Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have established strong local capabilities for perception and control, scaling these capabilities to extended, multi-stage tasks remains difficult. Agentic embodied systems address this c...
Min Chen, Sheng-Jun Zhang, Yu-Xin Li et al.· 0 citations
3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize...
Weichen Lyu, Chong Xia, Ming-Yu Dong et al.· 0 citations
This work introduces HarnessEval-W, an agentified evaluation pipeline that brings the harness paradigm from the LLM ecosystem to world model benchmarking, and applies HarnessEval-W to 18 representative world models over 330 evaluation cases.
Wei-Liang Chen, Haowen Sun, Jun Gao et al.· 3 citations
Experiments show that Code-as-World-VL achieves state-of-the-art performance on QuantiPhy and surpasses leading proprietary models, highlighting the potential of executable world representations as a scalable foundation for physical intelligence.
This work proposes ParallelWorld, a multi-horizon test-time scaling framework for embodied reasoning that empowers agents to simulate and evaluate multi-step future trajectories in parallel before committing to an action.
Min Chen, Shengjun Zhang, Yu-Xin Li et al.· 0 citations
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