Story · arXiv (via papers.cool)
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv (via papers.cool))
paper · Story page
Agents that iteratively edit their own prompts, tools, memory and control flow can post large in-distribution gains that shrink or vanish out of distribution. RRSI caps how many edits one candidate can bundle, pushes the proposer toward unexplored changes, and puts a critic and a pruner over the proposals.
In plain words
- Researchers proposed a method to help artificial intelligence assistants improve at unfamiliar tasks.
- It limits how many changes assistants propose at once to their instructions, tools, or stored information.
- It screens out changes tailored to familiar tests and removes changes that cost too much or no longer help.
- For users, the goal is improvements that still help when assistants face tasks beyond their practice examples.
Appeared in
- Dormant prompt injections land on nine production agents where direct orders fail
Sep 23, 2026 · in the sections
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