AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchWhen Judges Agree and Are Wrong: Consistency-Accuracy Dissociation as a Blind Spot for Label-Free Evaluation
arXiv:2610.21128v1 Announce Type: cross Abstract: Label-free evaluation methods, such as self-consistency filtering, majority-vote aggregation, and agreement-based reliability scoring, rely on an implicit assumption that…
- arXiv cs.AIagentsStronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation
arXiv:2610.21135v1 Announce Type: cross Abstract: LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported. A tampered deployment that preserves evaluated outputs pa…
- arXiv cs.AIresearchOnBodyAI - Why the Next AI Needs a Body
arXiv:2610.21137v1 Announce Type: cross Abstract: The next AI will not win by knowing more about the world. It will win because it knows you: because it lives on your body, works for you and answers to you. That is what…
- arXiv cs.AIresearchWhen is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
arXiv:2610.21139v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values. Yet LLM-assisted data c…
- arXiv cs.AIresearchCoSTA: Compressed Speech Token Adaptation for Efficient Spoken Dialogue Models
arXiv:2610.21143v1 Announce Type: cross Abstract: Spoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference. Lower-rate…
- arXiv cs.AIresearchCARE-SD: Explainable Reasoning Architecture for Local SLM Self-Disclosure Analysis in Textual Data
arXiv:2610.21151v1 Announce Type: cross Abstract: Cybersecurity interviews uncover critical security experiences, behaviors, and privacy concerns often not fully captured by conventional technical assessments. Consequent…
- arXiv cs.AIresearchLanguage Models Share a Readout Bottleneck in Working Memory
arXiv:2610.21176v1 Announce Type: cross Abstract: Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and errors concentrate on lur…
- arXiv cs.AIresearchJudging the Judges: Confidence and Disagreement in Rubrics as Rewards
arXiv:2610.21179v1 Announce Type: cross Abstract: Rubrics as Rewards (RaR) extends reinforcement learning to non-verifiable domains by using an LLM judge to score responses against a structured rubric. Prior work has foc…
- arXiv cs.AIresearchFedPix: Learning What to Share with Less Leakage in Federated Intelligence
arXiv:2610.21194v2 Announce Type: replace-cross Abstract: Existing federated intelligence paradigms primarily focus on how models are trained or aggregated, while overlooking a more fundamental question: \textbf{what inf…
- arXiv cs.AIresearchRecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation
arXiv:2610.21220v1 Announce Type: cross Abstract: Recommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trad…
- arXiv cs.AIresearchRepresentation Redistribution in Sparse Autoencoders
arXiv:2610.21240v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying request can redistrib…
- arXiv cs.AIagentsA Responsibility-Centered Coordination Mechanism for Decentralized Multi-Agent Systems
arXiv:2610.21243v1 Announce Type: cross Abstract: Centralized orchestration provides comprehensive multi-agent coordination but creates bottlenecks in scalability, adaptability, and collective performance. Decentralized…
- arXiv cs.AIresearchSystem1-Arena: Which Decisions Can Be Left to System 1?
arXiv:2610.21244v1 Announce Type: cross Abstract: Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human. System-1 models, such as Jev and the OpenAI Decisions API,…
- arXiv cs.AIresearchWhat do AI chatbots do after users decline therapeutic redirection for suicide thoughts?
