AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchExplore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning
arXiv:2610.17734v1 Announce Type: cross Abstract: Exploration bounds learning efficiency in reinforcement learning with verifiable rewards (RLVR). Current methods explore mostly through independent sampling, without full…
- arXiv cs.AIresearchTrain for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
arXiv:2610.17735v1 Announce Type: cross Abstract: Multi-sample reasoning measures candidate coverage, the probability that at least one of $K$ candidates is correct. However, minimizing the average loss of individual att…
- arXiv cs.AIresearchHiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data
arXiv:2610.17739v1 Announce Type: cross Abstract: Estimating potential outcomes under alternative treatment sequences is essential for personalized decision-making, but remains challenging with high-dimensional covariate…
- arXiv cs.AIresearchI3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
arXiv:2610.17748v2 Announce Type: replace-cross Abstract: Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizon whole-bo…
- arXiv cs.AIresearchEfficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
arXiv:2610.17767v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge th…
- arXiv cs.AIresearchPosterior-Averaged Bellman Flow Matching for Distributional Critics
arXiv:2610.17788v1 Announce Type: cross Abstract: Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown that flow-based crit…
- arXiv cs.AIresearchGIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation
arXiv:2610.17794v1 Announce Type: cross Abstract: Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expe…
- arXiv cs.AIresearchLearning from fracture: Teaching language models to design through physical experience
arXiv:2610.17804v1 Announce Type: cross Abstract: How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framework evaluated throug…
- arXiv cs.AIresearchSame Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers
arXiv:2610.17820v1 Announce Type: cross Abstract: TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns a probability for ea…
- arXiv cs.AIresearchChecking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR
arXiv:2610.17824v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs). It rests on the assumptio…
- arXiv cs.AIresearchQK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing
arXiv:2610.17829v2 Announce Type: replace-cross Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Net architec…
- arXiv cs.AIresearchDiff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
arXiv:2610.17831v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis r…
- arXiv cs.AIresearchA Tutorial on Quantum Amplitude Embedding with Data Re-Uploading
arXiv:2610.17836v1 Announce Type: cross Abstract: Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form in the normalized in…
- arXiv cs.AIresearchFASHION-RQ: Mining Hierarchical Semantic Structure for Automated Evaluation of Fashion Retrieval
arXiv:2610.17841v1 Announce Type: cross Abstract: Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queries encountered in re…
- arXiv cs.AIresearchMMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision
arXiv:2610.17856v3 Announce Type: replace-cross Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived from the s…
- arXiv cs.AIresearchTStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
arXiv:2610.17864v1 Announce Type: cross Abstract: Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. However, real-world time…
- arXiv cs.AIresearchRT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions
arXiv:2610.17867v3 Announce Type: replace-cross Abstract: Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. However, adapti…
- arXiv cs.AIresearchCross-Lingual Transfer of Evil Vectors Is Directionally Asymmetric
arXiv:2610.17880v1 Announce Type: cross Abstract: Persona vectors are linear directions in the residual-stream activations of LLMs that encode behavioral traits and enable fine-grained control over model behavior. Among…
- arXiv cs.AIresearchEvaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features
arXiv:2610.17898v1 Announce Type: cross Abstract: Sparse autoencoders decompose language model representations into interpretable features, but feature interpretation in this literature remains predominantly correlationa…
- arXiv cs.AIresearchVisual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP
arXiv:2610.17902v1 Announce Type: cross Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Although prior work…
- arXiv cs.AIresearchSpecCloak: Probing Harmful Code Generation through Engineering Specifications
arXiv:2610.17907v1 Announce Type: cross Abstract: The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to understand their safe…
- arXiv cs.AIresearchLinMorph: Learning to Morph Weights for Efficient Video DiT Linearization
arXiv:2610.17914v1 Announce Type: cross Abstract: Post-hoc linearization is an attractive way to accelerate pretrained Video Diffusion Transformers (Video DiTs), yet existing methods largely treat the quality loss induce…
- arXiv cs.AIresearchCineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation
arXiv:2610.17926v1 Announce Type: cross Abstract: Autoregressive video generation relies on memory retrieval to model long-range dependencies. This capability becomes particularly critical for multi-shot generation, wher…
- arXiv cs.AIresearchMERIT: Multimodal Evidence Reasoning over Image Tables
arXiv:2610.17937v1 Announce Type: cross Abstract: Table reasoning with language models typically assumes access to clean textual tables. In practice, tables are often serialized into text, which can corrupt cell contents…
- arXiv cs.AIresearchAI-Enhanced Hyperspectral Imaging for Intraoperative Detection of Low-Grade Glioma: A Clinical Study
arXiv:2610.17944v1 Announce Type: cross Abstract: Current image-guided neuronavigation techniques, such as intraoperative MRI and ultrasound, are limited by high costs, limited sensitivity and operational complexity. The…
- arXiv cs.AIresearchHaloBlock: Faster and Better Decoding for Diffusion Large Language Models
