arXiv AI By Zihan Chen, Di Zhu, Lei Nico Zheng

Auditing the Product, Not the Recursion: The Evaluation Evidence Behind Recursive Self-Improvement Claims

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arXiv AI
Oct 2

From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution

Praxa is an evidence‑bound harness for governed AI agent execution that explicitly represents states such as proposal, authority, dispatch, verified external effect, and promotion through deterministic admission, brokered execution, external read‑back, reconciliation, and reviewed promotion. The authors report four evidence lanes: a repository‑local audit passing all unit and Workerd tests; a pilot on 12 curated tasks where both baseline and reliability‑layer arms passed 17 of 36 trials; a coordination‑proxy comparison where both baseline and a source‑authored candidate completed all 180 trials with equal accuracy but the candidate used fewer tokens and steps; and deployed source/configuration evidence showing bounded reflection, recall accounting, memory compilation, and tool‑health paths. None of the evidence demonstrates superiority in security, safety, or user benefit. whyItMatters:"Praxa provides a testable architecture that makes authority‑to‑effect transitions explicit, offering a framework for verifying AI agent behavior, though current evidence does not prove improved security or performance."

By Stefan G. Creadore
arXiv AI
1d ago

Beneath the Surface: What We Overlook When We Talk About Agent Evolution

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.

By Xiaobo Wang, Zimu Wang, Siyu Ye, Jiaqi Li, Min Tang, Jun Bai, Yuling Shi, Ruobing Wang, Jingjing Zhang, Chaofan Wang, Kai Wu, Mengnan Qi, Kai Cai, Xiaodong Gu, Qi Liu, Zilong Zheng
arXiv AI
Oct 19

EvalResearchBench: Can AI Agents Build Their Own Feedback for Recursive Self-Improvement?

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.

By Yaolun Zhang, Tianyi Xu, Yujie Zhao, Jishen Zhao, Qingyun Wu, Huazheng Wang
arXiv AI
Oct 27

Trustworthy Recursive Self-Improvement in the Era of Large Models: Foundation, Risks, Evaluation and Open Challenges

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...

By Jiayue Pu, Ang Gao, Jinyi Bai, Qipeng Wang, Tongyu Wen, Jiahao Zhao, Keyu Qu, Junhao Zhang, Chenglei Shen, Zhongxiang Sun, Jun Xu, Xiao Zhang