AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchPW-Pruning: Phase-Aware Expert Selection for Speech-Centric Omni-Modal Models
arXiv:2610.26891v1 Announce Type: cross Abstract: Sparse routing reduces per-token computation but leaves the full expert bank resident in memory. We study expert pruning for speech-centric omni-modal models, where pooli…
- arXiv cs.AIresearchMOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems
arXiv:2610.26902v1 Announce Type: cross Abstract: Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation…
- arXiv cs.AIresearchUncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation
arXiv:2610.26909v1 Announce Type: cross Abstract: In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion. Blindly adapting to such low quality samples ris…
- arXiv cs.AIresearchSteerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
arXiv:2610.26914v1 Announce Type: cross Abstract: Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize…
- arXiv cs.AIresearchEnhancing analytical photon transport in turbid media through physics-guided neural network corrections
arXiv:2610.26917v1 Announce Type: cross Abstract: Analytical methods provide rapid and computationally efficient predictions of light transport in turbid media, making them attractive for real-time and inverse applicatio…
- arXiv cs.AIresearchMultilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
arXiv:2610.26927v1 Announce Type: cross Abstract: Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content. The neural basis of this cross-language semantic commonality remain…
- arXiv cs.AIagentsExpert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
arXiv:2610.26932v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, r…
- arXiv cs.AIagentsHow Intervention History Changes AI Agent Sabotage on Subsequent Tasks
arXiv:2610.26935v1 Announce Type: cross Abstract: AI control evaluations often reset agents' memory between tasks, although deployed agents may retain records of prior interventions. We ask whether learning that an earli…
- arXiv cs.AIresearchGRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation
arXiv:2610.26938v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and…
- arXiv cs.AIresearchBeyond Policy Discrepancy: Capability-Aware Multi-Teacher On-Policy Distillation
arXiv:2610.26950v2 Announce Type: replace-cross Abstract: Multi-teacher on-policy distillation (MOPD) integrates domain specialists into a shared student, but determining how strongly each teacher should supervise remain…
- arXiv cs.AIresearchFoundPDE: A generative foundation model for data-driven symbolic discovery of partial differential equations
arXiv:2610.26963v1 Announce Type: cross Abstract: Discovering governing partial differential equations (PDEs) from observed data is a central inverse problem in computational science and engineering: the goal is not only…
- arXiv cs.AIresearchSZED at ArGuard Shared Tasks: Multi-Task Label-Attention and Cross-Modal Fusion for Arabic Hateful Meme Detection
arXiv:2610.26972v1 Announce Type: cross Abstract: We present SZED's submission to ArGuard 2026 Track A: Arabic hateful meme detection. The task has two parts: binary classification (A1) and fine-grained multi-label taggi…
- arXiv cs.AIresearchDynamic Dual-Feedback Conformal Inference for Time Series Forecasting
arXiv:2610.26987v1 Announce Type: cross Abstract: Quantifying uncertainty in time series forecasting is particularly demanding because sequential data exhibit temporal dependence and are prone to distributional changes.…
- arXiv cs.AIresearch3D-MoE: Complementarity-Guided Hardware-Software Co-Design for MoE Decode on 3D-Stacked NMP
arXiv:2610.26991v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) have become a mainstream way to scale Large Language Models (LLMs), because each token activates only a small subset of experts, reducing inferen…
- arXiv cs.AIresearchCoASFL: Coupled Consistency Optimization for Asynchronous Split Federated Learning
arXiv:2610.26992v1 Announce Type: cross Abstract: Split Federated Learning (SFL) has emerged as a promising paradigm for training large-scale deep models on resource-constrained edge devices. Although asynchronous SFL al…
- arXiv cs.AIresearchHierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
arXiv:2610.27001v1 Announce Type: cross Abstract: Drug--target affinity (DTA) prediction plays an important role in computer-aided drug discovery, where reliable computational models can help prioritize compound--protein…
- arXiv cs.AIresearchExploration via Activation-Space Steering for Reinforcement Learning with Verifiable Rewards
arXiv:2610.27012v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model by sampling responses, scoring them with an automatic verifier, and updating the policy from…
- arXiv cs.AIresearchReliable Estimation of Differential Privacy under Selection Bias
