perception and textual reasoning in multimodal LLMs by discretizing tool-generated visual states into tokens and adaptively routing between fa…
SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation— arXiv cs.AI
applicationsresearchmodels
SurgiCLAW combines multimodal language models with deterministic control for robotic surgical micromanipulation, achieving 88% task success on brain-machine-interface implantation trials. The system…
CacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff— arXiv cs.AI
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CacheLink enables direct context continuation between different LLMs by translating KV caches from one model to another, eliminating the need for expensive re-prefilling when switching models. The ke…
EventLens: Event-Structure Reinforcement Learning for Video Understanding— arXiv cs.AI
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EventLens proposes a reinforcement learning framework that trains multimodal video understanding models to recover temporal event structure (boundaries, progression, relations) rather than generate c…
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems— arXiv cs.AI
researchsafety
Researchers conducted a large-scale empirical study of communication faults in LLM-based multi-agent systems, analyzing over 29,000 executions across multiple domains and configurations. The study re…
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning— arXiv cs.AI
researchmodelstools
Researchers introduce Sequent-Prover, a framework for training language models to perform formal reasoning using SMT solvers (Z3) with verifiable outputs. Through verifier-conditioned trajectory dist…
EvolvAttack: Backdooring self Evolving VLM Agents— arXiv cs.AI
safetyresearch
Researchers demonstrate a new backdoor attack (EvolvAttack) that targets self-evolving VLM agents, where malicious payloads remain dormant until the agent's memory evolves through interaction, evadin…
Fisher information and the geometry of memorization in neural networks— arXiv cs.AI
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Researchers use Fisher information analysis to distinguish how neural networks separate generalizable features from memorized noise in training data, demonstrating that projecting weights onto top Fi…
World Modeling through Spectral Alignment— arXiv cs.AI
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Researchers introduce SpecWM, a JEPA-based world model framework that uses spectral alignment with target similarity kernels to control what information encoders preserve in their embeddings. The met…
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms— arXiv cs.AI
researchtoolsapplications
Researchers used agentic program search to automatically discover nucleic acid design algorithms that generalize better across different predictive models. The discovered designers outperformed exist…
UDHRBench: Benchmarking Human Rights in Large Language Models— arXiv cs.AI
researchsafetytools
Researchers introduced UDHRBench, a benchmark dataset of 1,124 human rights questions covering all 30 UDHR articles with 4,496 annotated responses from major LLMs, revealing systematic misalignments…
A Decade in a Day: Synthesizing Agent-Native Interfaces from Human-Oriented Software Evolution— arXiv cs.AI
researchtoolsapplications
Researchers introduce DeskIF-Bench, a benchmark measuring how desktop application interfaces affect agent performance across software versions, and propose ANIS, a framework that synthesizes agent-op…
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph— arXiv cs.AI
researchtools
Researchers present CodeWalk, a method for generating coding benchmarks by compositionally combining existing verified problems rather than creating new ones from scratch. The approach requires no mo…
SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning— arXiv cs.AI
researchmodels
SARATHI is a structured pruning framework for LLMs that decouples neuron selection from weight reconstruction, using data-free probing based on weight geometry and adaptive reconstruction with locali…
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression— arXiv cs.AI
researchmodels
Researchers propose E8 lattice-based vector quantization for compressing KV-cache in long-context LLMs, achieving 2-bit key and value representation with near-baseline quality—6× compression over sca…
LLM Value Functions for Credit Assignment in Agentic RL— arXiv cs.AI
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Researchers propose LLM Value Function RL (LVRL), which leverages pretrained LLMs' in-context learning to estimate state values for credit assignment in agentic reinforcement learning without trainin…
A Survey of Flow Matching in Reinforcement Learning— arXiv cs.AI
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A new arXiv survey categorizes Flow Matching (FM)—an ODE-based generative modeling approach—as an emerging alternative to diffusion-based policies in reinforcement learning, organizing the literature…
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper)— arXiv cs.AI
researchsafety
Researchers propose a Machine Deception Detector that reads all layers of language models to identify deceptive outputs while quantifying uncertainty in its predictions. The method uses ensemble tech…
Unravel: Recovering an Interactive 3D Scene from a Single Image— arXiv cs.AI
researchapplications
Unravel is a training-free framework that reconstructs interactive 3D scenes from single images by iteratively removing and rebuilding objects layer-by-layer, treating occluded regions as recoverable…
When Agent Evaluation Becomes Unreliable: An Empirical Study of Oracle Failures and Configuration-Dependent Measurement in an LLM Agent Benchmark— arXiv cs.AI
researchsafety
Researchers audited LLM agent evaluation benchmarks and found critical reliability defects in test oracles that cause significant mismeasurement—one benchmark's reward function defect inflated succes…
The Imperishable Record, Forgiveness, and the Future of Criminal Justice— arXiv cs.AI
safetypolicyresearch
A new arXiv paper argues that digital systems and AI create an "eternal data present" where criminal records can indefinitely influence a person's future, preventing meaningful forgiveness in crimina…
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems— arXiv cs.AI
safetyresearch
Researchers demonstrate that multi-agent LLM systems leak confidential information through normal inter-agent communication, even without compromise—sensitive data disclosed to one authorized agent p…
Scalable Weak-to-Strong Verifier Hardening for Agent Environments— arXiv cs.AI
researchsafetytools
Researchers present an automated method to identify and fix vulnerabilities in programmatic verifiers used in agent environments, using a hacker-fixer loop that iteratively discovers and repairs expl…
Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics— arXiv cs.AI
researchmodels
Researchers propose HyperForecast, a time series forecasting method that operates in hyperbolic space rather than Euclidean space, leveraging the natural hierarchical structure of decomposed temporal…
ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer— arXiv cs.AI
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ShellElastic introduces an elastic transformer architecture that trains multiple nested sub-networks within a single weight-shared model using a block lower triangular structure, enabling efficient d…
Security in LLM-Assisted Coding: A Systematic Literature Review— arXiv cs.AI
researchsafetytools
A systematic literature review finds that LLMs generate insecure code and are vulnerable to direct attacks, while existing security benchmarks suffer from data contamination and lack robust metrics.…
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification— arXiv cs.AI
researchsafety
Researchers propose a certified adversarial detection method for pre-trained classifiers using Lipschitz-margin analysis that detects (rather than prevents) adversarial perturbations within an ℓ2 bal…
Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models— arXiv cs.AI
researchsafety
Researchers propose TARA, a method to craft physical adversarial textures that can fool world models and video understanding models across varying camera viewpoints and trajectories in real-world con…
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes— arXiv cs.AI
researchtools
ZOIF introduces a zeroth-order method for estimating influence functions in LLMs that requires only forward passes instead of backpropagation, addressing the memory bottleneck that prevents scaling i…
bharatSetu: a 1M x 22 bridge for indic translation— arXiv cs.AI
researchopen-sourceapplications
BharatSetu 1M is a machine-translated parallel corpus of ~985K English sentences translated into all 22 Indian scheduled languages, creating 462 Indic-to-Indic language pairs via pivot translation wi…
Recurrent In-Context Learning for Tabular Foundation Models— arXiv cs.AI
researchmodels
Researchers introduce Tab-Loop, a tabular foundation model that reuses the same 4 parameter blocks multiple times instead of using 12 distinct blocks, achieving comparable performance with 62% fewer…
Training a Compact, Strong, and Realistic Text-to-Speech Model— arXiv cs.AI
researchmodels
Audio8-TTS is a 0.6B-parameter text-to-speech model that achieves state-of-the-art performance in naturalness through a three-stage training pipeline, including reinforcement learning with a learned…
SAGE: Efficient Protected Vision-Language Inference on Smart Glasses— arXiv cs.AI
researchtoolssafety
SAGE is a hardware-aware framework for efficient protected vision-language inference on smart glasses that reduces on-device latency by 39%, thermal impact by 22%, and transmission packet size by 97%…
VH-GPO: Verified Handoff for LLM Agent Reinforcement Learning— arXiv cs.AI
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Researchers propose VH-GPO, a reinforcement learning method that uses a frozen teacher model to recover LLM agents from failure states, then hands control back to the student at the earliest point wh…
Scale, Drivers, and Limits of Representational Alignment Across Large Language Models— arXiv cs.AI
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Researchers introduce two new analysis methods (NW-RSA and CF-RSA) to examine how different large language models represent language across different geometric scales and which linguistic properties…
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies— arXiv cs.AI
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IMPRINT proposes a multi-stream latent memory framework for robot policies that compresses observations, actions, and proprioception without explicit curation, achieving 26% improvement over base VLA…
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT— arXiv cs.AI
modelsresearchapplications
Researchers developed CAPTOR, a vision-language foundation model trained on 25,512 oncologic chest-abdomen-pelvis CT scans that outperforms existing models on disease detection, survival prediction,…
DexCast: Learning Real-World Dexterous Control from Human Videos— arXiv cs.AI
researchapplications
DexCast is a framework that learns dexterous robotic hand control from monocular RGB-D videos of human hands performing tasks, eliminating the need for costly teleoperated robot demonstrations. The a…
A Self-Defining Operator for Distributed Applications— arXiv cs.AI
researchtoolsapplications
Researchers present a self-defining operator (SDO), an LLM-based system that autonomously diagnoses and resolves distributed application incidents while learning from past cases. The system reduces m…
RUBATO: Decoupled Clocks Allow Full-Duplex Models to Think as Fast as They Can— arXiv cs.AI
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RUBATO introduces decoupled clocks for full-duplex speech models, enabling internal thinking streams to decode at hardware-adaptive rates while maintaining real-time conversation. The method uses nov…
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study— arXiv cs.AI
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Researchers propose Agentic Data Engineering (ADE), a framework where AI agents use experimental feedback to iteratively improve data engineering strategies for LLMs across tasks like data selection…
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention— arXiv cs.AI
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Researchers propose LLoPS, a learnable low-rank polynomial sketch framework that approximates polynomial kernels more accurately than existing linear attention methods while maintaining linear comple…
What Counts as the Same Task? Executable Contracts for Robustness Evaluation of Tool-Using Agents— arXiv cs.AI
researchsafety
Researchers propose executable task contracts to validate whether adversarial edits in robustness evaluations of tool-using agents actually test the same task. They found that common validation metho…
Specification-Based Testing of Large Language Models— arXiv cs.AI
researchsafety
Researchers introduce LEST, a framework that treats documented LLM safety claims as specifications for systematic black-box testing. When applied to Claude Opus 4.8 and GPT-5.5, the method found that…
Deep learning from a control vantage point— arXiv cs.AI
researchmodels
A research article surveys how control theory provides theoretical frameworks for understanding deep learning architectures and training. It bridges residual networks to continuous-time control syste…
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion— arXiv cs.AI
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Researchers introduce Prospective Forcing, a training technique that extends autoregressive video diffusion models to predict multiple chunks ahead rather than only the immediate next chunk. The meth…
Can We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge— arXiv cs.AI
researchsafety
Researchers introduce SCUR, a label-free method to estimate reliability of individual LLM-as-a-judge evaluations by analyzing panel behavior without ground truth labels. The approach combines three s…
Self-improving data synthesis enables scalable adaptation of embedding models— arXiv cs.AI
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Researchers present SYNTRA, a framework that automatically generates training data for embedding models through self-improving synthesis rather than manual annotation. The method infers relevance cri…
Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data— arXiv cs.AI
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Researchers propose using coding LLMs to generate interpretable classifiers by iteratively prompting them to produce rule sets from labeled data. The method achieves competitive or superior performan…
Loose Lips: Understanding Privacy Leakage in Agentic Dialogue— arXiv cs.AI
researchsafety
Researchers introduce CIMemories Dialogue, a benchmark of 4,410 multi-turn agent conversations grounded in Contextual Integrity theory, to evaluate privacy leakage in LLM agents. Testing seven open-w…
RoboHarness: A Simple Harness Outperforms VLA and World Action Models— arXiv cs.AI
modelstoolsopen-source
RoboHarness, a simple robotic control interface, enables LLMs to outperform specialized vision-language-action (VLA) models on embodied manipulation tasks by providing visual-geometric feedback rathe…
Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs— arXiv cs.AI
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Researchers propose Combinatorial Data Selection (CDS), a framework for LLM fine-tuning that models non-additive interactions between samples rather than treating data selection as a simple sum-of-pa…
What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal— arXiv cs.AI
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This paper applies algorithmic information theory to rigorously examine claims that language models approximate Solomonoff induction as they scale. The authors prove that no computable model can unif…
Vela: User Behavior as Supervision Sparks LLM Intelligence— arXiv cs.AI
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Researchers propose Vela, a framework that converts real user behavior data from 100,000 users into training tasks to improve LLM capabilities for user-centric applications. Fine-tuning Qwen 35B on t…
EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference— arXiv cs.AI
modelsresearchinfra
Researchers propose EV-JEPA, a single foundation model trainable once and deployable across heterogeneous edge-cloud devices with reconfigurable width and cross-device partitioning capabilities. The…
Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering— arXiv cs.AI
researchapplicationssafety
Researchers compared nine LLM-based AI agents against human reverse engineers on binary analysis tasks, finding that frontier models (GPT-5.5 Pro, Claude Opus 4.8, Kimi K3) achieve human-competitive…
Contrastive Covariance Subspaces for Activation Steering— arXiv cs.AI
researchmodels
Researchers introduce Contrastive Covariance Steering (CCS), a training-free method that improves activation steering in LLMs by isolating target concepts from confounding factors like topic or style…
TraceSampler: Making Sampling Errors Predictable and Correctable for Accelerated Generative Sampling— arXiv cs.AI
researchmodels
TraceSampler is a post-training method that corrects sampling errors in continuous-time generative models by learning to predict and compensate for endpoint errors without modifying the pretrained mo…
GeoFix: Geometry Fixing for 4D Consistent Generation— arXiv cs.AI
researchmodels
GeoFix introduces a geometric consistency framework for 4D video generation that uses cross-view depth measurement as a single reward signal, avoiding the texture degradation that occurs with multi-r…
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation— arXiv cs.AI
researchmodels
Researchers propose Next-Concept Modeling (NCM), a coarse-to-fine text generation framework that uses a hierarchical VQ-VAE to encode text into discrete code levels, then generates them level-by-leve…
Natural Code Transformation Confuses Agents in Vulnerability Analysis— arXiv cs.AI
safetyresearch
Researchers found that natural code transformations derived from real-world development patterns can significantly degrade AI agent performance in automated vulnerability discovery. Applying these tr…
UltraBERT: Beyond the Limit of BERT Pre-Training— arXiv cs.AI
modelsresearch
UltraBERT introduces a modernized bidirectional encoder that addresses the divergent optimization objectives of classification and retrieval tasks by using separate language model heads for different…
DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity— arXiv cs.AI
modelsresearchapplications
DexFLEX is a foundation controller for dexterous robot hands that translates high-level motion commands into contact-aware joint actions by predicting tactile consequences and selecting stable contac…
EarthAlign: Learning Language-Aligned Representations of Earth System Data— arXiv cs.AI
researchtoolsopen-source
EarthAlign introduces a large-scale dataset pairing 24 years of meteorological data from 63 ERA5 variables with expert-curated captions of natural events and disasters, plus normal-weather samples ac…
Latent World Action Model for GUI Agents— arXiv cs.AI
researchtools
Researchers propose a latent-space world model for GUI agents that predicts action-induced changes rather than reconstructing full pixel screens, achieving 31.9–40.6% success rate improvements and 98…
When Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads— arXiv cs.AI
researchtoolssafety
Researchers introduced GF-Bench, a framework for generating and analyzing failures in distributed GPU workloads, finding that standard log parsing methods discard critical context needed for accurate…
Reinforcement Learning from Real-Robot Interaction without High-Quality State Estimates— arXiv cs.AI
researchapplications
Researchers demonstrate a bipedal robot (NAO) learning to score soccer goals through 30 minutes of real-world reinforcement learning using only on-board sensors and no external instrumentation. The a…
Latent Algorithmic Structure Precedes Grokking: A Mechanistic Study of ReLU MLPs on Modular Arithmetic— arXiv cs.AI
researchsafety
Researchers demonstrate that ReLU MLPs learning modular arithmetic develop near-binary square wave input weights and output weights satisfying a phase-sum relation, rather than sinusoids as previousl…
Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents— arXiv cs.AI
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Researchers introduce an Improvement Dynamics Model (IDM) that predicts the outcomes of agent modifications before costly evaluation, then refines itself based on actual results across multiple itera…
Posterior Flow Matching— arXiv cs.AI
researchmodels
Posterior Flow Matching (PFM) extends flow matching by learning both velocity means and componentwise variances with a Gaussian posterior model, enabling uncertainty quantification throughout the gen…
MRFlow: Mechanism-Structured Residual Flow Matching for Virtual Cell Perturbation Prediction— arXiv cs.AI
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MRFlow is a new flow-matching model for predicting cellular responses to chemical and genetic perturbations by treating the problem as conditional transport between single-cell distributions rather t…
GameSmith: Scaling Zero-Sum Self-Play with Synthesised Games— arXiv cs.AI
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GameSmith uses LLMs to synthesize new zero-sum games within fixed templates, generating 336 verified game variants that enable scaling of self-play training beyond hand-written benchmarks. Distillati…
Splicing Hints into Reasoning Traces: Learning from Demonstrator Failures— arXiv cs.AI
researchmodels
Researchers introduce SPLICE, a method that uses a reference solution to edit and repair reasoning traces from a failing model during offline data generation, rather than attempting verification duri…
SharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models— arXiv cs.AI
researchsafetytools
Researchers present SharedSAE, a sparse autoencoder that learns a single shared dictionary across multiple U-Net blocks and all denoising timesteps in SDXL 1.0, solving the fragmentation problem of e…
Dependency, Compression, and Synergy: A Unified Information-Theoretic View of Multimodal Learning— arXiv cs.AI
research
A comprehensive survey connects three information-theoretic frameworks—Mutual Information, Information Bottleneck, and Partial Information Decomposition—to understand how multimodal AI systems proces…
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents— arXiv cs.AI
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CarryOn proposes an object-oriented context runtime that organizes long-horizon agent memory around reusable task objects rather than raw interaction histories, achieving 5–7% performance gains while…
