Code World Model:
Coding Agent as World Brain

Yiwen Chen^1,2 · Guosheng Lin² · Chi Zhang¹

¹ AGI Lab, Westlake University ² Nanyang Technological University

Project Page | arXiv

https://github.com/user-attachments/assets/4c13c5fb-ab45-4a0e-9e59-b85499ef7390

Quick start

Follow the environment and installation guide.
From the repository root, download the public CWM LoRA and deterministically expand the 40 compact Hugging Face examples:

./scripts/install_release_assets.sh "$PWD/release"

Enter the prepared release directory and run a released configuration without changing its inputs, prompts, or seed:

cd release
CONFIG=examples/example_01/config.json
python -m cwm_h3_inference validate --config "$CONFIG"
CUDA_VISIBLE_DEVICES=0 python -m cwm_h3_inference prepare --config "$CONFIG"
CUDA_VISIBLE_DEVICES=0 python -m cwm_h3_inference generate --config "$CONFIG"

Use a new cache directory and output path for every changed experiment. The Hugging Face examples contain compact NPZ conditions, not caches or output videos; the installation script performs the required local format expansion.

buaacyw/code-world-model

計測したスター増加

概要

README.md

Code World Model:
Coding Agent as World Brain

Quick start

Documentation

計測したスター増加

概要

README.md

Code World Model:Coding Agent as World Brain

Quick start

Documentation

Code World Model:
Coding Agent as World Brain