arXiv:2610.27021v1 Announce Type: cross Abstract: Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms. Auditors typically use Membership Inf…
- arXiv cs.AIresearchBR-MoE: Budgeted Semantic Release for Privacy-Preserving Traffic Analytics
arXiv:2610.27026v2 Announce Type: replace-cross Abstract: Traffic cameras support useful analysis, but their outputs can reveal private information. Replacing images with text reduces direct visual exposure. It does not…
- arXiv cs.AIresearchRobustness of Internal Signals for Hallucination Detection in Vision-Language Models
arXiv:2610.27041v1 Announce Type: cross Abstract: When given an image and a text prompt, vision-language models (VLMs) can produce seemingly sophisticated text responses. However, they are also prone to hallucinate objec…
- arXiv cs.AIresearchCross-Lingual Reusability of LLM Pipelines: Financial Extraction, Multilingual Reasoning, and Semantic Screening
arXiv:2610.27043v1 Announce Type: cross Abstract: Financial institutions increasingly deploy large language models (LLMs) in multilingual markets, raising a practical question: can pipelines developed and validated in En…
- arXiv cs.AIresearchDiscrete Neural Interlingua for Ontology-Agnostic EHR Modeling
arXiv:2610.27047v1 Announce Type: cross Abstract: EHR foundation models promise scalable clinical prediction across health systems, yet their deployment is constrained by a tokenization bottleneck. The same clinical conc…
- arXiv cs.AIresearchJoint Constraint Aggregation for Qualifier-Sensitive Retrieval-Augmented QA
arXiv:2610.27114v1 Announce Type: cross Abstract: Retrieval-augmented QA can fail even when the correct answer is in the retrieved evidence. This can happen when a competing answer satisfies some, but not all, of the que…
- arXiv cs.AIresearchReconstruction-Optimized Expert Pruning for Mixture-of-Experts Language Models
arXiv:2610.27123v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models reduce computation through sparse expert activation, but deployment still requires storing a large number of expert parameters. We study p…
- arXiv cs.AIresearchMind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack
arXiv:2610.27135v1 Announce Type: cross Abstract: With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection. However, when edge devices are compr…
- arXiv cs.AIresearchOntic state recovery through prediction
arXiv:2610.27139v1 Announce Type: cross Abstract: We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed…
- arXiv cs.AIresearchRepresenting short-term movement to reveal animal identity and learning progress
arXiv:2610.27140v1 Announce Type: cross Abstract: How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major challenge is identifying…
- arXiv cs.AIresearchICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy
arXiv:2610.27142v1 Announce Type: cross Abstract: Data selection plays a central role in modern large language model training, as the quality of training data strongly influences downstream performance. A common approach…
- arXiv cs.AIresearchModel Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices
arXiv:2610.27152v1 Announce Type: cross Abstract: Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deploy…
- arXiv cs.AIresearchUnderstanding the Benefits of Scaffolds through Computational Separations
arXiv:2610.27157v1 Announce Type: cross Abstract: Inference-time harnesses, i.e., external procedures that call a model many times and keep state across calls, can substantially improve model capabilities. In this paper,…
- arXiv cs.AIagentsVisual Task Modeling and Automated Planning for Humanoid Robots in Education of Individuals with Autism Spectrum Disorder
arXiv:2610.27162v1 Announce Type: cross Abstract: In robot-assisted education, a robot must understand how to complete a task in order to effectively guide a student through it. We present a novel method that enables a s…
- arXiv cs.AIresearchOn the Fragility of Tasks under Composition: Implicit Budgeting in Language Models
arXiv:2610.27190v1 Announce Type: cross Abstract: Modern language models are increasingly asked to handle several tasks within a single prompt. Doing so requires solving each task correctly while integrating the resultin…
- arXiv cs.AIresearchCARLAR Influence: A Structured Hallucination Risk in Multimodal Cheque Extraction and an Exploratory Compressed-Image Countermeasure
arXiv:2610.27192v1 Announce Type: cross Abstract: Multimodal large language models (LLMs) are increasingly used for structured extraction from financial documents, but their behavior under visual ambiguity is poorly unde…
- arXiv cs.AIagentsLoWAgent: A Low-Resource Multilingual Benchmark for Web Agents
arXiv:2610.27205v2 Announce Type: replace-cross Abstract: Web agents are typically evaluated on English-language interfaces, leaving their ability to operate across the world's multilingual websites largely unexplored. T…
