AI news archive.
19,854 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsLost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
arXiv:2610.18522v1 Announce Type: new Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this desig…
- arXiv cs.AIresearchInput-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
arXiv:2610.18547v2 Announce Type: replace Abstract: People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeatedly applying a le…
- arXiv cs.AIresearchRethinking Quality in Large Language Model Routing: Security-Conditioned Model-and-Budget Selection
arXiv:2610.18566v2 Announce Type: replace Abstract: Large language model (LLM) routers reduce serving cost by choosing which model to call and how many output tokens to grant it for each query. Existing routers rank mode…
- arXiv cs.AIresearchConfiguring RAG Systems: What Breaks, Where Originates, and How to Fix
arXiv:2610.18567v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for improving Large Language Model (LLM) by intertwining it with other components, such as a vector/…
- arXiv cs.AIresearchOrder-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models
arXiv:2610.18570v1 Announce Type: new Abstract: Latent planners often rank candidate action sequences by terminal Euclidean distance to a goal latent. Exact agreement with transition distance would make this score ideal,…
- arXiv cs.AIagentsLLM Agents for Automated Post-Training via Structured Recipe Optimization
arXiv:2610.18572v1 Announce Type: new Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructing and optimizing a r…
- arXiv cs.AIagentsReading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops
arXiv:2610.18579v1 Announce Type: new Abstract: System One models choose an agent's next action by scoring a supplied set of options in a single pass. This makes them fast and cheap, but when they choose wrongly there is…
- arXiv cs.AIresearchCompare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling
arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequencies or path confiden…
- arXiv cs.AIagentsAuditing Future-Memory Use in Point-in-Time Language Agents
arXiv:2610.18602v1 Announce Type: new Abstract: Point-in-time (PIT) agents can recall post-cutoff facts even when their external evidence respects a historical cutoff. We examine whether this access predicts evidence gat…
- arXiv cs.AIagentsAgentStripes: An Evidence-Graded Maturity Model for AI Agents
arXiv:2610.18603v2 Announce Type: replace Abstract: Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with the controls that mak…
- arXiv cs.AIresearchBeyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces
arXiv:2610.18606v1 Announce Type: new Abstract: Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self-consistency (SC) im…
- arXiv cs.AIagentsCan an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents
arXiv:2610.18614v1 Announce Type: new Abstract: Controlling an acting LLM agent means deciding, at each step of a long-horizon task, whether to trust its next action, abstain, or hand off to a human. That decision needs…
- arXiv cs.AIagentsTwo Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference
arXiv:2610.18617v1 Announce Type: new Abstract: An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that…
- arXiv cs.AIresearchTo Align or Not To Align: Check Your COMPASS Before You Train
arXiv:2610.18638v1 Announce Type: new Abstract: Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-tuned checkpoints. Howe…
- arXiv cs.AIagentsKnowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
arXiv:2610.18684v1 Announce Type: new Abstract: Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing what the user and the a…
- arXiv cs.AIresearchUnlocking Token Rewards via Training-Free Reward Attribution
arXiv:2610.18704v1 Announce Type: new Abstract: In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model. Our core idea is t…
- arXiv cs.AIagentsBeyond the Verdict: Agentic Code Judging with Verifiable Feedback
arXiv:2610.18760v1 Announce Type: new Abstract: Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and…
- arXiv cs.AIresearchJeva: System One Models from First Principles
arXiv:2610.18773v2 Announce Type: replace Abstract: System one models like Jev enable direct, low-latency decision-making. Yet recent open-source practice of predicting an answer from serialized text does not, by itself,…
- arXiv cs.AIagentsBLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems
arXiv:2610.18780v1 Announce Type: new Abstract: Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Yet collaboration make…
- arXiv cs.AIagentsPack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
arXiv:2610.18784v2 Announce Type: replace Abstract: Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved. This raises a…
- arXiv cs.AIagentsSelf-Evolving Agent Harness
arXiv:2610.18813v1 Announce Type: new Abstract: Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We forma…
- arXiv cs.AIresearchRead the Room Before Your Model Generates
arXiv:2610.18820v3 Announce Type: replace Abstract: Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for selecting temperature…
- arXiv cs.AIagentsWho Improves the Robot? Toward Physical Recursive Self-Improvement
arXiv:2610.18842v1 Announce Type: new Abstract: Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physi…
- arXiv cs.AIresearchMFG: Multi-turn Feedback Guided Molecular Optimization
arXiv:2610.18851v1 Announce Type: new Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while main…
- arXiv cs.AIresearchAesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
arXiv:2610.18860v1 Announce Type: new Abstract: Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet…
- arXiv cs.AIresearchSubgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning
arXiv:2407.12703v5 Announce Type: cross Abstract: Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based methods focus solely on…
