AI

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

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Briefings

This article proposes a framework for verifying and self-improving agentic AI systems using bounded verification with hidden terminal randomness. The authors define a stage as a specification of admissible transcripts, polynomial bounds, and an alternating verification protocol, with a closure frontier allowing all supported inputs. They prove that independent majority amplification preserves verification languages, while existential acceptance over random tapes can admit incorrect outputs. The framework distinguishes between exact verification (zero randomness) and randomized-verifier classes, establishing relationships between complexity classes like BPP and P. Key findings include the preservation of verification languages under majority amplification and the separation of invariant acceptance from core-versus-support labels that may change under refactoring.

Key points

• Independent majority amplification preserves both verification languages and closure frontiers under uniform pointwise probability gaps, whereas existential acceptance over random tapes can admit incorrect outputs.

Context and limitations

The framework relies on bounded verification with hidden terminal randomness and assumes uniform pointwise probability gaps for task-relative soundness. Results are presented as theoretical proofs and definitions rather than empirical data from specific systems. The study focuses on formal languages and complexity classes, with limited coverage on practical implementation details or real-world performance metrics.

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