- arXiv cs.AIresearchLanguage Guidance for Supervised Vision Training: An Empirical Study of Generalization
arXiv:2610.27208v1 Announce Type: cross Abstract: Deep neural networks have achieved remarkable success on vision benchmarks, yet they continue to struggle with many generalization challenges. Supervised vision training…
- arXiv cs.AIresearchProfile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference
arXiv:2610.27213v1 Announce Type: cross Abstract: Personalized large language models (LLMs) increasingly rely on profile-conditioned inference, pairing public requests with rich, user-specific facts. This paradigm expose…
- arXiv cs.AIagentsMMedAgent-Plus: Scaling Multimodal Medical Agents across Modalities, Tasks and Workflows
arXiv:2610.27233v1 Announce Type: cross Abstract: Medical workflows often require multiple interdependent analyses, but existing specialist and generalist AI models are not designed to coordinate them. We introduce MMedA…
- arXiv cs.AIagentsAutoRedClaw: Automated Red-Teaming for Claw-Style Agents
arXiv:2610.27244v1 Announce Type: cross Abstract: Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain configuration files…
- arXiv cs.AIresearchTarget-Aware Curation of Hallucination Stress Tests for Large Language Models
arXiv:2610.27247v1 Announce Type: cross Abstract: Hallucination stress tests can yield similar average scores across language models even when different prompts elicit hallucinations in each model. We develop a procedure…
- arXiv cs.AIresearchValiAssist: Scaling LLM-generated Programming Feedback Validation by Demonstration
arXiv:2610.27248v1 Announce Type: cross Abstract: Generative AI can create programming feedback at scale, but validating its alignment with instructors' pedagogical goals and strategies remains challenging. Instructors m…
- arXiv cs.AIresearchWhen Models Know but Can't Linearly Say: Relational Encoding Limits Linear Vulnerability Classification in Code LLMs
arXiv:2610.27257v1 Announce Type: cross Abstract: Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear. We analyze 8 code models spanning 6.7B-…
- arXiv cs.AIresearchTheorem prover Arend
arXiv:2610.27264v2 Announce Type: replace-cross Abstract: This paper is an exposition of the theorem prover Arend developed at JetBrains Research. Arend language has been designed to facilitate the formalization of mathe…
- arXiv cs.AIresearchAuditing Safety Preference Supervision with Recorded Derivations
arXiv:2610.27266v1 Announce Type: cross Abstract: Preference labels align model training, but the judgments behind them are difficult to inspect or verify. In this paper, we examine how preference construction can be che…
- arXiv cs.AIagentsCertifying Repeatable Entry-Replay Witnesses for COBOL-to-Java Migration
arXiv:2610.27272v1 Announce Type: cross Abstract: Producing synthetic data for comparison testing a migrated COBOL application is a major challenge that agentic migrations face today. Our prior paper, the Agentic Method…
- arXiv cs.AIresearchVPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
arXiv:2610.27280v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based methods allow for plannin…
- arXiv cs.AIresearchWhen Does Hate Transfer? A Large-Scale Study of Cross-Lingual Hate Speech Detection
arXiv:2610.27285v1 Announce Type: cross Abstract: Effective detection of hate content is essential for maintaining healthy online communities, yet performance can vary substantially across languages. This work investigat…
- arXiv cs.AIresearchGeneralizing Multimodal Foundational Models in Context-Driven Class Incremental Learning
arXiv:2610.27298v1 Announce Type: cross Abstract: Class-Incremental Learning (CIL) aims to learn new classes continuously without forgetting the previously learned ones. Recent works in CIL have significantly advanced ac…
- arXiv cs.AIagentsEvaluating Context Compaction on the Questions That Come Later
arXiv:2610.27301v1 Announce Type: cross Abstract: Language-model agents compact their context for the question at hand, but the compacted context must also serve questions that come later. We evaluate compaction on these…
- arXiv cs.AIresearchGlobal Security Blind Spots: Translation as a Safety Bypass in Low-Resource Languages
arXiv:2610.27304v1 Announce Type: cross Abstract: Large language models are deployed globally, yet safety alignment and evaluation remain concentrated in English: harmful requests refused in English can elicit unsafe com…
- arXiv cs.AIresearchSteering Vision-Language Models for Visual-Contextual Privacy in Location Disclosure
arXiv:2610.27323v1 Announce Type: cross Abstract: Vision-language models (VLMs) can infer sensitive information such as geolocation from subtle visual cues, creating privacy risks when responses disclose more detail than…