AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchCode to Control: Synthesizing Parameterized Reactive Controllers
arXiv:2609.38733v1 Announce Type: new Abstract: Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decision, introducing late…
- arXiv cs.AIagentsLearning to Route in Visual Space via Multi-Step Embedding Retrieval
arXiv:2609.38743v2 Announce Type: replace Abstract: LLM agents rely on retrieval tools to access external knowledge, yet visual agentic search remains severely bottlenecked by standard single-step retrievers. In current…
- arXiv cs.AIagentsSelf-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization
arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorithms. Many successful…
- arXiv cs.AIagentsPathAnchor: Path-Structured Evidence for Scientific Agents
arXiv:2609.38766v1 Announce Type: new Abstract: Scientific agents can retrieve relevant passages yet still lose functional order, mix evidence across sources, or state conclusions that exceed the retrieved record. We int…
- arXiv cs.AIagentsAction Conditioned Bisimulation For GUI Agent Memory
arXiv:2609.38778v1 Announce Type: new Abstract: An agent that remembers what it did on a web page must decide when two pages count as the same. Memories built on observation similarity merge pages that look alike but beh…
- arXiv cs.AIagentsPersona and Persuasive Framing in AI Voice Agents: A $2\times2$ Field Experiment with Children
arXiv:2609.38782v1 Announce Type: new Abstract: Conversational agents increasingly interact with children, yet evidence on how their design shapes children's susceptibility to persuasion comes almost entirely from the la…
- arXiv cs.AIagentsPositive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening
arXiv:2609.38788v1 Announce Type: new Abstract: Organizations started listening to employees through conversational AI agents alongside structured surveys. Little is known about what these channels change in what employe…
- arXiv cs.AIagentsGraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics
arXiv:2609.38798v1 Announce Type: new Abstract: Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. How…
- arXiv cs.AIresearchWhen Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning
arXiv:2609.38817v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verification and persisten…
- arXiv cs.AIresearchWhose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening
arXiv:2609.38818v1 Announce Type: new Abstract: Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spoken voice. We introd…
- arXiv cs.AIresearchMore Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models
arXiv:2609.38827v2 Announce Type: replace Abstract: Direct-decision models enable efficient classification and evaluation by selecting from predefined candidates, but their reliability depends on more than prediction acc…
- arXiv cs.AIresearchDiversity Combining for Multi-Path LLM Reasoning
arXiv:2609.38829v2 Announce Type: replace Abstract: Multi-path reasoning methods such as self-consistency (SC) sample $K$ reasoning paths and choose the most frequent answer. However, their gains quickly plateau as $K$ i…
- arXiv cs.AIresearchOpenJev-RLCD: A Working RLCD Implementation
arXiv:2609.38850v1 Announce Type: new Abstract: Decision models such as Jev answer questions with probabilities, which are only useful if they are calibrated. Open-source reproductions rely on supervised fine-tuning plus…
- arXiv cs.AIagentsWhen Context Changes: Understanding Update Failures in LLMs
arXiv:2609.38866v2 Announce Type: replace Abstract: As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old value of the sa…
- arXiv cs.AIagentsTalk2Agent: Benchmarking Voice Interfaces for Text Agents
arXiv:2609.38867v1 Announce Type: new Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular interface for interacti…
- arXiv cs.AIresearchReasoning Externalization for Faithful Large Language Model Narratives of Stock Return Predictions
arXiv:2609.38869v2 Announce Type: replace Abstract: In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to understand. While la…
- arXiv cs.AIagentsSTRATA: Self-Learning Through Role-Aligned Tiered Agents for Real-Time Strategy Games
arXiv:2609.38881v1 Announce Type: new Abstract: Real-time strategy (RTS) games require agents to coordinate economic development, production and construction, base defense, unit organization, and attack timing over long…
- arXiv cs.AIagentsConsistent Plan-Act for Long-Horizon Agentic Tasks
arXiv:2609.38891v1 Announce Type: new Abstract: Long-horizon agentic tasks demand strong reasoning and efficient execution across successive interactions with dynamic environments. A common approach decouples high-level…
- arXiv cs.AIagentsComposing Task-specific Agent Harnesses at Test Time with Reusable Primitives
arXiv:2609.38912v1 Announce Type: new Abstract: Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting agent performance. Ho…
- arXiv cs.AIagentsRisk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets
arXiv:2609.38914v1 Announce Type: new Abstract: Evaluating interactive agents is expensive. Agent behavior is stochastic, so reliability must be measured over repeated trials, but failures are rare and differ widely in h…
- arXiv cs.AIresearchHow Much Can Reliability Drift Under a Fixed Confidence Distribution?