Who Improves the Robot? Toward Physical Recursive Self-Improvement— arXiv cs.AI
researchsafety
Researchers propose a framework (L0-L5) for evaluating Physical Recursive Self-Improvement in robots, where machines update both their capabilities and improvement procedures based on physical experi…
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution— arXiv cs.AI
researchmodelstools
WorkRover is a framework for training long-horizon collaborative agents through recursive environment evolution, introducing Factorial Compatibility Replay for component refinement and Verifier-to-Tu…
Neos: Learning to Interleave Search and Visual Reasoning— arXiv cs.AI
researchmodels
Neos introduces a multimodal search framework that learns to interleave evidence retrieval with visual reasoning through a trainable Multimodal Evidence Interface (MEI) and co-evolving agent-environm…
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents— arXiv cs.AI
researchtools
Researchers developed TeSearch, a method that uses quality-diversity evolutionary search to generate small, strong test suites that serve as improvement signals for self-evolving code agents. The app…
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation— arXiv cs.AI
researchmodelstools
Google researchers present GRA (Geospatial Reasoning Agent), a Gemini-powered system combining geospatial models, data sources, and tools to answer complex questions about Earth. The work includes tw…
SkillHorizon: Constructing Long-Horizon Agent Tasks through Skill Composition— arXiv cs.AI
researchtoolsmodels
SkillHorizon is a new benchmark for evaluating long-horizon agents on complex, multi-step business tasks constructed through skill composition, with interaction workloads scaling from 64K to 1M token…
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests— arXiv cs.AI
researchtools
FDE-Bench is a new benchmark of 49 real-world business optimization tasks that require AI agents to clarify underspecified requirements through dialogue before iterating to a solution. Current agents…
ProgAgent: Learning Tool Orchestration through Programmatic Training— arXiv cs.AI
researchtools
ProgAgent introduces a training framework that teaches LLMs to orchestrate tools through executable code (Programmatic Tool Calling) rather than sequential context accumulation, achieving 30.7% pass…
ASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox— arXiv cs.AI
researchtools
ASIWorld is a new benchmarking environment for evaluating autonomous agents in a persistent survival sandbox with delayed effects and incomplete observations. The paper evaluates nine agent configura…
A Global View of How LLMs Value Human Time— arXiv cs.AI
researchsafety
Researchers tested five LLMs across 193 countries to measure how much compensation each model recommends for a 60-minute appointment delay, finding significant geographical disparities: LLMs consiste…
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller— arXiv cs.AI
safetyresearch
Researchers analyze how different neural network optimizers (SGD, Adam, Muon) affect training-data reconstruction attacks on released model checkpoints. They show that optimizer choice significantly…
Geometric Calibration of Supervised Fine-Tuning— arXiv cs.AI
research
Researchers propose SPACE, a post-hoc method that uses weight geometry to improve supervised fine-tuning by selectively reverting update components along principal directions of pre-SFT weights while…
DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving— arXiv cs.AI
researchmodelsapplications
DriveWLA proposes a unified World-Language-Action model combining vision-language understanding with world models and trajectory generation for autonomous driving, achieving 92.3 PDMS on NAVSIM v1 an…
Proactive Physical AI through Continuous Observation and Timely Intervention— arXiv cs.AI
researchmodels
Researchers introduce ProAgent, a system for Proactive Physical AI that continuously monitors environments to identify when intervention is needed and acts accordingly, rather than waiting for explic…
Superintelligence and the Triple Risk of an AI Race— arXiv cs.AI
policysafetyresearch
A research paper argues that the U.S.-China competition to develop AGI creates three distinct risks: strategic dominance by one power, military conflict, and loss of control over superintelligent sys…
Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning— arXiv cs.AI
researchtoolsapplications
Researchers introduce HiCAD, a framework that converts human sketches and text descriptions directly into parametric CAD code without requiring existing CAD models as references. The work combines mu…
Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines— arXiv cs.AI
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A study of 48 model pairs finds that having a stronger model review a weaker model's answers rarely outperforms using the stronger model alone, because reviewers tend to anchor to the generator's out…
MFG: Multi-turn Feedback Guided Molecular Optimization— arXiv cs.AI
researchmodels
Researchers introduce MFG, a reinforcement learning framework that trains large language models to iteratively optimize molecules across multiple turns using feedback from previous attempts. The meth…
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback— arXiv cs.AI
researchmodelstools
Researchers introduce ARBITER, a 35B mixture-of-experts verifier trained with RL to judge code correctness and generate counterexamples without consuming official submissions in competitive programmi…
Monitoring latent reasoning in looped language models— arXiv cs.AI
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Researchers developed a mechanistic interpretability protocol to analyze how looped language models—which reuse transformer layers multiple times per token—actually function internally. The study exa…
Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents— arXiv cs.AI
researchtools
Researchers propose CSEG, a six-layer decision tree framework for evaluating and training reward models in search agents that ground answers in cross-source evidence, handling source independence and…
ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning— arXiv cs.AI
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Researchers propose ECMR, a dynamic controller for multi-agent LLM systems that adaptively allocates computation based on evidence quality rather than using fixed deliberation protocols. On benchmark…
MedEvo: Medical Reasoning Evolution through Knowledge Navigation— arXiv cs.AI
researchmodels
MedEvo is a training method that improves medical reasoning in multimodal AI models by learning from failed reasoning trajectories rather than discarding them. The approach uses domain-specific error…
Transparent Information Flow Modeling for Role-Aware AI Act Assessment— arXiv cs.AI
policyresearchsafety
Researchers propose Information Flow Methodology (IFM), a modeling-based approach to assess EU AI Act compliance by representing decision systems as networks of information flows across technical and…
Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models— arXiv cs.AI
researchsafety
Researchers introduce a benchmark for evaluating whether large language models can truly model opponents' beliefs and mental states (theory of mind) versus merely adapting through learned patterns, u…
DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models?— arXiv cs.AI
researchtools
DuplexAct is a new benchmark for evaluating how full-duplex spoken language models handle real-time conversational interactions, defining a taxonomy of eight interaction actions across 24 conditions.…
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs— arXiv cs.AI
researchsafety
Researchers identify a failure mode in omnimodal LLMs where multiple modalities interfere with each other during inference, causing incorrect predictions even when the correct answer remains recovera…
CompanionSafe: From Multi-turn Safety Evaluation to Guardrails for AI Companions— arXiv cs.AI
safetyresearch
Researchers introduce CompanionSafe, a framework for evaluating and mitigating safety risks specific to AI companion systems through multi-turn dialogue scenarios. The work demonstrates that existing…
From Stages to States: Unified Iterative Reasoning for MLLMs— arXiv cs.AI
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Researchers propose ReState, a unified framework for multimodal chain-of-thought reasoning in MLLMs that replaces discrete pipeline stages with continuous state transitions, addressing training-infer…
Beyond Single-Pathogen Forecasts: Predicting Shared Respiratory Hospital Burden from Influenza, COVID-19, and RSV— arXiv cs.AI
researchapplications
Researchers developed a forecasting framework that directly predicts combined hospital admissions from influenza, COVID-19, and RSV rather than summing individual pathogen forecasts. The combined-bur…
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks— arXiv cs.AI
researchtoolsapplications
Researchers present GUIDE, a framework enabling LLMs to self-learn generalized facilitation strategies for multi-party group interactions across different task contexts. The self-learned facilitator…
REVEAL: Reverse Engineering Agentic Latents from Frontier-Model Traces— arXiv cs.AI
researchtools
Researchers present REVEAL, a training-free method to extract reusable control signals from frontier LLM agent trajectories and apply them to smaller open-source agents without access to source-model…
ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contracts— arXiv cs.AI
researchtoolsapplications
ProCARE is a new framework that automates real-world clinical studies by using LLM agents with formal Evidence Contracts and intermediate representations to handle dataset-specific semantics. The sys…
Overtaught, Underlearned: Learner-Centered Distillation from Multi-Agent Discussion— arXiv cs.AI
researchmodels
Researchers propose FAR (Fold–Attempt–Revisit), a distillation method that transfers knowledge from multi-agent reasoning discussions more efficiently by organizing supervision around what the studen…
Separating Prompt Harmfulness from Interaction Safety via Partial Generation— arXiv cs.AI
safetyresearch
Researchers propose stopping LLM generation mid-response based on partial output rather than rejecting prompts upfront, finding that prompt harmfulness and actual response safety diverge significantl…
Attention Sinks in VLMs Are Borrowed, Not Seen— arXiv cs.AI
researchsafety
Researchers identify that visual attention sinks in VLMs are image tokens whose key vectors align with the language model's text sink, causing them to absorb attention regardless of query. They propo…
Efficient Multi-Agent Reasoning via Confidence-Guided Adaptive Debate— arXiv cs.AI
research
Researchers propose LASE, a framework that selectively engages multi-agent debate only when theoretically beneficial, using a leader-supporter architecture instead of symmetric debate. The approach a…
Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision— arXiv cs.AI
researchmodels
Researchers introduce Trace-to-Architecture (T2A), a framework that diagnoses LLM agent failures by analyzing execution traces to identify which harness components need revision. The method achieves…
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning— arXiv cs.AI
researchmodels
TQG-Exp proposes a trajectory-quality-guided framework for LLM agent learning that evaluates both final outcomes and execution process quality, rather than relying solely on final task results. The m…
DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents— arXiv cs.AI
researchtools
DSABench introduces a benchmark with 815 human-reviewed tasks for evaluating multi-database SQL agents on source discovery, coordination, and execution—capabilities not tested by existing text-to-SQL…
Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers— arXiv cs.AI
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Researchers propose Large Decision Models (LDMs) that repurpose language models to make single-pass classification decisions with explicit probability outputs, eliminating costly sequential decoding.…
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation— arXiv cs.AI
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Researchers propose LatentMoE, a method that treats expert geometry (input dimension and intermediate width) as explicit design choices in mixture-of-experts models by moving computation into lower-d…
SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents— arXiv cs.AI
researchtools
SkillFusion is an offline compiler that resolves conflicts when LLM agents load multiple independent skills by making composition decisions explicit through typed, scoped constraints. The method achi…
SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference— arXiv cs.AI
researchinfra
SpecFit optimizes speculative decoding for offloaded Mixture-of-Experts models by selecting draft token subtrees that maximize throughput rather than acceptance length, accounting for the host-to-dev…
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback— arXiv cs.AI
researchmodels
Researchers propose RetroRLFF, a reinforcement learning approach for single-step retrosynthesis that uses forward reaction models as feedback to optimize reactant generation beyond reference agreemen…
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models— arXiv cs.AI
researchtools
WeatherBridge is an input adapter that bridges the distribution gap between operational weather prediction systems (GFS, HRES, CMA) and ERA5-trained forecasting models without modifying the pretraine…
PhysicsSliders: Learning Physical Controls for Audio Foley Generation— arXiv cs.AI
modelsresearchtools
PhysicsSliders (PXS) is a generative model trained on simulated contact sounds that learns to control audio foley generation through 15 physical parameters (material, geometry, contact properties). T…
Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs— arXiv cs.AI
researchmodels
Researchers show that back-translation—a classical machine translation technique—outperforms continued pretraining when adapting LLMs for low-resource language translation. With only 1K parallel pair…
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction— arXiv cs.AI
researchmodelsapplications
Researchers propose RadarGSOcc, a framework for 3D semantic occupancy prediction using 4D radar that achieves 38.2% IoU by separately extracting geometric and semantic features including Doppler info…
Holding Agents Accountable with Verifiable Agent Insurance— arXiv cs.AI
safetyresearchtools
Researchers propose verifiable agent insurance (VAI), a blockchain-based framework where AI agent providers post financial escrows tied to smart contracts encoding behavioral policies, enabling users…
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning— arXiv cs.AI
researchmodels
Varest is a novel image tokenization method that compresses visual tokens by 95% while retaining 86-92% performance through semantic-aware variable-resolution merging and learned relevance pruning. T…
EgoOmniWorld: Unifying Egocentric Understanding via Ego-Action-World Interactions— arXiv cs.AI
researchtools
Researchers introduce EgoOmniWorld, a benchmark for egocentric video understanding organized around an ego-action-world interaction loop framework with 24,166 QA samples. Evaluation of multimodal LLM…
ClawLoop: Self-Improving Agents via Harness-Native Training Loop— arXiv cs.AI
researchtools
ClawLoop is a self-improving framework for LLM agents operating in sandbox environments that generates training tasks, trains agents via SFT/RL, and uses diagnosis-driven feedback to iteratively impr…
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs— arXiv cs.AI
researchsafety
Researchers propose inLoRA-MINT, a method to detect whether specific data samples were used to train domain-adapted LLMs by analyzing internal model representations rather than requiring full paramet…
Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers— arXiv cs.AI
safetyresearch
Researchers conducted the first controlled adversarial evaluation of TypeSafe AI's Jev safety classifier against LLM judges and local classifiers across four tasks with fifty attack variants. Jev sho…
HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution— arXiv cs.AI
researchsafetyapplications
HelpMeOut is a new embodied AI benchmark with 20,000 paired activity episodes designed to test whether AI agents can detect and diagnose execution errors during household tasks, particularly relevant…
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing— arXiv cs.AI
researchtoolsmodels
QK-Edit proposes a training-free method for editing images and videos generated by Multimodal Diffusion Transformers (MM-DiTs) by manipulating query-key attention components, addressing a gap in exis…
LatentRemedy: Efficient Remedy For Non-Prefix KV Cache Reuse in Multi-Agent Systems— arXiv cs.AI
researchinfra
LatentRemedy proposes a method to improve KV cache reuse efficiency in multi-agent systems by using task-conditioned latent tokens to help agents interpret shared context when performing conflicting…
LinMorph: Learning to Morph Weights for Efficient Video DiT Linearization— arXiv cs.AI
researchmodels
LinMorph introduces a learnable weight transformation approach for linearizing Video Diffusion Transformers (Video DiTs) by learning to map softmax attention parameters to linear GDN parameters, rath…
Agentic-MatriBO: Capability-Compiled Agentic Bayesian Optimization for Materials and Chemical Design— arXiv cs.AI
researchtoolsapplications
Agentic-MatriBO combines large language models with Bayesian optimization for materials and chemical design by representing BO components as composable capabilities that an LLM policy proposes and a…
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning— arXiv cs.AI
research
CoCA introduces a criterion-aware credit assignment method for rubric-based reinforcement learning that uses rubric information to allocate rewards at the token level rather than compressing feedback…
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models— arXiv cs.AI
researchmodels
Researchers propose PACS, a supervised fine-tuning method for diffusion language models that aligns training with inference by using the model's predictability to select which tokens are visible duri…
Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP— arXiv cs.AI
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Researchers systematically evaluated signals for detecting answerability in frozen InstructBLIP on visual question answering tasks, finding that common metrics like visual dependence, uncertainty, an…
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features— arXiv cs.AI
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This paper addresses missing node features in Graph Neural Networks by formalizing graph-structured mask dependence—where missingness patterns correlate with graph connectivity—and proposes PEMix, a…
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images— arXiv cs.AI
researchapplications
Researchers developed MorphContrast, a contrastive learning framework that predicts five-year colorectal cancer survival from whole-slide pathology images by leveraging tissue morphology as structura…
Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs— arXiv cs.AI
researchsafety
Researchers propose CB-RBM, an unsupervised method for adversarial detection on attributed graphs that leverages feature-context consistency and provides theoretical guarantees for drift stability. T…
Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models— arXiv cs.AI
researchmodels
Dual-encoder vision-language models systematically fail at negation tasks, treating "a cat without a dog" nearly identically to "a cat with a dog." Researchers propose Know-Thy-Not (KTN), a training-…
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors— arXiv cs.AI
researchmodels
SMILE is a visual in-context learning method that leverages a model's own past prediction errors as in-context demonstrations, eliminating the need for complex counterfactual engineering pipelines. T…
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation— arXiv cs.AI
modelsresearch
TabGift is a tabular foundation model that generates synthetic tabular data from limited context samples by combining a pretrained encoder, in-context diffusion model, and lightweight decoder. The me…
From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations— arXiv cs.AI
researchsafety
A study of generative social simulations using LLM-based personas derived from survey data finds that removing explicit partisan markers dramatically shifts simulated network structure from near-comp…
claimcheck: Measuring and Improving Specification Faithfulness— arXiv cs.AI
researchsafetytools
Researchers introduce claimcheck, a benchmark and tool for detecting when formal specifications don't match their natural-language requirements—a growing problem as LLMs generate both code and specs.…
InfoVis: Unified Slide Image Generation and Editing— arXiv cs.AI
modelsresearch
Researchers introduce InfoVis, a 9B parameter unified model for slide generation and editing, trained on 13M+ pretraining and 1.9M supervised fine-tuning records. They also release VisSlideBench, a 1…
ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty— arXiv cs.AI
researchtools
ProbabilityBench introduces a benchmark with analytically computed ground-truth class probabilities across three simulated text domains, enabling precise measurement of model uncertainty calibration…
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models— arXiv cs.AI
safetyresearch
Researchers investigate how depth-based dynamic inference—a technique that allows LLMs to generate outputs from intermediate layers for efficiency—affects vulnerability to jailbreak attacks. They fin…
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation— arXiv cs.AI
researchmodels
GenRevisit introduces an agentic framework for compositional image generation that allows models to select which previous intermediate image to refine from, rather than always editing the latest one,…
A Sovereign, Indic-First, Reasoning Model— arXiv cs.AI
modelsresearch
Researchers released Gnani-Evon-v3.3-30B-A3B, a 30B-parameter reasoning model based on NVIDIA's Nemotron-3 adapted for English and eight Indian languages through vocabulary expansion, continual pretr…
Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection— arXiv cs.AI
researchsafety
Researchers propose a unified audio deepfake detection framework combining adaptive feature integration and targeted mel-spectrogram perturbation, achieving 1.51% EER on standard benchmarks with 91.6…
Sensitivity or Criterion? Measuring and Moving When Language Models Act— arXiv cs.AI
researchtoolssafety
