arXiv AI By Wangcheng Tao, Zhanglu Yan, Han Wu, Weng-Fai Wong

Skill$^{\infty}$: Recursive Self-Improvement for Agent Skill Optimization

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arXiv AI
Jun 9

Skill Retrieval Augmentation for Agentic AI

arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.

By Weihang Su, Jianming Long, Qingyao Ai, Qiaozhi He, Yichen Tang, Changyue Wang, Yiteng Tu, Yingbo Wang, Yiqun Liu
arXiv AI
2d ago

RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training

RSI-Opt introduces Recursive Self-Improvement Optimizer Agents that evolve their own training policies by accumulating knowledge from previous runs, including prompts, playbooks, optimizer code, and decision cadences. A meta‑agent rewrites and archives these policies, enabling reusable procedures and code that adapt to new tasks. Across multiple generations and tasks, RSI‑Opt improves optimizer performance, achieving higher z‑scores on evolution suites, better cross‑model transfer, and significant error reductions on PDE benchmarks compared to fixed‑prompt baselines.

By Binghang Lu, Yige Sun, Guang Lin
arXiv AI
Oct 16

RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent

RSIFlow is a recursive self‑improvement framework that combines adaptive workflow orchestration with a meta‑agent that evolves its own harness. It diagnoses performance bottlenecks from task‑agent trajectories and environmental feedback, then dynamically selects component‑specific improvement workflows. Experiments on six benchmarks and two backbone models show that RSIFlow achieves state‑of‑the‑art average performance, with ablation studies confirming the benefits of adaptive orchestration and meta‑harness evolution.

By Huan Zhang, Mingju Chen, Dongxu Zhou, Can Lv, Sen Cui, Faguo Wu, Shiji Zhou