arXiv:2609.38917v1 Announce Type: new Abstract: A classifier's conditional accuracy can change while its confidence distribution stays exactly the same. We study the worst-case movement of the reliability relation under…
- arXiv cs.AIresearchPrototype-guided Bilateral Alignment Multimodal Federated Learning
arXiv:2609.38925v1 Announce Type: new Abstract: Multimodal federated learning (MFL) has emerged as a pivotal paradigm for leveraging distributed data to enhance model performance. However, existing methods predominantly…
- arXiv cs.AIresearchLearning What to Forget: Distributional Unlearning for LLM Representation Spaces
arXiv:2609.38929v1 Announce Type: new Abstract: Machine learning systems increasingly face the need to remove the influence of entire data domains, such as toxic language, harmful behavior, or topical content, rather tha…
- arXiv cs.AIresearchRouting Probes Can Improve Without New Information: An Exact-Null Audit of Uncertainty Beyond Model Outputs
arXiv:2609.38956v1 Announce Type: new Abstract: Routing signals of modern vision transformers -- expert gates, attention-residual weights and halting scores -- often improve probes that predict whether the model is corre…
- arXiv cs.AIresearchTargeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting
arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mechanisms underlying thi…
- arXiv cs.AIresearchAlleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering
arXiv:2609.38962v1 Announce Type: new Abstract: Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to estimate the model's con…
- arXiv cs.AIagentsWhen Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate
arXiv:2609.38964v1 Announce Type: new Abstract: Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents' opinions. We show th…
- arXiv cs.AIagentsRealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
arXiv:2609.38974v1 Announce Type: new Abstract: Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support…
- arXiv cs.AIresearchC-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories
arXiv:2609.39005v1 Announce Type: new Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, rea…
- arXiv cs.AIagentsSearch Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents
arXiv:2609.39026v1 Announce Type: new Abstract: Deep Research agents synthesize evidence into cited reports, yet a well-cited report can still reach a misleading conclusion. Citation correctness checks whether cited sour…
- arXiv cs.AIresearchMulti-LLM Collaborative Alignment via Stackelberg Games
arXiv:2609.39076v2 Announce Type: replace Abstract: A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructions used during t…
- arXiv cs.AIagentsBeyond Prediction: Steering VLM Agents with Retrospective World Modeling
arXiv:2609.39101v1 Announce Type: new Abstract: Equipping VLM agents with world modeling capabilities has shown strong potential for complex reasoning and long-horizon planning, while reducing the dependence of policy le…
- arXiv cs.AIresearchReserve-Aware Contrast Certificates for Conservative Bandits with Uncertain Baselines
arXiv:2609.39106v1 Announce Type: new Abstract: Conservative bandits must improve an incumbent policy without exhausting a prescribed performance budget. When the incumbent is uncertain, separately bounding candidate and…
- arXiv cs.AIagentsMASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Models
arXiv:2609.39107v1 Announce Type: new Abstract: Large Language Models (LLMs) have been applied in various fields. However, ensuring compliance and safety of LLMs, such as avoiding discrimination and bias, still remains a…
- arXiv cs.AIresearchBELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence
arXiv:2609.39139v1 Announce Type: new Abstract: Adaptive RAG uses signals such as confidence, relevance, support, and retrieval quality to decide when to search or correct evidence. In multi-step retrieval, however, thes…
- arXiv cs.AIagentsSchema: Discovering Unknown Environments via Agentic Program Induction
arXiv:2609.39140v1 Announce Type: new Abstract: Learning to complete tasks in unfamiliar environments with unknown rules remains a key challenge for LLM agents. Current LLM agents often record their discoveries in prose,…
- arXiv cs.AIagentsRefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent
arXiv:2609.39143v1 Announce Type: new Abstract: Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore need memory extract…
- arXiv cs.AIagentsMADBench: Benchmarking the Security of Multi-Agent Debate
arXiv:2609.39146v1 Announce Type: new Abstract: Multi-agent debate (MAD) can improve large language model (LLM) reasoning by allowing multiple agents to exchange and critique their answers to the same task. However, the…
- arXiv cs.AIagentsDo Self-Evolving Skills Generalize to Held-Out Tasks?
