Neural Interactive Proofs

• URL: http://arxiv.org/abs/2412.08897v1
• Date: Thu, 12 Dec 2024 03:21:53 GMT
• Title: Neural Interactive Proofs
• Authors: Lewis Hammond, Sam Adam-Day,
• Abstract summary: We study the case in which agents are represented using neural networks. We introduce a unifying framework based on prover-verifier games. We describe several new protocols for generating neural interactive proofs.
• Score: 1.519321208145928
• License:
• 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, 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.

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