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Code World Model: Coding Agent as World Brain

Research Multimodal & Generative

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Merged summary

TL;DR - Code World Model uses a coding agent to maintain executable, persistent world state while a conditioned video model renders the resulting scenes. This separation could enable more coherent, rule-consistent, and open-ended simulated worlds than models that learn dynamics solely from video.

  • A language-model coding agent reasons about events, updates state, and generates executable code governing world evolution.
  • A proxy representation encodes frame-level spatiotemporal constraints and is compiled into video that conditions the visual generator.
  • The authors construct aligned proxy-observation datasets from gameplay and real-world videos.
  • Fine-tuned MiniMax-H3 follows proxy specifications in simple interactive worlds while retaining detailed visuals and dynamics.

Sources (1)

Code World Model: Coding Agent as World Brain

arXiv cs.CV Yiwen Chen, Guosheng Lin, Chi Zhang 2026-08-26 arXiv:2608.25927
Public signals Hugging Face upvotes 40
Providers: Hugging Face · Upvotes 40 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:28:05.095422 UTC

TL;DR - Code World Model uses a coding agent to maintain executable, persistent world state while a conditioned video model renders the resulting scenes. This separation could enable more coherent, rule-consistent, and open-ended simulated worlds than models that learn dynamics solely from video.

  • A language-model coding agent reasons about events, updates state, and generates executable code governing world evolution.
  • A proxy representation encodes frame-level spatiotemporal constraints and is compiled into video that conditions the visual generator.
  • The authors construct aligned proxy-observation datasets from gameplay and real-world videos.
  • Fine-tuned MiniMax-H3 follows proxy specifications in simple interactive worlds while retaining detailed visuals and dynamics.
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