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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.
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Cited by 1 Pith paper
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Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference
ZK-verified LLM inference can be fooled: a provider can serve a small model while producing valid proofs for a much larger declared model by embedding structure-preserving ghost weights.
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