用大模型做智能代理，快速生成可信响应
Self-Supervised Inference of Agents in Trustless Environments
- 用大模型作为分类器实现代理数据推理与排序
- 验证延迟低于125毫秒，快一个数量级
- 适合需要高效安全的去中心化AI场景
本文提出一种新方法，使代理可形成群体，以高效生成高质量响应。该方法利用具备数据推断与排序能力的代理，通过大语言模型（LLMs）作为响应分类器实现有效部署。我们评估了现有无信任代理推理方法，定义了自身方法，并估算实际参数，建模多种恶意代理攻击类型。该方法利用群体智能，实现鲁棒、高效的去中心化AI推理，在准确性、安全性与可靠性方面表现更优。结果表明，该方法的验证延迟低于125毫秒，比其他无信任推理策略快一个数量级。
原文摘要 · Abstract (English)
In this paper, we propose a novel approach where agents can form swarms to produce high-quality responses effectively. This is accomplished by utilizing agents capable of data inference and ranking, which can be effectively implemented using LLMs as response classifiers. We assess existing approaches for trustless agent inference, define our methodology, estimate practical parameters, and model various types of malicious agent attacks. Our method leverages the collective intelligence of swarms, ensuring robust and efficient decentralized AI inference with better accuracy, security, and reliability. We show that our approach is an order of magnitude faster than other trustless inference strategies reaching less than 125 ms validation latency.
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