Project summary

I'm Igor. I'm a lawyer, not a scientist by training, and since August I've been working on one question: when an AI research agent says it discovered something, how do we know it didn't just fool itself?

SDCP is my answer so far. Before each run the agent writes down what it expects and seals it with a hash, so it can't adjust the story afterwards. It also runs on "empty" worlds with no real pattern, which shows how often it sees patterns that aren't there. A separate reviewer, not the agent, opens the sealed results.

Since October I've been testing it against things I don't control: an AI forecasting bot on Metaculus, where reality decides who was right, and next, NASA solar-wind data.

I write all the code with sufficient help of AI, but I edit them myself to my vision.

Project page: https://claude.ai/artifact/UYFp2CXdF78dLEPTU4H6VU

What are this project's goals? How will you achieve them?

1. Blinded real-data test: a structure is deliberately removed from the engine's vocabulary; false-discovery controls and success criteria are frozen and hashed before the run; the independent evaluator opens the result, and it is published either way.

2. Public verification protocol: a short, reusable specification.

3. One worked audit of a public "AI discovered X" claim using the protocol.

4. Open-source release of the tooling: data-custody kit, one-shot sealed executor, fail-closed evaluator with exact CRPS scoring.

How will this funding be used?

Funding goal $5,000: $2,400 for 6 months of AI model usage (generator and evaluator models), $2,100 partial stipend (includes income tax), $500 (10%) buffer. The minimum on this page covers compute only; any amount above it extends the time I can work on this full time.

Who is on your team? What's your track record on similar projects?

Just me, Igor Trofimenko, an independent, self-funded researcher, working with AI models: one generates, a separate reviewer session audits and can stop the work; I act as the authorizer. Until 11 October the reviewer was a model from a second company; since then it is a separate Claude session, the same model family, so its independence is weaker. Since August 2026 SDCP has produced calibrated false-discovery rates on null worlds, blinded runs opened only by the evaluator, an append-only record of my own withdrawn novelty claims and rejected engine versions, and a first pre-registered prediction on public NHANES data confirmed in an independent later cohort (preliminary; the effect is probably already known). No new discovery yet.

What are the most likely causes and outcomes if this project fails?

Most likely failure: the blinded test comes back negative. That is still a useful published result, because it measures the limits of an AI discovery engine under honest controls. Second: the protocol gets little uptake outside the project; the tooling and protocol stay public either way.

How much money have you raised in the last 12 months, and from where?

None received so far. Emergent Ventures (applied 2026-10-08, $12,000) declined on 2026-10-12. Pending: EA Funds Transformative AI Fund (applied 2026-10-08, $13,200). If more than one comes through, I will adjust amounts to avoid double funding.