AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsMetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing methods either optimize…
- arXiv cs.AIagentsISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
arXiv:2610.21502v1 Announce Type: new Abstract: Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and p…
- arXiv cs.AIresearchDecomposing Brain-LLM Alignment with Interpretable Semantic Representations
arXiv:2610.21508v1 Announce Type: new Abstract: Large language models (LLMs) predict neural responses during naturalistic language comprehension, but the linguistic information underlying this brain alignment remains dif…
- arXiv cs.AIagentsQREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering
arXiv:2610.21512v1 Announce Type: new Abstract: DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue intent-misaligned, under…
- arXiv cs.AIresearchSCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
arXiv:2610.21518v1 Announce Type: new Abstract: Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural setting…
- arXiv cs.AIagentsCriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games
arXiv:2610.21525v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown impressive capabilities across many tasks, including processing large amounts of text, following instructions, and generating ideas.…
- arXiv cs.AIresearchFunctional Subnetwork Localization for Efficient Continual Learning in Large Language Models
arXiv:2610.21537v1 Announce Type: new Abstract: Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In contrast, continual learning in large language m…
- arXiv cs.AIagentsAgent Status Protocol (ASP): A Lightweight Specification for State Reporting in Multi-Agent Systems
arXiv:2610.21538v1 Announce Type: new Abstract: The Agent Status Protocol (ASP) defines a lightweight way for heterogeneous AI agents to report their operational state to a status server, and for observers such as dashbo…
- arXiv cs.AIresearchReinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
arXiv:2610.21539v2 Announce Type: replace Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining…
- arXiv cs.AIagentsClaimScope: Label-Blind Witness Verification for Mathematical Claim Auditing
arXiv:2610.21581v1 Announce Type: new Abstract: Mathematical agents are commonly evaluated by answer accuracy or proof acceptance. We introduce ClaimScope, a 72-task benchmark for auditing proposed claims through a verdi…
- arXiv cs.AIresearchLOADBench: A Large Open Analog Dataset and Benchmark for Machine Learning in IC Design
arXiv:2610.21591v2 Announce Type: replace Abstract: Machine learning (ML) for analog IC design is constrained by limited open data and the absence of standardized evaluation: results reported in different papers are typi…
- arXiv cs.AIresearchLoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models
arXiv:2610.21594v1 Announce Type: new Abstract: Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves sequential computation an…
- arXiv cs.AIagentsEvaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large langu…
- arXiv cs.AIresearchLearning Intrinsic Dynamics in Latent World Models
arXiv:2610.21612v1 Announce Type: new Abstract: Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However, visual variations unrelated to the underlyin…
- arXiv cs.AIagentsAgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations
arXiv:2610.21624v2 Announce Type: replace Abstract: Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for jointly characteriz…
- arXiv cs.AIagentsThe Self-Improvement Problem
arXiv:2610.21646v1 Announce Type: new Abstract: With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to t…
- arXiv cs.AIagentsData World: Scaling Data Environments from Scratch
arXiv:2610.21656v1 Announce Type: new Abstract: Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-prope…
- arXiv cs.AIresearchIn-Context Preference-Conditioned Pareto Set Learning from Limited Offline Data
arXiv:2610.21736v1 Announce Type: new Abstract: Multi-objective optimization involves diverse preferences over conflicting objectives. Rather than returning a finite set of trade-off solutions, preference-conditioned Par…
- arXiv cs.AIagentsDeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents
arXiv:2610.21755v1 Announce Type: new Abstract: Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness. We investigate how this operating space shapes…
- arXiv cs.AIagentsFrom Research Goals to Scientific Closure: Evaluating Agents Through Evidence-Supported Physics Research
arXiv:2610.21767v1 Announce Type: new Abstract: Can AI agents organize scientific investigations that lead to well-supported conclusions? To evaluate this capability, we introduce PhysClosureBench, a benchmark of 50 expe…
- arXiv cs.AIagentsRSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent
arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question answering. Existing se…
- arXiv cs.AIresearchCo2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration
arXiv:2610.21804v1 Announce Type: new Abstract: Incremental learning (IL) aims to continually acquire new knowledge (plasticity) while retaining previously learned information (stability). However, striking a balance bet…
- arXiv cs.AIresearchCIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering
arXiv:2610.21805v1 Announce Type: new Abstract: Multimodal objects can require different partitions under different instructions, as when products in an e-commerce catalog are grouped by color, material, or both. Retriev…
- arXiv cs.AIresearchRoleQ: Role-Aligned Scales for Converting Language Models to Ternary
arXiv:2610.21808v1 Announce Type: new Abstract: Weight quantization below four bits shrinks memory and bandwidth at a perplexity that looks acceptable, but often at the cost of factual knowledge and task accuracy. Ternar…
- arXiv cs.AIagentsCoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness
