From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair

Repository-level automated program repair (APR) requires long-horizon reasoning over interdependent decisions. However, most LLM-based approaches reconstruct repair reasoning independently for each issue, failing to reuse successful patterns from prior repairs, even though realworld repositories contain many related issues with shared structure or constraints. Existing methods typically rely on forward exploration, which operates under outcome uncertainty, incurs substantial inference-time overhead, and can drift from the final correct patch. We propose Conditional Reasoning Distillation (ConRAD), which leverages in-repository resolved issues by reconstructing repair reasoning backward from verified patches and distilling outcome-consistent, stage-wise repair reasoning plans. Injected at inference time, these plans guide fault localization and patch generation, replacing open-ended exploration with constrained inference without fine-tuning or search. On SWE-Bench Lite, Con-RAD improves Pass@1 by 10.4% (GPT-4o), 8.6% (DeepSeek-V3), and 10.3% (GPT-5), demonstrating a scalable inference-time alternative to forward exploration for long-horizon APR. The code is publicly available at https://github. com/Reasoning4Code/ConRAD .