Verification and Self-Improvement in Agentic AI: Foundations and Limits

This research examines how agentic AI systems improve through longer searching, additional support, or modified output proposal and verification. The authors use bounded verification with hidden terminal randomness to compare these mechanisms and prove that independent majority amplification preserves specific languages.