arXiv:2610.21253v1 Announce Type: cross Abstract: Safety evaluations of artificial intelligence (AI) chatbots focus on therapeutic redirection, statements urging users who disclose suicide thoughts to seek help from cris…
- arXiv cs.AIresearchFractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
arXiv:2610.21257v1 Announce Type: cross Abstract: Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-train…
- arXiv cs.AIresearchBehavioral Compositionality of Sparse Autoencoder Representations
arXiv:2610.21261v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) represent model activations with sparse, linearly decoded features, but the downstream effects of those features need not compose additively. W…
- arXiv cs.AIresearchTRACER: A Multimodal Harness for Patient Care in Hospitals
arXiv:2610.21266v1 Announce Type: cross Abstract: Continuous video and clinical records provide complementary information for understanding how patient behavior evolves over a hospital stay. A central challenge is how to…
- arXiv cs.AIagentsTRACER: Trajectory-Aware Tool Retrieval for LLM Agents
arXiv:2610.21267v1 Announce Type: cross Abstract: Modern agents select tools from registries, but exposing entire registries incurs high token costs and can impair selection as they grow. We present TRACER, a retrieval f…
- arXiv cs.AIresearchLoud and Clear: Making Visual Evidence Count in Multimodal Document RAG
arXiv:2610.21318v1 Announce Type: cross Abstract: Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model reads the retrieved…
- arXiv cs.AIresearchDoes On-Policy Distillation Need to Complete Every Rollout?
arXiv:2610.21337v3 Announce Type: replace-cross Abstract: On-policy distillation (OPD) transfers teacher knowledge to a student through feedback on student-generated rollouts, but generating complete rollouts and computi…
- arXiv cs.AIresearchLTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models
arXiv:2610.21362v1 Announce Type: cross Abstract: Robotic manipulation often requires remembering interactions that are no longer visible. We introduce Latent Transition Memory (LTM), which stores history as compact late…
- arXiv cs.AIresearchESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases
arXiv:2610.21368v1 Announce Type: cross Abstract: With the expansion of pre-trained language model usage in recent years, the importance of datasets for performing tasks in specialized domains has significantly increased…
- arXiv cs.AIagentsMemorilla: Latent Semantic Memory for LLMs
arXiv:2610.21375v2 Announce Type: replace-cross Abstract: Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier act…
- arXiv cs.AIagentsPriorityFEC: Deadline-Aware Streaming FEC for KV-Cache Handoff in Edge Multi-Agent Systems
arXiv:2610.21390v2 Announce Type: replace-cross Abstract: Multi-agent large language model (LLM) systems are increasingly deployed at the edge to support complex tasks that require collaboration and information exchange…
- arXiv cs.AIagentsMatoi: Selective Revalidation of Consequence Evidence for Agent Authorization
arXiv:2610.21394v1 Announce Type: cross Abstract: Matoi separates reusable consequence evidence from current execution authority and durable occurrence history. Its finite query-aware contract covers branches, ordered co…
- arXiv cs.AIresearchWhen do quantum views help? Complementary classical-quantum representations in multi-view fusion
arXiv:2610.21395v1 Announce Type: cross Abstract: Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Research includes compari…
- arXiv cs.AIresearchMulti-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense
arXiv:2610.21404v1 Announce Type: cross Abstract: To improve task performance while encouraging desirable response properties, LLM post-training often combines a primary reward (e.g., correctness) with auxiliary rewards…
- arXiv cs.AIresearchWhere, How, and When: Query-Guided Grounding for Video Understanding
arXiv:2610.21422v1 Announce Type: cross Abstract: Understanding video content requires reasoning at multiple levels of granularity, from identifying objects to interpreting their interactions and localizing when specific…
- arXiv cs.AIresearchV-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
arXiv:2610.21448v1 Announce Type: cross Abstract: In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function i…
- arXiv cs.AIagentsX-Fly: Learning to Reach and Stop in Full-Chunk Aerial Vision-Language Navigation
arXiv:2610.21450v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) agents built on large vision-language models are expensive to query. Executing a complete predicted action chunk before replanning…
- arXiv cs.AIresearchFull-Duplex Interaction with Speech, Expressions and Gestures
arXiv:2610.21452v1 Announce Type: cross Abstract: Human face-to-face conversation requires coordinating speech, gestures and expressions across both speaking and listening. We present ELLSA 2, an end-to-end full-duplex d…
- arXiv cs.AIresearchCan Visual Generation Expand What Unified Multimodal Models Know?