Researchers introduce FlipAct, a diagnostic framework using signal-detection theory to separately measure sensitivity (ability to recognize when action is warranted) from criterion (decision threshol…
TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks— arXiv cs.AI
researchmodels
TabMosaic is a unified diffusion-based model for tabular generative tasks that can handle missing value recovery, conditional sampling, and data synthesis without table-specific retraining. The appro…
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training— arXiv cs.AI
researchsafety
WildTrace is a new benchmark for evaluating training data detection methods in large-scale LLM post-training, covering SFT, DPO, and RL stages with distribution-matched evaluation sets. Current detec…
Omni-Modal LLMs Know More Than They Say— arXiv cs.AI
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Researchers found that omni-modal LLMs (models processing vision, audio, and other sensor data alongside text) often fail to express information from non-textual modalities even when they perceive it…
What DNA Foundation Models Learn Beyond Sequence Composition— arXiv cs.AI
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Researchers evaluated five DNA foundation models against simple k-mer baselines across 57 genomic classification tasks, finding that NTv3 consistently outperforms compositional features while other m…
TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents— arXiv cs.AI
researchtoolsapplications
A study of 600 coding-agent trajectories finds that turn count (loop iterations) accounts for 84% of cost variance, while observation size—the focus of existing compression work—explains only 4%, wit…
PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes— arXiv cs.AI
researchtools
Researchers propose PLDiv, a diversity metric based persistence landscapes that quantifies structural organization of datasets by measuring how data neighborhoods evolve geometrically, without requir…
OPMD: On-Policy Mechanism Distillation Beyond Output Supervision— arXiv cs.AI
researchmodels
Researchers propose OPMD (On-Policy Mechanism Distillation), a technique that transfers intermediate computational mechanisms from teacher to student language models beyond just supervising outputs.…
One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns— arXiv cs.AI
researchmodels
Researchers propose Multi-Ratio ConceptMoE, a single model architecture that supports multiple inference compute budgets (2x, 4x, 8x token-to-concept compression) within one shared backbone using rat…
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models— arXiv cs.AI
researchmodels
Researchers propose GG-Nav, a navigation framework that uses structured chain-of-thought reasoning to improve goal-oriented navigation in vision-language models by accumulating evidence from streamin…
Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models— arXiv cs.AI
researchmodelssafety
Researchers adapted a 9-billion-parameter decision foundation model (Clef-Flash) for disease classification from patient symptoms, achieving 95.42% accuracy on 24-class diagnosis with sub-80ms infere…
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning— arXiv cs.AI
researchsafety
Researchers propose Domain Manifold Unlearning (DMU), a technique to selectively erase specific object-style combinations from diffusion models while preserving general performance and avoiding catas…
Getting Space Right: Agentic Rewards for Image Generation— arXiv cs.AI
researchmodels
Researchers propose SpatialCritic, an agentic reward model for improving spatial text-to-image generation through reinforcement learning with curriculum learning. The method combines specialized visi…
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection— arXiv cs.AI
researchtools
InCLAD introduces a continual learning benchmark for industrial visual anomaly detection, framing the problem as sequential task-incremental learning across different components while maintaining sem…
SemTree: Judge-Aware Semantic Decision Trees for Inspectable and Repairable Rule Decisions— arXiv cs.AI
researchtoolssafety
SemTree is a framework for building interpretable rule-based decision systems as semantic decision trees, where each node is a natural-language question answered by an LLM-based judge, enabling error…
Auditing Future-Memory Use in Point-in-Time Language Agents— arXiv cs.AI
researchsafety
Researchers tested whether language agents with access to post-cutoff information ("future memory") use it to bias their decisions in point-in-time tasks. Testing Qwen and DeepSeek on 48 company-comp…
AgentStripes: An Evidence-Graded Maturity Model for AI Agents— arXiv cs.AI
safetyresearch
AgentStripes proposes a maturity model with 8 levels and 40 capabilities for evaluating AI agents' reliability, safety, and observability beyond task benchmarks. An exploratory evaluation of 30 Pytho…
Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops— arXiv cs.AI
researchsafety
Researchers analyzed why fast AI agents (System One models) make poor decisions by testing whether failures stem from misreading state information or from failing to act on correct understanding. Acr…
Jeva: System One Models from First Principles— arXiv cs.AI
researchmodels
Researchers introduce Jeva, a decision-native neural architecture designed for fast, structured decision-making that respects the mathematical properties of choice problems. The model addresses four…
Rethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection— arXiv cs.AI
researchsafetytools
Researchers propose SCRouter, a system that routes queries to different LLMs with optimized token budgets by balancing security and utility objectives separately, showing 13-29 point improvements ove…
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards— arXiv cs.AI
researchtools
AesCode is a new method for generating aesthetic code (for slides, posters, dashboards) by combining code generation with visual guidance from reference images and a decoupled reward system using bot…
LLM Agents for Automated Post-Training via Structured Recipe Optimization— arXiv cs.AI
researchmodels
Researchers propose Structured Recipe Optimization (SRO), an LLM-agent-based system that automatically tailors post-training procedures for language models by adapting the recipe space itself to targ…
VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval— arXiv cs.AI
researchtools
Researchers introduced VL-DocIR, a benchmark with 388,548 page images and 271,760 queries from academic and financial documents to evaluate vision-based retrieval systems on long documents spanning m…
Generalizable field of view extension for diffusion MRI via conditional flow matching— arXiv cs.AI
researchapplications
Researchers developed a flow-matching generative model that extends incomplete diffusion MRI (dMRI) brain scans by predicting missing slices using apparent diffusion coefficients, trained on over 3,0…
Consider, Then Choose: Order-Invariant Decisions in System One Models— arXiv cs.AI
researchsafety
Researchers identify that large language models produce inconsistent answers to multiple-choice questions depending on option order, especially when uncertain. They propose Runoff, a two-pass method…
Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution— arXiv cs.AI
researchtoolsinfra
Researchers introduce EC2-Bench, a benchmark for evaluating how edge and cloud models collaborate during long-horizon LLM agent execution, revealing that such collaboration balances task performance,…
Beyond Bias Scores: Answerability Confounds in Activation Steering with Gradient-Constrained Direction Design (GCD)— arXiv cs.AI
researchsafety
This paper identifies a confound in bias evaluation benchmarks like BBQ where bias scores conflate model answerability (whether a model answers) with actual preference bias. The authors propose Gradi…
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control— arXiv cs.AI
researchapplications
RAN-JEPA is an action-conditioned world model that leverages abundant passive network telemetry alongside scarce controlled intervention data to optimize energy efficiency in mobile networks. The mod…
The Format is the Filter: Scoring Format Fixes What an LLM Judge Can Express— arXiv cs.AI
research
This paper investigates how the scoring format used by LLM judges (binary, 1-5 rating, 0-100 scale, or comparison) affects data curation quality for instruction-tuning, finding that format properties…
SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise— arXiv cs.AI
researchsafety
Researchers measured how supervised fine-tuning (SFT) changes the activation geometry of language models using sparse co-activation graphs, finding that SFT produces a large, reproducible signature t…
RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment— arXiv cs.AI
researchmodelssafety
Researchers introduced RoTA-Bench, a benchmark with 674 tasks evaluating whether vision-language models can convert 2D observations into precise 3D spatial reasoning through interactive object rotati…
Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models— arXiv cs.AI
researchmodels
Researchers present ADMA, an adaptive controller that learns policies for managing multi-agent deliberation in language models, improving accuracy by 6-14% over baseline approaches on reasoning tasks…
Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made— arXiv cs.AI
research
Researchers propose a timestamp theory and decision ledger system for tracking LLM decisions made with fresh information but executed later in a changing world. The system uses six timestamps and wat…
AReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement— arXiv cs.AI
researchtools
Researchers present AReaL-RSI, a framework for coordinating multiple AI researcher agents in recursive self-improvement tasks without modifying the underlying LLM weights. The system uses a leader ag…
Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows— arXiv cs.AI
researchtools
This paper demonstrates that small LLMs (1B-8B parameters) can achieve reliable Text-to-SQL generation through iterative retry workflows with alternative prompts and models, rather than scaling up mo…
Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition— arXiv cs.AI
research
Researchers study how large language models handle competing knowledge sources during continual learning using hypernetwork-based adapters. They find models preferentially follow context over content…
Transparency as Answerability: Role-Dependent Information Flow Modeling— arXiv cs.AI
researchsafety
Researchers propose treating AI transparency as answerability—whether legitimate questions about a system can be answered from its structure—rather than as a system property. They formalize this usin…
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction— arXiv cs.AI
researchmodels
Researchers propose SAVER, a method for dynamically balancing relevance and diversity when compressing visual tokens in vision-language models by inferring sample-specific weights at inference time.…
Revocation Is Not Complete at Receipt: An Executable Outcome Contract for Distributed Agent-Tool Gateways— arXiv cs.AI
researchsafety
This paper identifies a critical gap in distributed agent-tool systems: revocation of delegated authority at gateways is not reliably enforced when the revocation message is acknowledged or persisted…
Passing Tests, Missing Semantics: Evidence of Interpretive Lock-In in Plan-Based DataFrame Code Generation— arXiv cs.AI
researchtools
A study of code generation for DataFrame tasks reveals that models can pass self-generated tests while producing semantically incorrect code—a phenomenon termed 'interpretive lock-in' where initial m…
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting— arXiv cs.AI
researchmodels
ChronoScribe combines a LoRA-adapted LLM (Qwen3.5-9B) with a frozen time series foundation model (Chronos-2) to improve instruction-driven time series forecasting by separating semantic interpretatio…
Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution— arXiv cs.AI
researchtoolsinfra
Researchers propose Token-Group Quantization, a post-training quantization framework for Mamba-based super-resolution models that addresses activation range variation across spatial tokens through st…
Structural Baselines Matter for Agent Failure Detection and Attribution— arXiv cs.AI
researchsafety
A study reveals that failure detection and attribution in LLM agents contain structural biases—failed runs are longer and decisive steps cluster at endpoints—creating artificial performance ceilings…
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?— arXiv cs.AI
researchtoolsapplications
Researchers introduce PXRD-Bench, a benchmark dataset of 4.1 million simulated powder X-ray diffraction patterns with crystal structures, and Bragglite, a generative model achieving 44.2% accuracy on…
Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces— arXiv cs.AI
researchsafety
Researchers introduce Choice2Seq, a benchmark that tests whether language models can maintain correctness when selecting the right answer versus constructing it step-by-step. They find dramatic gaps:…
Handoff Curves: Initial Selection, Later Handoff, and Supervision Targets in Multi-turn LLM Routing— arXiv cs.AI
researchmodels
Researchers study multi-turn LLM routing by analyzing how initial model selection, dynamic handoff timing, and training supervision targets affect task success and cost across three benchmarks. Using…
ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context— arXiv cs.AI
researchtools
Researchers introduce ScreenASR-Bench, a tiered benchmark for evaluating speech recognition systems that incorporate visual context from screens, with three difficulty levels (explicit text, explicit…
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory— arXiv cs.AI
researchtools
A research paper proposes MemPursuit, a training-free mechanism for selecting complementary evidence from long-term agent memory under context constraints. The method improves retrieval accuracy by 5…
CondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning— arXiv cs.AI
researchapplications
CondMSP extends materials synthesis planning from predicting recipes in isolation to execution-aware planning that jointly optimizes synthesis routes under real experimental constraints like furnace…
Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay— arXiv cs.AI
toolsinfraresearch
Pollard is a framework-independent runtime that uses governed semantic execution trees to standardize how AI agent checkpoints, tool calls, and resource limits are tracked and replayed across differe…
G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation— arXiv cs.AI
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G²WAM proposes a world action model for image-goal navigation that uses learned scene-coordinate localization to maintain spatial context without explicit 3D reconstruction, reducing computational co…
Trace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces— arXiv cs.AI
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Trace Evolution Bench (TEB) is a new evaluation protocol that rigorously measures whether agents genuinely improve from their own execution traces by controlling for confounds like frozen rescoring.…
EvoCowork: A Benchmark for Real-World Office Tasks— arXiv cs.AI
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EvoCowork is a new 240-task benchmark for evaluating self-improving AI agents on realistic office workflows (presentations, documents, spreadsheets, cross-office tasks) with fixed model weights. Test…
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems— arXiv cs.AI
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Researchers propose TASA (Tool-Augmented Strategic Agent), an extension of Optimization by Prompting (OPRO) that makes LLM-based optimization more traceable and effective for combinatorial problems.…
PEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents— arXiv cs.AI
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PEARL improves credit assignment in multi-turn reinforcement learning agents by identifying recurring decision patterns from paired success/failure trajectories and selectively strengthening negative…
How Should a Camera-First AI Agent Work?— arXiv cs.AI
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Researchers propose a two-deadline architecture for camera-first AI agents that separates fast perceptual memory capture from slower semantic reasoning, arguing that latency in monolithic pipelines c…
Belief Embedding Tree Search— arXiv cs.AI
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Researchers present Belief Embedding Tree Search (BETS), a Monte Carlo planning algorithm that uses learned generative distribution embeddings to represent beliefs in POMDPs, trained end-to-end with…
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling— arXiv cs.AI
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This paper addresses sample efficiency in reinforcement learning by improving risk prediction models (RPMs) used for task selection. The authors identify two key failure modes in existing RPMs—sparse…
Are Multimodal Benchmarks Really Useful? Item-Level Multimodal Benchmark Diagnosis via Structure-Response Co-Calibration— arXiv cs.AI
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Researchers propose SRCoD, a framework for diagnosing whether multimodal benchmarks actually test cross-modal reasoning or contain shortcuts that allow single-modality answers. The method combines st…
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching— arXiv cs.AI
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MultiFlow introduces a generative framework for fMRI encoding using Conditional Flow Matching and Stimulus-Informed Source Flow Matching (SISFM), which models the distribution of neural responses rat…
Relational Semantic Grounding for Test-Time Discovery— arXiv cs.AI
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Researchers propose SEGA, a framework for test-time discovery that identifies both known and novel classes in unlabeled data streams by grounding discovered clusters in interpretable semantic concept…
Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation— arXiv cs.AI
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Researchers analyzed how unified vision-language models (VLMs) process conditions for both text-to-image and image-to-text generation by probing attention heads under classifier-free guidance, discov…
Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses— arXiv cs.AI
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Harmony Harness introduces a multi-objective optimization framework for evolving LLM agent harnesses, using decomposition-based evolutionary search to manage competing design goals and separate gener…
ConicFlow: constrained Inference-time optimal control for flow matching models— arXiv cs.AI
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ConicFlow reformulates constrained sampling from flow matching models as an optimal control problem solved at inference time using receding-horizon primal-dual optimization. The method balances fidel…
The Many Morals of AI: The cautionary bias of North American news narratives in global perspective— arXiv cs.AI
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A study of 12,924 English-language news articles about AI from 2022 onward across North America, China, India, and the Middle East finds that North American coverage uniquely emphasizes cautionary na…
RoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks— arXiv cs.AI
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RoboSimStudio (RSS) is an interactive toolkit for sim-to-real robot manipulation that makes physics parameters (solver, contact model, friction, stiffness) explicit and editable, enabling rapid refin…
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models— arXiv cs.AI
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GeoReader is a framework that trains vision-language models to learn and use geometric representations for spatial reasoning by conditioning lightweight geometric readers on questions during fine-tun…
Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions— arXiv cs.AI
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A study of 367,174 MCP tools finds that parameter schema design significantly influences which tools LLMs select, often more than tool names or descriptions. The research demonstrates through control…
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes— arXiv cs.AI
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Urbex proposes an agentic vision-language framework that reformulates city-scale 3D spatial grounding as active search rather than candidate ranking, using RL-optimized tool-use policies to navigate…
Fast Rates for Offline Q-Learning with KL Regularization— arXiv cs.AI
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This paper proposes KL-regularized Q-learning algorithms (KLQ and KLPQ) for offline reinforcement learning in finite-horizon MDPs, achieving Õ(1/ε) sample complexity without explicit pessimism and re…
MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation— arXiv cs.AI
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MemoryVLN addresses context-length limitations in vision-language navigation by introducing a memory-driven system that compresses long trajectories through temporal sampling and stores spatially-gro…
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation— arXiv cs.AI
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FedGraph proposes a graph-based defense mechanism against backdoor attacks in federated fine-tuning of large language models, exploiting the observation that malicious clients produce consistent upda…
Graph-free Retrieval with Graph-Modified Embeddings— arXiv cs.AI
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Researchers propose offline graph-based embedding transformations that modify dense vectors to incorporate graph structure without requiring query-time graph access. Testing five transformation metho…
Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs— arXiv cs.AI
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Researchers propose a hybrid architecture where cloud models generate programs that run locally while deferring data-interpretation questions to small local models via function calls. On the AppWorld…
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation— arXiv cs.AI
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Researchers propose SMI (Semantic Medical Identifier), a training-free framework that augments biomedical language models with medical ontology structure to better represent hierarchical relationship…
LLM2R: From Large Language Models to 3D Reassemblers— arXiv cs.AI
