arXiv:2608. 11181v1 Announce Type: cross Abstract: When a probabilistic predictor answers many conditional-probability queries, are its answers self-consistent, and can this be verified in polynomial time?
By Orr Paradise, Oliver Richardson, Yoshua Bengio, Shafi Goldwasser
arXiv:2603. 25414v4 Announce Type: replace-cross Abstract: A prevailing assumption in machine learning is that model correctness must be enforced after the fact.
By Houston Haynes
The paper investigates whether interactive arguments for oracle‑aided AI computations can be zero‑knowledge, meaning the verifier learns nothing beyond the correctness of the output. It proves that, in general, zero‑knowledge proofs for all oracle‑aided computations are impossible, even in the random oracle model, and this impossibility extends to debate protocols. However, if the oracle signs each answer with a cryptographic signature, then every oracle‑aided computation can be verified in zero‑knowledge with efficient provers and verifiers, assuming only collision‑resistant hash functions.
By Alessandro Chiesa, Ziyi Guan, Burcu Yildiz
arXiv:2608.31120v1 Announce Type: new
Abstract: The motivation for this paper is the investigation of the trade-offs implicit in probabilistic models used in machine learning. Models are often used t...
By Guy Emerson
arXiv:2607. 03561v1 Announce Type: new Abstract: As AI models continue to develop powerful capabilities, it becomes critical that we are able to verify that their output is aligned with our intentions.
By Liyan Chen, Yael Tauman Kalai, Zoe Xi
The paper introduces Rewindable Interactive Proofs (RIPs), a new model for scalable oversight where a weak verifier can rewind a powerful AI prover to an earlier state during interaction. RIPs enable verification of oracle‑aided computations that are robust to small changes in oracle answers, achieving near‑linear verifier time without relying on cryptography. The authors provide a construction, implement it on MNIST, and demonstrate that RIPs can outperform existing proof systems in practice.
By Ziyi Guan, Yael Tauman Kalai, Ron Rothblum, Zoe Xi