AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchModel-Based Online Decision Making via Generative Trajectory Planning
arXiv:2610.18677v1 Announce Type: cross Abstract: Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping that limits the pro…
- arXiv cs.AIagentsProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
arXiv:2610.18683v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO…
- arXiv cs.AIagentsRadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction
arXiv:2610.18686v1 Announce Type: cross Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adve…
- arXiv cs.AIresearchJust Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning
arXiv:2610.18690v2 Announce Type: replace-cross Abstract: Image-token compression must reduce computation without hurting the semantics. We introduce Varest, guided by semantic fungibility: tokens within a coherent seman…
- arXiv cs.AIresearchGeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models
arXiv:2610.18694v1 Announce Type: cross Abstract: Vision-language models (VLMs) struggle with metric and multi-view relationships that remain implicit in RGB observations. Geometry foundation models recover this structur…
- arXiv cs.AIresearchUniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
arXiv:2610.18697v2 Announce Type: replace-cross Abstract: World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these models learn sha…
- arXiv cs.AIresearchSteertuning Policies from a Few Human Videos
arXiv:2610.18711v1 Announce Type: cross Abstract: Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce Steertuning, a syste…
- arXiv cs.AIresearchA Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
arXiv:2610.18739v1 Announce Type: cross Abstract: We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bound…
- arXiv cs.AIresearchSolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts
arXiv:2610.18745v1 Announce Type: cross Abstract: Large language models increasingly generate Solidity smart contracts that, once deployed, can never be patched, making a single latent fault permanent and exploitable. Ye…
- arXiv cs.AIresearchPreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
arXiv:2610.18755v1 Announce Type: cross Abstract: Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze wha…
- arXiv cs.AIresearchWhere Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation
arXiv:2610.18786v2 Announce Type: replace-cross Abstract: LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used. We study a…
- arXiv cs.AIresearchBytes to Threats: Byte-Sequence Transformer For Malware Detection
arXiv:2610.18787v1 Announce Type: cross Abstract: Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files. These methods require speciali…
- arXiv cs.AIagentsSEAR: Self-Evolving Agentic Robotics
arXiv:2610.18792v1 Announce Type: cross Abstract: Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We int…
- arXiv cs.AIagentsOptimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
arXiv:2610.18808v1 Announce Type: cross Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordin…
- arXiv cs.AIresearchBack-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs
arXiv:2610.18818v3 Announce Type: replace-cross Abstract: Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource languages, the obv…
- arXiv cs.AIagentsRoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
arXiv:2610.18835v2 Announce Type: replace-cross Abstract: It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled cont…
- arXiv cs.AIresearchFast Rates for Offline Q-Learning with KL Regularization
arXiv:2610.18839v1 Announce Type: cross Abstract: This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regul…
- arXiv cs.AIresearchLIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models
arXiv:2610.18843v1 Announce Type: cross Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, calibrations drift,…
- arXiv cs.AIresearchTwo Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
arXiv:2610.18853v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling.…
- arXiv cs.AIagentsHybrid Reasoning Agents
arXiv:2610.18861v1 Announce Type: cross Abstract: How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through te…
- arXiv cs.AIresearchDoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
arXiv:2610.18862v1 Announce Type: cross Abstract: Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing ways to shorten the…
- arXiv cs.AIagentsTev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
arXiv:2610.18868v1 Announce Type: cross Abstract: Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to…
- arXiv cs.AIresearchWhat Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
arXiv:2610.18869v1 Announce Type: cross Abstract: How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filte…
- arXiv cs.AIagentsChunkAct: Action Chunking for Agentic Robotics
arXiv:2610.18874v1 Announce Type: cross Abstract: Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under t…
- arXiv cs.AIresearchIdentification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach
arXiv:2405.01259v2 Announce Type: replace Abstract: Argumentation is an important cognitive activity that involves the scrutiny of arguments and counterarguments. When given an argument, it is desirable that its premises…
- arXiv cs.AIresearchOvercoming a Learning Bottleneck in Impartial Games: A Circuit Complexity Approach
arXiv:2411.06403v4 Announce Type: replace Abstract: Why can neural networks struggle to learn optimal play in a game with a simple, efficient solution algorithm? Motivated by empirical difficulties with parity in imparti…
- arXiv cs.AIresearchRobustness and Cybersecurity in the EU Artificial Intelligence Act
arXiv:2502.16184v4 Announce Type: replace Abstract: The EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems. While prior work has sought to clarify some of these…
- arXiv cs.AIresearchPhysics: Benchmarking Foundation Models on University-Level Physics Problem Solving
arXiv:2503.21821v2 Announce Type: replace Abstract: We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving. It contains 1034 annotated, open-ended problems from PhD-qualify…
- arXiv cs.AIresearchLLM-BabyBench: Can Language Models Plan in Worlds They Can Simulate?