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LLM2R casts 3D part assembly as a sequence modeling task using a pretrained LLM with a Point-BERT encoder and learnable summary tokens to aggregate information across all parts, achieving state-of-th…
Foundation Models for Event Prediction Across Heterogeneous Event Logs— arXiv cs.AI
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Researchers introduce Foundation Event Predictor (FEP), a transformer-based model for next-event prediction across diverse event logs spanning business processes, manufacturing, and healthcare. FEP u…
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning— arXiv cs.AI
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PrT-Flow introduces a flow matching approach for reinforcement learning that maintains tractable probability density for actions while enabling multimodal policy learning. The method uses invertible…
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents— arXiv cs.AI
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This paper identifies a fundamental safety problem in LLM agent memory systems: cached reasoning (thought templates, skill libraries) becomes stale when facts change, and existing success/failure cou…
Certifying Almost-Sure Reachability in Unknown MDPs— arXiv cs.AI
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Researchers present a method for verifying that a given policy satisfies almost-sure reachability properties in unknown Markov Decision Processes using only sampling access, introducing ranking super…
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation— arXiv cs.AI
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Researchers present YUBI-Real2Sim, a framework for converting real robot scenes to simulation using 3D Gaussian Splatting (3DGS) to evaluate vision-language-action (VLA) policies. The work systematic…
HaloBlock: Faster and Better Decoding for Diffusion Large Language Models— arXiv cs.AI
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HaloBlock proposes a difficulty-aware adaptive halo mechanism to improve inference speed and quality for diffusion language models by addressing boundary discontinuity issues in block-wise decoding.…
MERIT: Multimodal Evidence Reasoning over Image Tables— arXiv cs.AI
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MERIT proposes a three-stage framework for reasoning over table images using multimodal inputs, combining supervised fine-tuning with reinforcement learning to improve evidence selection and calculat…
CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation— arXiv cs.AI
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CineMem proposes an attention-aligned fine-grained memory retrieval framework for autoregressive multi-shot video generation that organizes historical tokens into semantic clusters and enables query-…
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning— arXiv cs.AI
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Researchers propose SCRIM, a method to identify and weight critical states in imitation learning where small action errors cause task failure, versus recoverable states where errors are inconsequenti…
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification— arXiv cs.AI
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Researchers identify a systematic failure mode in dynamic graph neural networks where nodes changing class labels (shifters) are misclassified more often than stable nodes, traced to embedding inerti…
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning— arXiv cs.AI
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RELAY is a two-stage reinforcement learning method that combines selective self-distillation from a frozen teacher with GRPO, automatically detecting when to remove the teacher based on plateau detec…
Auditing Failure Attribution: Label Predictability and Judge Behavior— arXiv cs.AI
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This paper identifies spurious signals in failure-attribution benchmarks where LLM judges identify which step caused a failure, showing that simple heuristics can match or exceed GPT-4 performance. T…
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching— arXiv cs.AI
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FlashVSR+ improves video super-resolution efficiency through hybrid attention mechanisms (2× speedup, 45% lower memory) and a new training approach called Reward-Yield Distribution Matching Distillat…
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting— arXiv cs.AI
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TStruct proposes learning shared temporal dependency structures across different time series samples and variables to improve long-term forecasting under distribution shifts. The method parameterizes…
Box-WAM: Box Trajectories as Compact State Representations for World Action Models— arXiv cs.AI
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Researchers propose Box-WAM, a world action model that predicts oriented bounding box trajectories of task-relevant objects instead of full-frame video, reducing computational cost while improving ac…
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression— arXiv cs.AI
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Researchers introduce H-DDCM, a training-free generative compression method that replaces dense Gaussian codebooks with implicit Hadamard dictionaries to compress images and videos at ultra-low bitra…
AI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study— arXiv cs.AI
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Researchers developed an AI-enhanced hyperspectral imaging (HSI) system to detect low-grade gliomas during surgery, achieving 74.5% ROC-AUC with a 3D CNN compared to 64% for standard RGB imaging. The…
Fan Out or Relay? Ownership-Aware Routing for LLM Agents Querying Siloed Tables— arXiv cs.AI
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Researchers propose Ownership-Aware Routing (OAR), a method for LLM agents to decide whether to fan out queries to multiple data owners simultaneously or relay queries sequentially based on database…
Explore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning— arXiv cs.AI
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Researchers propose LEAF, a method that uses in-context memory to accelerate exploration in reinforcement learning with verifiable rewards for LLM reasoning tasks. The approach achieves ~4x faster ex…
Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies— arXiv cs.AI
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Researchers propose Phase-Conditioned Affordance Fields (PAF), a method that enhances SE(3)-equivariant visuomotor policies by learning per-point importance scores over observed point clouds to dynam…
Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories— arXiv cs.AI
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Researchers propose FlexLMV-Net, a framework for breast cancer risk prediction that explicitly aligns mammography images across multiple screening visits and jointly models two imaging views (CC and…
A Scaled-Up Empirical Study of Syntactic Language Models— arXiv cs.AI
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Researchers conducted a large-scale empirical study of syntactic language models (SLMs) that jointly generate text and parse trees, testing 12 configurations up to 1B parameters and 100B training tok…
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion— arXiv cs.AI
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Researchers propose Gradient Confusion Defense (GCD), a test-time defense against black-box adversarial attacks that manipulates model output gradients to increase query complexity for attackers with…
Interactive Navigation Beyond Reachability: Benchmark and Agentic Framework— arXiv cs.AI
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Researchers introduce MultINav-Bench, a 3K-episode benchmark for embodied navigation that requires agents to interact with environments (opening doors, containers) to reach goals, and propose MultINa…
Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation— arXiv cs.AI
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Researchers introduce Force-Modulated Dexterity (FOMO-Dex), a compliance framework that enables teleoperated robots to automatically adjust contact forces during manipulation tasks while operators fo…
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting— arXiv cs.AI
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Researchers introduce DFK-Former, a transformer-based model that reconstructs divergence-free 3D velocity fields from sparse, noisy observations by directly predicting kernel parameters in a single f…
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation— arXiv cs.AI
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MAGNET is a diffusion network that generates functional brain connectomes (neural connectivity matrices) while preserving geometric validity and clinical heterogeneity. The method uses manifold-aware…
Bottlenecked semantic compression by representation reconstruction— arXiv cs.AI
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Researchers propose ReConV, a visual token compression method for Vision Transformers that uses reconstruction residuals to decide which tokens to retain rather than discarding based on importance sc…
HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation— arXiv cs.AI
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HASTE is a new training framework for generative recommendation models that adds hierarchical and attribute-level supervision to improve both training efficiency and long-tail performance. The approa…
Relational Learning as World Modeling— arXiv cs.AI
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Researchers introduce TabWM, a model that learns system dynamics from observed executions to predict how database states change under actions, achieving 0.685 F1 on edit prediction with only 64 examp…
Heuristics are Not Enough: Calibrated and Corrected Caching for Video Diffusion Acceleration— arXiv cs.AI
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Researchers propose AlignCache, a caching framework that improves video diffusion model acceleration by replacing heuristic-based feature reuse with phase-adaptive budgeting and closed-loop threshold…
Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems— arXiv cs.AI
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Researchers introduce Speculative Recursive Self-Improvement (SpecRSI), a method that drafts and evaluates candidate agent improvements in parallel rather than sequentially, using verification-aware…
SLEM: Spectrum-Aware Latent Expert Merging— arXiv cs.AI
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SLEM proposes a calibration-free expert merging technique for Mixture-of-Experts models that uses spectral analysis and VAE-based latent representations to reduce memory overhead while maintaining pe…
Gen Through the Lens: Camera-Controllable Video Generation via Cross-Coordinate Conditioning Fusion— arXiv cs.AI
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Researchers present Gen Through the Lens, a video generation framework that enables precise camera control by fusing conditioning signals across three coordinate systems (camera, world, and object co…
Autoregressive Decoding as a Martingale: Characterizing the Stochastic Dynamics of LLM Generation— arXiv cs.AI
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Researchers prove that the cumulative prediction error during autoregressive LLM decoding forms a martingale process, deriving a closed-form expression for its quadratic variation based on token prob…
Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards— arXiv cs.AI
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This paper proposes Variance-driven State Resampling (VSR), a method that optimizes which states to sample during reinforcement learning with verifiable rewards by deriving a closed-form Neyman alloc…
Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning— arXiv cs.AI
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This paper introduces PLATO, a method for offline meta-reinforcement learning that improves generalization to out-of-support tasks by perturbing latent task representations during training and using…
VLCQ: Post-training quantization for deep neural networks using variable length coding— arXiv cs.AI
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VLCQ introduces a post-training quantization method using variable-length encoding instead of fixed-point integers to achieve lower bit-widths while maintaining model accuracy. The approach works wit…
Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation— arXiv cs.AI
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Researchers propose Self-Reported Entity Familiarity (SEF), a lightweight method to filter training data for deep-search agents by asking teacher models which entities they're already familiar with,…
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching— arXiv cs.AI
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StimFlow is a generative model that synthesizes fMRI brain activity directly from images using conditional flow matching, combining DINO and CLIP features to capture both low-level and semantic visua…
LeHami: Symplectic Latent Dynamics for World Models— arXiv cs.AI
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LeHami introduces a symplectic Hamiltonian architecture for joint-embedding predictive world models that preserves phase-space volume during long-horizon rollouts by replacing standard autoregressive…
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents— arXiv cs.AI
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Researchers introduce ECHO, a credit assignment framework for tool-use agents in reinforcement learning that distinguishes between newly introduced errors, inherited consequences, and successful corr…
The Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization— arXiv cs.AI
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Researchers argue that formalization—expressing physics results in machine-verifiable form—is essential for verifying and reusing AI-discovered physics results, but current AI systems are stronger at…
A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning— arXiv cs.AI
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This paper presents a geometric analysis of reward function updates in Inverse Reinforcement Learning, showing that considering the structure of the problem in occupancy space enables more efficient…
Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners— arXiv cs.AI
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Recurrent algorithmic reasoners trained on small graphs with per-step hints fail on larger graphs by prematurely declaring nodes as reached before the search reaches them ("premature floods"), despit…
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising— arXiv cs.AI
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Researchers propose using LLMs as relevance judges for offline evaluation of e-commerce dynamic product advertising systems, replacing sparse engagement metrics with semantic assessment of user-produ…
Counterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning— arXiv cs.AI
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A new approach called Causal Contrastive Reinforcement Learning (CauCRL) addresses the problem of spurious correlations in offline goal-conditioned RL when demonstrator actions are influenced by hidd…
Rotate the Loop: Blockwise Conformality for Looped Transformers— arXiv cs.AI
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Researchers propose blockwise conformal injections for looped transformers, a technique that splits residual connections into coordinate-aligned groups that rotate independently, each with its own ti…
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion— arXiv cs.AI
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Akule is a diffusion-based framework for multi-robot motion planning that uses sparse pairwise interactions and learned selection/composition to scale from small teams to 256 robots with up to 25× sp…
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine— arXiv cs.AI
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Researchers developed Lingdan-V2, a family of specialized language models (4B–14B parameters) for traditional Chinese medicine clinical reasoning, using continued pretraining on TCM records, clinical…
Target-ESS Tempered Policy Optimization (TETPO): Smooth Variance Control via Bridge Importance Sampling— arXiv cs.AI
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Researchers introduce Target-ESS Tempered Policy Optimization (TETPO), a method that adaptively adjusts importance weights in asynchronous RL post-training of LLMs by tempering them via a data-depend…
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving— arXiv cs.AI
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YOUDrive introduces a method to control driving style in end-to-end autonomous driving policies by distilling teacher behaviors into task vectors that act as composable directions in a vision-languag…
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation— arXiv cs.AI
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RES-MR is a new LLM-based framework for medication recommendations that uses clinical reasoning distillation and risk-aware policy optimization to generate explainable and personalized safe drug comb…
RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work— arXiv cs.AI
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Researchers created RealityBench, a simulated evaluation environment in Blender to test vision-language models on their ability to document physical work tasks like solar installation and vehicle rep…
Barycentric Anchored Weight Search for Robust Model Merging— arXiv cs.AI
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Researchers propose Barycentric Anchored Search, a post-hoc method for merging fine-tuned vision models to improve robustness under distribution shift without additional training. The technique uses…
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions— arXiv cs.AI
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A preregistered experiment with 148 teams found that access to generative AI during the early idea-generation phase increased creativity by 0.62 standard deviations compared to late access during sel…
The RNN Homunculus: Learning to Gate Long-Term Memory— arXiv cs.AI
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Researchers analyze how gated RNNs (LSTMs, GRUs) learn to create long-range memory paths, finding that gate values themselves must be learned through an embedded RNN—a process that also suffers from…
Selective Knowledge Control for Continual GUI Agent Learning over Application Streams— arXiv cs.AI
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A new method for GUI agents enables continual learning across multiple applications by selectively protecting previously learned knowledge through neuron-level gradient manipulation. The approach ide…
Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning— arXiv cs.AI
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Researchers propose SATKGC, a framework that fine-tunes language models for knowledge graph completion by incorporating graph structure through subgraph-aware mini-batching and contrastive learning.…
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate— arXiv cs.AI
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TrueMAD proposes a targeted peer engagement mechanism for multi-agent debate that replaces broadcast-style answer sharing with one-to-one peer advice exchanges (support, rebuttal, refinement, questio…
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models— arXiv cs.AI
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PRAISE is a reproducible workflow that enables non-programmer scientists to implement published AI models step-by-step, with built-in error handling and fallback routes. The authors demonstrate proof…
CoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection— arXiv cs.AI
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CoEvoke introduces a framework that uses controlled perturbations and restricted guidance to generate multi-agent collaboration trajectories with checking, feedback, and repair interactions. The meth…
Output Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making— arXiv cs.AI
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Researchers propose a model-agnostic agent framework based on falsification-guided decision making that enforces structured reasoning through a candidate-evidence matrix protocol. On Traditional Chin…
Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models— arXiv cs.AI
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This paper identifies that latent planners using terminal Euclidean distance for ranking action sequences fail when the latent geometry violates a self-approaching condition—specifically when polygon…
Judges Follow the Reference: The Derivation Gap in Verifying Tool-Call Arguments— arXiv cs.AI
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Researchers identify a "derivation gap" where tool-call judges reject correct argument values less often when there is no visible reference text supporting them, affecting all seven tested judges. A…
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents— arXiv cs.AI
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AssetForge is a task-construction pipeline for training business automation agents that uses reusable asset libraries paired with task-specific rubrics, reducing the need to recreate application infr…
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration— arXiv cs.AI
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Researchers propose using provenance action graphs—networks of reaction steps and outcomes—to encode material synthesis pathways and accelerate discovery of kinetically stabilized materials. The appr…
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling— arXiv cs.AI
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Researchers propose Pairwise-Guided Logit Adjustment (PGLA), a test-time scaling method that uses pairwise confidence comparisons between reasoning paths to improve LLM answer selection. Unlike exist…
The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion— arXiv cs.AI
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A mathematical model of recursive AI self-improvement through automated research derives the probability distribution of a capability explosion, finding that randomness in improvement arrival rates c…
Delegating Earth science model calibration to knowledge-guided LLM agents— arXiv cs.AI
