Recursive Experiential–Working Memory Evolution for Long-Horizon Agent Harnesses

Recuris is a recursive architectural framework designed to enhance the performance of large language model agents during complex, long-horizon tasks. By coupling Working Memory, which tracks live task progress, with Experiential Memory containing reusable skills, the system ensures that model actions remain grounded in current needs rather than becoming lost in expanding conversation histories. This integration allows the agent to produce structured execution traces, which a fixed Meta-Agent uses to pinpoint specific failures and apply targeted memory patches. Empirical results across various benchmarks demonstrate that this self-improving loop significantly boosts task success rates for both open-source and frontier models like GPT-5.6 and Claude Opus 5. By reducing common errors such as hallucinations and missed commands, Recuris provides a scalable foundation for agents to transform accumulated experience into increasingly reliable autonomous behavior.