RESEARCH · RESEARCH · #1779
Verification and self-improvement limits for agentic AI (arXiv:2610.10611v1)
This paper develops a formal framework for how agentic AI systems can improve via longer search, extra support, or changes to proposal/verification processes, using bounded verification with hidden terminal randomness. It defines stages (admissible transcripts, verifier, terminal checker), native reach and closure frontier languages, proves preservation under independent majority amplification but risks from existential acceptance over random tapes, relates randomized-verifier classes (Σ_k^{RV}) to classical complexity classes, and analyzes bounded recursive self-improvement, evidence/sampling audits, and a quota-enforced XOR-synthesis separation.
KEY POINTS
- This paper develops a formal framework for how agentic AI systems can improve via longer search, extra support, or changes to proposal/verification processes, using bounded verification with hidden terminal randomness.
- It defines stages (admissible transcripts, verifier, terminal checker), native reach and closure frontier languages, proves preservation under independent majority amplification but risks from existential acceptance over random tapes, relates randomized-verifier classes (Σ_k^{RV}) to classical complexity classes, and analyzes bounded recursive self-improvement, evidence/sampling audits, and a quota-enforced XOR-synthesis separation.
- It gives a complexity-theoretic and verification-oriented foundation tying self-improvement claims to concrete correctness, evidence and resource limits, which is relevant for rigorous AI evaluation and safety analyses.
WHY IT MATTERS
It gives a complexity-theoretic and verification-oriented foundation tying self-improvement claims to concrete correctness, evidence and resource limits, which is relevant for rigorous AI evaluation and safety analyses.