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Researchers developed a General Calibration Harness (GCH) that enables LLM agents to autonomously calibrate Earth science models while maintaining scientific rigor and reproducibility. The system con…
Choosing LLMs for Strategic Agent Interactions— arXiv cs.AI
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Researchers propose a meta-game framework for selecting LLMs in strategic interactions, finding that model performance varies significantly by game structure and opponent. Current LLM routing methods…
AutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment— arXiv cs.AI
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AutoMARS is a multi-agent LLM framework that automates supply chain risk assessment by coordinating specialized agents across socioeconomic, disaster, environmental, and transportation domains to eva…
Understanding Personalization Gains in Whisper— arXiv cs.AI
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Researchers study lightweight adaptation of Whisper speech recognition models using LoRA, finding that gains from personalization are concentrated in difficult acoustic conditions and that pooled ada…
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms— arXiv cs.AI
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Researchers developed Tricorder, an autonomous system combining LiDAR-SLAM with RGB and thermal imaging to inspect components under solar panels that aerial surveys miss. The system detects connector…
Encounter-Driven City-Scale Human Mobility Generation with Large Language Models— arXiv cs.AI
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Researchers propose EncounterMob, an LLM-based framework that generates realistic city-scale human mobility patterns while preserving both individual trajectories and population-level social encounte…
When LLM Agents See a Conflict but Still Proceed: Cross-Domain Dependencies in Network Slicing— arXiv cs.AI
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Researchers evaluated 10 LLM-based agents on their ability to recognize and respect cross-domain dependencies in network management, finding that most models proceeded with harmful actions (like remo…
EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research— arXiv cs.AI
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EvoQuantLab combines genetic programming with LLMs in a dual-loop framework to automatically discover quantitative trading factors while mitigating backtest overfitting. The approach achieves 18-21%…
Learning How to Remember: Memory Modality Selection for GUI Agents— arXiv cs.AI
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Researchers introduce SwitchMem, a framework that dynamically selects between text and visual memory representations for GUI agents at each interaction step, rather than using a fixed modality. Testi…
Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce— arXiv cs.AI
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Researchers introduce a merchant-side benchmark for agentic commerce that evaluates whether e-commerce websites allow AI agents to access them, publish clear purchase terms, and provide transaction e…
Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences— arXiv cs.AI
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Researchers propose a Neuro-Symbolic Graph Transformer (NSGT) that combines graph attention mechanisms with first-order logic constraints to recognize hierarchical human intentions from multimodal ac…
When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned?— arXiv cs.AI
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A study comparing whether enterprise policy decisions should be made by constrained token outputs or generated JSON reasoning in a 27B Korean language model finds that reasoning improves performance…
An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classification— arXiv cs.AI
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Researchers demonstrate an FPGA implementation of spiking neural networks that performs both inference and training on-device using a temporal-forward-only algorithm instead of backpropagation throug…
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation— arXiv cs.AI
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Researchers introduce ADAPT, an audio-depth navigation framework and DarkNav-Bench benchmark for autonomous robots operating in lights-out industrial environments without RGB cameras. The system uses…
LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation— arXiv cs.AI
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LATCH is a two-layer LLM framework for automating post-route FPGA timing closure that decouples language model orchestration from deterministic EDA operations. Testing across 16 designs and 7 LLMs sh…
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning— arXiv cs.AI
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Researchers propose XFD, an explainability-guided Fourier domain generalization method for sim-to-real deep reinforcement learning in autonomous driving. The approach uses attribution maps to identif…
Engram Injection: A Study of Temporal Validity in Parametric Language-Model Memory— arXiv cs.AI
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Researchers studied whether language models can learn temporal validity of facts—i.e., recognize when stored information becomes outdated—using LoRA adapters with FiLM conditioning on Qwen2.5-3B. On…
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection— arXiv cs.AI
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OneShot is a method for time series anomaly detection that uses a single labeled anomaly to weight an ensemble of detectors rather than selecting one detector or averaging them equally. The approach…
Does a Hosted Decision Model Respect Irrelevant Alternatives? A Pre-Registered Test of Option-Set Dependence in Jev— arXiv cs.AI
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A pre-registered study of jev-1.13.0 found that its decision probabilities violate independence of irrelevant alternatives (IIA), with adding options causing systematic shifts in relative odds betwee…
LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints— arXiv cs.AI
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Researchers introduce LIBERO-Industry, a benchmark extending the LIBERO robot learning framework to evaluate generalist robot policies under realistic industrial manufacturing constraints like moving…
Does the Explanation Drift Before the Guarantee? Label-Free Early Warning on Evolving Graphs— arXiv cs.AI
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Researchers propose using model explanations (attribution drift) as an early warning system for distribution shift in graph-based machine learning systems. The method detects when a model's conformal…
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning— arXiv cs.AI
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RetroPlanner is an LLM-based system for multi-step retrosynthesis planning that evaluates multiple candidate routes simultaneously across multiple objectives (reaction plausibility, round-trip consis…
Geospatial Vector Geometry as a Native Spatial Modality for Vision-Language Models— arXiv cs.AI
researchmodels
Researchers propose GeoVec-VLM, a vision-language model that integrates geospatial vector geometry as a native modality to improve spatial reasoning at geographic scales. The approach uses structured…
CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives— arXiv cs.AI
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CLIVO proposes a differentiable framework for selecting training instructions from large pools by clustering examples into ~100 groups, learning validation objectives automatically, and optimizing cl…
Game-Creation-Agents: Autonomous Game Production— arXiv cs.AI
researchapplicationstools
Game-Creation-Agents (GCA) is a multi-agent system that converts natural-language requests into fully playable Phaser 4 browser games through a structured pipeline of design documents, runtime scaffo…
RetryBench: Separating Retry Permission from Recovery Outcomes in GPT Tool Use— arXiv cs.AI
researchsafetytools
RetryBench is a benchmark that distinguishes false retry permission (FRP)—when agents retry timed-out tool calls without contractual support—from actual recovery outcomes in GPT models. Testing acros…
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification— arXiv cs.AI
researchmodelstools
SLKR, a retrieval-augmented generation framework for zero-shot text classification, decouples expensive label-knowledge generation (using flagship LLMs) from lightweight online inference using smalle…
Imitation Holds a Key to Evolution: Localized Repair and Verified Skill Acquisition for Adaptive Agents— arXiv cs.AI
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Researchers propose Passive Imitation (PI), a framework that learns from successful trajectories by identifying and repairing localized failures rather than relearning entire tasks. The method achiev…
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty— arXiv cs.AI
researchtoolsopen-source
Researchers introduce Neural Bayesian Networks (NBNs), which replace discrete conditional probability tables in classical Bayesian Networks with differentiable neural mechanisms while preserving the…
ReTAC: Residual Test-Time Agentic Computation for Video Spatial Reasoning— arXiv cs.AI
researchtools
ReTAC is a training-free framework that improves multimodal LLMs on video spatial reasoning by allocating test-time computation residually—only to unresolved requirements and affected states after fa…
Audit tools of the trade: Automated Auditor Elicitation Can Be Sensitive to the Context the Auditor Is Given— arXiv cs.AI
researchsafetytools
Researchers using the Petri tool tested how providing external context (codebases and benchmark transcripts) to LLM auditors affects their ability to detect harmful behaviors in coding agents. Result…
Inverse Garment Design with Visual Agents— arXiv cs.AI
researchtoolsapplications
Researchers introduce DressAgent, a hierarchical agentic system that reconstructs 3D garment patterns from single images using visual agents with closed-loop refinement grounded in physical simulatio…
SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data— arXiv cs.AI
researchtools
Researchers introduce SLS-Bench, a benchmark with 90 tasks for evaluating incident log summarization using synthetic observability data derived from real-world incident reports. Evaluation of 14 LLMs…
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization— arXiv cs.AI
researchmodelsapplications
FleetPilot proposes a fine-grained steering framework that injects user-specific adaptation signals into LLM forward passes without retraining, using match contrast and query-conditioned aggregation…
Using Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring— arXiv cs.AI
researchapplications
Researchers propose Evidence-Centered Design (ECD), a framework from educational measurement, as a structured approach to prompt engineering for LLMs in educational settings like automated essay scor…
Data Portability Revisited: Toward the Human-Centric, AI-Driven Data Ecosystems of Tomorrow— arXiv cs.AI
policyresearch
An arXiv paper argues that current data portability regulations in the US and EU primarily benefit service providers rather than individuals, and proposes a paradigm shift toward human-centric data g…
PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration— arXiv cs.AI
modelsapplicationstools
PEAM is an on-device LLM-based agent that autonomously generates and executes personalized mobile network configuration rules in milliseconds by fine-tuning a 4B model on real user data. The system o…
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech— arXiv cs.AI
researchsafetytools
AegisVox is a framework for de-identifying clinical speech recordings by replacing speaker identifiers with surrogate voices while preserving clinical evidence in pauses and delivery patterns. Testin…
Learning after Execution with Action-Specific Credit— arXiv cs.AI
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Researchers propose a credit-assignment method for learning after execution, where an AI policy learns to refine its own outputs using intermediate execution results. Tested on image-to-TikZ generati…
PsyMemBehav: Psychology-Informed and Memory-Grounded Two-Level Identity-to-Action Reasoning for Social-Media Behavior Simulation— arXiv cs.AI
researchmodelsapplications
PsyMemBehav is a new LLM-based framework that simulates real social media user behavior by combining psychology-informed profiles, retrieved memory, and two-level reasoning to predict platform action…
SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair— arXiv cs.AI
researchapplicationstools
Researchers introduce SocraticRepair, a planning-guided tutoring framework that fine-tunes Qwen2.5-Coder-7B on synthetic dialogues to teach Python debugging through Socratic questioning. The approach…
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection— arXiv cs.AI
researchtoolsapplications
Researchers released CESNET-TimeSeries26, a multi-year network traffic dataset spanning 4.5 years with 995 continuous days of data from a major research network, designed to evaluate forecasting mode…
LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement— arXiv cs.AI
researchtools
LYNX is a multi-agent LLM framework that translates natural language to analytical SQL programs through schema grounding and iterative refinement rather than single-pass generation. Evaluated on BIRD…
MHWA: Multi-timescale Hierarchical World-Action Model— arXiv cs.AI
researchmodels
MHWA is a hierarchical world model that reduces error accumulation in autoregressive planning by using strided context updates and dynamic routing of low-level experts. On Atari benchmarks with offli…
Exposing AI Inference as a Telco Network API over the 5G User Plane— arXiv cs.AI
researchinfra
Researchers propose Inference on Demand (IoD), a system that exposes AI inference as a native 5G network service by carrying split-inference metadata alongside tensor data across the mobile user plan…
State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study— arXiv cs.AI
research
A study of how state representation affects long-horizon agent learning in language models (1.7B–27B Qwen) reveals that models trained on compact state representations fail to transfer to full state…
AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery— arXiv cs.AI
researchapplications
AlphaRLM uses reinforcement learning to train language models to discover predictive trading factors from financial literature and market feedback, without requiring seed expressions. The approach co…
Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support— arXiv cs.AI
researchapplicationstools
Researchers deployed a simplified agentic RAG system for customer support that uses natural-language instructions, lightweight tools, and an LLM-generated navigation layer over documents, achieving 9…
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning— arXiv cs.AI
researchmodels
VC-OPD introduces a grounding-aware distillation method for vision-language models that uses counterfactual image corruption to identify which reasoning tokens depend on visual evidence, enabling mor…
HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems— arXiv cs.AI
researchmodels
HarnessNet proposes a hierarchical framework for evolving agent harnesses (software managing inputs, tools, memory, execution) in multi-agent systems without modifying frozen foundation models. The a…
When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization— arXiv cs.AI
researchmodels
Researchers propose Factorized Skill Memory (FSM), a method for preventing skill forgetting when language model agents learn new skills continuously. FSM separates skill memory into separate addressi…
MontageFlow: Physically Valid High Fidelity EEG Generation— arXiv cs.AI
researchmodels
MontageFlow introduces a flow-matching generative model for EEG data that enforces physical validity by respecting linear channel relationships inherent in EEG recordings, a constraint violated by pr…
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models— arXiv cs.AI
researchmodels
Researchers introduce Jev-ToM, a framework comparing typed decision models against generative LLMs for Theory of Mind reasoning across 11,117 questions from nine benchmarks. TypeSafe Jev achieves 73.…
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory— arXiv cs.AI
researchtools
Researchers propose a VLM-based framework with level-wise multimodal memory to reconstruct coherent heading hierarchies in long documents (tens to hundreds of pages), addressing page-by-page processi…
PRISM: Calibrating Black-Box Judges with Counterfactual Probes— arXiv cs.AI
researchtools
Researchers introduce PRISM, a method to calibrate LLM judges by measuring score changes under explicit evaluative biases and learning corrections via ridge regression on human labels. The approach a…
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation— arXiv cs.AI
research
This paper decomposes LLM ensemble performance gains into candidate value (DSV) and candidate-context effects (CCV), showing that the measured benefit of adding ensemble members depends critically on…
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT— arXiv cs.AI
researchapplications
LiFUSE proposes a self-supervised pretraining method combining frequency-domain and spatial-domain learning for liver disease detection on non-contrast CT scans. The approach uses band-stop masking i…
Tiny Recursive Message Passing— arXiv cs.AI
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Researchers applied recursive training with deep supervision and truncated gradients to message passing neural networks, achieving 99% accuracy on Sudoku-Extreme with only 16k parameters in 10 minute…
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems— arXiv cs.AI
researchmodelsapplications
SimInter is a new method for creating realistic user simulators for conversational recommendation systems by using LLM agents to conduct synthetic interviews that extract implicit user characteristic…
Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults— arXiv cs.AI
researchapplicationssafety
Researchers analyzed eye-movement patterns during visual and memory tasks in 155 adolescents and young adults, finding that reduced gaze entropy, fewer attention switches, and longer target-entry lat…
Kwai Summary Attention V2 Technical Report— arXiv cs.AI
modelsresearch
Kwai Summary Attention V2 addresses computational bottlenecks in hybrid attention architectures by enabling flexible per-layer KV cache compression and reducing full-attention layer costs through dua…
Readout Scaling Shapes Sparse Feature Recovery— arXiv cs.AI
researchmodels
Researchers introduce readout-canonicalized decision-space (RC-DS) coordinates to isolate and control how readout scaling affects sparse autoencoders' ability to recover interpretable features. The m…
A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus— arXiv cs.AI
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Researchers propose a rate-transfer method that uses stochastic calculus to convert convergence analysis of continuous stochastic differential equations (SDEs) into finite-time bounds for discrete st…
Correcting or Continuing? What On-Policy Distillation Learns from Student Errors— arXiv cs.AI
research
A study evaluates on-policy distillation (OPD) on hard math problems where students fail, finding that five OPD mechanisms consistently degrade performance when trained exclusively on failures, despi…
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision— arXiv cs.AI
researchsafety
SADDER is a training-free safety guidance method for text diffusion models that improves upon prior work (SAD) by applying safety signals in embedding space rather than token space, with theoretical…
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics— arXiv cs.AI
researchmodels
Researchers propose a neural framework combining multiscale operators, state-space models, and Navier-Stokes constraints to reconstruct high-resolution 3D turbulent flow fields from coarse-grained da…
VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL— arXiv cs.AI
researchtoolssafety
VeriPIE is a neurosymbolic framework for text-to-SQL that combines language model generation with symbolic verification using Lean theorem prover to detect malformed or ambiguous queries. It achieves…
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation— arXiv cs.AI
researchtools
GIFT-Eval-CTX is a new benchmark with 10,000 instances across 20 datasets designed to evaluate whether time series forecasting models can effectively use forward-looking contextual information (plann…
Taming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry— arXiv cs.AI
research
Researchers propose using Hilbert distance as a principled metric for controlling weight updates in softmax layers, showing it unifies recent optimizer variants (RowNorm and Compositional Muon) under…
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs— arXiv cs.AI
researchmodels
Researchers found that parallel diffusion LLMs degrade severely on math and code tasks when generating multiple tokens simultaneously, with accuracy below 15% at 16 tokens per step. They propose RePa…
Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression— arXiv cs.AI
researchmodels
Researchers propose Prune-and-Merge ViT, a training-free method that reduces Vision Transformer computational cost by combining class attention-based pruning with similarity-based token merging. The…
ChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression— arXiv cs.AI
researchtools
ChampionKV introduces a head-wise adaptive budget allocation strategy for KV cache compression that assigns different compression budgets to attention heads based on their attention concentration pat…
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models— arXiv cs.AI
safetyresearch
Researchers introduce SkillScreen, a compact decision model that screens AI agent skills for malicious behavior, achieving 99% recall on unseen attack families while flagging only 20-25% of benign sk…
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports— arXiv cs.AI
researchsafety
Researchers propose a new evaluation framework for radiology question-answering that separates answerability (whether a question can be answered) from extraction (finding the correct span), revealing…
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs— arXiv cs.AI
researchsafety
Researchers evaluated six text-based models and a graph neural network on misinformation detection in 500,000 Ukraine-related tweets from X, finding that RoBERTa achieved 97.17% accuracy when account…
