AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchAutoregressive Multimodal Anomaly Detection with Large Language Models for Microservice Systems
arXiv:2610.31722v1 Announce Type: cross Abstract: Microservice systems have become a dominant architecture for large-scale cloud applications, enabling rapid deployment and elastic scaling. However, ensuring the reliabil…
- arXiv cs.AIresearchLearning to Be Monitored: Causal Bounds and Intervention-Aware Post-Training Recipes for Chain-of-Thought Monitorability
arXiv:2610.31733v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring aims to detect potentially harmful behaviors in large language models (LLMs) from their CoTs. Intervention-based evaluations measure thi…
- arXiv cs.AIresearchGeneralized green and red concepts for artificial intelligence, big data, and information and communication technologies: Multidimensional framework and countermeasures
arXiv:2610.31739v1 Announce Type: cross Abstract: The rapid expansions of artificial intelligence (AI), big data (BD), and information and communication technologies (ICT) are driving substantial growth in electricity, w…
- arXiv cs.AIagentsBenchmarks Are Surprisingly Compaction-Friendly
arXiv:2610.31778v1 Announce Type: cross Abstract: Long horizon agentic tasks are now pushing LLM agents past their context window. To continue the task, the agent scaffold has to free up the context window, a process kno…
- arXiv cs.AIresearchPrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer
arXiv:2610.31812v1 Announce Type: cross Abstract: Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for learning robot behaviors aligned with human intent. However, its scalability is fun…
- arXiv cs.AIresearchWhat the Query Remembers: Query Abstraction Governs Lexical and Dense Retrieval of Classical Arabic Poetry
arXiv:2610.31821v1 Announce Type: cross Abstract: How well a poetry archive can be searched depends on what the user still remembers, not only on what the model understands. We show this for Classical Arabic poetry, benc…
- arXiv cs.AIresearchSEVI: Safe Evidential Real-World Reinforcement Learning
arXiv:2610.31829v1 Announce Type: cross Abstract: Real-world reinforcement learning has enabled contact-rich manipulation tasks to be trained directly on physical robots, but unsafe interactions during online learning re…
- arXiv cs.AIresearchRethinking Multi-View Fusion for Urban Representation Learning
arXiv:2610.31850v2 Announce Type: replace-cross Abstract: Urban region representation learning encodes heterogeneous urban signals, such as mobility flows, points of interest, and land-use patterns, into unified embeddin…
- arXiv cs.AIagentsSWE-Clar: Can Coding Agents Clarify Incomplete Client Requirements and Deliver Greenfield Software?
arXiv:2610.31854v1 Announce Type: cross Abstract: Greenfield software development in freelance settings requires coding agents to turn incomplete client requests into working software. This makes requirement clarificatio…
- arXiv cs.AIresearchMachine-Readable Operator Manuals over the ISO 11783 Network for Offline, Multilingual LLM Assistance
arXiv:2610.31863v1 Announce Type: cross Abstract: If agricultural machinery is to support intelligent, offline assistance, then the system must access technical documentation dynamically. In this paper, the Semantic Sync…
- arXiv cs.AIresearchFSVL: Taxonomic Fast-Slow Vision-to-Language Collaborative Framework for Interpretable Confusable Fish Species Identification and Knowledge Generation in the Middle Yangtze River
arXiv:2610.31880v1 Announce Type: cross Abstract: The Yangtze River Basin is a critical region for freshwater biodiversity conservation. The implementation of the Yangtze River Ten-Year Fishing Ban has increased the impo…
- arXiv cs.AIresearchEffectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima
arXiv:2610.31884v1 Announce Type: cross Abstract: While animals and people tend to learn a task more quickly and reliably when they first train on simpler versions of it, curriculum learning's effectiveness in artificial…
