This preprint introduces the Asymmetric Self-Consistency Hypothesis (ASC): if atheory is verified as internally self-consistent by multiple independent AIverifiers (Lean, Coq, and a GPT-based checker), then any disagreement withexperiment should be attributed to experimental limitations or incompletefoundational axioms, rather than to the theory’s internal logic.What’s in the paper• Formal statement of ASC and links to machine-checkable proofs.• Practical falsifiability criterion under the ASC framing.• Collider-oriented example (HL-LHC / FCC-hh) with acceptance & efficiency factors and systematics breakdown.• Demonstrative “first-principle micro-adjustment” preserving gauge invariance.• Reproducibility policy and cross-verification workflow.AI verification stack• Lean 4.0 and Coq 8.14 formal scripts.• GPT-based checker that cross-compares steps and emits a JSON report.• SHA-256 checksums for proofs, configs, and images.Availability• Dataset + code + proofs (reproducibility bundle): figshare DOI 10.6084/m9.figshare.30353326• Paper DOI: 10.6084/m9.figshare.30353320How to citePSBigBig (2025). Asymmetric Self-Consistency Hypothesis: AI-AssistedVerification and Falsifiability. figshare.https://doi.org/10.6084/m9.figshare.30353320Contacthello@onestardao.com