AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMisinformation Without Triggers: From Factual Answers to Downstream Decisions
arXiv:2610.02886v1 Announce Type: cross Abstract: Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and decisions that use…
- arXiv cs.AIresearchRevealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
arXiv:2610.02887v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However,…
- arXiv cs.AIresearchWhen Can We Trust the Matching Principle? Robust Deployment Geometry Under Finite-Sample and Model Uncertainty
arXiv:2610.02894v1 Announce Type: cross Abstract: Match only geometry you can identify; otherwise spread the penalty. We quantify that decision by the trust ratio tau = epsilon / gamma (estimation uncertainty over spectr…
- arXiv cs.AIresearchPost-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
arXiv:2610.02967v2 Announce Type: replace-cross Abstract: Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single rew…
- arXiv cs.AIresearchTemporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability
arXiv:2610.03000v1 Announce Type: cross Abstract: Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training within an optimization en…
- arXiv cs.AIresearchAvoKV-E: Payload-Aware KV Cache Eviction for Long Reasoning
arXiv:2610.03007v1 Announce Type: cross Abstract: Long-output reasoning shifts the KV-cache bottleneck from the fixed prompt to the generated trace. Existing reasoning-cache eviction methods largely treat cached entries…
- arXiv cs.AIresearchTailoring the Quantization Space for 1-Bit KV Cache Compression
arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidth. To mitigate this…
- arXiv cs.AIresearchNegT2IBench: When Negation Changes the Picture. A Polarity Benchmark for Text-to-Image Models
arXiv:2610.03084v1 Announce Type: cross Abstract: Text-to-image (T2I) models are judged by benchmarks that measure whether requested content appears, but these benchmarks largely overlook the complementary ability to sat…
- arXiv cs.AIresearchZephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines
arXiv:2610.03087v1 Announce Type: cross Abstract: Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caus…
- arXiv cs.AIresearchULTRADISCOVERY: Abductive Exploration in an Interconnected, Epistemically Open Universe
arXiv:2610.03092v3 Announce Type: replace-cross Abstract: Scientific discovery often begins when scattered clues call for a new way of describing the world. Such abductive exploration can require constructing the represe…
- arXiv cs.AIresearchBeyond Single Videos: Benchmarking and Active Evidence Seeking for E-Commerce Cross-Video Reasoning
arXiv:2610.03099v1 Announce Type: cross Abstract: E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However, existing multimoda…
- arXiv cs.AIresearchS2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales
arXiv:2610.03106v1 Announce Type: cross Abstract: The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water…
- arXiv cs.AIresearchHow to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning
arXiv:2610.03119v1 Announce Type: cross Abstract: In lifelong reinforcement learning, retaining previously learned policies is not sufficient for effective transfer to a new task. Useful knowledge may be distributed acro…
- arXiv cs.AIresearchForesight: planning future perception in streaming VLMs without retraining
arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference.…
- arXiv cs.AIresearchThe Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
arXiv:2610.03124v1 Announce Type: cross Abstract: Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recen…
- arXiv cs.AIresearchMultimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
arXiv:2610.03160v1 Announce Type: cross Abstract: Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity o…
- arXiv cs.AIresearchPredicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer
arXiv:2610.03163v1 Announce Type: cross Abstract: Adapting large language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: formal conventions leav…
- arXiv cs.AIresearchLiBRA: Detection-Aware Image Watermark Removal via Bidirectional Latent Optimization
arXiv:2610.03166v1 Announce Type: cross Abstract: Digital watermarking supports source attribution for AI-generated images, but its reliability depends on resistance to removal attacks. Some attacks attempt to remove wat…
- arXiv cs.AIresearchNot Until the Evidence Says So: Teaching LLM Investigators When to Close a Case
arXiv:2610.03190v1 Announce Type: cross Abstract: Accident, defect and outage investigations end with a decision that ordinary question answering never faces: whether the evidence gathered so far is enough to close the c…
- arXiv cs.AIresearchContextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
arXiv:2610.03202v1 Announce Type: cross Abstract: Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function evaluations. Existing…
- arXiv cs.AIresearchEvolving Hybrid Quantum-Classical Architectures for Image Classification
arXiv:2610.03220v2 Announce Type: replace-cross Abstract: Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their performance depen…
- arXiv cs.AIresearchUncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
arXiv:2610.03224v1 Announce Type: cross Abstract: Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs.…
- arXiv cs.AIresearchWAMpy: Efficient Synthesis of Prolog Programs in Python
arXiv:2610.03234v1 Announce Type: cross Abstract: We present WAMpy, a Python framework optimized for synthesizing Prolog programs. Unlike general-purpose Prolog systems, WAMpy targets workloads that repeatedly generate a…
- arXiv cs.AIresearchMapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery
arXiv:2610.03258v1 Announce Type: cross Abstract: Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several algorithmic familie…
- arXiv cs.AIresearchConsecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures
arXiv:2610.03261v1 Announce Type: cross Abstract: Solving severely ill-posed imaging inverse problems requires recovering image structures that are unobservable or weakly constrained by the measurements. Diffusion models…
- arXiv cs.AIresearchSPEAR: A Spectral-Disentangled MoE Neural Operator with Knowledge-Guided Expert Aggregation for Large-Scale PDE Pretraining
arXiv:2610.03265v1 Announce Type: cross Abstract: Large-scale pre-training has improved the generalization of neural operators across diverse PDEs. However, existing PDE foundation models still struggle with heterogeneou…
- arXiv cs.AIresearchTraining-Loss Guarantees for Muon with Finite-Step Newton--Schulz Orthogonalization
arXiv:2610.03306v1 Announce Type: cross Abstract: Existing convergence analyses of Muon either assume exact orthogonalization or analyze classical Newton--Schulz polynomials, and guarantee only stationarity, so it is unr…
- arXiv cs.AIresearchInformation Limits of Low-Rank Approximation Certification
arXiv:2610.03321v1 Announce Type: cross Abstract: Low-rank approximation can require additional matrix--vector products to verify that its error meets a prescribed tolerance. We characterize this certification cost for b…
- arXiv cs.AIresearchSyntaxBench: A Statistical Diagnostic Framework for Character-Level Reasoning in Large Language Models
arXiv:2610.03329v1 Announce Type: cross Abstract: Large language models are increasingly used where small syntactic errors matter, yet character-level reasoning is still evaluated mostly through isolated probes and aggre…
- arXiv cs.AIresearchEquivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation
arXiv:2610.03333v1 Announce Type: cross Abstract: Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient policy a key issue.…
- arXiv cs.AIresearchDriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift
arXiv:2610.03390v1 Announce Type: cross Abstract: Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step teachers. To avoid the…
- arXiv cs.AIresearchForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation
arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structures, severe occlusions…
- arXiv cs.AIresearchRethinking Epistemic Uncertainty in Node Classification through Information Growth
arXiv:2610.03418v1 Announce Type: cross Abstract: Epistemic uncertainty should decrease as additional information about the data-generating process (DGP) becomes available to the predictor. Yet, existing graph evidential…
- arXiv cs.AIresearchOptiSelect: How does the Optimizer Shape Data Curriculum?
