Search: “papers”

LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control

arXiv:2605.21086v1 Announce Type: new Abstract: While Large Language Models (LLMs) are increasingly integrated into in-vehicle conversational systems, identifying the optimal model remains challenging

local-aiarxiv-cs-cl

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Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs

DGX agent

arXiv:2605.21446v1 Announce Type: new Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world

safetyarxiv-cs-ro

21 May 2026

Measuring and mitigating overreliance to build human-compatible AI

arXiv:2509.08010v2 Announce Type: replace-cross Abstract: Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable o

applicationsarxiv-cs-cl

Mercer Large-Scale Kernel Machines from Ridge Function Perspective

arXiv:2307.11925v3 Announce Type: replace Abstract: To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from {it Fundamentality of

Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation

arXiv:2605.20894v1 Announce Type: new Abstract: Mobile imitation learning on portable demonstration interfaces faces two coupled bottlenecks: locomotion-contaminated action labels and inference-induce

safetyarxiv-cs-ro

Multi-agent Collaboration with State Management

arXiv:2605.20563v1 Announce Type: cross Abstract: Recent advances in multi-agent systems have shown great potential for solving complex tasks. However, when multiple agents edit a shared codebase conc

agentsarxiv-cs-cl

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

arXiv:2605.20255v1 Announce Type: new Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not capture the heterogeneity

Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching

arXiv:2509.17931v2 Announce Type: replace Abstract: Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. Howeve

local-aiarxiv-cs-cv

Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning

arXiv:2507.09180v4 Announce Type: replace Abstract: Depth information is robust to scene appearance variations and inherently carries 3D spatial details. Thus, a visual backbone based on the vision tr

applicationsarxiv-cs-cv

Tutorials

On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures

arXiv:2605.21388v1 Announce Type: new Abstract: Despite the remarkable empirical success of generative models, the available theory on their statistical accuracy in scientific computing remains largel

tutorialsarxiv-cs-lg

One-Step Distillation of Discrete Diffusion Image Generators via Fixed-Point Iteration

arXiv:2605.21484v1 Announce Type: new Abstract: Discrete diffusion models excel at visual synthesis but rely on slow, iterative decoding. Existing single-step distillation methods attempt to bypass th

local-aiarxiv-cs-cv

Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation

arXiv:2509.09946v2 Announce Type: replace Abstract: Multi-Target Multi-Camera Tracking (MTMC) is an essential computer vision task for automating large-scale surveillance. With camera calibration and

local-aiarxiv-cs-cv

Online Conformal Prediction with Corrupted Feedback

arXiv:2605.20515v1 Announce Type: new Abstract: Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Onli

applicationsarxiv-cs-lg

PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction

arXiv:2605.21414v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-languag

safetyarxiv-cs-cv

PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems

arXiv:2603.10726v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on optimizations like Automatic Prefix Caching (APC) to accelerate inference. APC works by reusing previousl

Q-ARVD: Quantizing Autoregressive Video Diffusion Models

arXiv:2605.21072v1 Announce Type: new Abstract: Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time inte

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation

arXiv:2605.20801v1 Announce Type: new Abstract: Adaptive robot navigation in dynamic environments requires policies that can reach the target reliably while producing efficient and stable trajectories

safetyarxiv-cs-ro

Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection

arXiv:2605.21164v1 Announce Type: new Abstract: Credit card fraud detection is fundamentally challenged by extreme class imbalance, where fraudulent transactions are rare yet operationally critical. T

Quadratic Characterizations for Reachability Analysis of Neural Networks

arXiv:2605.20482v1 Announce Type: new Abstract: Quadratic constraints (QCs) are widely used to characterize nonlinearities and uncertainties, but generic analytical characterizations can be conservati

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate

model-releasesarxiv-cs-lg

arXiv:2605.21486v1 Announce Type: new Abstract: Hyperparameter transfer allows extrapolating optimal optimization hyperparameters from small to large scales, making it critical for training large lang

QwenSafe: Multimodal Content Rating Description Identification via Preference-Aligned VLMs

model-releasesarxiv-cs-cv

arXiv:2605.20584v1 Announce Type: new Abstract: Mobile app marketplaces require developers to disclose standardized content rating descriptors (CRDs) to inform users about potentially sensitive or res

rePIRL: Learn PRM with Inverse RL for LLM Reasoning

arXiv:2602.07832v2 Announce Type: replace Abstract: Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking. In

Rethinking Cross-Layer Information Routing in Diffusion Transformers

arXiv:2605.20708v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization,

safetyarxiv-cs-cv

Retrospective Sparse Attention for Efficient Long-Context Generation

arXiv:2508.09001v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, i

researcharxiv-cs-cl

Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework

arXiv:2605.20721v1 Announce Type: new Abstract: Learning from implicit feedback in recommender systems is fundamentally challenged by pervasive label noise. While conventional denoising approaches oft