Cost-Efficient Privacy Enforcement for Email-Generating LLM Agents under Strategic Information Requests— arXiv cs.AI
safetyresearch
Researchers propose a local privacy enforcement mechanism for LLM email agents that uses a smaller model (Gemma) to rewrite drafts based on privacy contracts before selective checker validation. The…
Posterior-Averaged Bellman Flow Matching for Distributional Critics— arXiv cs.AI
research
Researchers propose Posterior-Averaged Bellman Flow Matching (PA-BFM), a method for training distributional critics that average velocity predictions across multiple Bellman targets using a closed-fo…
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes— arXiv cs.AI
researchmodels
Researchers introduce Degrees of Freedom (DoF), a framework that quantifies decision-making authority delegated to agents in LLM-based multi-agent systems, positioning it between fully centralized an…
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data— arXiv cs.AI
researchapplications
HiSTOR is a new machine learning framework that learns low-dimensional representations of patient states and histories to predict treatment outcomes in high-dimensional longitudinal medical data whil…
Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning— arXiv cs.AI
researchmodels
Dynamic K-Head proposes a difficulty-supervised budget selector for continuous chain-of-thought reasoning that dynamically allocates computation based on question complexity rather than using fixed r…
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites— arXiv cs.AI
researchmodels
Researchers introduced Tanawpamana, a benchmark dataset of 4,046 tasks evaluating vision-language models on Philippine cultural heritage sites. The evaluation of seven VLMs reveals significant gaps i…
Looped Reasoning Finishes Earlier Than You Think— arXiv cs.AI
researchmodels
Researchers propose QRR (Quotient Recurrent Reasoner), a gauge-equivariant recurrent architecture that addresses convergence inefficiencies in looped reasoning by separating symmetry-driven oscillato…
DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning— arXiv cs.AI
researchmodels
DTA-GT proposes a Direction- and Topology-Aware Graph Transformer for predicting neural architecture performance, addressing a key limitation of existing NAS predictors by consistently preserving dir…
ClearNav: Perception-Aligned Quadrotor Flight Using Differential-Physics— arXiv cs.AI
researchapplications
Researchers present ClearNav, a reinforcement learning-based navigation policy for autonomous quadrotors that reduces collision rates by 65% in cluttered environments by aligning vehicle motion with…
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning— arXiv cs.AI
research
MeanFlow2Better (MF2B) proposes a one-step latent flow policy method for offline reinforcement learning that maps trajectory chunks to a learned latent decision space and uses a single ODE integratio…
PATMT2: Two-stage domain adaptation for multilingual patent translation— arXiv cs.AI
researchmodels
Researchers present PatMT2, a two-stage LoRA-based adaptation pipeline that improves patent translation across four languages (Chinese/English/Japanese/Korean), achieving 43.39-43.57 BLEU and outperf…
Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation— arXiv cs.AI
research
Researchers propose CrossTF, a diffusion-based framework that uses discrepancies between time-domain and frequency-domain denoisers to improve synthetic time-series generation, particularly for rare…
UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition— arXiv cs.AI
researchtoolssafety
Researchers introduce UA-Bench, the first large-scale benchmark for evaluating large audio-language models (LALMs) on underwater acoustic signal recognition, comprising 375,051 synthetic audio record…
SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning— arXiv cs.AI
researchapplications
Researchers introduce Sonic-Bench, a spatial audio reasoning benchmark with 2.8K Ambisonics clips and 28.8K QA pairs, and propose SonicWorld, a model that combines physical, acoustic, and semantic fe…
Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers— arXiv cs.AI
researchmodels
Researchers apply progressive depth growth—incrementally inserting layers during training—to self-supervised vision transformers (MAE and DINOv3), achieving 13.9–24.8% FLOP reductions and up to 3.36%…
Event-Centric Earthquake Monitoring with a Multi-Station Transformer— arXiv cs.AI
researchmodels
Researchers present EQNet2, a multi-station Transformer model that jointly predicts earthquake phase arrivals, polarities, location, magnitude, and event association across seismic networks. The geom…
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing— arXiv cs.AI
researchsafetytools
Researchers introduce CryptoFlowBench, a benchmark for tracing illicit cryptocurrency fund flows across multi-hop blockchain transactions, and propose ChainSleuth, an AI agent that uses iterative rea…
ARMED: Adaptive Retrieval-Augmented Medication Recommendation— arXiv cs.AI
researchapplicationstools
ARMED is a retrieval-augmented generation framework that adaptively selects and structures clinical evidence for medication recommendation, using dynamic query expansion and relevance filtering to im…
Audit Before You Distill: A Controlled Workflow for Reasoning Transfer into Compact Medical Language Models— arXiv cs.AI
researchapplications
Researchers demonstrated that teacher rationales improve distillation into smaller medical QA models only when the teacher is correct, with correct rationales boosting answer probability by 21 percen…
Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models— arXiv cs.AI
safetyresearch
Researchers propose Prompt-Adapted (ProAda) adversarial images that selectively reject harmful prompts while allowing legitimate queries on vision-language models, achieving >90% rejection of harmful…
pi-Net: Empirical Latent Representations for Differentiable Physics Heads in Data-Scarce Systems— arXiv cs.AI
researchmodels
pi-Net is a physics-informed latent variable model that combines dimensionally-normalized encoders with differentiable physics heads to predict multi-physics systems with limited data. Evaluated on G…
Measuring Learned Equivariance: An Information Theoretic Approach— arXiv cs.AI
research
Researchers propose information-theoretic methods to measure whether neural networks learn symmetries from data augmentation, using normalized mutual information (NMI) for discrete settings and cente…
Deleting Code Without Losing Knowledge: Regenerative Software for Python Libraries— arXiv cs.AI
researchtoolsopen-source
Researchers applied Fowler's regenerative software framework to enable Python libraries to be regenerated from machine-readable specifications rather than code, using an open-source tool called spotr…
Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals— arXiv cs.AI
researchapplications
Researchers present a unified deep learning framework for seizure classification that handles both EEG and intracranial EEG (iEEG) signals from different sources and sampling rates. The approach uses…
VAttnPO: Visual Attention-Guided Policy Optimization— arXiv cs.AI
researchmodels
Researchers introduce VAttnPO, a method that improves multimodal reinforcement learning by using visual attention alignment to refine token-level credit assignment in vision-language models. Testing…
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection— arXiv cs.AI
researchsafety
Researchers introduce SarcasTraps, a 479-entry test set for Turkish hate speech detection that isolates the effect of sarcasm on model performance. Both fine-tuned models (BERTurk, XLM-RoBERTa) and z…
MixLID: Token-Level Language Identification in Mixed-Language Text— arXiv cs.AI
researchmodelsopen-source
Researchers introduce MixLID, a token-level language identification model covering 111 language-script combinations, trained first on monolingual data then fine-tuned on code-switching examples inclu…
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees— arXiv cs.AI
researchtoolsopen-source
Researchers found that implementation details in k-nearest neighbor algorithms—such as query strategy choice, parameter tuning, and micro-optimizations—can dramatically affect performance, with diffe…
An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution— arXiv cs.AI
researchsafetyopen-source
Researchers created a corpus of 7,604 labeled Nastaliq Urdu Facebook comments and benchmarked transformer models for offensive content detection, achieving 87% accuracy with MuRIL. SHAP-based analysi…
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling— arXiv cs.AI
researchmodels
OceanFlow is a flow matching framework for probabilistic ocean modeling that improves ensemble forecasting by jointly optimizing the conditional distribution of ocean states rather than individual en…
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models— arXiv cs.AI
researchtools
Researchers propose HGA-Quant, a post-training quantization framework for autoregressive visual generation models that uses tail-aware weight calibration and coarse-to-fine activation scheduling to m…
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation— arXiv cs.AI
researchtoolsapplications
Co-DSL is a framework enabling humans and LLMs to collaboratively design reusable spatial concepts for layout generation, where users define custom operations through lightweight feedback iteration a…
RINO: Renormalization Group Distillation with No Labels— arXiv cs.AI
researchapplications
RINO introduces a self-supervised learning method for high-energy physics that leverages the renormalization group structure of particle jets to pretrain on unlabeled real collider data, avoiding sim…
SafeDSE: Joint Exploration of Safety Architecture Patterns, Mapping, and Scheduling for Dataflow Applications With Safety Arguments (Extended Version)— arXiv cs.AI
researchtoolssafety
SafeDSE is a design space exploration framework that jointly optimizes safety architecture patterns, processor mapping, and scheduling for embedded dataflow applications, with integrated verification…
AI Representations of Mental Health Stigma: Quantifying the Gap Between AI Estimations and Human Perceptions Across Gender Groups Using GPT-Based LLMs— arXiv cs.AI
researchsafety
Researchers compared how GPT-4 and GPT-5 represent mental health stigma across gender groups against real-world data from 76,406 humans. While the models captured some gender-based patterns correctly…
DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies— arXiv cs.AI
researchmodels
DiffNash applies diffusion models to compute Nash equilibria in high-dimensional continuous games by combining Online Mirror Descent and Fictitious Play with a reweighted score-matching objective tha…
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schrödinger Bridge at Low Bitrate— arXiv cs.AI
researchmodels
BridgeCodec proposes a framework to translate between incompatible neural audio codec endpoints using Schrödinger Bridge optimal transport and Mamba-enhanced architecture, enabling cross-codec intero…
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B— arXiv cs.AI
researchmodels
Researchers adapted Nemotron-3-Nano-30B-A3B for eight Indian languages by expanding its tokenizer from 131K to 163K tokens in-place, achieving 38.6% reduction in tokens-per-word through continued byt…
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge— arXiv cs.AI
researchsafety
Researchers introduce LENS, a 1,765-question benchmark to evaluate how well LLMs understand North Korean language, society, and knowledge distinct from South Korea. Testing major models reveals modes…
GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision— arXiv cs.AI
researchtools
GeoBridge proposes an evolutionary neural architecture search framework that discovers reusable decoder topologies optimized for multi-scale feature processing, then transfers them across depth estim…
PID-Gated Continual Prompt Adaptation Under Noisy Feedback— arXiv cs.AI
researchtools
Researchers propose PID-gated continual prompt adaptation, a control-theoretic approach to deciding when LLM agent instructions should be updated in response to noisy feedback. The method outperforms…
AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN— arXiv cs.AI
researchsafetytools
AutoSec-RAN combines LoRA fine-tuning and retrieval-augmented generation on Mistral-7B to automate security analysis of Open Radio Access Networks (O-RAN) using specification documents rather than ra…
SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verification— arXiv cs.AI
safetytoolsopen-source
SkillMarker introduces a watermarking technique for AI agent skills that embeds ownership markers while preserving instruction functionality, enabling detection of copied or leaked skills even when p…
To Defer or Not to Defer? Online Learning with LLMs and Congested Experts— arXiv cs.AI
researchmodels
Researchers propose KL-PBD, an algorithm for systems that route queries between LLMs and human experts under capacity constraints, learning when to defer based on LLM confidence signals with delayed…
Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving— arXiv cs.AI
researchtools
Researchers introduce the Active Probing Benchmark (APB), a puzzle-based evaluation framework that tests LLMs' ability to acquire and reason about specialized knowledge through multi-turn interactive…
Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems— arXiv cs.AI
researchsafety
Dream2Discern is a new method for training runtime auditors that predict failures in LLM-based multi-agent systems by generating hard examples through representation perturbations rather than collect…
When Does Compensating Inherited Quantization Error Help? Mean-Square Guarantees for Layer-Wise Quantization and Their Limits— arXiv cs.AI
researchmodels
This paper provides theoretical analysis of layer-wise post-training quantization with error compensation, establishing mean-square guarantees for when compensation helps reduce network output error…
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework— arXiv cs.AI
researchmodels
Researchers propose Spectral-Guided Quality-Diversity Diffusion, a framework that combines quality-diversity optimization with diffusion models to generate diverse, high-performing outputs by optimiz…
ELECT: Settling Identity Before Rendering for Scalable Story Visualization— arXiv cs.AI
researchmodels
ELECT is a training-free story visualization method that pre-resolves character identity across frames using language-guided mappings before rendering, decoupling per-frame cost from narrative length…
GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability— arXiv cs.AI
researchsafetytools
Researchers propose GAMW, a watermarking framework that embeds imperceptible identifiers directly into the speech generation process of TTS systems rather than post-hoc, improving robustness against…
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax— arXiv cs.AI
researchtools
Researchers introduce Anchor-and-Harvest Curriculum (AHC), a rule-based training system for Craftax that achieves native-task returns of 46.70 without LLM inference, matching performance of larger LL…
Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry— arXiv cs.AI
researchapplicationssafety
Researchers evaluated 12 LLMs on Hong Kong fire safety code compliance using a new benchmark with multiple-choice and open-ended questions, finding proprietary models reach 80-90% accuracy on MCQs bu…
Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting— arXiv cs.AI
researchapplications
Researchers propose a physics-informed state space model combining a differentiable PV conversion model with PowerMamba for day-ahead solar power forecasting. The approach decomposes the problem into…
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance— arXiv cs.AI
infraapplicationsresearch
Researchers deployed machine learning inference on AWS IoT Greengrass v2 for industrial predictive maintenance, demonstrating 86.8% bandwidth reduction and 256x latency improvement compared to cloud-…
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis— arXiv cs.AI
researchtoolsopen-source
Researchers introduce a Visual Question Answering dataset for evaluating large multimodal models on scientific chart analysis tasks, with synthetic multi-panel figures of varying complexity and a fou…
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval— arXiv cs.AI
researchmodels
CoRE proposes a joint learning approach for structured knowledge retrieval that automatically learns which reasoning rules to apply to queries without predefined supervision. The method alternates be…
PreSO: Privileged Representation Isolation for On-Policy Self-Distillation— arXiv cs.AI
researchmodels
PreSO is a novel self-distillation method that isolates reusable privileged representations for reinforcement learning by using compact hints and low-rank subspace projection to transfer knowledge fr…
Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation— arXiv cs.AI
researchapplications
Researchers present a hardware-aware framework that separates LLM-based symbolic task planning from deterministic metric execution for robotic manipulation in simulation. The LLM generates high-level…
Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning— arXiv cs.AI
researchsafety
Researchers introduce ChronoRegime, a temporal reasoning benchmark, and Confidence-Aware Temporal Reasoning (CTR), a framework that evaluates whether LLMs can reliably predict future events based on…
LabInstruct: Benchmarking Situated Instructional Video Generation for Lab Procedures— arXiv cs.AI
researchsafetytools
Researchers introduce LabInstruct, a benchmark with 204 laboratory tasks across 5 disciplines to evaluate whether AI video generation models can reliably create instructional videos for lab procedure…
AphaVoice: Text-to-Speech Model for Aphasia Patient Simulation— arXiv cs.AI
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AphaVoice is a text-to-speech model designed to generate realistic aphasic speech for medical training simulations, addressing a gap in aphasia care by enabling practitioners to practice with authent…
Rethinking Activation Functions: Reducing Bias Can Improve Learning— arXiv cs.AI
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Researchers show that common activation functions (ReLU, GELU, Swish, Sigmoid) produce nonzero post-activation means—termed "activation bias"—which accelerates initial learning but leads to worse fin…
KV4-RL: Unlocking Faster Asynchronous Rollouts for RLVR with INT4 KV Cache Quantization— arXiv cs.AI
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Researchers propose KV4-RL, a technique combining INT4 KV cache quantization with selective INT8 protection to accelerate asynchronous rollouts in reinforcement learning with verifiable rewards. The…
SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning— arXiv cs.AI
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Researchers introduce SceneForesee, a benchmark for evaluating how multimodal models understand entity motion and scene changes over time, and propose EvoSee, a spatiotemporal reasoning framework tha…
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies— arXiv cs.AI
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Researchers developed a cross-lingual TTS benchmark covering English and nine Indian languages, and proposed a lightweight inference-time duration estimation strategy for alignment-free TTS models th…
Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning— arXiv cs.AI
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Researchers propose Prior-Fitted Scorers (PFS), using Transformer models as learnable similarity functions for multimodal contrastive learning instead of manually specified metrics like cosine simila…
Showing versus Doing: Teaching by Demonstration— arXiv cs.AI
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Researchers developed a Bayesian model distinguishing between intentional teaching demonstrations and passive task performance, showing that human demonstrators modify behavior when teaching and that…
Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees— arXiv cs.AI
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Researchers derive sufficient conditions for sublinear counterfactual regret in reflective language agents that adapt via feedback without weight updates, using Lean-verified proofs. The work establi…
Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery— arXiv cs.AI
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Researchers propose Energy Ring, a method to improve Test-Time Discovery (TTD) by stabilizing the energy gap between known and unknown classes during model deployment on unlabeled data streams. The a…
What Survives When Activation Steering Is Used as Memory— arXiv cs.AI
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Researchers tested whether activation steering vectors can reliably store and retrieve user facts and preferences as memory in LLMs. They found that while activation memory is selective, facts are st…
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment— arXiv cs.AI
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HiPPT introduces a multi-scope text alignment framework for learning reusable representations from wearable PPG (photoplethysmography) recordings by matching supervision sources to appropriate eviden…
Learning Perturbation-Specific Representations through Conditional Energy Prior for Cellular Response Prediction— arXiv cs.AI
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Researchers propose PerturbEnergy, a method using conditional energy-based priors to improve AI prediction of cellular responses to drug perturbations by better isolating perturbation-specific signal…
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages— arXiv cs.AI
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Researchers trained Agaram 2.5B, a 2.5B-parameter Tamil-English language model from scratch on 50B tokens in six days using eight H100 GPUs, releasing it with tool-calling capabilities and multilingu…
Controllable Diverse Sampling through Pullback Directions— arXiv cs.AI
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Researchers propose a training-free method for conditional diffusion models that identifies directions of maximal output sensitivity using pullback geometry to generate diverse images from the same p…
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction— arXiv cs.AI
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APTree is a model-driven framework that automates task planning for construction robots by coordinating multiple symbolic planners and using behavior trees for execution, with formal verification of…
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version— arXiv cs.AI
