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LAW, a closed-loop VLM--WAM framework that unifies language reasoning, action generation, and world modeling through semantic subtask progress, is presented, establishing a new open-source state of the art on RoboTwin.
Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual tokens results in high computational costs. While many methods have been proposed to reduce the number of visual tokens, most of them rely on heuristics and are prone to...
WorldEcho is introduced, which probes action following over a broader action distribution using visual integrity and SE(3) trajectory alignment, and WorldSync is proposed, which strengthens action following along three complementary axes: distributional coverage, representational grounding, and intervention-effect alig...
These findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion, and propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework.
This work formalizes Observable Simulator Contract, a minimal contract that any action-conditioned physical simulator should satisfy: supplied actions must induce corresponding agent motion, and environment responses must be grounded in that realized motion.
JarvisHub is introduced, a canvas-native creative agent harness for long-horizon multimodal creation, where agents can progressively plan, generate, revise, and organize multimodal projects while users remain able to inspect, guide, and intervene throughout the process.
DeepSearch-Evolve is presented, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools that enables scalable self-evolution for long-horizon web agents.
This work introduces Robo-Dopamine 2.0, a history- and OOD-aware process reward model with a pairwise prediction interface that combines history-conditioned pairwise rewards that use source-aligned reference panels for synthetic OOD queries and observed rollout history for online queries, while preserving the queried e...
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