Research & announcements

Accuracy benchmarks ask whether the system was right. That is the wrong question for production. The right question is whether the system behaved well given what it actually knew—and that is what epistemic integrity measures.

AI has already proven it can write, analyze, and classify. What it has not proven, in the institutional sense, is that its reasoning can be trusted for the decisions a regulated business is actually built on. The blocker is not capability. It is accountability.

Updated: Colorado repealed SB 24-205 before it ever took effect and replaced it with the narrower SB 26-189, effective January 1, 2027. The original analysis stands on the record, with what the replacement dropped and what survives — including the question that outlives both statutes: can you document how the system reasoned?

Apply different prompt sets to the same reasoning graph and you get different convergence paths, different abstention rates, and different epistemic integrity scores. Prompt engineering isn’t cosmetic—it’s the configuration of an AI system’s epistemic personality.

Traditional validation assumes you can read a model’s mechanics, test it on holdout data, and stress-test it against known scenarios. LLMs break all three assumptions. What validation teams actually need isn’t holdout accuracy—it’s reasoning traces.

Hallucination rates aren’t evenly distributed. They increase with document complexity and input ambiguity—which means they concentrate in the domains where accuracy matters most: medicine, law, finance. The cause is architectural, and so is the fix.