AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation
arXiv:2505.17613v2 Announce Type: replace Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for comp…
- arXiv cs.AIagentsReducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
arXiv:2508.08882v5 Announce Type: replace Abstract: Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning sy…
- arXiv cs.AIagentsLLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
arXiv:2508.17692v2 Announce Type: replace Abstract: Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways. This survey introdu…
- arXiv cs.AIresearchMoloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to elec…
- arXiv cs.AIresearchA framework for auditing grounding claims
arXiv:2512.06205v3 Announce Type: replace Abstract: The symbol grounding problem asks how a token such as cat can be about cats. We propose a framework for auditing grounding claims against a declared semantic standard.…
- arXiv cs.AIagentsAstroAgentBench: Evaluating Agentic Planning on Space Mission Planning Tasks
arXiv:2601.11354v3 Announce Type: replace Abstract: Recent LLM-for-Space systems address mission planning, scheduling, operations support, simulator control, and autonomy, but their evaluations use different task contrac…
- arXiv cs.AIresearchSONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding
arXiv:2601.21666v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to…
- arXiv cs.AIagentsWhy Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory
arXiv:2601.22984v3 Announce Type: replace Abstract: Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evaluation, obscuring inte…
- arXiv cs.AIresearchAligning Language Model Benchmarks with Pairwise Preferences
arXiv:2602.02898v5 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world downstream performance. However, many recent works find that benchmarks oft…
- arXiv cs.AIresearchDistilling LLM Reasoning into Graph of Concept Predictors
arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces…
- arXiv cs.AIresearchScalable Delphi: Large Language Models for Structured Risk Estimation
arXiv:2602.08889v2 Announce Type: replace Abstract: Quantitative risk assessment relies on structured expert elicitation to estimate unobservable properties. The Delphi method produces calibrated, auditable estimates but…
- arXiv cs.AIagentsPhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning
arXiv:2602.13691v2 Announce Type: replace Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However, long-horizon multi-…
- arXiv cs.AIresearchTool Use Reduces Depth-Induced Collapse in OOD Reasoning
arXiv:2602.21061v3 Announce Type: replace Abstract: Humans can apply ideas learned in one context to substantially different situations. We call this process of searching for and constructing novel recombinations of lear…
- arXiv cs.AIresearchMan and machine: artificial intelligence and judicial decision making
arXiv:2603.19042v5 Announce Type: replace Abstract: The integration of artificial intelligence (AI) into judicial decision making -- particularly in pretrial, sentencing, and parole contexts -- has generated a substantia…
- arXiv cs.AIagentsDetecting Multi-Agent Collusion Through Multi-Agent Interpretability
arXiv:2604.01151v5 Announce Type: replace Abstract: Non-verbalised or secret collusion may impose a ceiling on transcript-level monitoring in multi-agent deployments. Internal model activations are known to provide an ad…
- arXiv cs.AIagentsThe Hitchhikers Guide to Rubric Quality Understanding and Enrichment
arXiv:2604.01375v4 Announce Type: replace Abstract: Rubrics distill notions of expert quality and measure agent performance. However, the quality of rubrics themselves have not been systematically measured and are often…
- arXiv cs.AIresearchGenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation
arXiv:2604.01997v2 Announce Type: replace Abstract: Gait analysis provides an objective characterization of locomotor function and is widely used to support diagnosis and rehabilitation monitoring across neurological and…
- arXiv cs.AIresearchWhat Does a Sharing Question Add? Auditing LLM Survey Scores for Misinformation
arXiv:2604.06820v3 Announce Type: replace Abstract: Evaluating misinformation requires distinguishing whether readers believe content from whether they would share it. Asking large language models (LLMs) both questions y…
- arXiv cs.AIagentsSpatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
arXiv:2604.12102v3 Announce Type: replace Abstract: We describe compute-grounded reasoning (CGR), a design pattern in which code computes selected sub-problems from explicit intermediate representations before a language…
- arXiv cs.AIagentsSWE-chat: Coding Agent Interactions From Real Users in the Wild
arXiv:2604.20779v2 Announce Type: replace Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We prese…
- arXiv cs.AIresearchMulticalibration for Unbiased Model-Based Prevalence Estimation
arXiv:2604.21549v2 Announce Type: replace Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to…
- arXiv cs.AIagentsSkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents
arXiv:2605.08386v2 Announce Type: replace Abstract: Skill libraries have become a practical way for LLM agents to reuse procedural experience across tasks. However, existing systems typically treat skills as flat, single…
- arXiv cs.AIresearchLearning Local Constraints for Reinforcement-Learned Content Generators
arXiv:2605.13570v2 Announce Type: replace Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually satisfying game…
- arXiv cs.AIresearchSenses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
arXiv:2605.13737v2 Announce Type: replace Abstract: When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perception or in act…
- arXiv cs.AIresearchProbing an Embodied LLM: When Higher Observation Fidelity Hurts Problem Solving
arXiv:2605.20072v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain suc…
- arXiv cs.AIresearchPlaying Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancy
arXiv:2605.21006v4 Announce Type: replace Abstract: Language models are often sycophantic: they agree with a user's stated opinion whether or not it is correct. Prior work has shown that this trait can be controlled by s…
- arXiv cs.AIagentsRMA: Context-Orchestrated Research Math Agents