- arXiv cs.AIagentsKnowing When to Stop: The Evidence-Action Gap in Coding Agents
arXiv:2610.31895v2 Announce Type: replace-cross Abstract: Coding agents are trained to complete tasks, and they keep trying even when the environment makes completion impossible. The evidence that only outside help can r…
- arXiv cs.AIresearchLearning Structured Sparsity for Large Models: Bridging Model Compressibility and Efficient Inference
arXiv:2610.31897v1 Announce Type: cross Abstract: Scaling laws continue to reward increases in model scale, while the resulting computational and memory demands drive greater reliance on cloud infrastructure for inferenc…
- arXiv cs.AIresearchENCL: Enabling Efficient Neuromorphic Continual Learning with Local Learning Rule and Sub-Network Freezing Strategy
arXiv:2610.31927v1 Announce Type: cross Abstract: Continual learning (CL) in neural networks is a promising learning approach for updating the knowledge of deployed systems based on new data encountered at run time, that…
- arXiv cs.AIresearchKolibri: A Sovereign European Model on the Pareto Frontier
arXiv:2610.31945v1 Announce Type: cross Abstract: This report introduces Kolibri, an English-German mixture-of-experts transformer with 78.1B total parameters and 3.46B active parameters per token, released as open weigh…
- arXiv cs.AIresearchRobustNCL: Robustifying Neuromorphic Continual Learning under Transient Faults with Lightweight Detect-and-Cure Strategy
arXiv:2610.31954v1 Announce Type: cross Abstract: In neural networks, continual learning (CL) is a prominent approach for enabling deployed systems to adapt to dynamic operational environments by effectively learning fro…
- arXiv cs.AIresearchA Mathematical Theory of the Jacobian Lens
arXiv:2610.31955v1 Announce Type: cross Abstract: Recent work from Anthropic introduced a new interpretability tool, called the Jacobian lens (J-lens, Gurnee et al., 2026). This method identified linear directions in the…
- arXiv cs.AIagentsAgentic AI Defense: Decode-Time Provenance Binding for MCP
arXiv:2610.31974v1 Announce Type: cross Abstract: Agentic AI systems using the Model Context Protocol (MCP) are vulnerable to indirect prompt injection through untrusted tool outputs. We demonstrate cross-modal trust lau…
- arXiv cs.AIresearchSpectra: Multi-Scale Implicit Decomposition for Deepfake Detection
arXiv:2610.31979v1 Announce Type: cross Abstract: Deepfakes increasingly undermine trust in digital media, yet existing detectors often struggle to generalize across unseen manipulation techniques, compression levels, an…
- arXiv cs.AIresearchOpenHome: From Perception to Insight in Smart-Home Camera Videos
arXiv:2610.31986v1 Announce Type: cross Abstract: Vision language models (VLMs) have been increasingly adopted in consumer smart-home camera systems to interpret recorded videos and provide insights. However, video clips…
- arXiv cs.AIresearchMI-MIDI 2: From Observation to Control with Sparse Autoencoders in Text-to-MIDI Generation
arXiv:2610.32007v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) can expose features of a generative model without predefined labels, but in language models the evidence used to interpret these features has p…
- arXiv cs.AIresearchRethinking Representation Geometry in Supervised Contrastive Learning under Long-Tailed Data
arXiv:2610.32039v1 Announce Type: cross Abstract: Supervised contrastive learning and its balanced variants have demonstrated their effectiveness for representation learning under long-tailed distributions. However, alth…
- arXiv cs.AIresearchACVA-Bench: Beyond Prompt Following in Abstract Concept Visualization Ability for Text-to-Image Models
arXiv:2610.32058v1 Announce Type: cross Abstract: Text-to-image (T2I) models have achieved remarkable progress in image quality and instruction following, prompting growing research interest in their capacity for deep un…
- arXiv cs.AIresearchConcept Acupuncturist: An Efficient and Precise Concept Erasure Method for Diffusion Models via Sparse Autoencoders
