{"total":77,"rows":[{"id":"2506.13757","title":"AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning","created":"2025-06-16","cited_by_count":260,"area":"lr"},{"id":"2411.17465","title":"ShowUI: One Vision-Language-Action Model for GUI Visual Agent","created":"2024-11-26","cited_by_count":232,"area":null},{"id":"2503.15558","title":"Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning","created":"2025-03-18","cited_by_count":164,"area":"lr"},{"id":"2511.00088","title":"Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail","created":"2025-10-30","cited_by_count":146,"area":"lr"},{"id":"2510.12796","title":"DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving","created":"2025-10-14","cited_by_count":134,"area":"wm"},{"id":"2510.07077","title":"Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications","created":"2025-10-08","cited_by_count":84,"area":"mllm"},{"id":"2505.04769","title":"Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges","created":"2025-05-07","cited_by_count":74,"area":"mllm"},{"id":"2506.24044","title":"A Survey on Vision-Language-Action Models for Autonomous Driving","created":"2025-06-30","cited_by_count":72,"area":"mllm"},{"id":"2510.26125","title":"WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios","created":"2025-10-30","cited_by_count":62,"area":"mllm"},{"id":"2509.19012","title":"Pure Vision Language Action (VLA) Models: A Comprehensive Survey","created":"2025-09-23","cited_by_count":49,"area":"mllm"},{"id":"2507.10672","title":"Vision Language Action Models in Robotic Manipulation: A Systematic Review","created":"2025-07-14","cited_by_count":38,"area":"vla"},{"id":"2412.15206","title":"AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving","created":"2024-12-19","cited_by_count":35,"area":"lr"},{"id":"2504.03164","title":"NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving","created":"2025-04-04","cited_by_count":35,"area":"lr"},{"id":"2512.16760","title":"Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future","created":"2025-12-18","cited_by_count":33,"area":"vla"},{"id":"2601.22032","title":"Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving","created":"2026-01-29","cited_by_count":30,"area":"driving"},{"id":"2412.09951","title":"WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model","created":"2024-12-13","cited_by_count":28,"area":"lr"},{"id":"2512.10719","title":"SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving","created":"2025-12-11","cited_by_count":28,"area":"lr"},{"id":"2601.01528","title":"DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving","created":"2026-01-04","cited_by_count":21,"area":"wm"},{"id":"2506.06218","title":"STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving","created":"2025-06-06","cited_by_count":17,"area":"mllm"},{"id":"2604.01765","title":"DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning","created":"2026-04-02","cited_by_count":17,"area":"wm"},{"id":"2604.19710","title":"SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model","created":"2026-04-21","cited_by_count":17,"area":"lr"},{"id":"2501.00226","title":"Generative Emergent Communication: Large Language Model is a Collective World Model","created":"2024-12-31","cited_by_count":15,"area":"wm"},{"id":"2412.05337","title":"ACT-Bench: Towards Action Controllable World Models for Autonomous Driving","created":"2024-12-06","cited_by_count":14,"area":"wm"},{"id":"2604.23775","title":"Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms","created":"2026-04-26","cited_by_count":13,"area":"vla"},{"id":"2506.11234","title":"Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving","created":"2025-06-12","cited_by_count":11,"area":"driving"},{"id":"2602.08440","title":"SteerVLA: Steering Vision-Language-Action Models in Long-Tail Driving Scenarios","created":"2026-02-09","cited_by_count":11,"area":"vla"},{"id":"2604.09059","title":"Learning Vision-Language-Action World Models for Autonomous Driving","created":"2026-04-10","cited_by_count":11,"area":"wm"},{"id":"2508.21824","title":"DriveQA: Passing the Driving Knowledge Test","created":"2025-08-29","cited_by_count":10,"area":"driving"},{"id":"2504.10873","title":"Can Vision-Language Models Understand and Interpret Dynamic Gestures from Pedestrians? Pilot Datasets and Exploration Towards Instructive Nonverbal Commands for Cooperative Autonomous Vehicles","created":"2025-04-15","cited_by_count":9,"area":"mllm"},{"id":"2505.20124","title":"TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos","created":"2025-05-26","cited_by_count":9,"area":null},{"id":"2605.31572","title":"nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving","created":"2026-05-29","cited_by_count":9,"area":"driving"},{"id":"2507.02190","title":"cVLA: Towards Efficient Camera-Space VLAs","created":"2025-07-02","cited_by_count":8,"area":"vla"},{"id":"2505.00156","title":"V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving","created":"2025-04-30","cited_by_count":7,"area":"mllm"},{"id":"2509.15968","title":"CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine","created":"2025-09-19","cited_by_count":7,"area":"driving"},{"id":"2602.00500","title":"Inject Once Survive Later: Backdooring Vision-Language-Action Models to Persist Through Downstream Fine-tuning","created":"2026-01-31","cited_by_count":7,"area":"vla"},{"id":"2509.00789","title":"CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving","created":"2025-08-31","cited_by_count":5,"area":"mllm"},{"id":"2602.04184","title":"Natural Language Instructions for Scene-Responsive Human-in-the-Loop Motion Planning in Autonomous Driving using Vision-Language-Action