FASE: Fast Adaptive Semantic Entropy for Code Quality
Multi-agent code generation offers a promising paradigm for autonomous software development by simulating the human software engineering lifecycle. However, system reliability remains hindered by LLM hallucinations and error propagation across interacting agents. While semantic entropy provides a principled way to quantify uncertainty without ground-truth answers, current methods often rely on costly LLM-driven equivalence checks. In this work, we introduce Fast Adaptive Semantic Entropy (FASE), a novel metric that approximates functional correctness based on the minimum spanning tree of structural and semantic dissimilarity graphs. Evaluations on HumanEval and BigCodeBench demonstrate th...
| Authors | Shizhe Lin, Ladan Tahvildari |
|---|---|
| Primary category | cs.SE |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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