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RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

Published 16 Sept 2026arXiv:2609.15364

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Updated 7 h ago · first seen 14 Sept 2026

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Abstract

Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor, and verifier agents to continually explore the environment, validate outcomes, and retain environment-specific knowledge, including reusable causal relationships between actions, conditions, and consequences. It further adopts a broad-then-deep exploration strategy, combining parallel broad recursive self-exploration for discovering diverse environment structures with focused deep self-exploration for uncovering hard cases, hidden constraints, boundary conditions, and previously unknown causal dependencies. The resulting memory is frozen and can be directly reused for downstream tasks without updating model parameters. Experiments on OSWorld-v2 and Agent's Last Exam show that RSIAgent substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.

Authors

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Biwei HuangKun ZhouShicheng FanSibo ZhuWenyi WuXinyue Wang

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arXiv (Atom API + RSS)rss.arxiv.org/rss/cs.CV feedT1· Official7 h ago4
arXiv (Atom API + RSS)rss.arxiv.org/rss/cs.AI feedT1· Official7 h ago5
arXiv (Atom API + RSS)rss.arxiv.org/rss/cs.CL feedT1· Official7 h ago4
Hugging Face Hub (public pages, model cards, papers)huggingface.co/papers listingT2· Quality secondary4 h ago30

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