arXiv:2609.39148v2 Announce Type: replace Abstract: AI agents can externalize what they learn from past tasks into reusable \emph{skills}, such as procedures, checklists, code, or other executable artifacts, that can be…
- arXiv cs.AIagentsRep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
arXiv:2609.39149v2 Announce Type: replace Abstract: Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolutio…
- arXiv cs.AIagentsBeyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds
arXiv:2609.39166v4 Announce Type: replace Abstract: Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved, including during…
- arXiv cs.AIresearchReinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation
arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing frameworks rely on c…
- arXiv cs.AIresearchLearning Process Rewards via Reasoning State Propagation
arXiv:2609.39220v1 Announce Type: new Abstract: Process reward models (PRMs) have demonstrated notable effectiveness in test-time scaling and reinforcement learning by providing fine-grained signals for evaluating interm…
- arXiv cs.AIagentsFyan: A Human--AI Harness with Semantic Auditing for Document-Level Formalization
arXiv:2609.39228v1 Announce Type: new Abstract: We present FYAN, a human--AI harness for document-level mathematical formalization. Rather than treating theorems in isolation, FYAN coordinates an end-to-end workflow span…
- arXiv cs.AIresearchEffective Does Not Mean Useful: Conditional Functional Substitutability for Redundancy and Scaling in Transformers
arXiv:2609.39259v1 Announce Type: new Abstract: Modern neural networks scale predictably, yet the mechanisms behind these regularities remain unclear. Neural redundancy is typically characterized by component importance…
- arXiv cs.AIresearchANI: Adaptive Numerical Injection for Unifying Semantic and Arithmetic Representations in Numerical Reasoning
arXiv:2609.39294v1 Announce Type: new Abstract: Precise numerical reasoning with Large Language Models (LLMs) is essential for expanding their applicability to complex real-world tasks. However, text-based tokenization o…
- arXiv cs.AIagentsMiniRep: Robust Reputation-Based Aggregation for Multi-Agent Debate
arXiv:2609.39297v1 Announce Type: new Abstract: Autonomous agents powered by large language models (LLMs) are rapidly evolving into an open agentic ecosystem. To support trustworthy collaboration, industry initiatives in…
- arXiv cs.AIagentsWorkGenesis: Building the Worlds That Teach Agents to Work
arXiv:2609.39325v1 Announce Type: new Abstract: The ability of Large Language Model (LLM) agents to complete daily and professional work is receiving increasing attention. Training such agents requires realistic work sce…
- arXiv cs.AIresearchUnderstanding as No-Arbitrage: Bounded Dutch Books as a Definition and Training Objective for Language Models
arXiv:2609.39341v1 Announce Type: new Abstract: Does a language model merely predict tokens, or does it understand what it says? We make this question measurable by defining "understanding" through the lens of no-arbitra…
- arXiv cs.AIresearchThe Golden Path Hypothesis: Reusable Schedules in Diffusion Caching
arXiv:2609.39343v1 Announce Type: new Abstract: Diffusion caching accelerates generation by replacing transformer computation with cached or predicted features at selected denoising steps. We introduce the Golden Path Hy…