arXiv:2610.21910v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance, they are increasingly expected to perform spatiotemporal reasoning over motion, actions, and view changes beyond what the input im…
- arXiv cs.AIagentsOntoAct: Hypothesis-Based Neuro-Symbolic Agents through Ontological Abstraction
arXiv:2610.21983v1 Announce Type: new Abstract: Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation. We introduce OntoAct, a hypothesis-based neuro-symbolic…
- arXiv cs.AIagentsCARA: Causally Guided Retrieval Agent for Question Answering
arXiv:2610.22017v1 Announce Type: new Abstract: Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textu…
- arXiv cs.AIagentsGraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents
arXiv:2610.22031v1 Announce Type: new Abstract: Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents. However, existing appro…
- arXiv cs.AIresearchProgress Reward Model for Reinforcement Learning via Large Language Models
arXiv:2610.22056v1 Announce Type: new Abstract: Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large la…
- arXiv cs.AIagentsFAOPs: Normal Forms and an Algebra for AI Agent Workflows
arXiv:2610.22061v1 Announce Type: new Abstract: Organizations increasingly encode operational procedures as multi-step agent skills, here called Agent Operating Procedures (AOPs), that must be traceable, auditable, repro…
- arXiv cs.AIresearchPrism-1: Position- and Rival-Independent Scoring for System One Models
arXiv:2610.22066v2 Announce Type: replace Abstract: Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass,…
- arXiv cs.AIresearchWhen Reasoning Is Not Enough: Proactive Inquiry for Cross-Modal Safety in Multimodal Large Language Models
arXiv:2610.22068v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly. Beyond implicit reasoning risk, where reasoning…
- arXiv cs.AIresearchLearning from Unverified Peers via Shared Residual Distillation
arXiv:2610.22080v1 Announce Type: new Abstract: A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones. Many post-training methods turn these tra…
- arXiv cs.AIagentsPROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents
arXiv:2610.22124v1 Announce Type: new Abstract: Modern agentic systems extend language models with external tools, enabling them to complete complex tasks through multi-turn interactions. As these agents are deployed in…
- arXiv cs.AIresearchDropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction
arXiv:2610.22130v1 Announce Type: new Abstract: Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory growth. While recen…
- arXiv cs.AIresearchCART: Compatible Routing for Multimodal Continual Instruction Tuning
arXiv:2610.22137v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world applications often require them to continually acquire new ca…
- arXiv cs.AIagentsBelieved but Not Said: Measuring Hidden Belief Collapse in Multi-Agent LLM Discussions
arXiv:2610.22138v1 Announce Type: new Abstract: LLM systems increasingly rely on groups of agents that discuss, critique, and check one another, assuming that peers will catch individual errors. We test this assumption a…
- arXiv cs.AIresearchAre You Sure Your LLM Is Sure? Repairing and Auditing Confidence
arXiv:2610.22139v1 Announce Type: new Abstract: Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself.…
- arXiv cs.AIresearchRAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
arXiv:2610.22150v1 Announce Type: new Abstract: In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a relation drawn from a cl…
- arXiv cs.AIagentsRealtime-Bench: Can Frontier Models Solve Real-time Interactive Problems?
arXiv:2610.22155v3 Announce Type: replace Abstract: Real-time interaction requires agents to perceive evolving environments and act under temporal constraints, yet existing evaluations of real-time streaming models focus…
- arXiv cs.AIresearchMulti-sourced AI Tutor for Non-Native English Speaker Students
arXiv:2610.22163v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student que…
- arXiv cs.AIagentsTerminal-RSI: Recursive Self-Improving Terminal Agents via Recursive Self-Rewrite and Policy-Refine
arXiv:2610.22192v3 Announce Type: replace Abstract: Converting verifier outcomes into useful policy updates and partial progress into complete task resolution is crucial for terminal agents. We present Terminal-RSI, whic…
- arXiv cs.AIagentsAutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
arXiv:2610.22194v1 Announce Type: new Abstract: Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising tra…
- arXiv cs.AIagentsHTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
arXiv:2610.22195v2 Announce Type: replace Abstract: Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches provide execution fl…
- arXiv cs.AIresearchGNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP
arXiv:2610.22214v1 Announce Type: new Abstract: Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed s…
- arXiv cs.AIagentsShortcut Sensitivity in Agent-Safety Evaluations
arXiv:2610.22243v1 Announce Type: new Abstract: AI agents take actions through tools. Some actions are sensitive, so guards that supervise them must themselves be evaluated reliably. Yet the data used to evaluate a guard…
- arXiv cs.AIresearchDisagreement-Guided Weak-to-Strong Routing
arXiv:2610.22245v1 Announce Type: new Abstract: Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable d…
- arXiv cs.AIagentsX-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning
arXiv:2610.22269v1 Announce Type: new Abstract: The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control. While the RAN Intelligent Controller (RIC) enables centralized optimiz…
- arXiv cs.AIagentsLEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents
arXiv:2610.22280v1 Announce Type: new Abstract: Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails. Weak-…
- arXiv cs.AIagentsImproving Environment-Agnostic Agents: The Role of Scaffolding
arXiv:2610.22289v3 Announce Type: replace Abstract: Agents built on stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We inves…