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Get Started Free →yogsoth-ai/circular-validation-audit
Strategy: Run BEFORE building any validator (sandbox/simulation/benchmark). Builds a non-circularity matrix of theory-claim × validator-assumption to detect when a validator would 'confirm' a theory only because it was built on the theory's own premises. A circular validator's PASS carries zero evidential weight. Methods: Cartwright nomological machines, Winsberg sanctioning-of-simulations, tautology detection.
Passed 17 of 23 test cases with the skill, and 6 of 23 without it.
and 13 more in the full report
13 fixed6 passed either way5 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
What are the exact three classification labels permitted for cells inside the non-circularity evaluation matrix?
| Checked | Without skill | With skill |
|---|---|---|
| Lists CIRCULAR, INDEPENDENT, and PARTIAL as the three cell verdicts | Missed | Passed |
The instructions your model reads when the skill loads.
A pre-build gate for any validator we are about to construct — a sandbox, simulation, or benchmark that is supposed to verify a theory. The catastrophic failure mode: we build the validator using the theory's own assumptions as the validator's ground-truth machinery, run the theory's method on the validator's output, and celebrate when it "recovers" the truth — but the recovery was guaranteed by construction, not by the theory being right. The PASS carries zero evidential weight. This is the single most expensive mistake available, because a circular validator can burn enormous compute producing confident, meaningless confirmations. So this audit runs FIRST, on the validator DESIGN, before a line of it is built.
A validator V tests a theory T by: generating data D from a ground-truth generator G, running T's recovery method R on D, and checking if R(D) matches G's hidden truth. The test is non-circular only if G is NOT itself an instance of T's central assumptions. If G is built from the same functional factorization / the same noise law / the same mechanism that T claims to discover, then "R recovers G" is a tautology: we encoded the answer into the question. The audit's job is to find every place where G secretly contains T.
Rows = the theory's load-bearing claims/assumptions (the ones the validator is meant to test). Columns = the validator's ground-truth construction choices (how each mechanism is implemented, what functional forms or noise laws the generator uses, what intervention semantics the validator actually applies).
Each cell asks: does this construction choice ASSUME this claim?
| validator: construction choice 1 | validator: construction choice 2 | validator: construction choice 3 | … | |
|---|---|---|---|---|
| claim: load-bearing claim A | ? | ? | ? | ? |
| claim: load-bearing claim B | ? | ? | ? | ? |
| claim: load-bearing claim C | ? | ? | ? | ? |
| … | ? | ? | ? | ? |
Fill rows with each load-bearing claim the validator must test. Fill columns with each ground-truth construction choice in the validator. Cell verdicts:
test cases passed, vs 6 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.