Michael Rothrock

What I built: I built BioSurface Audit, a deterministic tool that verifies the claims in an AI‑assisted single‑cell / Perturb‑seq analysis. It takes an AI assistant's prose write‑up, turns it into explicit structured claims, and checks each one across several independent verification surfaces. The model drafts the claims and suggests missing evidence, but a deterministic engine decides what the data supports. This is a clear trust boundary. Each claim ends up with a reproducible status tied to the exact evidence behind it, plus two summary numbers: Trust Coverage (how much of the required evidence was actually checked) and Corroboration (the share of checkable claims the independent surfaces agree on). What I found: I ran it on the published Marson data and a second Replogle K562 dataset to check that it generalizes. It catches the kinds of mistakes an AI assistant tends to make: a reversed fold‑change direction, or a target "nominated" from expression alone with no pathway or network support. In a blind test it caught all five errors I planted, each tied to the exact contradicting row, e.g. a claim that RASA2 knockdown lowers CXCL8 when the data shows it going up. The output is byte‑for‑byte reproducible, so it can gate a CI pipeline. Why it matters: The lesson we are learning in software development is that LLMs generate far more than human reviewers can digest. We need to augment them with deterministic tests that allow us to make guarantees about output, paired with stochastic evaluation that suggests where our limited human attention is best spent. For this domain, my starting point is that trust isn't any one engine's verdict, it's where independent lines of evidence converge. BioSurface makes that triangulation mechanical: it reports what it can't check rather than guessing, and every result is reproducible. It's the single‑cell instance of the Verification Surface framework I've been developing (https://michael.roth.rocks/research), so the same idea extends well beyond biology.