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Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axe...
This work distinguishes strict and pretrain-exposed zero-shot transfer, where the target embodiment is absent from all training data, from pretrain-exposed zero-shot transfer, where it appears only during pretraining, and conducts a controlled analysis of four factors: state-action representations, pretraining embodime...
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.
Method is presented, a dual-system WAM that preserves broader-horizon world-action generation while enabling high-frequency closed-loop action updates by decoupling global planning and local refinement and shows that it naturally supports edge--cloud deployment with substantially lower communication overhead than the b...
GIF, an agentic Generation framework for Interactive and Functional object compositions, recast this problem as disentangled reconstruction followed by relative pose recovery, revealing diversity scaling in both simulation and real-world deployment.
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.
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