REFIXAGENT: ITERATIVE REFLECTION AND LOCAL- IZATION FOR AUTOMATED PROGRAM REPAIR

Automated program repair via Large Language Models (LLMs) has achieved re- markable advancements, yet prevailing agent-centric paradigms exhibit limited exploration of diverse remediation strategies. Although multi-sampling method- ologies have been introduced to alleviate this constraint, they frequently converge toward homogeneous solutions owing to the stochastic nature of autoregressive language models. To mitigate these deficiencies, we introduce ReFixAgent, a col- laborative multi-agent architecture that enables systematic exploration of diverse candidate localization sets and implements iterative introspection and refinement for patch synthesis, thereby augmenting the strategic search landscape of program repair. Our framework initiates with multi-faceted localization set sampling, op- tionally augmented with code dependency graph-enhanced fault localization for complex remediation tasks. This is succeeded by iterative introspective refinement to generate high-fidelity patches, culminating in a final-generation phase guided by distilled knowledge from all historical remediation attempts. Empirical evaluations on the SWE-bench Verified benchmark demonstrate that ReFixAgent attains excep- tional performance, achieving 74.4% pass@1 with DeepSeek-V3.2—surpassing state-of-the-art methodologies under equivalent model configurations by up to 5.0%. The framework also exhibits robust generalization across diverse backbone models including Claude-4, GPT-5, and Qwen-Coder, while delivering superior fault localization precision compared to existing approaches.