Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember

Self-play lets AI agents generate their own training problems, but without persistent memory, past failures don\'t shape future practice in a lasting way. SESA introduces an evolving skill memory system where a \"challenger\" poses problems, a solver retrieves relevant skills, and failures get distilled into reusable skills written back to memory --- creating a feedback loop where task difficulty and skill knowledge co-evolve. Tested across seven QA benchmarks, it improves accuracy over strong baselines while supporting both memory-based and memory-free deployment. This benefits agentic AI systems needing continual improvement in search, tool use, and multi-hop reasoning. Authors: Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai, Haoyu Wu, Minghui Wu, Paper: https://arxiv.org/abs/2607.29468v1