arXiv:2505.12135v3 Announce Type: replace Abstract: Interactive benchmarks for LLMs usually report a single metric, even though succeeding in them requires models to show several different skills. While those benchmarks…
- arXiv cs.AIagentsAgent-Environment Alignment via Automated Interface Generation
arXiv:2505.21055v3 Announce Type: replace Abstract: Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with environment through inte…
- arXiv cs.AIresearchDecomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning
arXiv:2506.06121v3 Announce Type: replace Abstract: Large-scale itinerary planning is a variant of the traveling salesman problem, aiming to determine an optimal path that maximizes the collected point-of-interest (POI)…
- arXiv cs.AIresearchCite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
arXiv:2506.17585v4 Announce Type: replace Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving documents at inference time…
- arXiv cs.AIresearchToward a Universal Characterization of Sudoku Puzzle Difficulty
arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question "What constitutes Sudoku difficulty rating across different Sudoku websites?" Using two distinct methods that can both solve…
- arXiv cs.AIresearchOPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
arXiv:2508.12943v3 Announce Type: replace Abstract: Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER,…
- arXiv cs.AIagentsAre LLM Agents Behaviorally Coherent? A Latent Profiling Approach for Social Simulation
arXiv:2509.03736v3 Announce Type: replace Abstract: LLM agents are increasingly proposed as substitutes for human participants in social research, usually on the strength of survey responses that match those of the human…
- arXiv cs.AIresearchLIFT: Likelihood-Based Intervention Faithfulness Training
arXiv:2509.13334v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on the stated reasoning steps t…
- arXiv cs.AIresearchCompositional Machine Design as Program Synthesis with LLMs
arXiv:2510.14980v4 Announce Type: replace Abstract: Large language models (LLMs) have shown strong abilities in writing and revising programs, yet many program-synthesis benchmarks still evaluate programs in symbolic or…
- arXiv cs.AIagentsToward Agents That Reason About Their Computation
arXiv:2510.22833v3 Announce Type: replace Abstract: While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationally efficient as they improv…
- arXiv cs.AIresearche1: Learning Adaptive Control of Reasoning Effort
arXiv:2510.27042v3 Announce Type: replace Abstract: Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning. Users may prefer to allocate…
- arXiv cs.AIagentsMulti-agent Undercover Gaming: Hallucination Removal via Counterfactual Test for Multimodal Reasoning
arXiv:2511.11182v2 Announce Type: replace Abstract: Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a…
- arXiv cs.AIresearchAlignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria
arXiv:2511.17937v2 Announce Type: replace Abstract: Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while pre…
- arXiv cs.AIresearchAlignMerge - Alignment-Preserving Large Language Model Merging via Fisher-Guided Geometric Constraints
arXiv:2512.16245v2 Announce Type: replace Abstract: Merging large language models (LLMs) is a practical way to compose capabilities from multiple fine-tuned checkpoints without retraining. Yet standard schemes (linear we…
- arXiv cs.AIresearchROI-Reasoning: Rational Optimization for Inference via Pre-Computation Meta-Cognition
arXiv:2601.03822v2 Announce Type: replace Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computation a task requires.…
- arXiv cs.AIresearchGDEPO: Group Dual-dynamic and Equal-right Advantage Policy Optimization for Sample-Efficient Reinforcement Learning
arXiv:2601.06795v4 Announce Type: replace Abstract: Automated Theorem Proving (ATP) is a fundamental AI challenge, requiring machine-verifiable proofs in formal languages like Lean to evaluate reasoning. Reinforcement Le…
- arXiv cs.AIresearchStochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition
arXiv:2601.07239v2 Announce Type: replace Abstract: Deterministic inference is a comforting ideal in classical software: the same program on the same input should always produce the same output. As large language models…
- arXiv cs.AIagentsReCreate: Reasoning and Creating Domain Agents Driven by Experience
arXiv:2601.11100v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, making their creation a…
- arXiv cs.AIagentsRisky-Bench: Evaluating Agentic Safety Risks in Real-World Life-Assist Deployments
arXiv:2602.03100v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm through tool u…
- arXiv cs.AIresearchSimple Baselines are Competitive with Code Evolution
arXiv:2602.16805v2 Announce Type: replace Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code. While code evolution…
- arXiv cs.AIresearchThe Token Games: Evaluating Language Model Reasoning with Puzzle Duels
arXiv:2602.17831v3 Announce Type: replace Abstract: Evaluating the reasoning capabilities of Large Language Models is increasingly challenging as models improve. Human curation of hard questions is highly expensive, espe…
- arXiv cs.AIresearchStrategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance
arXiv:2602.22583v2 Announce Type: replace Abstract: Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when…