Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses
Score
75提出递归记忆演化新架构，并给出跨 10 个模型的系统评测，增量明显。
实验覆盖多个基准与模型，结果统计扎实，分析到失败模式层面。
开源代码与明确收益让工程团队可直接验证并复现。
贴合当前长程 Agent 与 RSI 研究热点，发布及时。
来自 arXiv 预印本，作者团队可信但尚未经同行评审。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
Strong novelty, broad benchmark validation, and released code make this a high-impact contribution.
AI commentary
「What I find compelling is not the benchmark gains alone, but how Recuris turns memory evolution into a bounded, validation-gated loop that pinpoints failures to components. Teams building long-horizon agents or RSI systems should read the paper and try the code.」
Quick overview
Recuris introduces a recursive Experiential-Working Memory architecture that lets long-horizon agents decouple task history from skill selection. A working memory tracks progress and guides skills from experiential memory, turning execution into structured evidence. A fixed Meta-Agent converts that evidence into localized, validation-gated updates to skill memory, forming a bounded recursive evolution loop. Across four long-horizon benchmarks and ten models, Recuris improves success in 35 of 37 completed pairs, pushing frontier models to SOTA: +17.8 points for GPT-5.6 Sol on tau-bench and +15.6 for Claude Opus 5, reaching 87.9%, plus +16.6/+13.5 on SkillFlow for Qwen3.6-27B/35B. Gains widen with longer horizons, up to +32.2 points on the longest tasks, while common long-horizon failures drop by up to 80%. Code is open-sourced.
- Introduces Recuris, a recursive Experiential-Working Memory architecture for long-horizon agents.
- Localizes failures to memory components and applies validation-gated updates via a fixed Meta-Agent.
- Achieves SOTA or near-SOTA on tau-bench and SkillFlow across ten models.
- Improvements grow with task horizon, up to +32.2 points, and reduce common failures by up to 80%.
Full text
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