Avoiding Obfuscation with Prover-Estimator Debate

Abstract:Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks. A promising approach to this problem is to amplify human judgement by leveraging the power of two competing AIs in a debate about the correct solution to a given problem. Prior theoretical work has provided a complexity-theoretic formalization of AI debate, and posed the problem of designing protocols for AI debate that guarantee the correctness of human judgements for as complex a class of problems as possible. Recursive debates, in which debaters decompose a complex problem into simpler subproblems, hold promise for growing the class of problems that can be accurately judged in a debate. However, existing protocols for recursive debate run into the obfuscated arguments problem: a dishonest debater can use a computationally efficient strategy that forces an honest opponent to solve a computationally intractable problem to win. We mitigate this problem with a new recursive debate protocol that, under certain stability assumptions, ensures that an honest debater can win with a strategy requiring computational efficiency comparable to their opponent.

Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)

arXiv:2506.13609 [cs.AI]

(or arXiv:2506.13609v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2506.13609 Focus to learn more arXiv-issued DOI via DataCite

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