In-Context World Modeling for Robotic Control
Paper • 2606.26025 • Published • 63
WM-EAI
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Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs
Paper • 2606.27378 • Published • 60 -
Infinite Worlds with Versatile Interactions
Paper • 2607.07534 • Published • 46 -
MentalThink: Shaping Thoughts in Mental SVG World
Paper • 2607.03530 • Published • 14 -
Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling
Paper • 2607.02980 • Published • 84 -
Orca: The World is in Your Mind
Paper • 2606.30534 • Published • 477 -
Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model
Paper • 2607.11643 • Published • 45 -
KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill
Paper • 2607.12625 • Published • 81 -
LightMem-Ego: Your AI Memory for Everyday Life
Paper • 2607.11487 • Published • 47 -
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Paper • 2607.12395 • Published • 100 -
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Paper • 2607.05382 • Published • 88 -
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory
Paper • 2607.10350 • Published • 85 -
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Paper • 2607.10383 • Published • 102 -
Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Paper • 2607.15330 • Published • 73 -
Mental World Modeling
Paper • 2607.27201 • Published • 103 -
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Paper • 2607.27924 • Published • 14 -
Metis: Memory Foundation Model
Paper • 2607.26760 • Published • 270 -
World Action Models: A Survey
Paper • 2606.20781 • Published • 56 -
Fast LeWorldModel
Paper • 2606.26217 • Published • 28 -
WorldClaw: Agentic 3D Open-World Generation at Scale
Paper • 2608.05248 • Published • 67 -
GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks
Paper • 2608.03764 • Published • 27 -
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Paper • 2608.06197 • Published • 43