SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
An RSI-style approach that scales automated research loops across many diverse coding-agent environments to optimize the harness layer itself. The process yields reusable improvements that cut coding agents' token use and API costs while preserving task performance.
Why it matters
It shows token-efficiency of agent harnesses can itself be the target of automated search, pointing toward self-improving agent infrastructure.
Importance: 2/5
Notable agents paper, 94 upvotes on HF Daily Papers
Sources
official
HF Daily Papers: SoL-Pi (94 upvotes)