LLMs accelerate literature triage and formalization drafts. They do not replace versioned experiments, baseline comparisons, or the inventor's verification step before anything goes public.
ML is a dynamical observer on structured carriers — not an authority. Outputs ship with claim ceilings, comparator passes, and explicit abstention when baselines still win.
Structured results, human report, checksum. No upgrade from summary to validated claim without bound evidence at reproducible artifacts.
Tools assist; the inventor owns public language. AI disclosure is plain; overclaim is treated as a failure mode, not a marketing option.
Same electron-flow discipline as the System diagram — directed edges, dwell at gates, emit only on pass.
Interpretable early-warning features vs deep-learning baselines on published benchmark families — scoped, publication-dependent claims only.
Methods-ready · not universal predictor
Composition-to-phase signals on public elasticity datasets — classical methods demonstration without quantum hold-up for filing narrative.
Methods-demonstrated on cited public N
Hypothesis-generating phenotyping and cohort de-mixing — research-stage, not diagnostic clearance.
Hypothesis-generating only
Statements in library or finite certificates cited externally — not unsourced "solved" language.
COMPILED gate for proof claims