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results/leaderboard.md

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+# UnityReviewBench — Leaderboard
+
+Bug detection on real-world Unity gameplay code (24 bugs / 22 clean methods).
+Recall = % of real bugs flagged. Quiet = % of clean methods left alone (no false alarm). Balance = harmonic mean.
+
+| Model | Recall | Quiet (specificity) | Balance |
+|---|---|---|---|
+| Qwen3-Coder + project-context | 16/24 (67%) | 18/22 (82%) | **73** |
+| Qwen3-Coder (open) | 22/24 (92%) | 5/22 (23%) | **36** |
+| Gemini 2.5 Pro | 16/24 (67%) | 4/22 (18%) | **29** |
+| Claude Sonnet 4.5 | 11/24 (46%) | 4/22 (18%) | **26** |
+| GPT-5.5 | 24/24 (100%) | 2/22 (9%) | **17** |
+
+> Note: binary recall/specificity is *necessary but not sufficient*. See README — a judge-based
+> precision pass reveals that high recall often hides false-positive flags. Project-context (RAG)
+> dramatically improves specificity on this set.