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This method shifts physical AI from pixel-based prediction to executable code generation, enabling agents to recover and verify real-world dynamics using MuJoCo simulations. By treating video frames as evidence for underlying physics rather than just visual data, the approach provides exact supervision labels that standard training methods lack. This advancement directly addresses the gap between generative video models and true physical reasoning capabilities.

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Meet ‘Code-as-World’: An Agentic Loop That Rewrites Real Videos Into Executable MuJoCo Physics Programs

MarkTechPostAugust 30, 20264 min read

Code-as-World recovers editable MuJoCo scene code from real video, then uses those verified worlds to train physical reasoning. The post Meet ‘Code-as-World’: An Agentic Loop That Rewrites Real Videos Into Executable MuJoCo Physics Programs appeared first on MarkTechPost.

MirroS released Code-as-World: a paradigm that represents physical worlds through executable world representations. The argument is narrow and testable: pixels are evidence of a physical scene, not its ontology. A video model can predict plausible frames without ever representing mass, contact, or gravity. So instead of pixels, latents, or captions, Code-as-World represents a scene as executable code — a scene.json that MuJoCo can run, that an agent can verify against the…

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