FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement

2026-06-02

AI Summary

This work addresses the challenge that code generated by large language models often contains defects, yet existing feedback signals are too coarse-grained to precisely localize errors. To this end, the authors propose FLARE, a novel framework that introduces lightweight line-level suspiciousness prediction and a multi-candidate region search mechanism for the first time. By leveraging execution outcomes to rank and select among repair candidates, FLARE enables fine-grained, iterative code refinement. Experimental results on LiveCodeBench and BigCodeBench demonstrate that FLARE outperforms the strongest baseline by 1.72%–7.42% on average. Moreover, employing a 10-candidate search strategy yields an additional 8.50% improvement over single-candidate approaches, substantially enhancing the accuracy and effectiveness of code repair.

Abstract

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 BigCodeBench with five base LLMs show that, even without candidate search (k=1), Flare outperforms the strongest baseline with an absolute improvement from 1.72% to 7.42%. Furthermore, searching over 10 candidates yields an average improvement of 8.50% compared with no candidate search. When evaluated in isolation, our lightweight diagnostic model achieves the best performance compared with recent fault localization methods, demonstrating that it can provide reliable fine-grained guidance for code refinement.

Problem

• bug localization
• code refinement
• diagnostic feedback
• large language models
• fine-grained feedback

Innovation

• fine-grained feedback
• bug localization
• code refinement
• diagnostic model
• LLM code generation