arXiv:2610.03432v1 Announce Type: cross Abstract: Online data selection has demonstrated substantial efficiency gains for LLM pretraining by training on the most valuable candidates within each batch. Since a candidate's…
- arXiv cs.AIresearchCorrupted but Correct: Why Vision-Language Models Lie to Themselves Internally
arXiv:2610.03445v2 Announce Type: replace-cross Abstract: A targeted adversarial perturbation can drive a vision-language model's (VLM's) teacher-forced training loss for a fixed target caption to near zero, yet the same…
- arXiv cs.AIresearchMeasure Less, Know More: Self-Supervised Test-Time Feature Acquisition
arXiv:2610.03454v1 Announce Type: cross Abstract: Recent progress in multimodal, high-dimensional learning has enabled foundation models to process heterogeneous, large-scale data. However, at test time, acquiring all fe…
- arXiv cs.AIresearchPreserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans
arXiv:2610.03467v1 Announce Type: cross Abstract: Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected predictions due to com…
- arXiv cs.AIresearchSingle or Multiple Policies for Phase-Structured Reinforcement Learning?
arXiv:2610.03475v1 Announce Type: cross Abstract: Many reinforcement-learning (RL) problems are non-stationary yet structured and can be decomposed into phases, each with its own transition probabilities and reward funct…
- arXiv cs.AIresearchAREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching
arXiv:2610.03483v1 Announce Type: cross Abstract: We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tractable part of the s…
- arXiv cs.AIresearchDetect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
arXiv:2610.03498v2 Announce Type: replace-cross Abstract: Adversarial patches can disrupt Vision-Language-Action (VLA) models by manipulating visual observations, leading to failures in robot control. However, it remains…
- arXiv cs.AIresearchCertified Mechanistic Edits: Behavioral Guarantees for Skill Removal and Preservation
arXiv:2610.03502v1 Announce Type: cross Abstract: Mechanistic edits (ablations, weight edits, activation steering) are the standard tools for unlearning a harmful capability from a neural network while preserving useful…
- arXiv cs.AIresearchWeave Forcing: Compositional Memory Routing for Interactive Long Video Generation
arXiv:2610.03510v1 Announce Type: cross Abstract: Recent advances in autoregressive video generation have improved temporal consistency over extended durations, yet interactive storytelling requires more than continuous…
- arXiv cs.AIresearchBeyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
arXiv:2610.03526v1 Announce Type: cross Abstract: Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a m…
- arXiv cs.AIresearchCephalonauts One: A deep fMRI dataset for decoding naturalistic speech in the human brain
arXiv:2610.03558v1 Announce Type: cross Abstract: Cephalonauts One is a whole-brain 3 Tesla (3T) functional magnetic resonance imaging (fMRI) dataset recorded while subjects listened to audio podcasts. Three healthy subj…
- arXiv cs.AIresearchRethinking What to Cache in Few-Step Diffusion Transformers: Solver-Aware Target Selection
arXiv:2610.03577v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) can generate high-quality images and videos, but generating each sample requires multiple costly DiT forward passes. Two common ways to acce…
- arXiv cs.AIresearchWhen a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game
arXiv:2610.03598v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to obtain. There are fi…
- arXiv cs.AIresearchLoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation
arXiv:2610.03636v1 Announce Type: cross Abstract: Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera mov…
- arXiv cs.AIresearchOn-Board Anomaly Detection for Efficient Marine Environmental Monitoring
arXiv:2610.03649v1 Announce Type: cross Abstract: Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human activities. Advances…
- arXiv cs.AIresearchRevisiting Input Time-frequency Representations in Multi-pitch Estimation for Vocal Ensembles
arXiv:2610.03656v2 Announce Type: replace-cross Abstract: Multi-pitch estimation in vocal ensembles is challenging because singers occupy overlapping pitch ranges and often sing at closely spaced fundamental frequencies,…
- arXiv cs.AIresearchFrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution
arXiv:2610.03675v1 Announce Type: cross Abstract: LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. How…