LAW: Unifying Language, Action, and World Modeling for Agentic Physical Intelligence

Jiajun Cao; Kai Tang; Bohua Zhang; Zezhong Qian; Ying Li; Xiaozhu Ju; Xiaobao Wei; Jiaming Liu; Yang Ding; Sixiang Chen; Yiding Sun; Siyuan Qian; Tinghao Wang; Jixian Wu; Yichen Guo; Yi Zhang; Shilong Zou; Yong Dai; Jian Tang; Shanghang Zhang

arXiv · 2610.23334

LAW: Unifying Language, Action, and World Modeling for Agentic Physical Intelligence

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Abstract

World-action models jointly predict actions and future world states, but long-horizon manipulation further requires language reasoning to plan subtask objectives, assess execution progress, and guide action selection. We present LAW, a closed-loop VLM--WAM framework that unifies language reasoning, action generation, and world modeling through semantic subtask progress. Within the LAW agent, a hybrid-expert vision-language model plans the remaining subtasks, estimates the values of and fuses actions proposed by a world-action model, and asynchronously assesses execution status to support continued execution and subtask transitions. Within the world-action model, Mixture-of-Transformer Experts integrates modality-specific and shared computation for joint video--action modeling. Dual-stream hierarchical visual memory maintains temporal context for both models, while subtask-local value learning aligns action evaluation with semantic objectives and preserves supervision from successful prefixes of failed trajectories. LAW achieves 94.21% average success across 50 RoboTwin tasks. Replacing the WAM backbone with OpenWAM-$alpha$ further raises the average success rate to 95.56%, establishing a new open-source state of the art on RoboTwin. Despite having only 2B parameters, our VLM also outperforms variants using Embodied-R1.5 and GPT-5.5 High. On three long-horizon real-robot tasks, LAW achieves 65% average success, compared with 28.33% for $pi_{0.5}$ and 25% for Fast-WAM.

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Jiajun Cao, Kai Tang, Bohua Zhang, Zezhong Qian, Ying Li, Xiaozhu Ju, Xiaobao Wei, Jiaming Liu, Yang Ding, Sixiang Chen, Yiding Sun, Siyuan Qian, Tinghao Wang, Jixian Wu, Yichen Guo, Yi Zhang, Shilong Zou, Yong Dai, Jian Tang, Shanghang Zhang. 2026-10-16. LAW: Unifying Language, Action, and World Modeling for Agentic Physical Intelligence. https://arxiv.org/abs/2610.23334

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