{"total":1761,"rows":[{"id":"2506.09985","title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","created":"2025-06-11","cited_by_count":809,"area":"wm"},{"id":"2602.15922","title":"World Action Models are Zero-shot Policies","created":"2026-02-17","cited_by_count":465,"area":"wm"},{"id":"2506.21539","title":"WorldVLA: Towards Autoregressive Action World Model","created":"2025-06-26","cited_by_count":367,"area":"wm"},{"id":"2603.16666","title":"Fast-WAM: Do World Action Models Need Test-time Future Imagination?","created":"2026-03-17","cited_by_count":349,"area":"wm"},{"id":"2510.10274","title":"X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model","created":"2025-10-11","cited_by_count":303,"area":"vla"},{"id":"2511.14759","title":"$\\pi^{*}_{0.6}$: a VLA That Learns From Experience","created":"2025-11-18","cited_by_count":303,"area":"vla"},{"id":"2601.16163","title":"Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning","created":"2026-01-22","cited_by_count":300,"area":"diffusion"},{"id":"2512.13030","title":"Motus: A Unified Latent Action World Model","created":"2025-12-15","cited_by_count":251,"area":"wm"},{"id":"2506.07339","title":"Real-Time Execution of Action Chunking Flow Policies","created":"2025-06-09","cited_by_count":216,"area":null},{"id":"2507.04447","title":"DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge","created":"2025-07-06","cited_by_count":213,"area":"vla"},{"id":"2509.08827","title":"A Survey of Reinforcement Learning for Large Reasoning Models","created":"2025-09-10","cited_by_count":180,"area":"lr"},{"id":"2505.12705","title":"DreamGen: Unlocking Generalization in Robot Learning through Video World Models","created":"2025-05-19","cited_by_count":179,"area":"wm"},{"id":"2510.03827","title":"LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization","created":"2025-10-04","cited_by_count":179,"area":"vla"},{"id":"2510.10125","title":"Ctrl-World: A Controllable Generative World Model for Robot Manipulation","created":"2025-10-11","cited_by_count":167,"area":"wm"},{"id":"2606.02800","title":"Cosmos 3: Omnimodal World Models for Physical AI","created":"2026-06-01","cited_by_count":165,"area":"wm"},{"id":"2509.09674","title":"SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning","created":"2025-09-11","cited_by_count":151,"area":"vla"},{"id":"2505.23705","title":"Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better","created":"2025-05-29","cited_by_count":146,"area":"mllm"},{"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":"2509.09372","title":"VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model","created":"2025-09-11","cited_by_count":145,"area":"mllm"},{"id":"2512.15692","title":"mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs","created":"2025-12-17","cited_by_count":142,"area":"vla"},{"id":"2603.17240","title":"GigaWorld-Policy: An Efficient Action-Centered World--Action Model","created":"2026-03-18","cited_by_count":139,"area":"wm"},{"id":"2604.15483","title":"${\\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","created":"2026-04-16","cited_by_count":138,"area":null},{"id":"2509.24527","title":"Training Agents Inside of Scalable World Models","created":"2025-09-29","cited_by_count":136,"area":"wm"},{"id":"2505.17016","title":"Interactive Post-Training for Vision-Language-Action Models","created":"2025-05-22","cited_by_count":135,"area":"vla"},{"id":"2507.12440","title":"EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos","created":"2025-07-16","cited_by_count":128,"area":"vla"},{"id":"2505.03233","title":"GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data","created":"2025-05-06","cited_by_count":123,"area":"vla"},{"id":"2602.10098","title":"VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model","created":"2026-02-10","cited_by_count":118,"area":"wm"},{"id":"2507.15493","title":"GR-3 Technical Report","created":"2025-07-21","cited_by_count":111,"area":"vla"},{"id":"2505.19789","title":"What Can RL Bring to VLA Generalization? An Empirical Study","created":"2025-05-26","cited_by_count":108,"area":"vla"},{"id":"2505.11917","title":"OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning","created":"2025-05-17","cited_by_count":107,"area":"vla"},{"id":"2601.18692","title":"A Pragmatic VLA Foundation Model","created":"2026-01-26","cited_by_count":103,"area":"vla"},{"id":"2602.11236","title":"ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning","created":"2026-02-11","cited_by_count":103,"area":null},{"id":"2507.15597","title":"Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos","created":"2025-07-21","cited_by_count":102,"area":"vla"},{"id":"2507.01925","title":"A Survey on Vision-Language-Action Models: An Action Tokenization