Socratic-SWE 解决了智能体训练数据依赖人工标注的瓶颈，做 AI 编程或智能体开发的团队可以直接借鉴其闭环进化思路，提升模型在真实仓库中的修复能力。
Socratic-SWE 是一种新型闭环自我进化框架，它利用 LLM 驱动的软件工程智能体的历史解决追踪来生成训练信号。与传统的固定突变或漏洞注入方法不同，该框架将追踪提炼为结构化技能，总结重复失败和有效修复模式，并指导生成针对性的修复任务。通过执行验证和求解器梯度对齐奖励筛选任务，Socratic-SWE 在 SWE-bench Verified 等基准测试上经过三次迭代达到 50.40% 的准确率，持续超越同等计算预算下的自我进化基线。这表明解决追踪可作为可扩展的自我进化基础，为提升编程智能体能力提供了新路径。
Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-quality SWE tasks. Existing synthetic data methods typically create tasks through fixed mutation or bug-injection procedures, making the resulting distributions largely independent of the agent's own weaknesses and training progress. We introduce Socratic-SWE, a closed-loop self-evolution framework that reuses the agent's historical solving traces as a source of training signal. Rather than treating traces only as evidence for reward computation, Socratic-SWE distills them into structured agent skills that summarize recurring failures and effective repair patterns. These skills then guide the generation of targeted repair tasks in real repositories. Candidate tasks are checked through execution-based validation and scored with a solver-gradient alignment reward, so that the retained tasks are both verifiable and useful for improving the Solver. The updated Solver produces new traces, enabling the task curriculum to adapt over successive rounds. Across SWE-bench Verified, SWE-bench Lite, SWE-bench Pro, and Terminal-Bench 2.0, Socratic-SWE consistently improves over self-evolving baselines under the same compute budget, reaching 50.40% on SWE-bench Verified after three iterations. These results suggest that solving traces can serve as a scalable substrate for self-evolving SWE agents.