Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills

Software engineering agents are among the most commercially consequential AI systems being developed today, yet improving them has been constrained by the cost and scarcity of high-quality training tasks. Socratic-SWE turns this problem inside out: rather than sourcing improvement from external data, it mines the agent's own failure history. Every time the agent struggles or succeeds, that experience becomes curriculum material for the next training round. The approach is both efficient and self-correcting, targeting exactly the weaknesses the current model exhibits. For teams building coding assistants, automated debugging tools, or autonomous development pipelines, this self-improvement loop offers a scalable path toward agents that genuinely get better through use. Authors: Chuan Xiao, Zhengbo Jiao, Shaobo Wang, Wei Wang, Bing Zhao, Hu Wei, Linfeng Zhang, Lin Qu Paper: https://arxiv.org/abs/2606.07412v1