arXiv Artificial Intelligence

On The Statistical Limits of Self-Improving Agents

1 day ago 12.08.2026 07:00 👁 3 views
On The Statistical Limits of Self-Improving Agents

Quick summary

arXiv:2510.04399v3 Announce Type: replace Abstract: We develop a learning-theoretic framework for analyzing self-improving agents by decomposing self-modification into five axes. Within this framework, we prove a sharp boundary: under standard i.i.d. assumptions, distribution-free PAC learnability is preserved if and only if the policy-reachable family remains uniformly capacity-bounded. If reachable capacity can grow without bound, utility-rational self-changes can make learnable tasks unlearnable. We further introduce a simple Two-Gate guardrail -- a validation-improvement requirement plus a

Key takeaways

  • arXiv:2510.04399v3 Announce Type: replace Abstract: We develop a learning-theoretic framework for analyzing self-improving agents by decomposing self-modification into five axes.
  • Within this framework, we prove a sharp boundary: under standard i.i.d.
  • assumptions, distribution-free PAC learnability is preserved if and only if the policy-reachable family remains uniformly capacity-bounded.

Why it matters

“On The Statistical Limits of Self-Improving Agents” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