FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
Large language models often generate code with bugs. Existing methods rely on feedback signals such as test failures and self-critiques to iteratively refine the generated code. Such signals are either too coarse-grained or too high-level, which is not sufficient to inform the model where to fix the bug. In this work, we present Flare, an iterative framework with a lightweight diagnostic model that predicts line-level suspiciousness signals for bug localization and code refinement. Given the inherent uncertainty of diagnostic predictions, Flare searches over the top-k suspicious regions and selects the best candidate according to execution outcomes. Experiments on LiveCodeBench and BigCod...
| Authors | Yinsheng Yao, Hongxiang Zhang, Weixi Tong et al. |
|---|---|
| Primary category | cs.SE |
| Published | 2 June 2026 |
| Last updated at source | 16 September 2026 |
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