The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

A paper submitted to arXiv on 10 Sep 2026, titled "The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement," proposes a development roadmap for recursive self-improvement (RSI) in AI systems. The paper uses the Headroom-Closed Index (HCI) to identify problems in existing large language models, then outlines five stages of RSI: improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, environment-adaptation autonomy, and recursive meta-improvement. The authors examine RSI across scientific discovery, embodied intelligence, and software engineering, and identify key challenges to achieving genuine RSI.

Computer Science Machine Learning Submitted on 10 Sep 2026 Title:The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement View PDF /pdf/2609.11873 HTML experimental https://arxiv.org/html/2609.11873v1 Abstract: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. Current browse context: cs.LG References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .