The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
- Published
- Source
- arXiv
- Paper number
- 1084
- Field
- Machine Learning
- arXiv ID
- 2609.11873
Key points
- The paper proposes a five-level autonomy framework (B0–L5) that maps scattered self-improvement research onto a single landscape.
- The authors directly surveyed more than 100 industry cases including Anthropic, DeepSeek, and Tencent, and graded them by level.
- It quantifies development cost: multi-agent workloads use roughly 15 times the tokens of standard chat, and DeepSeek-V3.2 spent over 10% of pretraining compute on post-training.
- It cites the GPT-5.6 case where AI directly designed and ran experiments, improving token-generation efficiency by more than 15%.
- It identifies reliable verification and learning environments as the core bottleneck for recursive self-improvement.
Paper links
External research summaries. These are not HDATF publications or measured product results.