AI RESEARCH
FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
arXiv CS.AI
•
ArXi:2606.03852v1 Announce Type: cross 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.