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arXiv:2504.13443cs.AI

Trust, but verify

Michael J. Yuan, Carlos Lospoy, Sydney Lai, James Snewin, Ju Long

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Abstract

Decentralized AI agent networks, such as Gaia, allows individuals to run customized LLMs on their own computers and then provide services to the public. However, in order to maintain service quality, the network must verify that individual nodes are running their designated LLMs. In this paper, we demonstrate that in a cluster of mostly honest nodes, we can detect nodes that run unauthorized or incorrect LLM through social consensus of its peers. We will discuss the algorithm and experimental data from the Gaia network. We will also discuss the intersubjective validation system, implemented as an EigenLayer AVS to introduce financial incentives and penalties to encourage honest behavior from LLM nodes.

arXiv comments
Journal reference
IEEE Computer ( Volume: 59, Issue: 2, February 2026)
DOI
doi:10.1109/MC.2025.3600630
Subjects
cs.AIArtificial Intelligence cs.DCDistributed, Parallel, and Cluster Computing cs.MAMultiagent Systems econ.GNGeneral Economics

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  • Show HN: A new way to verify remote AI model execution (no TEEs, no ZK)2 points0 comments

    We’ve published a whitepaper proposing a simple method to verify that a remote AI node is executing a specific model — without using trusted execution environments (TEEs) or zero-knowledge proofs. The method relies on a minimal set of reference outputs to test model behavior, enabling trust without full re-execution. Curious to hear thoughts from folks working in ML, distributed systems, or trustless compute. https://arxiv.org/abs/2504.13443

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