arXiv:2610.21459v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) receive image-generation supervision that exposes them to visual information not fully documented in text. Can learning to generate image…
- arXiv cs.AIagentsTrajectory-Level Speculation with Semantic Verification for Tool-Using Agents
arXiv:2610.21463v1 Announce Type: cross Abstract: Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps…
- arXiv cs.AIresearchCan LLMs Tell Stories Out of Order? A Corpus and Benchmark for Nonlinear Narrative Generation
arXiv:2610.21468v1 Announce Type: cross Abstract: Every story has two orders: the order in which its events happen and the order in which they are told. A story is nonlinear when the two differ. We introduce a benchmark…
- arXiv cs.AIagentsPoison Valleys: Diagnosing Path-Dependent Failure in LLM Code Repair
arXiv:2610.21514v1 Announce Type: cross Abstract: When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed. Poison Valleys trac…
- arXiv cs.AIresearchHierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck
arXiv:2610.21520v1 Announce Type: cross Abstract: Joint Acoustic Scene Classification (ASC) and Audio Tagging (AT) models traditionally rely on unconstrained multihead architectures. However, this design ignores the natu…
- arXiv cs.AIresearchWhen Is Muon Optimal? Radial Fragility and Minimax Geometry
arXiv:2610.21549v1 Announce Type: cross Abstract: Muon replaces a matrix gradient by an approximate polar factor and has shown strong empirical performance in neural-network training. The Isotropic Curvature Model (ICM)…
- arXiv cs.AIresearchUncertainty Quantification in Pairwise Difference Learning for Classification
arXiv:2610.21554v1 Announce Type: cross Abstract: Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two ins…
- arXiv cs.AIresearchWhen Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark
arXiv:2610.21555v1 Announce Type: cross Abstract: Muon's gains in data efficiency motivate evaluating its performance under explicit data, time, and tuning budgets. On a single A100 GPU, we compare a reference single-dev…
- arXiv cs.AIagentsDialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents
arXiv:2610.21561v5 Announce Type: replace-cross Abstract: Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has received exte…
- arXiv cs.AIresearchBiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition
arXiv:2610.21565v3 Announce Type: replace-cross Abstract: On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse en…
- arXiv cs.AIagentsIndependent Data-Integrity Verification for LLM-Driven Functional Coverage Closure in Hardware Design
arXiv:2610.21570v1 Announce Type: cross Abstract: LLM agents can write directed tests that close functional coverage, but coverage only shows that a state was reached, not that the data on the way there was correct. This…
- arXiv cs.AIagentsRethinking the Role of Tool Graph Memory in Multi-Turn Tool Use
arXiv:2610.21571v1 Announce Type: cross Abstract: Large language model (LLM) agents solve complex tasks by calling external tools over multiple turns. Recent work has proposed tool graph memory, which stores past experie…
- arXiv cs.AIresearchLearning to Search for Executable Patternmaking from Certified Programs
arXiv:2610.21579v1 Announce Type: cross Abstract: Recent models generate sewing patterns from images or text, and program-based representations make these patterns executable. These systems produce a finished pattern and…
- arXiv cs.AIresearchAVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
arXiv:2610.21586v1 Announce Type: cross Abstract: Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users. We address this human-familiar, robot-unseen settin…
- arXiv cs.AIresearchDecoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker
arXiv:2610.21597v1 Announce Type: cross Abstract: Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for the protobuf messages…
- arXiv cs.AIresearchQwen-Audio-Jev: A Unified Interface for Audio Decisions
arXiv:2610.21614v2 Announce Type: replace-cross Abstract: Large audio language models (LALMs) have made substantial progress in audio understanding. However, autoregressive text generation introduces computational overhe…
- arXiv cs.AIresearchEfficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection
arXiv:2610.21644v1 Announce Type: cross Abstract: Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances ra…
- arXiv cs.AIresearchRouter Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning
arXiv:2610.21682v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training. The resulting mismatch is esp…
- arXiv cs.AIagentsHuman Interventions as Labels: Localizing, Steering, and (Sometimes) Editing the Computations Behind LLM Agent Failures
arXiv:2610.21708v1 Announce Type: cross Abstract: When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data. We treat it instead as a measurement: a label poi…