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How to score Code as World checkpoints on QuantiPhy validation with Code as World

Run the released 4B or 9B model on QuantiPhy videos and get metrics from the official evaluator.

  • Top pick
  • Research
  • Advanced
  • 2 hours
  • From the README
  • Any agent

Who it is forA researcher or ML engineer comparing physical reasoning models who needs reproducible QuantiPhy numbers for Code as World on a CUDA machine.

The problemReproducing a benchmark result means wiring together weights, the QuantiPhy dataset, and the official evaluator without a clear path.

The outcomeA prediction CSV, a metric summary, and raw generations in outputs/quantiphy/, plus official metrics in outputs/quantiphy_metrics.

What you need first

  • Python 3.10 or 3.11
  • A CUDA host for the checkpoints
  • git and the hf CLI to clone repos and download weights
  • Disk space and network access for the 4B and 9B weights and the QuantiPhy validation videos

Steps

  1. Clone the repo with git clone https://github.com/MirroS-Lab/Code-as-World.git, run cd Code-as-World, create a environment with python -m venv .venv, activate it with source .venv/bin/activate, and install with pip install -r requirements/inference.txt.
  2. Download the 4B checkpoint with hf download MirroS-Lab/Code-as-World-VL-4B --local-dir weights/4b.
  3. Clone the evaluator repo with git clone https://github.com/Paulineli/QuantiPhy.git /path/to/QuantiPhy and download the validation videos with hf download PaulineLi/QuantiPhy-validation --repo-type dataset --local-dir /path/to/QuantiPhy-validation. (inferred, check the README)
  4. Run the evaluation with python -m code_as_world.evaluation 4b --input-csv /path/to/QuantiPhy/quantiphy_validation.csv --video-dir /path/to/QuantiPhy-validation/validation_videos. (inferred, check the README)
  5. Install pandas with pip install pandas, then score the outputs with python /path/to/QuantiPhy/evaluator.py outputs/quantiphy outputs/quantiphy_metrics --gt_file /path/to/QuantiPhy/quantiphy_validation.csv. (inferred, check the README)

Prompt for your agent

In the Code-as-World repo at <repo_path>, run `python -m code_as_world.evaluation 4b` against the QuantiPhy validation CSV at <csv_path> and videos at <video_dir>, then run the official QuantiPhy evaluator on outputs/quantiphy. Expect a prediction CSV, a metric summary, and an outputs/quantiphy_metrics result; verify that the prediction CSV row count matches the ground-truth CSV.

Caveat. The README documents evaluation and inference only, so reported numbers depend on running the released checkpoints on the QuantiPhy validation set you download.

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