Recuris

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

National University of Singapore  ·  Stanford University  ·  University of Oxford  ·  Princeton University

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💡 Introduction

Recuris is a recursive self-improvement framework that improves a long-horizon agent by evolving its memory instead of its weights or its prompt. A frozen agent is paired with a Skill Memory M = (E, W, ρ, C). A meta-agent reads structured execution traces, locates each failure in one component of that memory, and patches only that component. A deterministic validation gate then decides, on paired held-out evidence, whether the patch survives. Recuris has the following key features:

• State-grounded memory use. Working memory drives skill invocation, so retrieval is conditioned on verified task state rather than on a chat history that grows until the state is buried.
• Targeted memory evolution. Structured trajectories (w_t, E_t, a_t, o_t) locate a failure in a specific component, instead of nudging a monolithic prompt from outcomes alone.
• Bounded by a validation gate. Candidates are admitted by paired held-out arithmetic and nothing else. No model votes on its own patch.
• Training-free and model-agnostic. The downstream agent stays frozen, and a memory evolved on one model transfers to others unchanged.

Overall, Recuris achieves higher task success, larger gains on longer horizons, and substantially fewer long-horizon failures, on both frontier and open-source agents.

🔔 News

• [2026-08] 🎉 Initial release: evaluation and evolution code, the evolved Skill Memory packages, and the frozen evaluation splits.

📊 Results

Task success (avg@4, %), each model run with the benchmark's own reference agent alone and with that same agent plus Recuris. Bold marks the better of each pair, the subscript is Δ, † marks a paired task-clustered bootstrap 95% CI excluding zero, and n/a means the model was not run on that benchmark.