Auditable AI-Assisted Scientific Discovery: From Noncausal Processes to Quantum Measurement Rigidity

Abstract

Large language models can accelerate scientific exploration, but generated arguments are not evidence. We report a human–AI research workflow in which model outputs are treated as conjectures and claims are admitted only after proof or deterministic, independently checked computation. The workflow is illustrated by a connected sequence of results in mathematical physics. An exhaustive exact search of 19,683 rotation-symmetric ternary process functions finds 111 logically consistent cases, of which 108 are noncausal, and yields a projection-rigidity result over 432 source completions and 5,184 coarse-grainings. These structures generate qutrit product bases. For all 108 noncausal cores, exact orthogonality-preserving measurement calculations certify local rigidity and therefore impossibility of perfect finite-round local-operations-and classical-communication discrimination. An independent exact sweep of all 744 consistent binary tripartite process functions reproduces the qualitative causality/QNLWE boundary implied by recent general theory while additionally certifying local orthogonality-preserving rigidity for all 64 noncausal binary cases. We further obtain a closed-form rigidity spectrum and quantitative finite-round error bounds. Three of four pre-run structural predictions in the first campaign were falsified, and subsequent conjectures were retained when killed. We use this record to distinguish final-answer verification from discovery provenance, contrasting our control-first architecture with the large-scale multi-agent search and Lean verification reported in OpenAI’s September 2026 Navier–Stokes announcement. The general process-function/product-basis correspondence and its qualitative causality/QNLWE equivalence are credited to prior work; the contribution here is the audited research architecture together with the exact ternary census, projection rigidity, stronger local measurement-rigidity certificates, and quantitative results.