Perspective","created":"2025-07-02","cited_by_count":96,"area":"vla"},{"id":"2509.00576","title":"Galaxea Open-World Dataset and G0 Dual-System VLA Model","created":"2025-08-30","cited_by_count":92,"area":"vla"},{"id":"2505.22159","title":"ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation","created":"2025-05-28","cited_by_count":91,"area":"vla"},{"id":"2508.20072","title":"Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies","created":"2025-08-27","cited_by_count":90,"area":"vla"},{"id":"2603.22435","title":"CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation","created":"2026-03-23","cited_by_count":88,"area":"vla"},{"id":"2505.16278","title":"DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving","created":"2025-05-22","cited_by_count":86,"area":"vla"},{"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":"2510.13778","title":"InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy","created":"2025-10-15","cited_by_count":84,"area":"vla"},{"id":"2506.07961","title":"BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models","created":"2025-06-09","cited_by_count":80,"area":"mllm"},{"id":"2506.09937","title":"SAFE: Multitask Failure Detection for Vision-Language-Action Models","created":"2025-06-11","cited_by_count":80,"area":"vla"},{"id":"2603.04356","title":"RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots","created":"2026-03-04","cited_by_count":79,"area":null},{"id":"2511.00091","title":"Self-Improving Vision-Language-Action Models with Data Generation via Residual RL","created":"2025-10-30","cited_by_count":78,"area":"vla"},{"id":"2602.08971","title":"WorldArena: A Unified Benchmark for Evaluating Perception and Functional Utility of Embodied World Models","created":"2026-02-09","cited_by_count":76,"area":"wm"},{"id":"2603.10448","title":"DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control","created":"2026-03-11","cited_by_count":76,"area":"vla"},{"id":"2605.00078","title":"Being-H0.7: A Latent World-Action Model from Egocentric Videos","created":"2026-04-30","cited_by_count":75,"area":"wm"},{"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":"2508.21046","title":"CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification","created":"2025-08-28","cited_by_count":74,"area":"mllm"},{"id":"2511.17502","title":"RynnVLA-002: A Unified Vision-Language-Action and World Model","created":"2025-11-21","cited_by_count":74,"area":"wm"},{"id":"2507.17520","title":"InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation","created":"2025-07-23","cited_by_count":72,"area":"lr"},{"id":"2509.15937","title":"A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning","created":"2025-09-19","cited_by_count":71,"area":"vla"},{"id":"2510.21571","title":"Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos","created":"2025-10-24","cited_by_count":70,"area":"vla"},{"id":"2511.09515","title":"WMPO: World Model-based Policy Optimization for Vision-Language-Action Models","created":"2025-11-12","cited_by_count":70,"area":"wm"},{"id":"2508.13073","title":"Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey","created":"2025-08-18","cited_by_count":69,"area":"vla"},{"id":"2505.19017","title":"WorldEval: World Model as Real-World Robot Policies Evaluator","created":"2025-05-25","cited_by_count":68,"area":"wm"},{"id":"2510.20328","title":"MemER: Scaling Up Memory for Robot Control via Experience Retrieval","created":"2025-10-23","cited_by_count":67,"area":null},{"id":"2601.12993","title":"Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization","created":"2026-01-19","cited_by_count":66,"area":"vla"},{"id":"2506.18123","title":"RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies","created":"2025-06-22","cited_by_count":64,"area":null},{"id":"2512.05964","title":"Training-Time Action Conditioning for Efficient Real-Time Chunking","created":"2025-12-05","cited_by_count":64,"area":"vla"},{"id":"2507.12898","title":"Vidar: Embodied Video Diffusion Model for Generalist Manipulation","created":"2025-07-17","cited_by_count":62,"area":"diffusion"},{"id":"2511.19861","title":"GigaWorld-0: World Models as Data Engine to Empower Embodied AI","created":"2025-11-25","cited_by_count":62,"area":"wm"},{"id":"2506.22355","title":"Embodied AI Agents: Modeling the World","created":"2025-06-27","cited_by_count":61,"area":"wm"},{"id":"2603.03596","title":"MEM: Multi-Scale Embodied Memory for Vision Language Action