arXiv:2610.32059v1 Announce Type: cross Abstract: Diffusion-based image generation models produce high-quality images but may generate harmful or policy-violating content, motivating machine unlearning (MU) to selectivel…
- arXiv cs.AIresearchControlled Evaluation of EEG Foundation Models: Random Initialisation, Band-Power and Validation-Selected Probes
arXiv:2610.32067v1 Announce Type: cross Abstract: EEG foundation models report downstream gains, yet their protocols seldom include three controls. These are the same encoder untrained, spectral features fitted on the sa…
- arXiv cs.AIresearchGuided Zero-Shot Anomaly Explanation for Anonymized Crowd Video Monitoring: Context, Categorization, and Spatial Localization
arXiv:2610.32073v1 Announce Type: cross Abstract: Despite the promising development of deep learning for video anomaly detection (VAD), explainability (XAI) of these models remains a challenge. Explainable and privacy-pr…
- arXiv cs.AIagentsAgentTrace: Execution-Level Fingerprinting of Black-Box LLM Agents
arXiv:2610.32092v1 Announce Type: cross Abstract: Verifying the provenance of autonomous LLM agents is a critical security challenge. Existing semantic attribution is easily obfuscated by system prompts, while active wat…
- arXiv cs.AIresearchTTT in VLA Models: Exploring Training with Human Demonstrations
arXiv:2610.32094v1 Announce Type: cross Abstract: Long-horizon robot tasks require retaining and using past information. We integrate Test-Time Training (TTT), which stores context in fast weights, into InternVLA-A1 and…
- arXiv cs.AIresearchReMedI: Inference-Time Refinement and Self-Distillation for Controllable Molecular Optimization
arXiv:2610.32103v1 Announce Type: cross Abstract: In drug development, lead optimization must improve competing properties, such as increasing permeability and reducing toxicity, while limiting scaffold changes that coul…
- arXiv cs.AIresearchREACT: Representational Early Analysis and Control of Chain-of-Thought Trajectories
arXiv:2610.32113v1 Announce Type: cross Abstract: Chain-of-thought (CoT) generation offers an opportunity to intervene before a reasoning model completes its answer. But does the point at which risk is easiest to detect…
- arXiv cs.AIresearchTampered ML: Evaluating LLM Judgment of Machine Learning Research
arXiv:2610.32114v1 Announce Type: cross Abstract: One approach to accelerating progress on AI alignment is to automate parts of alignment research itself. Doing so requires systems that can perform reliably on hard-to-su…
- arXiv cs.AIagentsImageRSI: Verification-Driven Recursive Self-Improvement for Agentic Image Generation
arXiv:2610.32120v1 Announce Type: cross Abstract: Complex text-to-image requests remain difficult to satisfy reliably. Verifying and repairing an image can fix an individual output, but a successful repair does not neces…
- arXiv cs.AIresearchBeyond Data Schedule: Fast LLM Pretraining on Data Mixtures by Loss-Reweighting
arXiv:2610.32141v1 Announce Type: cross Abstract: Large language models often acquire specialized capabilities through successive pretraining stages, but these gains can come at the cost of forgetting earlier knowledge.…
- arXiv cs.AIresearchRethinking LoRA Initialization for Robust Asymmetric Learning Rates
arXiv:2610.32142v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) is often highly sensitive to learning-rate (LR) choices. Recent empirical studies suggest that, once LRs are carefully tuned, vanilla LoRA can…
- arXiv cs.AIresearchImplicit Neural Representations for Variational Problems on Graphons
arXiv:2610.32147v1 Announce Type: cross Abstract: We propose a neural exploratory framework for variational problems over graphons, i.e., symmetric measurable functions $[0,1]^2 \to [0,1]$, that arise as limits of dense…
- arXiv cs.AIagentsThe LLENS Platform for On-Premises Large Language Model Agents in a Japanese University Hospital: Development and Evaluation Study
arXiv:2610.32151v2 Announce Type: replace-cross Abstract: Large language model agents that use tools could take on part of the electronic health record and desk work that occupies physicians' time, but in many hospitals…
