Prism: A Multi-solution Reasoning and Synthesis Framework for Repository-Level Issue Resolution

Abstract

Large language model (LLM)-based agents have achieved notable success in repository-level issue resolution, yet existing frameworks still face two key bottlenecks: insufficient diversity among candidate repair solutions and limited ability to combine complementary strengths across candidate solutions. To address these challenges, we introduce Prism, a multi-solution reasoning and synthesis framework that adopts a coarse-to-fine paradigm to systematically generate, refine, and integrate diverse repair solutions. Prism comprises three stages: (1) a global exploration mechanism with contrastive semantic constraints that broadens the search space by encouraging divergence across historical solutions; (2) an A* algorithm-inspired local exploration mechanism that identifies suitable branching points to generate divergent sub-solutions; and (3) a multi-agent collaborative solution synthesis process where reviewer and judge agents jointly evaluate, complement, and integrate partially correct candidate solutions. We evaluate Prism on SWE-bench Verified and SWE-bench-Live Lite. With Claude-4.6-Sonnet and DeepSeek-V3.2-Reasoner, Prism achieves Pass@1 rates of 82.0% and 80.0% on SWE-bench Verified, respectively, outperforming the compared baselines. On SWE-bench-Live Lite, designed to reduce data leakage concerns, Prism outperforms the reported baselines. Further analyses show that Prism expands the solution space and that its solution synthesis stage uniquely resolves 15 issues unaddressed by isolated candidates.