Models","created":"2026-03-04","cited_by_count":61,"area":"vla"},{"id":"2603.02115","title":"Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons","created":"2026-03-02","cited_by_count":60,"area":"alignment"},{"id":"2510.14830","title":"RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning","created":"2025-10-16","cited_by_count":59,"area":"deeprl"},{"id":"2506.12723","title":"SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration","created":"2025-06-15","cited_by_count":58,"area":"vla"},{"id":"2509.22195","title":"Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting","created":"2025-09-26","cited_by_count":58,"area":"lr"},{"id":"2512.01031","title":"VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference","created":"2025-11-30","cited_by_count":58,"area":"vla"},{"id":"2604.27792","title":"Motubrain: An Advanced World Action Model for Robot Control","created":"2026-04-30","cited_by_count":57,"area":"wm"},{"id":"2509.19301","title":"Residual Off-Policy RL for Finetuning Behavior Cloning Policies","created":"2025-09-23","cited_by_count":55,"area":null},{"id":"2607.00272","title":"ASPIRE: Agentic /Skills Discovery for Robotics","created":"2026-06-30","cited_by_count":53,"area":"agents"},{"id":"2606.15768","title":"LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies","created":"2026-06-14","cited_by_count":52,"area":"wm"},{"id":"2505.02152","title":"Interleave-VLA: Enhancing Robot Manipulation with Interleaved Image-Text Instructions","created":"2025-05-04","cited_by_count":51,"area":"vla"},{"id":"2511.16651","title":"InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy","created":"2025-11-20","cited_by_count":50,"area":"vla"},{"id":"2601.02456","title":"InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation","created":"2026-01-05","cited_by_count":50,"area":"mllm"},{"id":"2602.13977","title":"WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL","created":"2026-02-15","cited_by_count":50,"area":"wm"},{"id":"2605.02881","title":"MolmoAct2: Action Reasoning Models for Real-world Deployment","created":"2026-05-04","cited_by_count":50,"area":"lr"},{"id":"2605.12090","title":"World Action Models: The Next Frontier in Embodied AI","created":"2026-05-12","cited_by_count":50,"area":"wm"},{"id":"2606.17846","title":"Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models","created":"2026-06-16","cited_by_count":50,"area":null},{"id":"2505.21906","title":"ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge","created":"2025-05-28","cited_by_count":49,"area":"lr"},{"id":"2509.19012","title":"Pure Vision Language Action (VLA) Models: A Comprehensive Survey","created":"2025-09-23","cited_by_count":49,"area":"mllm"},{"id":"2510.13054","title":"VLA-0: Building State-of-the-Art VLAs with Zero Modification","created":"2025-10-15","cited_by_count":49,"area":"vla"},{"id":"2505.23189","title":"TrackVLA: Embodied Visual Tracking in the Wild","created":"2025-05-29","cited_by_count":47,"area":null},{"id":"2607.08448","title":"Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents","created":"2026-07-09","cited_by_count":47,"area":"agents"},{"id":"2602.09153","title":"SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes","created":"2026-02-09","cited_by_count":46,"area":"agents"},{"id":"2605.00080","title":"World Model for Robot Learning: A Comprehensive Survey","created":"2026-04-30","cited_by_count":46,"area":"wm"},{"id":"2606.09811","title":"AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing","created":"2026-06-08","cited_by_count":46,"area":"wm"},{"id":"2507.23523","title":"H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation","created":"2025-07-31","cited_by_count":45,"area":"diffusion"},{"id":"2512.01801","title":"GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation","created":"2025-12-01","cited_by_count":45,"area":"vla"},{"id":"2602.12684","title":"Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution","created":"2026-02-13","cited_by_count":45,"area":"vla"},{"id":"2510.19430","title":"GigaBrain-0: A World Model-Powered Vision-Language-Action Model","created":"2025-10-22","cited_by_count":44,"area":"wm"},{"id":"2511.02832","title":"TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System","created":"2025-11-04","cited_by_count":44,"area":null},{"id":"2505.21457","title":"ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning","created":"2025-05-27","cited_by_count":43,"area":null},{"id":"2506.13751","title":"LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction","created":"2025-06-16","cited_by_count":43,"area":"vla"},{"id":"2604.03208","title":"Hierarchical Planning with Latent World Models","created":"2026-04-03","cited_by_count":43,"area":"wm"},{"id":"2602.12063","title":"VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model","created":"2026-02-12","cited_by_count":41,"area":"wm"},{"id":"2505.17006","title":"CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning","created":"2025-05-22","cited_by_count":40,"area":null},{"id":"2512.23703","title":"Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation","created":"2025-12-29","cited_by_count":40,"area":"lr"}],"next_cursor":"100"}