Research & announcements

We’re publishing irg-reference, an open implementation of the Iterative Reasoning Graph protocol. It runs against seven LLM providers, ships with a trace navigator that makes every reasoning step inspectable, and is licensed CC BY-SA so anyone can use it, fork it, and build on it.

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.

The Colorado AI Act applies to deployers, not just developers, and it covers AI used in credit, employment, housing, insurance, education, healthcare, and legal services. Compliance turns on whether you can document how the system reasoned, not just what it produced.

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.