#evals

Part 3 left me a label-free signal that responds to a real regression. It does not come with a threshold. Here is where the line actually goes, what a false page costs, and why the reflex answer computes to a negative number.

I could not detect a deleted guardrail with an LLM judge, a judge-free assertion, or six label-free signals. Three instruments, one null. The instrument was never the problem: shorten the source and the same gate goes from p = 1.000 to p = 0.0020.

I deliberately broke my summariser's prompt, then failed to detect it two ways: with an LLM judge over a golden dataset, and with a judge-free deterministic assertion. Removing the judge changed nothing. Here is the calculation that would have told me first.

I built an LLM-as-judge eval on my own blog and got a suspiciously perfect 16/16. Here's the three-round test I ran before trusting that number: single-variable corruption, and a self-consistency check the research says most teams skip.