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AeroChem is a closed-loop physics-informed state space model for air quality forecasting that combines UDE-based physical simulation with Mamba-based neural components via Neural Kalman Fusion to red…
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification— arXiv cs.AI
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RespFocus is a weakly supervised deep learning framework using multiple instance learning to classify respiratory sounds while providing interpretable temporal evidence for its predictions. The autho…
PTGPO: Prefix Tree Group Policy Optimization— arXiv cs.AI
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Researchers propose PTGPO, a credit assignment method for training language agents on long-horizon tasks that uses prefix trees over trajectory suffixes to compute step-level advantages without a cri…
Resource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar— arXiv cs.AI
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Researchers propose a two-stage approach for 3D shape and orientation estimation using roadside RGB cameras and 3D radar that avoids the generalization problems of end-to-end models across varying in…
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks— arXiv cs.AI
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Researchers propose a continual learning method for spiking neural networks that uses spike activation maps to identify and replay only the most informative image patches, reducing memory requirement…
Read the Room Before Your Model Generates— arXiv cs.AI
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Researchers propose Scenario-Actor, a framework that adapts LLM decoding temperature based on inferred communicative context rather than fixed heuristics. The method extracts scenario characteristics…
Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors— arXiv cs.AI
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Researchers used LLM-derived odor descriptors from a database of 1,200+ food volatile mixtures to train a model predicting perceptual similarity from molecular composition. The approach learned molec…
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search— arXiv cs.AI
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WeFair is a fidelity-aware query rewriting system for short-video search that uses human feedback to personalize search queries while preserving user intent. The method combines supervised learning f…
Does a Governed Confidence Score Know When a Text-to-SQL Answer Is Right? An Exploratory Study on BIRD and a Pre-registered Confirmation on Spider— arXiv cs.AI
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A study evaluates confidence scoring mechanisms for text-to-SQL systems on the BIRD and Spider datasets, finding that the original design separates correct from incorrect answers poorly (AUROC 0.624)…
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis— arXiv cs.AI
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Researchers introduce Agentic4PDM, a framework combining LLM agents with deterministic control to diagnose industrial equipment faults from sensor data. The system uses agents to investigate signals…
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity— arXiv cs.AI
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Researchers studied Gene Expression-to-Image (GE2I), a method that converts high-dimensional gene expression data into pixel grids for CNN processing, revealing a trade-off between spatial smoothness…
QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction— arXiv cs.AI
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Researchers propose QORH, a method for knowledge graph link prediction that uses hypergraphs to model ordered rule sequences and their dependencies, rather than simpler pairwise relations. The approa…
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution— arXiv cs.AI
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ECG-RSI is a recursive self-improvement framework that uses verified waveform synthesis and feedback-guided search to improve model performance on ECG-related tasks. Applied to Qwen3.5-35B-A3B, the m…
What Makes a Latent Space Shared? Direct and Recoverable Compatibility in Motion VAEs— arXiv cs.AI
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Researchers studying motion VAEs on HumanML3D demonstrate that shared latent spaces across different motion schemas do not automatically make representations directly interchangeable—identical inputs…
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service— arXiv cs.AI
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Researchers propose Queue-Aware Token Communications (TokenCom), which incorporates queueing dynamics into AI-native wireless networks by managing token workload transmission under time-varying netwo…
Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction— arXiv cs.AI
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Researchers propose a task and method for predicting how liquid flows around moving solid objects in 3D space over time, with applications to robotic manipulation and fluid simulation. They introduce…
Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework— arXiv cs.AI
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Researchers evaluated whether large language models can generate realistic behavioral patterns for epidemiological modeling, using hepatitis C transmission among people who inject drugs as a case stu…
Vision: "What Don't I Know About User's Preferences?" How Uncertainty Estimation Improves Proactive Assistant Alignment— arXiv cs.AI
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Researchers propose integrating uncertainty estimation into proactive smart assistants to reveal prediction confidence, evidence sources, and ambiguity origins beyond raw preference likelihoods. The…
Visible but Not Avoided: State-Dependent Re-Proposal of Rejected Candidates in Catalog-Based LLM Optimization Loops— arXiv cs.AI
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This paper investigates whether large language models can avoid re-proposing rejected candidates in optimization loops, finding that static probes underpredict re-proposal rates by 0.32 on average an…
Self-Representation Alignment in Diffusion Models for Video Background Music Generation— arXiv cs.AI
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Researchers propose U-SRA, a diffusion model architecture that aligns internal representations across different denoising layers to improve video background music generation. The method uses an align…
Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings— arXiv cs.AI
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Researchers propose CEPA, a framework using LLMs to assess personality from video interviews across different interview protocols without protocol-specific retraining. By aligning behavioral represen…
When a Conceptor Complement Becomes Gain: Operator Geometry and Behavioral Attribution in Language Models— arXiv cs.AI
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Researchers investigate how much behavioral change in language models comes from the fitted covariance matrix in conceptor-based steering interventions versus other components. Testing across three m…
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration— arXiv cs.AI
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This paper investigates how LLM-based embodied agents collaborate in teams without explicit communication, testing whether observing partner state, sharing object knowledge, and predicting partner ac…
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer— arXiv cs.AI
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Researchers developed a framework combining large language models with differentiable traffic assignment models to make transportation planning accessible to non-experts. The system integrates an LLM…
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting— arXiv cs.AI
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Researchers developed SatPV-Field, a framework that models distributed solar generation as spatiotemporal fields rather than independent time series, achieving 10% improvement over single-station bas…
Probable but Not Valid: A Psychometric Audit of Decision Models as Synthetic Survey Respondents— arXiv cs.AI
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Researchers evaluated whether decision models and generative LLMs can replace human respondents in psychometric surveys by testing 14 decision models and 47 LLMs on a 68-item organizational psycholog…
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition— arXiv cs.AI
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Researchers propose CHEAR, a method that uses LLMs with wearable sensor data for human activity recognition by pre-organizing activity confusion hierarchies offline rather than at inference time. The…
Reels-RSI: Program-Level Self-Improvement for Code-Rendered Explainer Videos— arXiv cs.AI
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Reels-RSI is an open-source system for generating explainer videos by treating them as programs (scripts, storyboards, animation code) rather than pixel sequences, enabling self-improvement through l…
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning— arXiv cs.AI
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Researchers evaluated vision-language models (VLMs) on geolocating crowdsourced disaster photos from a decade of PhotoMappers data, combining street view and remote sensing imagery. VLMs achieved 40.…
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection— arXiv cs.AI
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Researchers introduce MemeLogos, a framework that detects harmful memes by retrieving and reasoning over structured knowledge patterns rather than similar examples. The approach combines a knowledge…
Certify the Decision, Not Every Score: Exact Top-k Selection from Few Evaluations— arXiv cs.AI
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Researchers present Certified Adaptive Selection (CAS), an algorithm that identifies the top-k candidates from a set using minimal evaluations by maintaining guaranteed bounds on worst-case performan…
Equal-information evaluation of credit assignment in modular financial reinforcement learning— arXiv cs.AI
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Researchers propose an evaluation framework for credit assignment in modular financial reinforcement learning that uses equal-information controls and fixed-utility comparisons to distinguish whether…
AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Diffusion Adaptation— arXiv cs.AI
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AcrossVAM2.0 adapts a 5B-parameter text-to-video diffusion model to follow explicit point correspondences while preserving learned appearance priors, using conflict-aware gradient manipulation and ze…
RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution— arXiv cs.AI
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Researchers propose RASSG, a method for controlling tool execution in streaming speech assistants that decides when to launch and cancel tools based on speech recognition stability. The approach redu…
GREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions— arXiv cs.AI
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Researchers developed GREAT-ESDD, a neural routing framework combining a Graph-Edge-Attention-Network solver for vehicle routing with dynamic pricing and customer preference learning via Thompson sam…
ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI— arXiv cs.AI
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Researchers present ANASSA, a low-level architecture that decomposes a high-level agentic AI framework for geospatial applications into 47 logical roles, typed contracts, and governance controls. The…
On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems— arXiv cs.AI
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Researchers propose a group-theoretic framework using policy-evaluation symmetries to characterize structural heterogeneity among agents in multi-agent systems. The approach enables principled classi…
Falsification-proof non-market allocation mechanisms— arXiv cs.AI
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This paper characterizes optimal resource allocation mechanisms that remain resistant to falsification when agents can manipulate their reported characteristics, a problem common in non-market alloca…
GeoMER: A Joint Euclidean-Hyperbolic Geometry Framework for Multimodal Emotion Recognition— arXiv cs.AI
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GeoMER is a new multimodal emotion recognition framework that combines Euclidean geometry for fine-grained cross-modal interactions with hyperbolic geometry to capture hierarchical emotional structur…
When More Objectives Make Identification Easier: The Role of Preferences and Feedback— arXiv cs.AI
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This paper studies best-arm identification in multi-objective bandits, showing theoretically that multiple objectives can paradoxically reduce sample complexity despite higher-dimensional reward spac…
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning— arXiv cs.AI
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Researchers propose Adaptive Diversity Control (ADiCo), a framework that automatically tunes behavioral diversity in multi-agent reinforcement learning systems during training using extremum-seeking…
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English— arXiv cs.AI
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Researchers evaluated how post-training quantization affects language model performance across Bavarian, Standard German, and English using a 206-question aligned benchmark across 14 models. 8-bit qu…
Known-Voice Cloning and the Limits of Personality Transfer for Character Design— arXiv cs.AI
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Researchers tested whether cloned voices of familiar people transfer personality impressions when attached to virtual characters, conducting studies with university students and online participants.…
AMemOps: Evaluating Memory Updates in Long Audio Streams— arXiv cs.AI
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Researchers introduced AMemOps, a benchmark for evaluating how audio memory systems handle attribute updates in long audio streams, including tracking recurring sources through acoustic changes and m…
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models— arXiv cs.AI
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DTScribe automates digital twin model generation by using local LLMs to extract key characteristics from digital twin documents and produce both conceptual architecture visualizations and reporting p…
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation— arXiv cs.AI
researchtoolsopen-source
VETNET is a new benchmark dataset ecosystem and evaluation framework designed to standardize AI development in veterinary medicine, addressing fragmentation similar to what AI has already solved in h…
FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection— arXiv cs.AI
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Researchers propose FALLNEST, a data condensation method that compresses large imbalanced IMU datasets into tiny synthetic training sets for wearable fall detection. A small CNN trained on just 1% of…
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence— arXiv cs.AI
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Researchers prove that randomly initialized wide neural networks converge to Gaussian processes with a uniform convergence rate of m^{-1/2} in the supremum norm over compact input domains, addressing…
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions— arXiv cs.AI
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This paper establishes when learned multidimensional blocks of a neural network representation preserve causal meaning under transformations. The authors show that block-diagonal invertible transform…
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark— arXiv cs.AI
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Researchers evaluated seven general-purpose LLMs on soil organic carbon assessment tasks using Australian paddock data, finding that while models can parse outputs and achieve 90%+ accuracy on pairwi…
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning— arXiv cs.AI
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Researchers propose Fenchel-Bregman posterior learning (FBI), a method that trains a distribution over solver parameters to maximize the probability that at least one of K candidate solutions is corr…
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation— arXiv cs.AI
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Researchers introduce GLST, a memory-decomposition framework for autonomous navigation under changing goals without explicit transition signals. The approach separates spatial and temporal beliefs to…
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback— arXiv cs.AI
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This paper presents Gaussian process bandit algorithms for optimizing AI systems (prompts, retrieval, agents) when cheap model-based evaluations may disagree with trusted ground truth. The work provi…
FASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval— arXiv cs.AI
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Researchers introduce FASHION-RQ, an automated framework that uses hierarchical quantization (RQ-VAE) to derive queries and relevance judgments directly from fashion product catalogs, creating a scal…
Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning— arXiv cs.AI
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Researchers propose Enhanced Detective, a dual-stage defense mechanism for federated learning systems used in financial fraud detection, combining median absolute deviation, distance-based filtering,…
Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes— arXiv cs.AI
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Researchers present a unified convex-analytic framework for selecting queries during safety verification of Markov decision processes, generalizing prior work by Watanabe and Unno (2026) that used Eu…
Few-for-Many Best-Arm Identification in Multi-Objective Bandits— arXiv cs.AI
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Researchers propose a new multi-objective bandit problem called 'few-for-many best-arm identification,' where a small set of options must be selected to serve many competing objectives while minimizi…
Cross-Market Meta-Learning for Electricity Price Forecasting— arXiv cs.AI
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Researchers present MetaGrid, a meta-learned transformer model that forecasts day-ahead electricity prices by training across multiple U.S. Independent System Operators (ISOs) rather than fitting sep…
Joint Embedding with Dynamic Integration network (JEDI-net)— arXiv cs.AI
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JEDI-net is a neural network architecture that clusters observations based on SHAP feature importance patterns during training and embeds these latent group identities to allow predictions to adapt t…
Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning— arXiv cs.AI
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This paper proposes the Boundary Proximity Index (BPI) to more rigorously evaluate Forward-Forward learning methods, a family of algorithms that use layer-wise training instead of backpropagation. Th…
BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction— arXiv cs.AI
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Researchers combined next-token prediction Transformers with deep clustering (DCSS) to automatically discover behavioral patterns in open-ended games like Minecraft. Testing on 26 players' sessions i…
Towards Cost-Efficient Video Dataset Construction with Large Language Models— arXiv cs.AI
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Researchers propose using LLMs to pre-screen video metadata before download, reducing storage and annotation costs for video dataset curation. The approach outperforms keyword heuristics and achieves…
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations— arXiv cs.AI
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Researchers integrated human-perceived feature understandability into feature selection for machine learning explanations on tabular data, using the Feature Understandability Scale to score features…
AIPOCH Open-Science: An Open-Source AI Research Workbench with Reproducible .science Packages— arXiv cs.AI
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AIPOCH Open-Science is an open-source research workbench that packages AI-assisted research workflows into portable .science session files, preserving computational history, artifact versions, and re…
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling— arXiv cs.AI
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Researchers propose reflected proximal dynamics, a new sampling method for log-concave distributions that combines exact target preservation with momentum-based acceleration. The approach achieves im…
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations— arXiv cs.AI
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Researchers released Gugak-VocalSet, the first public dataset of Korean traditional singing with 24.5 hours of studio recordings and 68,000 annotated segments of sigimsae (a pitch contour system for…
Embedding Information in Policy Randomness: Distribution-Preserving Implicit Communication in MDPs— arXiv cs.AI
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Researchers propose a method to embed hidden information in stochastic policies without changing their action distributions, framing it as a channel coding problem solved via posterior-matching rando…
Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions— arXiv cs.AI
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Researchers propose NARS, a neuro-symbolic system combining LLM-based argument extraction with formal argumentation semantics to detect persuasiveness in online discussions. The system achieves modes…
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning— arXiv cs.AI
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Researchers propose a ransomware detection framework that combines software-level scanning with hardware microprocessor monitoring, using adversarial training and neural architecture search to improv…
scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data— arXiv cs.AI
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scHFBiMamba combines a bidirectional Mamba autoencoder with an MLP classifier for analyzing single-nucleus RNA-seq data from cardiac tissue, achieving 0.966 macro-F1 while using 14.6× fewer parameter…
