人类建造的最后AI：迈向真正的递归自我改进
The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
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中文总结 AI 辅助
本文提出递归自我改进（RSI）概念及发展路线图，用HCI揭示现有LLM问题，分析多场景需求，结合行业实践识别实现真正RSI的关键挑战。
中文摘要 AI 辅助
递归自我改进（RSI）使AI系统能够将经验和反馈转化为持久的改变，从而提升其能力以及未来改进的过程。我们首先使用Headroom-Closed Index（HCI）揭示现有大语言模型的问题，然后介绍RSI概念及其发展路线图：从改进执行自主性、改进策略自主性、经验获取自主性、环境适应自主性，到递归元改进。接下来，我们考察RSI在多种场景（如科学发现、具身智能、软件工程）中的应用，强调它们的不同需求和开发速度。借鉴多样的行业实践和初步实证证据，我们将RSI研究与实际系统联系起来，并识别实现真正RSI的关键挑战。
英文摘要
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement. Next we examine RSI across scenarios (e.g., scientific discovery, embodied intelligence, software engineering), highlighting their distinct requirements and development speeds. Drawing on diverse industry practices and preliminary empirical evidence, we connect RSI research with practical systems and identify key challenges to achieving genuine RSI.