arXiv:2605.22875v2 Announce Type: replace Abstract: Long-horizon mathematical reasoning fails less often because a model cannot produce a valid next step than because an agent fails to maintain and expose the right seman…
- arXiv cs.AIresearchHyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models
arXiv:2605.24140v5 Announce Type: replace Abstract: Searching over alternative continuations lets large language models (LLMs) compare the downstream consequences of a reasoning step before committing to it, but expandin…
- arXiv cs.AIresearchThe Confidence Shortcut: A Reasoning Failure Mode of Masked Diffusion Models
arXiv:2605.29123v2 Announce Type: replace Abstract: Chain-of-thought reasoning helps autoregressive models solve complex problems by generating intermediate steps that support later predictions. Masked diffusion models (…
- arXiv cs.AIresearchAccelerating Constrained Decoding with Token Space Compression
arXiv:2605.29986v2 Announce Type: replace Abstract: To guarantee that an LLM's outputs conform to a specified structure, context-free grammar (CFG) decoding engines force the selection of next tokens to produce strings t…
- arXiv cs.AIresearchNot All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation
arXiv:2606.04402v2 Announce Type: replace Abstract: Test-time compute has emerged as an effective paradigm for improving large language model capability at inference time. Existing allocation strategies primarily priorit…
- arXiv cs.AIagentsArchitect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
arXiv:2606.10953v2 Announce Type: replace Abstract: Furnished floor plans support real-estate visualization, interior design, and architectural workflows, yet automatic furnishing remains challenged by limited real-world…
- arXiv cs.AIresearchReasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
arXiv:2606.13607v4 Announce Type: replace Abstract: When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but…
- arXiv cs.AIresearchGreed Is Learned: Visible Incentives as Reward-Hacking Triggers
arXiv:2606.16914v2 Announce Type: replace Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dashboard naming which action…
- arXiv cs.AIresearchSkin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations
arXiv:2606.22676v2 Announce Type: replace Abstract: Refusal on a safety benchmark does not reveal how stable that behavior will remain after model updates. Benign downstream fine-tuning can weaken refusal, yet behavioral…
- arXiv cs.AIresearchMINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration
arXiv:2606.22826v2 Announce Type: replace Abstract: Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a process that can take tens…
- arXiv cs.AIresearchSPIRAL: Learning to Search and Aggregate
arXiv:2606.23595v2 Announce Type: replace Abstract: Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequential reasoning withi…
- arXiv cs.AIagentsSeparating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment
arXiv:2607.01674v4 Announce Type: replace Abstract: In multi-source ECG deployment, new sources may arrive when earlier raw ECGs cannot be retained or replayed. Isolating source-specific classifiers on a frozen backbone…
- arXiv cs.AIresearchQuantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift
arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles. From fi…
- arXiv cs.AIagentsSemantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
arXiv:2607.18255v2 Announce Type: replace Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and…
- arXiv cs.AIresearchMasked Self-Distillation: Internalizing the Chain-of-Thought in Language Models
arXiv:2607.22629v3 Announce Type: replace Abstract: Large Reasoning Models produce long, explicit chains of intermediate steps before generating a final answer at inference time. These intermediate traces dominate latenc…
- arXiv cs.AIagentsESCROW: Guarded and Dual-Objective Continual Maintenance for Agents in Policy-Governed Enterprise Workflows
arXiv:2608.01772v2 Announce Type: replace Abstract: LLM agents increasingly run policy-bound enterprise workflows, where they must apply rules consistently and stay auditable. Deploying such an agent is the start of its…
- arXiv cs.AIagentsFinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
arXiv:2608.06144v2 Announce Type: replace Abstract: Agents used over time encounter recurring professional work: each case requires different evidence and judgment, while the underlying workflow can be reused. Benchmarks…
- arXiv cs.AIagentsiARCS: Iterative Agentic RL for Controllable 3D Scene Generation
arXiv:2608.06161v3 Announce Type: replace Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism witho…
- arXiv cs.AIresearchFull-bandwidth transformer
arXiv:2608.08888v2 Announce Type: replace Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth. Dense attention gives each token broad hor…
- arXiv cs.AIresearchForesight Without Seeing: Latent Futures for World Action Models
arXiv:2608.11605v2 Announce Type: replace Abstract: World Action Models (WAMs) connect visual prediction with robot control, but supplying predictive context often requires expensive future-video generation. Direct polic…
- arXiv cs.AIresearchImplicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites
arXiv:2608.24471v2 Announce Type: replace Abstract: Maritime moving-target observation scheduling with agile Earth observation satellites is a dynamic, sequence-dependent combinatorial optimization problem. Sea-surface t…
- arXiv cs.AIresearchProgram Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs
arXiv:2608.28421v2 Announce Type: replace Abstract: Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capability inside the mode…
- arXiv cs.AIresearchRethinking World Models for Safety-Critical Embodied Systems
arXiv:2609.03774v2 Announce Type: replace Abstract: World models have progressed from compact latent dynamics to generative, controllable, and interactive simulators of embodied environments. However, high predictive lik…
- arXiv cs.AIagentsSE-GoS: Self-Evolving Graph-of-Skills for Skill Library at Scale
arXiv:2609.08228v2 Announce Type: replace Abstract: LLM agents use large libraries of reusable skills. At thousands of skill entries, retrieval becomes the bottleneck. Graph-of-Skills (GoS) retrieves dependency-aware bun…