arXiv:2610.17948v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have attracted increasing research attention due to their bidirectional attention and arbitrary order decoding. However, there are…
- arXiv cs.AIresearchFETALFusion: Resolution-Aware State-Space Modeling for Multi-Domain Fetal Head Segmentation
arXiv:2610.17951v1 Announce Type: cross Abstract: Head circumference (HC) measured from 2D ultrasound is a routine marker of fetal growth, and automating it needs segmentations whose boundaries hold up on individual scan…
- arXiv cs.AIresearchVirtual Head Attention
arXiv:2610.17959v1 Announce Type: cross Abstract: The design of attention mechanisms in long-context large language models faces an ``impossible triangle'' among KV cache size, training and prefill FLOPs, and model quali…
- arXiv cs.AIresearchBetter Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
arXiv:2610.17963v1 Announce Type: cross Abstract: Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment o…
- arXiv cs.AIresearchBELT : BEhavior-Aligned Low-Rank KV Cache Compression with Learned Top-k Rank Allocation
arXiv:2610.17966v1 Announce Type: cross Abstract: Long-context inference makes the key-value (KV) cache a major memory bottleneck for large language models. We propose BELT, a low-rank KV cache compression framework. Its…
- arXiv cs.AIresearchSeeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models
arXiv:2610.17989v1 Announce Type: cross Abstract: A medical vision-language model may analyze a given CT scan and correctly detect both kidneys in view. Continuing this conversation, if a user provides a prescription wit…
- arXiv cs.AIresearchSaliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
arXiv:2610.17998v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentia…
- arXiv cs.AIresearchMatch the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models
arXiv:2610.18001v1 Announce Type: cross Abstract: Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a fixed left-to-righ…
- arXiv cs.AIresearchCorrecting or Continuing? What On-Policy Distillation Learns from Student Errors
arXiv:2610.18011v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher, mitigating the distribution mismatch of off-policy supervision. While prior work…
- arXiv cs.AIresearchChampionKV: Head-Wise Adaptive Budget Allocation for KV Cache Compression
arXiv:2610.18026v1 Announce Type: cross Abstract: Some observation-window-based KV cache compressors use prompt attention to identify important tokens but allocate uniform or predefined budgets across heads, overlooking…
- arXiv cs.AIresearchCan Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
arXiv:2610.18027v1 Announce Type: cross Abstract: Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their tra…
- arXiv cs.AIresearchScaling Vision Transformers Without Training: Prune-and-Merge Token Compression
arXiv:2610.18039v2 Announce Type: replace-cross Abstract: Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions. Training-free token reduction methods improve…
- arXiv cs.AIresearchHigh-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics
arXiv:2610.18045v1 Announce Type: cross Abstract: High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numerical simulation or hig…
- arXiv cs.AIresearchinLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
arXiv:2610.18051v1 Announce Type: cross Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific…
- arXiv cs.AIresearchFrom Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning
arXiv:2610.18057v1 Announce Type: cross Abstract: Transformers can implement reinforcement-learning algorithms in context, yet how off-policy control emerges within Transformers remains poorly understood. We study Q-lear…
- arXiv cs.AIresearchUncertainty Evaluation: Bridging Machine Learning and Measurement Science
arXiv:2610.18066v1 Announce Type: cross Abstract: Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical applications. This pa…
- arXiv cs.AIresearchA CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge
arXiv:2610.18108v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative wh…
- arXiv cs.AIresearchWhen Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
arXiv:2610.18113v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the…
- arXiv cs.AIresearchCertifying Almost-Sure Reachability in Unknown MDPs
arXiv:2610.18119v1 Announce Type: cross Abstract: {\em Specification-Guided Reinforcement Learning (RL)} has largely concerned itself with \emph{learning} an optimal policy against a formal specification in an unknown {\…
- arXiv cs.AIresearchH-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression
arXiv:2610.18125v1 Announce Type: cross Abstract: Training-free diffusion-based compression methods repurpose the generative priors of a diffusion or a flow model to compress data without retraining them as task-specific…
- arXiv cs.AIresearchWhen Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions
arXiv:2610.18155v2 Announce Type: replace-cross Abstract: Standard identifiability theory assumes one-dimensional latent factors, but learned representations can encode latent information across multidimensional blocks w…
- arXiv cs.AIresearchClaim Framing as a Driver of LLM Sycophancy
arXiv:2610.18159v1 Announce Type: cross Abstract: Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions without a clear gro…
- arXiv cs.AIresearchLoOPSD: On-Policy Self-Distillation with Looped Teachers
arXiv:2610.18162v1 Announce Type: cross Abstract: On-policy self-distillation improves a model using guidance from the model itself, but it still requires a teacher that provides more useful predictions than the student.…
- arXiv cs.AIresearchLifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence
arXiv:2610.18166v1 Announce Type: cross Abstract: Randomly initialised neural networks of width $m$ converge to Gaussian processes as $m \to \infty$, with two types of quantitative bounds characterising this convergence.…
- arXiv cs.AIresearchLooped Reasoning Finishes Earlier Than You Think
arXiv:2610.18174v1 Announce Type: cross Abstract: Looped reasoning with dynamic depth can be viewed as relaxation toward solution attractors in a learned dynamical landscape. In practice, however, latent trajectories oft…