DySCoR: Stage-Constrained Dynamic Sparse Context Routing for Repository-Level Code Repair

Agentic code repair systems increasingly combine large language models with retrieval, tools, and structured context packs, yet most existing approaches expose similar repository evidence throughout task understanding, localization, patch generation, and feedback revision. This stage-agnostic context construction wastes token budget and can distract repair agents with evidence that is useful only in specific phases. We propose DySCoR, a stage-constrained dynamic sparse context routing framework for repository-level code repair. DySCoR decomposes repository information into fine-grained evidence units, assigns stage-aware visibility states, routes compact evidence under token budgets, and updates evidence confidence and visibility after validation using test outcomes, traceback changes, and patch diffs. Experiments on SWE-Bench Lite, SWE-Bench Verified, and RepoRepair-Hard show that DySCoR consistently improves repair effectiveness, localization quality, and context efficiency. It achieves an average resolve rate of 33.8%, outperforming ToolAgent+StaticPack, StaticPack, and Hybrid-RAG by 4.5, 7.7, and 10.0 absolute points, respectively. DySCoR also reaches 54.8% file-level Hit@1, 72.4% file-level Hit@5, 34.2% function-level Hit@1, and 55.8% function-level Hit@5, while reducing average input tokens to 58.4K, a 43.5% reduction over ToolAgent+StaticPack, and lowering redundant context from 36.4% to 18.5%.