Models","created":"2026-02-04","cited_by_count":5,"area":"vla"},{"id":"2602.13329","title":"HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving","created":"2026-02-11","cited_by_count":5,"area":"vla"},{"id":"2510.07067","title":"Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models","created":"2025-10-08","cited_by_count":4,"area":"vla"},{"id":"2512.11362","title":"An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges","created":"2025-12-12","cited_by_count":4,"area":"vla"},{"id":"2603.23607","title":"Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset","created":"2026-03-24","cited_by_count":4,"area":"lr"},{"id":"2606.19641","title":"Scaling Self-Play for End-to-End Driving","created":"2026-06-17","cited_by_count":4,"area":"driving"},{"id":"2509.10570","title":"Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey","created":"2025-09-11","cited_by_count":3,"area":"driving"},{"id":"2510.03075","title":"What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives","created":"2025-10-03","cited_by_count":3,"area":null},{"id":"2602.01834","title":"Concept-Based Dictionary Learning for Inference-Time Safety in Vision Language Action Models","created":"2026-02-02","cited_by_count":3,"area":"vla"},{"id":"2603.06054","title":"Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving","created":"2026-03-06","cited_by_count":3,"area":"mllm"},{"id":"2607.07103","title":"A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving","created":"2026-07-08","cited_by_count":3,"area":"driving"},{"id":"2608.13556","title":"V-RAE: Rethinking Video Latent Spaces for Generation","created":"2026-08-13","cited_by_count":3,"area":null},{"id":"2503.06138","title":"System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2505.03174","title":"Automated Data Curation Using GPS & NLP to Generate Instruction-Action Pairs for Autonomous Vehicle Vision-Language Navigation Datasets","created":"2025-05-06","cited_by_count":2,"area":"driving"},{"id":"2507.12463","title":"MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding","created":"2025-07-16","cited_by_count":2,"area":null},{"id":"2507.19262","title":"OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models","created":"2025-07-25","cited_by_count":2,"area":"mllm"},{"id":"2602.05863","title":"Constrained Group Relative Policy Optimization","created":"2026-02-05","cited_by_count":2,"area":"lr"},{"id":"2604.06750","title":"How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study","created":"2026-04-08","cited_by_count":2,"area":"mllm"},{"id":"2605.23176","title":"DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving","created":"2026-05-22","cited_by_count":2,"area":"driving"},{"id":"2608.13395","title":"FIRE-VLA: Failure-Informed Self-Evolution for Vision-Language-Action Models in Autonomous Driving","created":"2026-08-13","cited_by_count":2,"area":"vla"},{"id":"2505.16594","title":"Temporal Object Captioning for Street Scene Videos from LiDAR Tracks","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2507.06072","title":"MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding","created":"2025-07-08","cited_by_count":1,"area":"driving"},{"id":"2604.04857","title":"The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models","created":"2026-04-06","cited_by_count":1,"area":"mllm"},{"id":"2604.11707","title":"Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2604.19145","title":"ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving","created":"2026-04-21","cited_by_count":1,"area":"mllm"},{"id":"2511.06496","title":"A Low-Rank Method for Vision Language Model Hallucination Mitigation in Autonomous Driving","created":"2025-11-09","cited_by_count":0,"area":"mllm"},{"id":"2511.08439","title":"Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.14499","title":"Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM","created":"2025-11-18","cited_by_count":0,"area":"mllm"},{"id":"2511.23311","title":"Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach","created":"2025-11-28","cited_by_count":0,"area":"mllm"},{"id":"2512.17724","title":"SAVeD: A First-Person Social Media Video Dataset for ADAS-equipped vehicle Near-Miss and Crash Event Analyses","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2602.07668","title":"Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2607.23962","title":"Development of Vision-Language Model-based GNSS Spoofing Detection for Autonomous Vehicle Navigation","created":"2026-07-27","cited_by_count":0,"area":"driving"},{"id":"2608.14586","title":"Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures","created":"2026-06-19","cited_by_count":0,"area":"vla"},{"id":"2608.14767","title":"NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.03572","title":"Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving","created":"2026-09-03","cited_by_count":0,"area":"wm"},{"id":"2609.31648","title":"Energy Vision--Language--Action: A Controlled Multimodal Benchmark for Intent-Conditioned Residential Energy Management","created":"2026-09-13","cited_by_count":0,"area":null},{"id":"2609.38028","title":"doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving","created":"2026-09-29","cited_by_count":0,"area":"driving"},{"id":"2610.00926","title":"A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform","created":"2026-10-01","cited_by_count":0,"area":"driving"},{"id":"2610.01942","title":"Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models","created":"2026-10-01","cited_by_count":0,"area":"wm"},{"id":"2610.11662","title":"Lamarck's Driving School: Discovering Autonomous Driving Training Strategies through Evolutionary Competition","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.17856","title":"MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision","created":"2026-10-13","cited_by_count":0,"area":"mllm"}],"next_cursor":null}