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 source video, and that anyone can edit and re-simulate. An agentic loop recovers those programs from real footage in up to five rounds. The verified worlds then become training data with exact physical labels, which real video does not carry. Trained on that supervision, Code-as-World-VL-9B scores 55.4 MRA on QuantiPhy-validation, above Gemini-3.1 Flash at 54.8 and roughly 15 points above the strongest open-weight baseline.
Is it deployable?
Yes, at the research and internal-prototype tier. MirroS shipped the GitHub repo and two checkpoints — Code-as-World-VL-4B and Code-as-World-VL-9B — under Apache 2.0, fine-tuned from Qwen3.5-4B and Qwen3.5-9B. Both are BF16 safetensors served by vLLM behind an OpenAI-compatible /v1 endpoint, with 16 sampled frames per video and --max-model-len 4608.
