AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchLearning Transferable Sensor Models via Language-Informed Pretraining
arXiv:2603.11950v2 Announce Type: replace Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured from diverse healthcare sens…
- arXiv cs.AIresearchLearning Dynamic Belief Graphs for Theory-of-mind Reasoning
arXiv:2603.20170v2 Announce Type: replace Abstract: Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under…
- arXiv cs.AIresearchMonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models
arXiv:2603.28590v4 Announce Type: replace Abstract: Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the Co…
- arXiv cs.AIresearchMeasuring (some aspects of) the metacognition of AI
arXiv:2603.29693v4 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks. Because artificial intelligence (AI) systems ar…
- arXiv cs.AIresearchRefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv:2604.00790v2 Announce Type: replace Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly…
- arXiv cs.AIagentsCommunication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning
arXiv:2604.03785v3 Announce Type: replace Abstract: Communication is essential for coordination in \emph{cooperative} multi-agent reinforcement learning under partial observability, yet \emph{cross-timestep} delays cause…
- arXiv cs.AIagentsMMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems
arXiv:2604.05075v2 Announce Type: replace Abstract: Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-age…
- arXiv cs.AIagentsAuditable Agents
arXiv:2604.05485v4 Announce Type: replace Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in the world, the question is no longer only whe…
- arXiv cs.AIresearchAcceptance Dynamics Across Cognitive Domains in Speculative Decoding
arXiv:2604.14682v2 Announce Type: replace Abstract: Speculative decoding accelerates large language model (LLM) inference. It uses a small draft model to propose a tree of future tokens. A larger target model then verifi…
- arXiv cs.AIagentsAgentic Forecasting with Structured Linguistic Beliefs
arXiv:2604.18576v5 Announce Type: replace Abstract: We propose the Bayesian Linguistic Forecaster (BLF), which is a search-based ReAct style agent for (binary) forecasting that augments its context with a linguistic 'bel…
- arXiv cs.AIagentsAlignment has a Fantasia Problem
arXiv:2604.21827v3 Announce Type: replace Abstract: In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.g., from brainstorming ideas to writing an essay). With the advent of…
- arXiv cs.AIagentsWhat Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis
arXiv:2605.03354v3 Announce Type: replace Abstract: Agent memory failures are silent: an LLM-based agent can produce a fluent response even when it fails to extract, retain, or retrieve the information needed across sess…
- arXiv cs.AIagentsBALAR : A Bayesian Agentic Loop for Active Reasoning
arXiv:2605.05386v2 Announce Type: replace Abstract: Large language models increasingly operate in interactive settings where solving a task requires multiple rounds of information exchange with a user. However, most curr…
- arXiv cs.AIresearchHindsight Compacts but Does Not Repair: Rethinking On-Policy Self-Distillation in Reasoning Models
arXiv:2605.06188v2 Announce Type: replace Abstract: On-Policy Self-Distillation (OPSD) has emerged as a promising post-training method: requiring only privileged context, it enables token-level credit assignment from a s…
- arXiv cs.AIagentsPrefixGuard: Online Failure Warning and Trace-Grounded Diagnosis for LLM Agents
arXiv:2605.06455v2 Announce Type: replace Abstract: Large language model (LLM) agents execute multi-step tasks, but terminal verdicts arrive too late for intervention. Agent traces mix messages, tool calls, and feedback,…
- arXiv cs.AIresearchHow Deep Can LLMs Learn to Reason? Expressiveness Is Key
arXiv:2605.06638v4 Announce Type: replace Abstract: Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has b…
- arXiv cs.AIagentsARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning
arXiv:2605.07103v2 Announce Type: replace Abstract: Reaction feasibility prediction, as a fundamental problem in computational chemistry, has benefited from diverse tools enabled by recent advances in artificial intellig…
- arXiv cs.AIresearchSearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks
arXiv:2605.09038v4 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especially important in…
- arXiv cs.AIresearchHow LLMs Are Persuaded: A Few Attention Heads, Rerouted
arXiv:2605.09314v2 Announce Type: replace Abstract: Large language models can answer a factual question correctly, yet switch to an incorrect answer when exposed to persuasive context. Why this happens inside the model r…
- arXiv cs.AIresearchPACE: Policy-Native Adaptive Decision Timing for Long-Horizon Reasoning
arXiv:2605.09860v5 Announce Type: replace Abstract: Long-horizon reasoning requires deciding not only what actions to take, but how many to execute open-loop before replanning. This number, the execution depth, balances…
- arXiv cs.AIagents$\delta$-mem: Efficient Online Memory for Large Language Models
arXiv:2605.12357v2 Announce Type: replace Abstract: Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is…
- arXiv cs.AIresearchDistribution-Aware Programming: Learning Specialized Solvers from Experience
arXiv:2605.14141v3 Announce Type: replace Abstract: Many optimization problems are solved repeatedly on instances drawn from the same underlying distribution. In this setting, a system can begin with a general-purpose so…
- arXiv cs.AIagentsContext Pruning for Coding Agents via Multi-Rubric Latent Reasoning
arXiv:2605.15315v2 Announce Type: replace Abstract: LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at hand. Existing…
- arXiv cs.AIagentsLatent Action Reparameterization for Efficient Agent Inference
arXiv:2605.18597v3 Announce Type: replace Abstract: Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. Whi…
- arXiv cs.AIresearchUniversal Quantum Transformer
arXiv:2606.00045v4 Announce Type: replace Abstract: Classical continuous-space neural networks fundamentally struggle to lock into exact formal rules, whether mathematical, such as modular arithmetic and non-Abelian grou…
- arXiv cs.AIresearchCapability Self-Assessment in Large Language Models
