The Divine Blueprint is a public open-source repository we are using as a stress test for AI-assisted adversarial review. Repo: https://github.com/phx/blueprint The interesting AI question is not whether a model “believes” the paper. The useful question is whether an AI system can audit a mixed artifact made of:
- README claims
- LaTeX/PDF paper text
- mathematical formulas
- CSV registries
- Python implementations
- pytest assertions
- symbolic / ARG-like surface structure A possible audit workflow: Parse the paper into discrete claims. Map each claim to formula registry rows and validation-matrix entries. Locate corresponding implementation functions and tests. Ask a model to separate internal consistency from empirical proof. Run contradiction search across README, paper, code, tests, and docs. Generate missing tests for any claim that is executable but untested. Require exact file, formula, function, or test references for every criticism. The limitation is obvious but important: passing tests can only demonstrate internal consistency. It does not prove the external truth of the framework. The reason this repo is a useful stress target is that it mixes formal structure with speculative claims and an intentional puzzle-box layer. That combination exposes common AI failure modes: over-endorsement, vague debunking, missed traceability, hallucinated citations, and failure to distinguish metaphor from executable structure. Question for this sub: What would you add to this audit protocol to make AI systems better at finding the first real flaw instead of producing either hype or generic dismissal? submitted by /u/rubynorails
Originally posted by u/rubynorails on r/ArtificialInteligence
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