Toward Recursive Self-Improvement in Physical AI
Recursive self-improvement (RSI) describes a system that improves not only its behavior but also the process by which it improves.
ProofPaper ↗
Key points
- Recent digital agents test this idea by rewriting their own code and checking each version with executable tests.
- We position RSI within robot learning and trace pathways from improving robot capabilities to improving the processes that generate them.
- We examine how physical experience can strengthen these improvement mechanisms and when the resulting gains can support further self-improvement of the mechanisms themselves.
- We synthesize these findings into evaluation principles and research directions for machines that, trial by trial, safely improve the way they improve.
Sources (1)
- [1]Toward Recursive Self-Improvement in Physical AIarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:54 PM
Recursive self-improvement (RSI) describes a system that improves not only its behavior but also the process by which it improves.
Recent digital agents test this idea by rewriting their own code and checking each version with executable tests.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 21, 2026Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
- Oct 20, 2026MaD-RSI: Model-as-Data for Recursive Self-Improvement of Agent Harnesses
- Oct 20, 2026Elicitation or Expansion? Demystifying Gains from Agent Harness Optimization
- Oct 20, 2026RSI Control: Can AI Models Perform Covert Sabotage in the Self-Improvement Process?
- Oct 20, 2026Auditing the Product, Not the Recursion: The Evaluation Evidence Behind Recursive Self-Improvement Claims
- Oct 20, 2026Cache Engineering for Agents