Collections
Discover the best community collections!
Collections including paper arxiv:2604.10333
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WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Paper • 2511.09515 • Published • 20 -
Robot Learning from a Physical World Model
Paper • 2511.07416 • Published • 32 -
WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
Paper • 2512.02425 • Published • 25 -
MobileWorldBench: Towards Semantic World Modeling For Mobile Agents
Paper • 2512.14014 • Published • 3
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GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
Paper • 2503.14734 • Published • 7 -
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Paper • 2401.02117 • Published • 33 -
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Paper • 2506.01844 • Published • 158 -
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Paper • 2506.16035 • Published • 89
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Endless Terminals: Scaling RL Environments for Terminal Agents
Paper • 2601.16443 • Published • 18 -
Linear representations in language models can change dramatically over a conversation
Paper • 2601.20834 • Published • 21 -
Scaling Embeddings Outperforms Scaling Experts in Language Models
Paper • 2601.21204 • Published • 102 -
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Paper • 2601.18778 • Published • 42
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WorldVLA: Towards Autoregressive Action World Model
Paper • 2506.21539 • Published • 40 -
LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
Paper • 2509.05263 • Published • 11 -
VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
Paper • 2510.00406 • Published • 67 -
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
Paper • 2510.19430 • Published • 53
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Endless Terminals: Scaling RL Environments for Terminal Agents
Paper • 2601.16443 • Published • 18 -
Linear representations in language models can change dramatically over a conversation
Paper • 2601.20834 • Published • 21 -
Scaling Embeddings Outperforms Scaling Experts in Language Models
Paper • 2601.21204 • Published • 102 -
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Paper • 2601.18778 • Published • 42
-
WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Paper • 2511.09515 • Published • 20 -
Robot Learning from a Physical World Model
Paper • 2511.07416 • Published • 32 -
WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
Paper • 2512.02425 • Published • 25 -
MobileWorldBench: Towards Semantic World Modeling For Mobile Agents
Paper • 2512.14014 • Published • 3
-
WorldVLA: Towards Autoregressive Action World Model
Paper • 2506.21539 • Published • 40 -
LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation
Paper • 2509.05263 • Published • 11 -
VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
Paper • 2510.00406 • Published • 67 -
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
Paper • 2510.19430 • Published • 53
-
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
Paper • 2503.14734 • Published • 7 -
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Paper • 2401.02117 • Published • 33 -
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Paper • 2506.01844 • Published • 158 -
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Paper • 2506.16035 • Published • 89