Recursive Harness Self-Improvement (RHI): Refining Agent Harnesses from Execution Feedback
Sakana AI
Sakana AI and UC Berkeley researchers introduce Recursive Harness Self-Improvement, an iterative algorithm that refines user-constructed agent harnesses using pairwise feedback drawn from the agent's own execution history, without retraining the underlying model.
Why it matters
Extends Sakana AI's recently launched Recursive Self-Improvement research program, part of a broader industry push toward AI systems that improve their own tooling rather than relying solely on more compute.
Importance: 2/5
Notable extension of an active research program on agent self-improvement, below the upvote bump threshold (15 upvotes).
Sources
official
HuggingFace Daily Papers, 2026-07-20