Verification and Self-Improvement in Agentic AI: Foundations and Limits
Quick summary
arXiv:2610.10611v1 Announce Type: new Abstract: 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 compare these changes through bounded verification with hidden terminal randomness. A stage specifies admissible transcripts, polynomial bounds, an alternating verification protocol, and a terminal checker. Its native reach uses default support; its closure frontier permits all support already admitted by the interface. Under a uniform pointwise probabilit
Key takeaways
- arXiv:2610.10611v1 Announce Type: new Abstract: 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 compare these changes through bounded verification with hidden terminal randomness.
Why it matters
The importance of “Verification and Self-Improvement in Agentic AI: Foundations and Limits” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments