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Contributing to C3

Thanks for your interest in improving C3.

Scope

This repository is a research codebase for reproducing the paper results. We prioritize:

  • reproducibility,
  • deterministic data preparation,
  • clear provenance for models and datasets,
  • small and reviewable pull requests.

Development setup

  1. Create Python 3.11 environment.
  2. Install pinned dependencies:
python -m pip install -U pip
python -m pip install -r requirements.txt --no-build-isolation
python -m pip check
  1. Prepare datasets:
bash scripts/data/prepare_all.sh --out_dir data
  1. Run sanity checks:
bash scripts/reproduce/smoke.sh
bash scripts/audit/pre_release.sh

Pull request expectations

  • Keep changes focused; avoid unrelated refactors.
  • Update docs when behavior or CLI contracts change.
  • Add/adjust tests or checks when changing data, eval, or training logic.
  • Preserve backward compatibility of public CLI flags unless the PR explicitly documents a breaking change.
  • Do not commit generated local outputs such as data/, artifacts/, ckpt/, runs/, wandb/, or models/.

Reproducibility requirements

If your change modifies dataset preparation outputs:

  1. Recompute hashes:
bash scripts/data/prepare_all.sh --out_dir data --update_manifest_sha256 1
  1. Commit configs/data_manifest.yaml.
  2. Verify strict mode:
bash scripts/data/prepare_all.sh --out_dir data --strict 1

Security and disclosure

Please do not open public issues for potential security problems. Follow SECURITY.md for private disclosure.