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Verification and Self-Improvement in Agentic AI: Foundations and Limits
self-improvementverificationagentic AIrandomized verification
2610.10611
Builder Relevance
2h ago70%
Abstract
The paper explores the mechanisms of self-improvement in agentic AI systems through bounded verification and hidden terminal randomness.
Reality Card
Core Claim
The study proves that independent majority amplification preserves well-defined languages in the context of agentic AI self-improvement.
Method / Result
Exact verification is established as the zero-randomness case, with placement and completeness results.
Limitations
The framework's reliance on explicit complexity assumptions may limit reproducibility and general applicability.
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