Add greenproof to §8 (verifiers) - #50
Conversation
|
The mechanism here is genuinely novel and I want to be clear that I like it: snapshot the tests before the agent runs, then re-run the agent's final code against the untouched originals, and let the exit code decide. Judge-free, deterministic, and it targets a real failure mode that outcome-only scoring misses. The blocker is evidence, not design. The entry cites "caught every constructed cheat and flagged no legitimate edit across 15 hand-built scenarios on 5 real Python repos" — but the repo contains no scenario definitions, no harness, and no result artifacts, only Publish the 15-scenario harness and its outputs and I'll take another look — with that in the repo this becomes a straightforward merge. Two smaller things: please disclose that you're the author (the PR doesn't say so), and note the README's |
|
Three corrections — I was unfair to this submission in two specific ways. 1. I said the repo has "no harness." Wrong. 2. I truncated your own hedge and then criticised you for not hedging. Your entry says "Self-reported: … ; not a statistical benchmark." You labelled the evidence correctly. I quoted it without the hedge and then argued the claim was overstated. That was backwards. 3. The The one real blocker, restated narrowly: the 15 scenarios across 5 repos and their outputs aren't in the repo, so a reader can't re-run the result. Publish those and this becomes a straightforward merge — the counterfactual mechanism is genuinely good and §8 is thin on judge-free verification. Please also add an authorship line to the PR body. Sorry for the noise. |
|
Published: https://github.com/zxyasfas/greenproof/tree/main/validation
Two things I had wrong in my own README, now fixed: flashtext has 39 tests, so the "82 to 407" range was wrong, and I'd written "delete or weaken" when every scenario here deletes rather than weakens. Authorship line added to the PR body. The framing in your second comment is right, it is self-reported and I'd rather it stay labelled that way. |
greenproof re-runs an agent's current code against the tests it started with (snapshotted before the agent touched anything) to check whether a claimed-green suite is real or only green because the tests got edited. No LLM in the loop — it's a deterministic re-execution, so it sidesteps the judge-reliability problem this section is about rather than adding another judge. Tested on 15 hand-built scenarios across 5 real Python repos (self-reported, not an independent benchmark — noted as such in the entry).
Authorship: I wrote greenproof. Submitting my own project.