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arXiv:2610.27610v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements. Evaluat...
arXiv:2609.33180v2 Announce Type: replace-cross Abstract: As recursive self-improvement (RSI) rapidly advances, reliable evaluation becomes critical for guiding adaptive search. RSI typically relies...
The paper investigates how agent harness evolution supports recursive self‑improvement without changing model weights, assessing four methods across general and coding tasks. Repeated tests and component interventions show that all evolved agents acquire new tasks while losing some previously mastered ones, with reliability gains sometimes offset by higher deployment costs. The study highlights that the highest‑scoring agent does not always deliver the greatest mean improvement, and that stricter admission criteria can eliminate useful alternatives; it proposes preservation‑aware evaluation and a follow‑up tool, EvoAudit, to refine agent design.
EvalResearchBench (ERB) is a benchmark that tests whether AI agents can autonomously design evaluation tasks for recursive self‑improvement. In ERB, a researcher agent selects or synthesizes tasks, builds graders, and refines them through pilot tests before producing a final evaluator that runs within fixed time and API budgets. Experiments with nine researchers and thirteen candidate models show that the best automated evaluators achieve about 75% concordance with target benchmarks, below the 91% ceiling set by target disagreements, and that human‑designed task samples still outperform many automated approaches.
arXiv:2607. 26587v1 Announce Type: cross Abstract: Automated research systems use experimental scores both to deliver artifacts and to decide which ideas to retain, transfer, and pursue.
arXiv:2610.35193v1 Announce Type: new Abstract: AI systems built around large models increasingly use experience and feedback to make persistent changes to models, prompts, memories, tools, and workf...