编码智能体老学不会新技能？试试 GSE 这套框架，在测试生成和假阳性过滤上比现有方法准不少，还通过了工业级验证。
GSE 是一个全局化技能进化框架，通过联合优化技能兼容性和泛化性，解决现有方法容易过拟合的问题。它维护技能关系图来显式建模技能间关联，并用聚类整合与重放验证来提升泛化能力。在 bug 揭示测试生成和假阳性 bug 报告过滤两项任务上，GSE 在 OpenHands 和 mini-SWE-agent 上均取得最佳精确率、召回率和 F1。相比现有进化技术，测试生成的精确率和召回率分别提升 6.1%~34.1% 和 31.8%~180.0%，假阳性过滤分别提升 15.4%~96.4% 和 13.1%~19.8%。内部工业智能体部署进一步带来 61.4% 的 F1 提升。
Learning Globally Reusable Skills for Coding Agents
Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evolution as a sequence of local updates, overlooking relationships among skills and often producing overfitted skill updates that fail to generalize across tasks. We propose GSE, a globalized skill evolution framework that jointly optimizes skill compatibility and skill generalization. To preserve consistency across the skill bank, GSE maintains a Skill Relation Graph (SRG) that explicitly models and co-evolves inter-skill relationships. To improve generalization, GSE performs cluster-based skill consolidation to abstract reusable capabilities from local updates and employs replay-driven verification to prevent overfitting and behavioral regressions. We evaluate GSE on two representative software engineering tasks: bug-revealing test generation and false-positive bug report filtering. Across two state-of-the-art coding agents, OpenHands and mini-SWE-agent, GSE consistently achieves the best precision, recall, and F1-score. Compared with existing evolution techniques, GSE improves precision and recall by 6.1%~34.1% and 31.8%~180.0% for test generation, and by 15.4%~96.4% and 13.1%~19.8% for false-positive filtering. Deployment on an internal industrial agent further yields a 61.4% improvement in F1-score, demonstrating the effectiveness and generalizability of GSE for evolving effective skills.