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results/leaderboard.md

@@ -1,26 +1,30 @@
 # Review track — a methodology finding, not a leaderboard
 
-We ran five models on 24 buggy + 22 "post-fix" Unity gameplay methods, expecting to rank them by
+We ran seven models on 24 buggy + 22 "post-fix" Unity gameplay methods, expecting to rank them by
 recall and specificity. We are **not** publishing a balance ranking — here's why.
 
 | Model | Recall (real bug flagged) | Flag rate on "post-fix" methods |
 |---|---|---|
 | GPT-5.5 | 100% | 91% |
+| Opus 4.8 | 100% | 100% |
 | Qwen3-Coder (open, no context) | 92% | 77% |
 | Gemini 2.5 Pro | 67% | 82% |
-| Claude Sonnet 4.5 | 46% | 82% |
 | Qwen3-Coder + project context (RAG) | 67% | 18% |
+| Gemini 3.1 Pro | 62% | 55% |
+| Claude Sonnet 4.5 | 46% | 64% |
 
-A naive harmonic-mean "balance" would rank GPT-5.5 last. **That is wrong.** We hand-read every flag
-GPT-5.5 raised on the "post-fix" methods: they are overwhelmingly **real, reasonable review issues**
-(unchecked return values, missing null guards, non-atomic reward grants, missing idempotency,
-`Resources.Load` null, `OnDestroy` on scene-unload) — not false positives. GPT-5.5 even returned
-`CLEAN` on the two methods with no residual issue, so it isn't flagging blindly.
+A naive harmonic-mean "balance" would rank GPT-5.5 and Opus 4.8 last. **That is wrong.** We hand-read
+their flags on the "post-fix" methods, then ran a convention-aware per-flag judge (cross-checked by
+two independent judges) across all models: the thorough frontier models' flags are **predominantly
+real review issues** (unchecked return values, missing null guards, non-atomic reward grants, missing
+idempotency, `Resources.Load` null, `OnDestroy` on scene-unload), not false positives.
 
 The ground truth is the problem: a "post-fix" method is just the version where *one* bug was fixed,
 not a method with *zero* issues — real shipping game code always has residual concerns. So
 "specificity" punishes the most thorough reviewer, and a balance score built on it is upside-down.
 
-**Takeaway:** binary recall/specificity is the wrong instrument for code-review quality. The right
-one is a per-flag verdict (real / nitpick / false-positive); see the repo README for the full
-methodology. Treat this track as a case study in why bug-detection benchmarks are hard.
+**Takeaway:** binary recall/specificity is the wrong instrument for code-review quality. The right one
+is a per-flag verdict (real / nitpick / false-positive) — but two independent judges disagree enough
+(46–88% agreement) that absolute precision needs human gold labels before it can be ranked, which is
+why only recall + flag-rate are shown. Treat this track as a case study in why bug-detection
+benchmarks are hard.