History-Aware and Scope-Constrained Skill Evolution for Coding Agents

Large language model (LLM)-based coding agents have made substantial progress in automated software engineering, and recent studies further enable them to accumulate reusable knowledge from completed task trajectories. However, as code repositories continuously evolve, accumulated knowledge may become outdated or even mislead agents on new tasks, making it necessary for knowledge to evolve alongside the repository. Existing knowledge self-evolution methods typically update knowledge based on its current state and newly acquired experience, overlooking both the history of successive updates and the scope within which knowledge remains applicable. This leads to knowledge degeneration as repeated updates make knowledge increasingly redundant or overly task-specific, and cross-repository contamination as repository-specific experience is inappropriately used to modify knowledge beyond its applicable scope. To address these limitations, we propose EvoGraph, a history-aware and scope-constrained framework for knowledge self-evolution in coding agents that represents reusable knowledge as skills. To mitigate knowledge degeneration, EvoGraph maintains an Evolution History Graph (EHG) that preserves historical skill versions and their revision, derivation, and consolidation relations, enabling proposed updates to be assessed against earlier versions and evolution-related skills. To reduce cross-repository contamination, the EHG further records the repository scope of each skill, based on which EvoGraph constrains both skill retrieval and skill updates, restricting inappropriate knowledge reuse and modification across repositories. Experiments on SWE-bench Verified and Pro with Qwen3.6-27B, GPT-5 mini, and DeepSeek-V4-Flash show that EvoGraph achieves average relative improvements of 6.90%–14.04% in resolution rate over all baselines across the three models. All code and data are available at https://anonymous.4open.science/r/EvoGraph-B1C4.