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 efficient verification of computations whose outputs are robust to small changes in oracle answers, achieving near-linear verifier time without relying on cryptography. The authors implement the protocol, demonstrate a preliminary MNIST experiment, and show that an RIP can outperform a state‑of‑the‑art deployed proof system.
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 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:2607. 06341v1 Announce Type: cross Abstract: Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort.
By Shuangxiang Kan, Shuanglong Kan, Sebastian Ertel
arXiv:2607. 16372v1 Announce Type: cross Abstract: Interactive theorem proving (ITP) underpins program verification and formalized mathematics, but its manual effort limits scalability.
By Qiyuan Xu, Joshua Ong Jun Leang, Renxi Wang, Wenda Li, Haonan Li, Luke Ong, Conrad Watt
Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large language models (LLMs) promise to generate these proofs automatically, yet existing approaches wire a fixed, human-designed proof strategy into the system and constrain the model to follow it (retrieving premises and predicting tactics one step at a time, or splitting goals by divide-and-conquer), and still prove only a fraction of their target theorems.