- arXiv cs.AIresearchEmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
arXiv:2501.06597v1 Announce Type: cross Abstract: The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigat…
- arXiv cs.AIresearchLatent Algorithmic Structure Precedes Grokking: A Mechanistic Study of ReLU MLPs on Modular Arithmetic
arXiv:2603.23784v1 Announce Type: cross Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has been memorized-has been charac…
- arXiv cs.AIagentsSelective Knowledge Control for Continual GUI Agent Learning over Application Streams
arXiv:2609.06530v1 Announce Type: cross Abstract: Continual learning is a crucial capability for Graphical User Interface (GUI) agents to adapt to evolving applications while retaining knowledge acquired from previous ap…
- arXiv cs.AIresearchDependency, Compression, and Synergy: A Unified Information-Theoretic View of Multimodal Learning
arXiv:2609.14421v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement information. Mutual Infor…
- arXiv cs.AIresearchMRFlow: Mechanism-Structured Residual Flow Matching for Virtual Cell Perturbation Prediction
arXiv:2610.13966v1 Announce Type: cross Abstract: Predicting cellular responses to chemical and genetic perturbations is a central test for AI virtual cells, yet many single-cell models reduce each condition to an averag…
- arXiv cs.AIagentsPredict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents
arXiv:2610.13991v2 Announce Type: replace-cross Abstract: Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only after costly…
- arXiv cs.AIresearchPosterior Flow Matching
arXiv:2610.13994v1 Announce Type: cross Abstract: Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an intermediate state. Thi…
- arXiv cs.AIresearchGameSmith: Scaling Zero-Sum Self-Play with Synthesised Games
arXiv:2610.13995v1 Announce Type: cross Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to general reasoning. Exis…
- arXiv cs.AIresearchSplicing Hints into Reasoning Traces: Learning from Demonstrator Failures
arXiv:2610.13996v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards improves mathematical reasoning when a final answer can be checked, but a correct answer does not certify the derivation be…
- arXiv cs.AIresearchSharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models
arXiv:2610.13997v1 Announce Type: cross Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-timestep dictionar…
- arXiv cs.AIresearchEnhanced Trading Systems with Intelligent Market Analysis
arXiv:2610.13998v1 Announce Type: cross Abstract: This paper proposes a hybrid reinforcement learning trading framework integrating intraday market data with transformer-based financial sentiment signals. The system extr…
- arXiv cs.AIresearchSemTree: Judge-Aware Semantic Decision Trees for Inspectable and Repairable Rule Decisions
arXiv:2610.13999v1 Announce Type: cross Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-information cases, and an er…
- arXiv cs.AIagentsVH-GPO: Verified Handoff for LLM Agent Reinforcement Learning
arXiv:2610.14000v1 Announce Type: cross Abstract: Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every trajectory in a gro…
- arXiv cs.AIresearchPreSO: Privileged Representation Isolation for On-Policy Self-Distillation
arXiv:2610.14001v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning with verifiable r…
- arXiv cs.AIresearchShowing versus Doing: Teaching by Demonstration
arXiv:2610.14004v1 Announce Type: cross Abstract: People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demo…
- arXiv cs.AIagentsCoding LLMs Can Be Harnessed to Learn Interpretable Models from Data
arXiv:2610.14006v1 Announce Type: cross Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from dat…
- arXiv cs.AIresearchWhat Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal
arXiv:2610.14007v1 Announce Type: cross Abstract: A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them closer to Solomonoff i…
- arXiv cs.AIresearchWhen Log Parsing Removes What Matters: Detecting Failures in Distributed GPU Workloads
arXiv:2610.14009v2 Announce Type: replace-cross Abstract: Large AI models increasingly rely on distributed GPU execution, yet it remains unclear whether established anomaly detection methods transfer effectively to this…
- arXiv cs.AIresearchPhysics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting
arXiv:2610.14014v1 Announce Type: cross Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models o…
- arXiv cs.AIresearchBias Robustness of Genetic Multi-Criteria Sorting versus Learned Rankers under Proxy Skew and Label Contamination
arXiv:2610.14015v1 Announce Type: cross Abstract: Learning-to-rank (LTR) models can align rankings with undesignated signals when those signals appear as features or leak into training labels. We compare label-free genet…
- arXiv cs.AIresearchBarycentric Anchored Weight Search for Robust Model Merging
arXiv:2610.14017v1 Announce Type: cross Abstract: We propose Barycentric Anchored Search, a post-hoc weight-space procedure for improving fine-tuned vision models under distribution shift without additional gradient trai…
- arXiv cs.AIresearchRoboHarness: A Simple Harness Outperforms VLA and World Action Models
arXiv:2610.14018v2 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world. One stra…
- arXiv cs.AIresearchLabInstruct: Benchmarking Situated Instructional Video Generation for Lab Procedures
arXiv:2610.14020v1 Announce Type: cross Abstract: Self-driving laboratories (SDLs) aim to automate the full experimental loop, from scientific decision-making to physical execution. Ideally, AI-generated plans could be c…
- arXiv cs.AIagentsCoEvoke: Evoking Multi-Agent Collaboration for Trajectory Collection
arXiv:2610.14021v1 Announce Type: cross Abstract: Training multi-agent collaboration requires trajectories of checking, feedback, and repair, yet task-solving rollouts may end in immediate approval or repeat unsuccessful…