Safety-Critical Control for Smoothed Implicit Contact Dynamics

model-releasesarxiv-cs-ro

arXiv:2605.21138v1 Announce Type: new Abstract: Smoothed implicit contact dynamics enables gradient-based planning and control for contact-rich tasks without predefined mode sequences. However, safety

Seeing Through Fog: Towards Fog-Invariant Action Recognition

model-releasesarxiv-cs-cv

arXiv:2605.20645v1 Announce Type: new Abstract: Foggy conditions are commonly encountered in real-world applications; however, existing action recognition approaches typically assume favorable weather

Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs

arXiv:2605.20191v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) have recently attracted much attention for their ability to simulate human behavior and generate text that reflects

safetyarxiv-cs-cl

Testing Support Size More Efficiently Than Learning Histograms

arXiv:2410.18915v4 Announce Type: replace-cross Abstract: Consider two problems about an unknown probability distribution p: 1. How many samples from p are required to test if p is supported on n elem

Text Analytics Evaluation Framework: A Case Study on LLMs and Social Media

model-releasesarxiv-cs-cl

arXiv:2605.21338v1 Announce Type: new Abstract: LLMs have demonstrated exceptional proficiency in a wide range of NLP tasks. However, a notable gap remains in practical data analysis scenarios, partic

Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination

arXiv:2605.20625v1 Announce Type: cross Abstract: Advanced Air Mobility (AAM) operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic managemen

safetyarxiv-cs-ro

torchtune: PyTorch native post-training library

arXiv:2605.21442v1 Announce Type: new Abstract: Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface f

Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G

arXiv:2605.21395v1 Announce Type: cross Abstract: The proliferation of emerging applications, such as autonomous driving and immersive experiences, demands cellular networks that are not only faster,

agentsarxiv-cs-lg

Towards the Anonymization of the Language Modeling

arXiv:2501.02407v3 Announce Type: replace Abstract: Rapid advances in Natural Language Processing (NLP) have revolutionized many fields, including healthcare. However, these advances raise significant

applicationsarxiv-cs-cl

Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning

model-releasesarxiv-cs-lg

arXiv:2605.20803v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Re

UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models

arXiv:2504.13109v2 Announce Type: replace Abstract: Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are

USV: Towards Understanding the User-generated Short-form Videos

arXiv:2605.20838v1 Announce Type: new Abstract: Several large-scale video datasets have been published these years and have advanced the area of video understanding. However, the newly emerged user-ge

Validating Navmesh using Geometry: Voxel-Based Analysis with Prioritized Exploration

arXiv:2605.21397v1 Announce Type: cross Abstract: Navigation mesh (Navmesh) inconsistencies affect the player experience by directly impacting the navigation systems used by non-playable characters (N

applicationsarxiv-cs-ro

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations

arXiv:2603.19545v2 Announce Type: replace-cross Abstract: Many core problems in nonlinear systems analysis and control can be recast as solving partial differential equations (PDEs) such as Lyapunov a

Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification

model-releasesarxiv-cs-cv

arXiv:2605.21268v1 Announce Type: new Abstract: Land Use Scene Classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable reso

WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning

model-releasesarxiv-cs-lg

arXiv:2605.20311v1 Announce Type: new Abstract: Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. Howe

Why Latent Actions Fail, and How to Prevent It

arXiv:2605.20223v1 Announce Type: new Abstract: Latent action models (LAMs) aim to learn action-like representations from unlabeled videos by compressing frame-to-frame changes. The frames of in-the-w

agentsarxiv-cs-cv

Tutorials

WiXus: A Wheeled-Legged Robot with Wire-Driven Environmental Utilizing to Integrate Mobility and Manipulation

arXiv:2605.20932v1 Announce Type: new Abstract: Wheeled-legged robots, which have wheels at their feet and achieve high mobility by coordinating wheel drive and leg drive, have been developed. These r

tutorialsarxiv-cs-ro

A Data-Driven Approach to Idiomaticity Based on Experts' Criteria in Theoretical Linguistics

arXiv:2605.19575v1 Announce Type: new Abstract: The article observes data analysis of 286 multi-word expressions (MWEs) based on 16 lexical, grammatical and other criteria described in theoretical boo

researcharxiv-cs-cl

A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond

arXiv:2510.19382v2 Announce Type: replace-cross Abstract: Understanding the dynamics of feature learning in neural networks (NNs) remains a significant challenge. The work of (Mousavi-Hosseini et al.,

A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texas

arXiv:2603.13609v2 Announce Type: replace Abstract: Despite progress in deep learning for shared micromobility demand prediction, the systematic design and statistical validation of temporal input str

A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images

arXiv:2605.19595v1 Announce Type: cross Abstract: The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded in

agentsarxiv-cs-ai

A Practical Framework of Key Performance Indicators for Multi-Robot Lunar and Planetary Field Tests

arXiv:2601.20529v3 Announce Type: replace Abstract: Robotic prospecting for critical resources on the Moon, such as ilmenite, rare earth elements, and water ice, requires robust exploration methods gi

researcharxiv-cs-ro