arXiv:2606.00251v2 Announce Type: replace Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or seek help is fundamental to reliable intelligent systems. Yet modern large langu…
- arXiv cs.AIagentsForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment
arXiv:2606.00644v3 Announce Type: replace Abstract: AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We i…
- arXiv cs.AIagentsSkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
arXiv:2606.01314v2 Announce Type: replace Abstract: LLM agents increasingly store capabilities in external skill and tool libraries. Libraries can grow, but execution must fit a finite context window and improvement cons…
- arXiv cs.AIagentsAgentCL: Toward Rigorous Evaluation of Continual Learning in Language Agents
arXiv:2606.02461v3 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes. Continu…
- arXiv cs.AIresearchNovelAPIBench: Diagnosing How A Code LLM Learns to Use Novel APIs
arXiv:2606.03657v2 Announce Type: replace Abstract: Rapidly evolving software libraries require coding language models to use unfamiliar APIs. Yet failed solutions alone tell us little about what API knowledge models nee…
- arXiv cs.AIresearchMultilingual Fine-Tuning via Localized Gradient Conflict Resolution
arXiv:2606.05613v2 Announce Type: replace Abstract: Cross-lingual versatility has become a defining capability of Large Language Models (LLMs). However, fine-tuning these models often induces negative interference, where…
- arXiv cs.AIresearchSafety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack
arXiv:2606.05614v2 Announce Type: replace Abstract: Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe…
- arXiv cs.AIagentsPRISM: Recovering Instruction Sets from Language Model Activations
arXiv:2606.09563v2 Announce Type: replace Abstract: As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This is difficult wh…
- arXiv cs.AIagentsMind the Perspective: Let's Reason Recursively for Theory of Mind
arXiv:2606.11724v2 Announce Type: replace Abstract: Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Existing prompting…
- arXiv cs.AIresearchMaking AI Scientists Auditable from Evidence to Claim
arXiv:2606.18874v4 Announce Type: replace Abstract: AI research systems can report improved results without establishing that the tested implementation matches the method described in their conclusions. We introduce Xcie…
- arXiv cs.AIagentsAutonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale
arXiv:2606.20058v2 Announce Type: replace Abstract: Enterprise agent language models must coordinate specialist agents over continuous business event streams, yet most multi-agent evaluations assume discrete request-resp…
- arXiv cs.AIresearchCliff Tokens: Analyzing Failure Trigger Tokens in LLM Mathematical Reasoning
arXiv:2606.25524v3 Announce Type: replace Abstract: Large language models reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the correct answer while others f…
- arXiv cs.AIresearchTandem Reinforcement Learning with Verifiable Rewards
arXiv:2606.28166v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven substantial gains in language-model reasoning. However, models fine-tuned with RLVR need not remain com…
- arXiv cs.AIresearchMechanistic Personality Analysis of LLMs: Steering Personality via Latent Feature Interventions
arXiv:2606.28770v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text. Previous efforts have focused on prompt en…
- arXiv cs.AIresearchFacePlex: Toward Natural Full-Duplex Conversational Avatars
arXiv:2606.30145v2 Announce Type: replace Abstract: Natural human conversation is inherently a real-time interaction in which speech and facial behavior continuously evolve. Enabling such interaction requires a conversat…
- arXiv cs.AIagentsDesign and Implementation of Agentic Orchestrations and Orchestration of Agents
arXiv:2606.31518v2 Announce Type: replace Abstract: Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents, can be balanced…
- arXiv cs.AIagentsOPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
arXiv:2607.01531v3 Announce Type: replace Abstract: Learning a useful world model from minimal interaction is central to building agents that adapt to unfamiliar tasks. Programmatic world modeling approaches such as Worl…
- arXiv cs.AIresearchWhat is Left for Us? Second Scholarship Against the Degradation of Research by AI
arXiv:2607.04049v3 Announce Type: replace Abstract: We argue that generative AI can degrade research by eroding the very practices through which scholarly judgement is formed. As constitutive conditions for the productio…
- arXiv cs.AIagentsTurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
arXiv:2607.05804v2 Announce Type: replace Abstract: On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent…
- arXiv cs.AIresearchTowards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning
arXiv:2607.08393v2 Announce Type: replace Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formal…
- arXiv cs.AIagentsTopoExplore: Homology as an Exploration Signal
arXiv:2607.09971v2 Announce Type: replace Abstract: Exploration signals in reinforcement learning are currently computed from what an agent has seen: visitation counts, density estimates, or a model's prediction error at…
- arXiv cs.AIagentsAgents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents
arXiv:2607.10526v5 Announce Type: replace Abstract: Self-improving personal agents write profiles, memories, and reusable skills that carry over from one chat to the next. Prior works ask whether a model agrees with the…
- arXiv cs.AIresearchThe Steering Budget: Examples beat Knobs
arXiv:2607.14246v2 Announce Type: replace Abstract: Generative models are steered with knobs -- prompts, guidance scales, property tags. Turn one as hard as you like and, past a point, it stops moving the property you ca…
- arXiv cs.AIresearchSemantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
arXiv:2607.17742v2 Announce Type: replace Abstract: In retrieval-augmented generation (RAG), semantic relevance asks whether a source matches a query in meaning, while answerability asks whether it contains sufficient in…
- arXiv cs.AIresearchSAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
arXiv:2607.17973v2 Announce Type: replace Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and…