新论文的PhoenixRepair能更系统地探索代码修复位置，在SWE-bench上比SWE-agent提升了7.8%，做软件工程的值得一看。
PhoenixRepair是一个多智能体框架，通过多位置采样和迭代反思扩大修复策略搜索空间。在SWE-bench-Verified上，相比SWE-agent在DeepSeek-V3.1下实现7.8%的相对提升。在MiniMax-M2.5下达到76.0% Pass@1的最高解决率。该框架的故障定位准确率也优于现有方法。
PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of repair strategies. This insufficiency manifests in two key aspects. First, the exploration of multiple potential edit locations is limited. Second, the exploration of repair attempts at each location is also insufficient. To address these challenges, we present PhoenixRepair, a multi-agent framework that systematically explores multiple candidate edit locations and performs iterative reflection and refinement on patch generation, thereby expanding the search space of repair strategies. Our framework begins with multi-location sampling, optionally augmented with graph-based localization information for difficult tasks, followed by iterative reflection and refinement to generate better patches, culminating in final-round generation guided by distilled insights from all historical attempts. Experiments on SWE-bench-Verified demonstrate that PhoenixRepair achieves the largest relative improvement of 7.8\% over SWE-agent under DeepSeek-V3.1, and attains the highest resolved rate of 76.0\% Pass@1 under MiniMax-M2.5. Meanwhile, it achieves higher fault localization accuracy than existing approaches. Our code is available at https://github.com/DeepSoftwareAnalytics/PhoenixRepair.