Agentic AI systems can improve by searching longer, receiving additional support, or modifying how they propose and verify outputs. A performance score does not distinguish these mechanisms. We compar...
The paper examines the challenge of safely allowing autonomous AI agents to improve themselves. It formalizes the problem by modeling agents as programs and defining improvement via a potentially uncomputable semantic score. The authors prove that in general, no terminating procedure can reliably accept only genuine improvements while never missing any, but they identify a practical bounded regime where exhaustive simulation can achieve these guarantees, noting its limitations.
By Brian B. Moser, Stanislav Frolov, Tobias C. Nauen, Federico Raue, Andreas Dengel
arXiv:2605. 27381v3 Announce Type: replace-cross Abstract: Agents increasingly modify the procedures by which they solve tasks and improve themselves.
By Chien-Ping Lu
arXiv:2608. 03588v1 Announce Type: cross Abstract: AI coding agents are stochastic workflows: prompts are interpreted, artifacts are sampled, validators produce observations, and orchestrators commit or repair.
By Corrado Priami
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
By Alex Iacob, Andrej Jovanovi\'c, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccol\`o Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
By Seth Dobrin, {\L}ukasz Chmiel