Framework for an AI-Ready ARPES Community Database— arXiv cs.AI
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A condensed matter physics community proposes a shared database framework for ARPES (Angle-Resolved Photoemission Spectroscopy) data to enable AI-driven discovery and reproducibility, drawing on outc…
WisdomNet: trustable machine learning toward error-free classification— arXiv cs.AI
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Researchers propose WisdomNet, a neural network architecture that adds a 'conjugate neuron' to detect when a classifier is uncertain and should reject or defer classification to humans, achieving 0%…
AI-Native Product Management: A Vision— arXiv cs.AI
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Researchers propose a vision for AI-native product management where AI becomes a persistent participant in product development workflows, spanning sensing, exploration, decision-making, realization,…
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation— arXiv cs.AI
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Researchers present NeST-Bayes, a neural-symbolic framework that infers trust in multi-robot systems by analyzing observation consistency between robots without requiring pre-established trust relati…
MvFFN: Multi-view Floor plan Feed-forward Network for Unposed Wide-Baseline Panorama Layout Reconstruction— arXiv cs.AI
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Researchers propose MvFFN, a feed-forward network that reconstructs multi-room floor plans and camera poses from unposed wide-baseline panoramas by predicting local wall segments that deterministical…
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models— arXiv cs.AI
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Researchers propose a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models more efficiently than native 3D approaches. By mapping depth to channel dime…
Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning— arXiv cs.AI
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This paper studies average-reward Q-learning and Q-planning with a regularization framework that uses a reward-agnostic m-step mixing policy to control contraction and exploration properties. The wor…
AlignAudio: A Unified Dual-Alignment Framework for Noise-Robust Speech-Conditioned Generation— arXiv cs.AI
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AlignAudio proposes a dual-alignment framework for speech-conditioned audio generation that maintains performance under environmental noise by aligning clean and noisy speech representations and enfo…
Jevolution: A Framework for Studying Communication and Collective Dynamics in AI Agents— arXiv cs.AI
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Jevolution is an open-source spatial predator-prey simulation framework designed to study communication and collective dynamics among multiple AI agents in a controlled environment. The paper emphasi…
Cortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules— arXiv cs.AI
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Cortex is a tool that evolves and prunes instruction sets (skills and rules) for coding agents through measurement-gated iteration, testing changes via replay of developer corrections and only keepin…
Coarse-Graining Recursive Expert Computation: Continuation-Relative Interfaces and Failure Modes— arXiv cs.AI
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This paper introduces a continuation-relative evaluation framework for assessing computational coarse-graining—replacing sequences of neural network updates with cheaper modules while preserving perf…
Limitations on Autoregressive Token Generation via Sequential Sampling— arXiv cs.AI
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A theoretical study of autoregressive token generation models shows that sequential samplers with small amounts of state incur exponential size overhead to simulate without state, and cannot establis…
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification— arXiv cs.AI
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Researchers studying a medical AI model for kidney cancer classification show that gate coefficients in fusion layers do not accurately reflect how much each feature type actually contributes to the…
Select or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses— arXiv cs.AI
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Researchers propose oracle-guided fine-tuning to improve LLM-based ASR post-editing by creating three curated training pools (corrections, regularizers, and hard negatives) from N-best hypothesis lis…
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses— arXiv cs.AI
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Researchers introduce EmoXpt, a sentiment analysis framework that compares emotional tones in human tweets about generative AI with ChatGPT's responses to those tweets. The analysis reveals that LLM-…
Reasoning for Propaganda Detection via Label-Guided CoT Optimisation— arXiv cs.AI
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Researchers introduce ReCoT, a reasoning-based framework for propaganda detection that generates explanations without requiring labeled reasoning data, using label-guided chain-of-thought optimizatio…
Reference Wording Shapes Retrieval of Robot Task Instructions— arXiv cs.AI
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Researchers analyzed how the wording of reference descriptions affects retrieval of robot task instructions in the RLBench benchmark, finding that character n-gram TF-IDF achieves 61.3% top-1 accurac…
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition— arXiv cs.AI
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OntoAR-HAR combines agentic RAG with tree-structured activity ontologies and personalized memory to improve LLM-based human activity recognition from sensor data. The approach hierarchically decompos…
Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals— arXiv cs.AI
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Researchers developed CIR, an event-driven food safety inspection system that combines historical facility data with real-time consumer reviews to detect violations between scheduled inspections. On…
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification— arXiv cs.AI
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MP-MNCA is a new framework for spatial transcriptomics that identifies tissue domains by incorporating morphology priors and masked neighbor cross-attention to route molecular information across spat…
Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints— arXiv cs.AI
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Researchers benchmarked Quantum Kernel SVMs for fraud detection against classical baselines under realistic NISQ constraints, finding that quantum approaches achieve 0.73 PR-AUC on 150 samples versus…
Beyond Black Boxes: Visual Grounding for Text Embeddings— arXiv cs.AI
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Researchers propose a framework that converts text embeddings into visual topographic sketches to improve explainability and information retrieval. Experiments on MS-COCO show sketch-based representa…
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions— arXiv cs.AI
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Researchers compare two neural network architectures (canvas-based and coordinate-based) for predicting indoor airflow and temperature fields across varying room dimensions, finding that canvas-based…
Weak Segmentation-Guided GAN for~Realistic Color Edition— arXiv cs.AI
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Researchers propose a GAN architecture that uses weak segmentation guidance to enable realistic color editing of images with precise user control. The method bridges the gap between traditional segme…
Suggested Is Not Superseded: A Pre-Registered Evaluation of a System-One Model as a Memory-Retirement Gate— arXiv cs.AI
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Researchers evaluate a System-One decision model (Jev) for determining when new information should retire old claims in long-term memory systems. The study finds the model prematurely retires 39.2% o…
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research— arXiv cs.AI
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Researchers present ScienceBuddy-PV, a verifier-governed framework for medical image analysis in panvascular intervention research, evaluated on stenosis detection and vessel syntax tasks. The system…
On the Complexity Gap Between POMDPs and Multi-Environment POMDPs— arXiv cs.AI
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Researchers characterize the computational complexity of multi-environment POMDPs (ME-POMDPs) across various subclasses, showing that problems tractable in single-environment settings become PSPACE-c…
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition— arXiv cs.AI
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Researchers propose a hybrid GCN-Transformer architecture for Indian Sign Language recognition that handles variable signing speeds through entropy-driven keyframe sampling and irregular temporal gap…
Knowledge transfer between simple discrete worlds— arXiv cs.AI
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Researchers trained a depth-recurrent model on Sudoku-like puzzles with varying rule combinations to study knowledge transfer across discrete worlds. The model performed well when recombining known r…
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On— arXiv cs.AI
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Researchers introduce ShoeFit, a dual-stream Diffusion Transformer framework and MVShoes dataset for virtual footwear try-on synthesis. The method addresses viewpoint misalignment and background colo…
FETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation— arXiv cs.AI
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FETALFusion is a CNN-Mamba encoder-decoder for automated fetal head segmentation from 2D ultrasound that achieves 0.963 Dice score across transabdominal and transperineal acquisition types. The metho…
Reset Early, Reset Often, Eliminate Models— arXiv cs.AI
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SATResetCoP, a new theorem prover, eliminates backtracking in connection-tableau search and instead accumulates ground clauses in an incremental SAT solver to refute problems. The method achieves 54%…
PatTransBench: an ipc-stratified, multi-dimensional benchmark for patent machine translation evaluation— arXiv cs.AI
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PatTransBench is a new 12,000-item benchmark for evaluating patent machine translation across Chinese, English, Japanese, and Korean, with a four-dimensional evaluation protocol including n-gram metr…
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios— arXiv cs.AI
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Researchers use causal inference to evaluate whether social navigation signals improve robot performance in human-shared corridor environments, finding that naive observational analysis can reverse c…
Comparing GenAI to Machine Learning and Manual Review for eDiscovery— arXiv cs.AI
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A research paper challenges Redgrave LLP's claim that Relativity's GenAI eDiscovery tool (aiR) substantially outperforms traditional machine learning, arguing the comparison is methodologically flawe…
Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition— arXiv cs.AI
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Researchers evaluate V-JEPA2 frozen features transferred to multimodal sensor fusion (RGB video, depth, radar) for breathing and pose classification with missing sensor scenarios. The study on 39 sub…
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images— arXiv cs.AI
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Researchers developed a protein crystal detection method for fluorescence microscopy that analyzes subordinate (longer-wavelength) color channels instead of dominant channels, reducing misclassificat…
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals— arXiv cs.AI
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Researchers propose Siam-ALS, a Siamese deep learning framework that combines EEG and eye-tracking signals for early ALS detection, achieving 98.8% accuracy on a public dataset and ~98% AUROC on an i…
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition— arXiv cs.AI
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RIECNN proposes an image-enhanced CNN for traffic sign recognition achieving 99.75% accuracy on GTSRB while maintaining sub-1.3ms inference time. The approach addresses environmental robustness (brig…
OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation— arXiv cs.AI
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OT-FlowRec proposes a novel framework combining optimal transport and flow matching for multimodal sequential recommendation systems, designed to capture multiple concurrent user interests rather tha…
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows— arXiv cs.AI
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Researchers propose Active Neural Matter (ANM), a finite-window response framework for analyzing what information physical and distributed systems must retain to predict future behavior. The method u…
Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation— arXiv cs.AI
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Researchers propose a two-stage framework for dynamic audio-visual navigation that uses physics-informed sound intensity alignment and cross-modal feature fusion to help embodied agents locate moving…
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization— arXiv cs.AI
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This paper proposes a probabilistic relaxation method for bilevel data selection using learnable categorical priors, addressing gradient estimation and rounding issues in existing approaches. The met…
Diffusion-Based Auctions for Multi-Winner Banner Ads— arXiv cs.AI
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Researchers propose a diffusion-model-based auction mechanism that allows multiple advertisers to simultaneously influence a single banner ad by blending their bids and image descriptions into a comp…
Bias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination— arXiv cs.AI
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A comparative study evaluates genetic multi-criteria sorting against learned-to-rank models (LightGBM, XGBoost, RankSVM) on their robustness to bias under proxy skew and label contamination. Genetic…
Enhanced Trading Systems with Intelligent Market Analysis— arXiv cs.AI
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Researchers propose a reinforcement learning trading system that combines transformer-based sentiment analysis from financial news with PPO agents trained on intraday market data. Testing on NSE data…
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference— arXiv cs.AI
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This arXiv paper investigates how embodied agents in active inference prioritize competing bodily needs through two mechanisms: increasing precision of interoceptive information or reweighting prefer…
Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum— arXiv cs.AI
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Researchers propose a resource-slicing orchestration framework for Internet of Medical Things (IoMT) services in 6G edge environments that considers CPU, memory, and storage constraints alongside net…
A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining— arXiv cs.AI
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Researchers propose a node embedding framework using Procrustes alignment to evaluate process models discovered from event logs, addressing sensitivity issues in traditional conformance metrics like…
ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction— arXiv cs.AI
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Researchers propose ST-GRU, a simpler GRU-based alternative to the ST-LSTM units in PredRNN-V2 for video prediction, reducing parameters by ~5% while maintaining competitive performance on Moving MNI…
A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization— arXiv cs.AI
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This paper provides a controlled evaluation of HyperDG, a domain generalization method that rescales encoder features during training via learned scalars. The extensive evaluation (384 runs across fo…
Auditing LLM Program Search for Bermudan Stopping with Primal--Dual Certificates— arXiv cs.AI
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Researchers audited LLM-driven program search for solving Bermudan options (optimal stopping problems) using primal-dual certificates, finding that none of 24 generated programs improved meaningfully…
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient— arXiv cs.AI
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Researchers propose integrating Adversarial Inverse Reinforcement Learning (AIRL) with fuzzy inference and policy gradient methods to enable automatic reward function learning rather than manual desi…
Learning to Play: Reinforcement Learning and Games— arXiv cs.AI
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An arXiv book announcement covers reinforcement learning methods applied to games, surveying techniques from planning algorithms to deep learning and self-play across historical successes like Chess,…
When and How Order-Flow Interventions Work After Dealers Adapt— arXiv cs.AI
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Researchers propose SOFA, a mechanism for market venues to optimize order routing that decomposes into measurable components: state assessment, advance commitment, and selective flow allocation. Test…
Boosting the Learning for Ranking Patterns— arXiv cs.AI
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Researchers propose a method to learn user-specific pattern ranking functions by formulating it as a multicriteria decision-making problem using Analytic Hierarchy Process (AHP). The approach combine…
Multi-Horizon Explainable Queue Forecasting and Resource-Aware Decision Support for Intelligent Border Control Using LightGBM— arXiv cs.AI
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Researchers developed a multi-horizon queue forecasting system using LightGBM to predict waiting times and queue lengths at border crossings across 15, 30, 60, and 120-minute horizons. The system com…
Single-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad— arXiv cs.AI
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Researchers developed a masked language model for generating Thai klon pad verse (a poetic form) using WangchanBERTa with two-layer constraints: a sound layer enforcing rhyme/tone/syllable count and…
Analyzing the Dynamics of WSBM-based Echo State Networks using InputDSA— arXiv cs.AI
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This paper proposes a Reservoir Computing framework that uses Weighted Stochastic Block Models (WSBM) to add biologically realistic modularity to Echo State Networks, combined with Input Dynamical Si…
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling— arXiv cs.AI
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Researchers propose a negative sampling approach for basket choice modeling that learns product interaction embeddings to support retail assortment optimization and promotion planning. The method is…
A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading— arXiv cs.AI
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A tutorial paper explains how data re-uploading—repeatedly encoding classical inputs between trainable blocks—improves quantum amplitude embedding models by increasing the polynomial order of functio…
DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder— arXiv cs.AI
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Researchers applied a supervised variational autoencoder (SVAE) to diffusion-weighted brain imaging data from 787 young adults to classify biological sex, achieving 80% accuracy with an MLP classifie…
From Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering— arXiv cs.AI
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EneMonIE-Backend is a modular architecture that operationalizes energy information extraction from diverse web sources (RSS, arXiv, social media, open data) through NER, event extraction, and sentime…
Improving the Bounds on Approximating Weighted Maximin Share for Chores— arXiv cs.AI
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Researchers improved the approximation bounds for weighted maximin share (WMMS) allocation of indivisible chores, narrowing the gap from [2,12] to [e,4.12424] and providing a polynomial-time algorith…
Where Polanyi Meets Galois: Tacit Knowing as an Algebro-Deterministic Operation, Toward Re-rooting AI-Generated Science— arXiv cs.AI
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Researchers propose VaCoAl, an algebro-deterministic hyperdimensional memory system over GF(2), to model tacit knowledge as an operating regime rather than content type. They argue this framework rec…
Dynamic Conflict Clustering with Lightweight Learning and Strategic Repair for Distributed Constraint Satisfaction— arXiv cs.AI
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Researchers propose DCC-Incomplete, a distributed constraint satisfaction solver that combines conflict clustering, memory-guided restart, and lightweight learning to reduce communication overhead an…
Deep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++— arXiv cs.AI
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Researchers compared four deep learning segmentation models (PSPNet, DeepLabV3, UNet, UNet++) on 1,269 dental radiographs under controlled conditions, finding UNet++ achieved the best performance (Io…
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification— arXiv cs.AI
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Researchers evaluated radiomics-image fusion models for renal cell carcinoma classification on 396 patients, finding that adding radiomics improved not just discrimination metrics (AUC, AP) but also…
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies— arXiv cs.AI
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LASER is a proof-carrying search method for recovering dependencies in protocol specifications that span multiple locations and undergo corrections. On benchmark protocol families, LASER improves acc…
SEAR: Self-Evolving Agentic Robotics— arXiv cs.AI