Introduction
AI-based formal proof verification is rapidly transforming the way scientific theories are evaluated—not only in mathematics, but also in physics, machine learning, and logic.
This paper introduces the Asymmetric Self-Consistency Hypothesis:
If a scientific theory passes comprehensive self-consistency checks by multiple independent AI verifiers (such as Lean, Coq, GPT, etc.), then any contradiction with experimental results should be attributed to measurement error or the choice of foundational axioms—not to a failure of the theory’s internal logic.
This approach reframes Popper’s principle of falsifiability for the AI era.
AI-verified self-consistency becomes the new gold standard for rigorous theory evaluation—making theoretical logic auditable, reproducible, and transparent for all.
How does it work?
- The hypothesis is tested using multiple independent formal verifiers: Lean, Coq, and large language models (GPT-4, Claude, etc.).
- All proof scripts, CI pipelines, and datasets are open sourced and fully reproducible.
- Example: In quantum field theory (QFT), we use AI to check every step of two-loop β-function calculations, resonance window predictions, and systematic error propagation—before any experimental investment.
Case Study: QFT Resonance Model
- The paper demonstrates the approach by running full two-loop calculations for a QFT resonance model.
- All calculations, including micro-adjustments (such as adding a tiny Lorentz-violating term), are verified by both symbolic proof engines and LLMs.
- Result: Out of 1000 formal steps, 99.8% pass rate, with only 2 minor script errors (both automatically corrected in the CI pipeline).
- Full code, data, and verification scripts are provided.
Why does this matter?
- Prevents waste of billions on experimental setups for theories that are already internally inconsistent.
- Reduces ambiguity: If an AI-audited theory disagrees with experiment, the problem is most likely with the experiment or foundational axioms, not the math.
- Sets a new standard for scientific rigor, transparency, and reproducibility across disciplines.
How to reproduce?
- Download the scripts and data from Zenodo or GitHub.
- Run the provided Docker image or step-by-step notebook.
- Every step is logged and auditable.
Open Call
I invite researchers, developers, and philosophers from all domains—AI, physics, logic, mathematics—to test, challenge, or extend this hypothesis.
Can your theory pass independent, multi-AI consistency checks? Try it out, propose edge cases, or fork the pipeline for your own field!
AI is not just a tool for creating new theories—it’s a new referee for scientific logic itself.
Let’s make reproducible, AI-audited science the new
Author: PSBigBig (Purple Star)