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

TL;DR - Recuris is a recursive memory architecture that helps long-horizon agents track task progress, select relevant skills, and improve those skills through validation-gated updates. It increased task success across nearly all completed model-benchmark evaluations, with larger gains on longer tasks.

• Working Memory represents current progress and guides skill retrieval from Experiential Memory instead of relying on the full interaction history.
• Execution evidence localizes failures to memory components, enabling a fixed Meta-Agent to make bounded, validated updates to Skill Memory.
• Recuris improved 35 of 37 completed model-benchmark pairs across four benchmarks and ten models.
• Reported gains include +17.8 points for GPT-5.6 Sol and +15.6 for Claude Opus 5 on tau-bench, with improvements reaching +32.2 points on the longest tasks.