RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments
- 类型：arxiv
- 标识：2609.15364
- 链接：https://arxiv.org/abs/2609.15364
- 主分类：agent
- 形态：method
- TLDR：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 br
- 副分类：engineering
- 待LLM分类：否
- 来源文件：
- /inbox/tom/_candidates/2026-09-15-agent-rag-longcontext-candidates.json