通过节点间共识验证，确保去中心化AI网络中模型运行合规。
Trust, but verify
- 利用邻居节点的共识判断异常模型运行
- 在多数节点诚实的前提下可准确识别违规节点
- 结合经济激励与惩罚机制提升网络可信度
去中心化AI代理网络（如Gaia）允许个人在其设备上运行定制化的大型语言模型（LLM），并对外提供服务。然而，为保障服务质量，网络必须验证各节点确实在运行指定的LLM。本文证明，在大多数节点诚实的集群中，可通过其同行的社会共识检测出运行未经授权或错误模型的节点。我们介绍了相关算法，并展示了来自Gaia网络的实验数据。此外，还提出了一个作为EigenLayer AVS实现的互主体验证系统，引入经济激励与惩罚机制，以鼓励LLM节点保持诚实行为。
原文摘要 · Abstract (English)
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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