AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsCompMat-Bench: Benchmarking AI Agents for Computational Materials Science
arXiv:2610.00636v2 Announce Type: replace Abstract: Evaluating AI agents on scientific research tasks is constrained by the time and resources required for the underlying experiments or calculations. In computational mat…
- arXiv cs.AIagentsIncident-Arena: Getting agents to the last nine of reliability
arXiv:2610.00648v1 Announce Type: new Abstract: AI coding agents are ubiquitous in engineering workflows amongst industry and academia. Yet, despite their use in app coding, relatively less attention has been paid to the…
- arXiv cs.AIagentsAgent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard
arXiv:2610.00651v1 Announce Type: new Abstract: Agent evaluations are increasingly used to compare LLMs and inform deployment decisions, yet ranks can reflect not only the model but also the effects of the evaluation con…
- arXiv cs.AIresearchWhen More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization
arXiv:2610.00654v1 Announce Type: new Abstract: Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historical behavior and pref…
- arXiv cs.AIresearchBackdoor Containment via Expert Quarantine and Shutdown in LLMs
arXiv:2610.00663v1 Announce Type: new Abstract: Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing defenses span four s…
- arXiv cs.AIagentsA Simple Doxastic Deontic Logic for Norm-Guided Decision Making
arXiv:2610.00668v1 Announce Type: new Abstract: Making decisions despite conflicting norms and incomplete or unreliable information is a fundamental challenge for autonomous systems. We introduce a simple doxastic deonti…
- arXiv cs.AIresearchOntology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI
arXiv:2610.00682v1 Announce Type: new Abstract: Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informa…
- arXiv cs.AIresearchBackdoor Purification for LoRA-Tuned LLMs via Null-Space Projection
arXiv:2610.00685v1 Announce Type: new Abstract: With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more severe. Existing ba…
- arXiv cs.AIresearchR-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing
arXiv:2610.00700v1 Announce Type: new Abstract: Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsunclear.Markush struc…
- arXiv cs.AIagentsMeta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-re…
- arXiv cs.AIagentsReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality
arXiv:2610.00710v1 Announce Type: new Abstract: As large language model (LLM) agents become widely adopted, they are increasingly deployed for tasks that require persistent monitoring or recurring actions (e.g., market a…
- arXiv cs.AIresearchRobust Nash Alignment under Preference Uncertainty
arXiv:2610.00715v1 Announce Type: new Abstract: Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are uncertain: noisy, hetero…
- arXiv cs.AIresearchEnterprise Representation Simplification (ERS): Reducing Representational Complexity for Enterprise AI
arXiv:2610.00791v1 Announce Type: new Abstract: Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirements. These structures…
- arXiv cs.AIagentsSapien: A Stateful Policy Engine for Autonomous AI Agents
arXiv:2610.00797v1 Announce Type: new Abstract: Contextual security defenses prevent AI agents from taking rogue actions by synthesizing a task-specific policy and enforcing it on the agent's tool calls. In multi-step ta…
- arXiv cs.AIagentsKepler: Auditable World Models for ARC-AGI-3
arXiv:2610.00834v1 Announce Type: new Abstract: ARC-AGI-3 evaluates agents in interactive environments whose rules and objectives must be inferred from observation. We present Kepler, an open-source harness that represen…
- arXiv cs.AIresearchLearning Multiple Timescales for Goal-Conditioned Reinforcement Learning
arXiv:2610.00849v1 Announce Type: new Abstract: Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks. Discounting shrinks value differences between distant states…
- arXiv cs.AIagentsAn Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design
arXiv:2610.00870v1 Announce Type: new Abstract: We present an educator-guided LLM pedagogical agent for scaffolded feedback in conceptual database design. Integrated into an entity--relationship diagram (ERD) editor, the…
- arXiv cs.AIagentsMemFit: Efficient Long-Term Agentic Memory
arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memory systems rely on LL…
- arXiv cs.AIagentsActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization
arXiv:2610.00906v1 Announce Type: new Abstract: Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. Howev…
- arXiv cs.AIresearchOR for AI That Does OR: Routing LLMs up the Escalator inside the OSCAR Framework
arXiv:2610.00912v1 Announce Type: new Abstract: Large language models can translate business descriptions into optimization models, but executable code may misrepresent constraints or objectives. A solver can then return…
- arXiv cs.AIagentsFinding the Right Fit: Model-Harness Interactions across Agent Tasks
arXiv:2610.00917v1 Announce Type: new Abstract: Choosing an agent system means choosing both a language model and the harness through which it acts. We ask whether a strong model, harness, or pairing stays strong when th…
- arXiv cs.AIresearchABDA-NL: A Natural-Language Scenario Explorer for Argument-Based Reasoning
arXiv:2610.00947v1 Announce Type: new Abstract: ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users see which conclusions…
- arXiv cs.AIagentsPG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning
arXiv:2610.00949v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on offline agent trajectories is the standard approach for training specialized tool-using agents, but forcing models to imitate reasoning and…
- arXiv cs.AIagentsCybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation
arXiv:2610.00961v1 Announce Type: new Abstract: As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a shift CS-education r…
- arXiv cs.AIagentsVeriHarness: Scaling Agentic Verification for Long-Horizon Tasks
arXiv:2610.00972v1 Announce Type: new Abstract: As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be stre…
- arXiv cs.AIagentsRISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation
arXiv:2610.00979v1 Announce Type: new Abstract: Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data…
- arXiv cs.AIresearchEvaluating LLM-Generated Preference Distributions
arXiv:2610.01000v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as probabilistic generators for simulation, synthetic data generation, and decision support in settings where real-world…
- arXiv cs.AIresearchCalibration-risk routing for controlled world-model adaptation
arXiv:2610.01001v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correcting the simulator…
- arXiv cs.AIresearchWhat Can Analogy Tell Us About Artificial Consciousness?