- arXiv cs.AIresearchSparsity-driven emergence of functional modularity in multitask neural networks
arXiv:2610.32153v1 Announce Type: cross Abstract: Neural networks can learn multiple tasks, but how learning organizes shared computation into specialized and reusable components remains poorly understood. In this work,…
- arXiv cs.AIresearchMG-REPA: Multi-Granularity Representation Alignment for Generative Target Speaker Extraction
arXiv:2610.32161v1 Announce Type: cross Abstract: Generative target speaker extraction (TSE) models achieve high-quality extraction, but suffer from considerably slow training convergence. Representation Alignment (REPA)…
- arXiv cs.AIresearchSearching with Sparse Histories: Cross-Task Architecture Search from Incomplete Search Logs and Zero-Cost Proxies
arXiv:2610.32179v1 Announce Type: cross Abstract: Neural architecture search (NAS) is expensive, but cross-task transfer can reduce costs by reusing knowledge from previous tasks. However, existing methods struggle with…
- arXiv cs.AIresearchUni-Stream: A Unified Streaming Speech Language Model for Speech Synthesis and Recognition
arXiv:2610.32187v1 Announce Type: cross Abstract: Unified streaming speech language models aim to support speech recognition and synthesis within a single model, enabling real-time human-computer interaction. However, jo…
- arXiv cs.AIresearchDual-Adaptive Z-Score PPO: Dynamic Clipping Using Action Confidence and Normalized Advantage Magnitude
arXiv:2610.32194v1 Announce Type: cross Abstract: Proximal Policy Optimization (PPO) remains a popular on-policy reinforcement learning algorithm owing to its stability and ease of implementation. Its clipping threshold,…
- arXiv cs.AIresearchPrincipia: Test-Time Hypotheses to Principle Construction for Scientific Discovery
arXiv:2610.32195v1 Announce Type: cross Abstract: Scientific discovery requires identifying the scientific principles underlying observed phenomena to guide further investigation. As these empirical principles are often…
- arXiv cs.AIresearchDo VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems
arXiv:2610.32208v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as powerful general-purpose robot policies, but their successes and failures remain difficult to diagnose and understand.…
- arXiv cs.AIresearchSOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection
arXiv:2610.32212v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, but suffer from hallucination issues where they generat…
- arXiv cs.AIresearchHead Dimension Matters in Language Models for Long-Context Retrieval
arXiv:2610.32213v1 Announce Type: cross Abstract: Multi-head attention retrieves and combines context representations through query--key interactions and value aggregation, with head dimension constraining the per-head r…
- arXiv cs.AIresearchEasy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction
arXiv:2610.32250v1 Announce Type: cross Abstract: Single-image 3D scene construction is a fundamental problem in 3D vision. Recent methods such as SAM-3Dfy enable end-to-end reconstruction of 3D assets and their scene la…
- arXiv cs.AIresearchAnti-coherence Regularized Absolute Scaling for Out-of-Distribution Detection
arXiv:2610.32255v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is recognized as a critical problem in AI safety. SCALE is an effective post-hoc OOD detection method in the activation-shapin…
- arXiv cs.AIresearchRepresentation Learning via Composite Subspace Flow Matching
arXiv:2610.32256v1 Announce Type: cross Abstract: Distilling semantic meaning from raw image data remains a challenge in machine learning. Current self-supervised paradigms face a trade-off: joint-embedding architectures…
- arXiv cs.AIresearchHeterogeneous Space Data Centers: A Key Paradigm for Future Non-Terrestrial and In-Orbit Services
arXiv:2610.32260v1 Announce Type: cross Abstract: Lower space-launch cost and advances in AI are driving space-based data centers (SDCs) that provide AI-centric computing and communication services to in-orbit and non-te…