The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
Position paper defines recursive self-improvement for AI, introduces the Headroom-Closed Index and an autonomy roadmap toward genuine recursive meta-improvement.
The paper uses the Headroom-Closed Index to diagnose limitations of existing LLMs and frames recursive self-improvement (RSI) as a staged roadmap: improvement-execution, improvement-strategy, experience-acquisition, and environment-adaptation autonomy, culminating in recursive meta-improvement. It examines RSI across scientific discovery, embodied intelligence, and software engineering, highlighting differing requirements and development speeds. Drawing on industry practices and preliminary empirical evidence, it connects RSI research with practical systems and identifies key challenges to achieving genuine RSI.
- Introduces Headroom-Closed Index to reveal limits of existing LLMs
- Defines four autonomy stages up to recursive meta-improvement
- Analyzes RSI in scientific discovery, embodied AI, and software engineering
- Connects RSI research with industry practices and empirical evidence
From the original article
1 min read at arxiv.orgRecursive 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.
By Yi Duan · arxiv.org
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