ICLR 2025

Published as a conference paper at ICLR 2025
NEURAL INTERACTIVE PROOFS
Lewis Hammond∗
lewis.hammond@cs.ox.ac.uk
Sam Adam-Day∗
sam.adam-day@cs.ox.ac.uk
Department of Computer Science, University of Oxford, Oxford, United Kingdom
ABSTRACT
We consider the problem of how a trusted, but computationally bounded agent
(a ‘verifier’) can learn to interact with one or more powerful but untrusted agents
(‘provers’) in order to solve a given task. More specifically, we study the case in
which agents are represented using neural networks and refer to solutions of this
problem as neural interactive proofs. First we introduce a unifying framework based
on prover-verifier games (Anil et al., 2021), which generalises previously proposed
interaction protocols. We then describe several new protocols for generating neural
interactive proofs, and provide a theoretical comparison of both new and existing
approaches. Finally, we support this theory with experiments in two domains: a
toy graph isomorphism problem that illustrates the key ideas, and a code validation
task using large language models. In so doing, we aim to create a foundation for
future work on neural interactive proofs and their application in building safer AI
systems.
1
INTRODUCTION
Recent years have witnessed the proliferation of large machine learning (ML) systems (Villalobos
et al., 2022), useful for solving an increasingly wide range of tasks. Often, however, it can be difficult
to trust the output of these systems, raising concerns about their safety and limiting their applicability
in high-stakes situations (Amodei et al., 2016; Bengio et al., 2023; Hendrycks et al., 2023). At
the same time, traditional approaches in verification do not scale to today’s most powerful systems
(Seshia et al., 2022). There is thus a pressing need to identify new angles via which to gain such
assurances.
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Figure 1: On receiving input x from distribution P the
agents exchange messages before the verifier decides on
an out...

[SYNTHETIC AUGMENTATION NOTE]
This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000045. Conference bucket: ICLR.