ASC Hypothesis — Reproducibility Dataset (proofs, CI, HEP configs)

This dataset contains everything needed to reproduce the results of the paper“Asymmetric Self-Consistency Hypothesis: AI-Assisted Verification andFalsifiability” (DOI 10.6084/m9.figshare.30353320).<br>Contents (top-level)• README.md — quick start and structure• checksums.txt — SHA-256 for all files• Dockerfile — reproducible environment (Ubuntu 22.04 + Lean 4.0 + Coq 8.14 + Python)• proofs/ - Proofs.lean, AdjustedProof.lean — Lean scripts - Proofs.v, AdjustedProof.v — Coq scripts - gpt_report.json — AI verification summary - gpt_verify.py, cross_diff.py — verification helpers - adversarial/ — deliberately corrupted variants for stress tests• configs/ (if present) — HL-LHC and FCC-hh YAML configs• run_all.sh, run_delphes.py — batch/analysis helpers• figures/ — theory_blueband_cms.png, theory_blueband_moedal.png, exclusion_overlay.png, gpt_checker_summary.png• utils: check_images.py, generate_plots.py, etc.<br>How to reproduce (short)1) Build container: docker build -t asc_env .2) Run Lean/Coq inside container: docker run --rm -it -v $(pwd)/proofs:/workspace/proofs asc_env /bin/bash cd /workspace/proofs lean --make Proofs.lean &amp;&amp; lean --make AdjustedProof.lean coqc Proofs.v &amp;&amp; coqc AdjustedProof.v3) Validate GPT report: python3 gpt_verify.py --input gpt_report.json4) (Optional) Collider sim: cd /workspace python3 run_delphes.py --config configs/hl_lhc.yaml python3 run_delphes.py --config configs/fcc_hh.yaml5) Verify checksums: sha256sum -c checksums.txt<br>Notes• Licence for the dataset item is CC BY 4.0. Individual source files may additionally include a permissive software licence header when applicable.• No confidential or personal data included.<br>Recommended citationPSBigBig (2025). ASC Hypothesis — Reproducibility Dataset (proofs, CI, HEPconfigs). figshare. https://doi.org/10.6084/m9.figshare.30353326<br>[e-mail]<br>