arXiv:2610.01002v1 Announce Type: new Abstract: Who or what is conscious? Because subjective experience is directly accessible only in the first person, judgments about consciousness in other entities depend partly on an…
- arXiv cs.AIresearchBeyond Answer Confidence: A Controlled Audit of Self-Knowledge in a Black-Box Decision Model
arXiv:2610.01006v1 Announce Type: new Abstract: Decision models return probabilities intended for routing, abstention and automated action. Calibration makes those probabilities useful on average, but does not establish…
- arXiv cs.AIresearchFrom Discovery to Decision: Finite-Budget Recoverability in LLM Voting
arXiv:2610.01014v1 Announce Type: new Abstract: Voting over multiple LLM responses is a common primitive in test-time scaling and ensemble inference. Collecting more responses can expand the candidate pool and increase t…
- arXiv cs.AIagentsPay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization
arXiv:2610.01017v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly construct multi-agent workflows that decompose a complex task and assign specialist agents from a pool. However, building such…
- arXiv cs.AIagentsBeyond Final Accuracy: Auditing Communication in LLM Multi-Agent Systems
arXiv:2610.01042v1 Announce Type: new Abstract: Multi-agent communication aims to help agents benefit from one another's information. Yet improvements in system performance leave a fundamental ambiguity: do they reflect…
- arXiv cs.AIagentsEmpty Commitments: When Agents Promise What They Cannot Deliver
arXiv:2610.01045v2 Announce Type: replace Abstract: A chatbot that says "I will remind you tomorrow" will not run again until the user writes. We call such a promise an empty commitment: a promise of action after the cur…
- arXiv cs.AIresearchNetwork World Models as Environments for Algorithm Design on Complex Systems
arXiv:2610.01048v1 Announce Type: new Abstract: World models, which simulate an environment and predict how it changes under actions, are increasingly used in real-world applications such as robotics. Complex systems cal…
- arXiv cs.AIresearchImproving Math Reasoning through Value-guided Informative Search
arXiv:2610.01080v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the mathematical reasoning capabilities of large language models. Recent work introduces se…
- arXiv cs.AIagentsYouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents
arXiv:2610.01097v1 Announce Type: new Abstract: End-to-end research agents can now produce complete scientific papers, yet manuscript claims often diverge from executed experiments. This gap is structural: research state…
- arXiv cs.AIresearchBeyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research
arXiv:2610.01116v1 Announce Type: new Abstract: As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning communi…
- arXiv cs.AIagentsAbsorbEvo: An Agentic Framework for Autonomous Inverse Design of Microwave Absorbers
arXiv:2610.01119v1 Announce Type: new Abstract: Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, and entails time-cons…
- arXiv cs.AIresearchCortexBridge: Cortical Alignment of EEG Montages for Foundation Models
arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer difficult when electrode l…
- arXiv cs.AIresearchGrounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting
arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economic dynamics. We test…
- arXiv cs.AIagentsAuditing Action Settlement in LLM Agent Environments: Order, Progress, and Replay
arXiv:2610.01138v1 Announce Type: new Abstract: Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a typed snapshot-settlem…
- arXiv cs.AIresearchReSolve: Reusing Candidate Reasoning through Selective Generative Moderation
arXiv:2610.01140v2 Announce Type: replace Abstract: Sampling multiple solutions spends computation on intermediate deductions and unfinished arguments as well as final answers. We introduce ReSolve, a training-free infer…
- arXiv cs.AIresearchWhen Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design
arXiv:2610.01188v1 Announce Type: new Abstract: Exercise-specific joint selection can improve skeleton-based correctness classification, but what does that gain establish? We audit 1,057 repetitions from ten REHAB24-6 su…
- arXiv cs.AIagentsFederated Agent Optimization
arXiv:2610.01195v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowled…
- arXiv cs.AIagentsDependency-Aware Reward Shaping for Agentic Reinforcement Learning
arXiv:2610.01207v1 Announce Type: new Abstract: When training large language models with reinforcement learning, terminal rewards provide little guidance about which steps matter. Common methods for assigning step credit…
- arXiv cs.AIagentsReputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models
arXiv:2610.01222v1 Announce Type: new Abstract: As generative AI (Gen AI) systems take on increasingly autonomous roles in economically and socially consequential interactions, understanding their propensity to cooperate…
- arXiv cs.AIresearchHHR: Hierarchical Hash Retrieval for Efficient LLM Generation
arXiv:2610.01230v2 Announce Type: replace Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval offers an efficient…
- arXiv cs.AIresearchLearning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget
arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models with the strong reason…
- arXiv cs.AIagentsRevision-Aware Independent Agent Graphs for Dynamic Reasoning
arXiv:2610.01249v1 Announce Type: new Abstract: Conventional reasoning protocols present a fixed, preselected task, so they cannot test whether an agent propagates relevant updates, preserves unaffected work, or reconstr…