{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "mirros-lab-code-as-world--evaluate-code-as-world-on-quantiphy",
 "repo": "MirroS-Lab/Code-as-World",
 "repo_url": "https://github.com/MirroS-Lab/Code-as-World",
 "repo_slug": "mirros-lab-code-as-world",
 "title": "Score Code as World checkpoints on QuantiPhy validation",
 "page_title": "How to score Code as World checkpoints on QuantiPhy validation with Code as World",
 "summary": "Run the released 4B or 9B model on QuantiPhy videos and get metrics from the official evaluator.",
 "who": "A researcher or ML engineer comparing physical reasoning models who needs reproducible QuantiPhy numbers for Code as World on a CUDA machine.",
 "problem": "Reproducing a benchmark result means wiring together weights, the QuantiPhy dataset, and the official evaluator without a clear path.",
 "outcome": "A prediction CSV, a metric summary, and raw generations in outputs/quantiphy/, plus official metrics in outputs/quantiphy_metrics.",
 "steps": [
  "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`.",
  "Download the 4B checkpoint with `hf download MirroS-Lab/Code-as-World-VL-4B --local-dir weights/4b`.",
  "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)",
  "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)",
  "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)"
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The README documents evaluation and inference only, so reported numbers depend on running the released checkpoints on the QuantiPhy validation set you download.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/mirros-lab-code-as-world/evaluate-code-as-world-on-quantiphy/",
 "md": "https://dedupedai.com/use-cases/mirros-lab-code-as-world/evaluate-code-as-world-on-quantiphy/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/mirros-lab-code-as-world--evaluate-code-as-world-on-quantiphy.json",
 "api_version": "v1"
}