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A Function-Level Vulnerability Score Measures Flag Rate More Than the Model: Protocol Effects on Paired Benchmarks

arXiv:2609.32890v1 Announce Type: new Abstract: Language models are increasingly evaluated as vulnerability detectors, and scores reported for similar models differ widely between papers. We measured

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A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation

DGX agent

arXiv:2510.26184v2 Announce Type: replace Abstract: Public resource allocation involves distributing resources, including urban infrastructure, energy, and transportation, which are typically limited

applicationsarxiv-cs-lg

29 Sept 2026

A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty

arXiv:2609.35217v1 Announce Type: cross Abstract: Modern probabilistic machine learning models increasingly produce multivariate outputs with complex dependence structure, from multi-step time-series

A Journey to the Edge of Stability

arXiv:2609.32290v1 Announce Type: new Abstract: It has recently been found that deep learning often occurs at the 'edge of stability (EoS),' where the maximum Hessian eigenvalue of the model is stabil

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning

arXiv:2509.11070v4 Announce Type: replace-cross Abstract: We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer

A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services

arXiv:2609.31877v1 Announce Type: cross Abstract: Cloud-based Large language model (LLM) services create a network-level traffic side channel that can expose model, prompt, and task behavior despite e

A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning

arXiv:2609.33424v1 Announce Type: cross Abstract: Self-supervised pretraining learns representations that are broadly transferable across downstream tasks, yet direct fine-tuning can be suboptimal due

A Multi-Dataset Benchmark of YOLO-Based Weed Detection in Precision Agriculture

arXiv:2609.33991v1 Announce Type: new Abstract: Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Althou

A multi-modal tactile fingertip design for robotic hands to enhance dexterous manipulation

arXiv:2510.05382v2 Announce Type: replace Abstract: Tactile sensing is critical for enhancing manipulation precision and versatility, but its adoption in robotic hands remains limited due to high sens

applicationsarxiv-cs-ro

A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation

arXiv:2609.35121v1 Announce Type: new Abstract: Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodont

A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

arXiv:2404.14019v3 Announce Type: replace Abstract: Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentatio

A Novel Unified Approach to Deepfake Detection

arXiv:2601.03382v2 Announce Type: replace Abstract: Advancements in the field of AI are increasingly giving rise to various threats, one of the most prominent being the synthesis and misuse of deepfak

Local Ai

A Solvable Theory of Pre-training Data Poisoning: Regime-Dependent Scaling Exponents

arXiv:2609.32288v1 Announce Type: new Abstract: Pre-training data poisoning of large language models is usually studied using targeted backdoors and their survival through safety post-training, which

local-aiarxiv-cs-lg

A Spectral Theory of Compositional Learning

arXiv:2609.33708v1 Announce Type: new Abstract: How does compositional reasoning emerge during learning? We address this question by mathematically analyzing the learning dynamics of deep linear netwo

A Statistical Perspective on Knowledge Distillation: Foundations, Classical Methods, and Large Language Model Extensions

arXiv:2609.33727v1 Announce Type: cross Abstract: Knowledge Distillation (KD) has emerged as a vital paradigm for transferring the capabilities of high-capacity models to efficient ``student'' counter

A Surgical Foundation Model Reveals Task-Dependent Label Efficiency

arXiv:2609.31821v1 Announce Type: new Abstract: Developing label-efficient models is a central challenge in surgical AI due to the high cost and scarcity of expert annotation. While self-supervised fo

A Survey on Efficient Vision-Language-Action Models

arXiv:2510.24795v3 Announce Type: replace Abstract: Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-wor

A Survey on Reinforcement Learning Applications in SLAM

arXiv:2408.14518v3 Announce Type: replace-cross Abstract: Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estima

A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149v1 Announce Type: cross Abstract: Linear bandits model sequential decision-making problems with noisy rewards that are linear in the decision variable, where an agent must simultaneous

A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion

arXiv:2609.35703v1 Announce Type: new Abstract: Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existi

A Unifying Framework of Concept-based Explainable AI with Completeness Guarantees

arXiv:2609.34750v1 Announce Type: cross Abstract: Concept-based explanations describe neural network predictions through human-understandable properties of inputs called concepts. The field encompasse

A Visual Classification Dataset and Model Evaluation for Historical Manuscript Illustrations

arXiv:2609.33449v1 Announce Type: new Abstract: Historical manuscript illustrations preserve rich visual evidence of past cultures. They depict people, animals, plants, diagrams, music notations, and

Agents

A Weak Notion of Symmetry for Control Systems

arXiv:2609.34173v1 Announce Type: cross Abstract: Symmetry (or invariance) is a powerful structural property that enables efficient, effective solutions for estimation and control. However, the constr

agentsarxiv-cs-ro

ABC-Align: Prediction-Powered Alignment with Adaptive Bias Control

arXiv:2609.34374v1 Announce Type: new Abstract: Language model post-training is often bottlenecked by the need for human-collected preference data, which is expensive and difficult to scale. Reinforce

ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis

arXiv:2601.00416v2 Announce Type: replace Abstract: Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation

safetyarxiv-cs-cv

ABO-Med: Accelerated Bilevel Optimization for Few-Shot Medical Image Classification

arXiv:2609.33176v1 Announce Type: new Abstract: In recent years, bilevel optimization has been widely used in a variety of machine learning tasks. However, prior bilevel optimization algorithms genera

Hardware

Accelerator Choice Is Not Enough: AlphaFold2 Inference on Cloud TPUs

arXiv:2609.34818v1 Announce Type: cross Abstract: AlphaFold2 is written in JAX, so the same inference code compiles and runs unchanged on CPUs, GPUs and Google Cloud TPUs. That portability makes the a

hardwarearxiv-cs-lg

Accurate Sampling from Diffusion Models

arXiv:2609.29902v1 Announce Type: cross Abstract: A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using

Achieve What You Imagined: Learning to Align Actions with Visual Plans

arXiv:2609.33832v1 Announce Type: cross Abstract: World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual prediction

Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application

arXiv:2609.31759v1 Announce Type: cross Abstract: Domain Generalization (DG) aims to develop models that remain robust to conditions unseen during training. While DG has been systematically studied in

applicationsarxiv-cs-lg

ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs

arXiv:2609.34558v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) face significant computational inefficiencies caused by the large number of visual tokens. Existing visual token pr

Action-Driven Processes for Continuous-Time Control

arXiv:2510.26672v2 Announce Type: replace-cross Abstract: At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally funda

Action Sequence Transfer via LLMs for Heterogeneous Environments

arXiv:2609.34730v1 Announce Type: new Abstract: We present an action sequence transfer system that adaptively transfers user action sequences across different target spaces. Given an input action sequ

researcharxiv-cs-ro

Action Shaping: Policies Absorb What They Can Express

arXiv:2609.32752v1 Announce Type: cross Abstract: Reward shaping has a theorem: a potential-based term can be removed without changing the optimal policy. The same practice on the action channel, an o

ActionEngine: From Reactive to Programmatic Web Agents via State Machine Memory

arXiv:2602.20502v2 Announce Type: replace-cross Abstract: Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and r

ActionGround: Training-Free Runtime Refinement of Frozen VLA Policies

arXiv:2609.33256v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions directly to robot actions, but they do not explicitly represent th

safetyarxiv-cs-ro

ActionLens: Diagnosing Spatial-Temporal Binding Failures in Vision-Language Models

arXiv:2609.34547v1 Announce Type: new Abstract: Video-capable vision-language models score above 80% on popular benchmarks yet struggle with spatial-temporal binding: associating the right action with

ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning

arXiv:2609.34982v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, thi

applicationsarxiv-cs-cv

Activation Flow: Manufacturing Activations for Steering

arXiv:2609.32530v1 Announce Type: new Abstract: Difference-in-means steering requires activations recorded while a model shows the desired behavior, which a sandbagging model withholds by deliberately

applicationsarxiv-cs-lg

Active 3D weaves for load-bearing and damage-resilient locomotion

arXiv:2609.32099v1 Announce Type: new Abstract: The craft of weaving, where different materials are interlaced, has tremendous potential for creating active and functional systems for use in soft robo

applicationsarxiv-cs-ro

Active Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions

arXiv:2609.31675v1 Announce Type: new Abstract: We introduce the Active Causal Discovery Benchmark (ACDB), an SCM-grounded environment for evaluating whether LLM agents recover causal graph structure

Active Data Acquisition with Side Information via Discrete Diffusion Priors

arXiv:2609.32252v1 Announce Type: cross Abstract: Acquiring data is costly: higher measurement fidelity costs power and storage and risks collecting irrelevant content, while aggressive cost reduction

Active Feature Acquisition With Incomplete Training Data

arXiv:2609.32325v1 Announce Type: new Abstract: In many prediction tasks, acquiring all features can be a prohibitively expensive or outright impossible task. Further, in many cases a static subset of

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving

arXiv:2609.34085v1 Announce Type: cross Abstract: Autonomous driving requires extit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first s

Agents

AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Driving

arXiv:2510.27047v2 Announce Type: replace Abstract: This paper presents the Autonomous Driving Segment Anything Model (AD-SAM), a foundation-model adaptation framework for semantic segmentation in aut

agentsarxiv-cs-cv

Adam or Gauss-Newton? A Comparative Study In Terms of Basis Alignment and SGD Noise

arXiv:2510.13680v2 Announce Type: replace Abstract: Preconditioned methods are central to deep learning optimization. Predominant approaches include computationally light diagonal preconditioners such

Adapting neural operators for mechanics decisions under changing operating conditions

arXiv:2609.33978v1 Announce Type: cross Abstract: Neural operators can accelerate repeated nonlinear mechanics calculations, but their accuracy can deteriorate as operating conditions move beyond the

Adapting Nonstationary Multi-output Gaussian Processes to Bayesian Optimization

arXiv:2609.32464v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) commonly relies on independent Gaussian processes (GPs) with stationary kernels, limiting its ability to re