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GraphAlignCoder boosts code generation accuracy by 31.6% to 43.8% by embedding formal proof structures into the training process.
SCOPE's structured feedback mechanism boosts code generation accuracy, achieving a notable 39.4% pass rate on LiveCodeBench V6鈥攐utperforming existing methods by a significant margin.
Fixing label errors in RVL-CDIP can enhance model performance by over 8 percentage points, but removing test-train duplicates may paradoxically hurt accuracy.
VERITAS reveals that incorporating verifier feedback can boost theorem proving success rates by over 10%, challenging the efficacy of traditional binary pass/fail approaches.
LLMs can now reliably fix decompiled code, but only if you make them *execute* it.
