Asymmetric Self-Consistency Hypothesis: AI-Assisted Verification and Reproducible Research Dataset

Abstract:This dataset, curated by independent researcher PSBigBig, consolidates all materials required for end-to-end reproduction and AI-assisted verification of the Asymmetric Self-Consistency Hypothesis. The central objective is to demonstrate that, once a theoretical framework T is independently confirmed as logically self-consistent by multiple formal proof systems (including Lean 4.0, Coq 8.14, and GPT-based validation scripts), any discrepancy between experimental measurements and T’s predictions must stem from limitations in the measurement apparatus or from underlying axioms—rather than from logical flaws in T itself. Included within this dataset are: Formal Proof Artifacts: Lean scripts (proofs/Proofs.lean, proofs/AdjustedProof.lean) complete with dependency manifests. Coq files (proofs/Proofs.v, proofs/AdjustedProof.v) and associated tactic guides. A GPT validation report (proofs/gptreport.json) generated via automated consistency checks. CI/CD Configuration: GitHub Actions workflow (.github/workflows/proof.yml) orchestrating cross-platform proof compilation, automated theorem checking, and environment setup. A Dockerfile ensuring a reproducible Ubuntu 22.04 environment preinstalled with Lean, Coq, and Python dependencies. High-Energy Physics Simulation Data: Delphes simulations for resonance cross-section predictions (σₜ(s)) at HL-LHC (300 fb⁻¹) and FCC-hh (20 ab⁻¹) energies. Systematic uncertainty breakdowns (ISR/FSR, pile-up, detector noise, etc.) and a tabulated “self-consistency check” of predicted versus excluded resonance regions (95 % CL) using CMS 2025 preprint data. Non-Perturbative Validation and Tuning Examples: Lattice-based non-perturbative checks, 2PI diagrams, and numerical routines with accompanying uncertainty estimates. A documented fine-tuning scenario illustrating the addition of a tiny Lorentz-violation term (δL = ε ψ̄ γ⁰ ∂₀ ψ, ε ≈ 10⁻²⁰) and its impact on two-loop β-function corrections (δβ ≲ 10⁻¹⁰). All source code, scripts, raw log files, and SHA-256 checksums are provided to guarantee bitwise reproducibility. Users should begin by launching the Docker container, executing the formal proofs, and cross-referencing simulation outputs against published CMS exclusion curves. This dataset equips theorists, experimentalists, and software engineers alike to rigorously validate the Asymmetric Self-Consistency Hypothesis, fostering transparent, falsifiable research practices. Contact Information (English): Name: PSBigBig Affiliation: Independent Researcher / Developer Email: [e-mail] Website: https://onestardao.com