面向AI智能体的公平补偿分布式信息检索与增强

中文摘要 AI 辅助

自主AI智能体对外部和分布式知识源的日益依赖，为去中心化信息市场引入了一个根本性挑战：检索智能体必须在购买前评估数据的质量和相关性，而数据提供者则必须避免在获得有保障的补偿之前泄露有价值的信息。这一悖论在无信任的多智能体环境中变得尤为关键，因为在这种环境中，没有中心化的中介机构可以强制各方之间的公平性。在本文中，我们提出了一种用于自主智能体网络中分布式信息检索与增强的公平补偿协议。我们的框架使检索智能体能够在不了解候选文档明文内容的情况下安全地评估和排序这些文档，同时确保数据提供者仅在有效信息成功交付时才获得补偿。我们进一步分析了该协议针对恶意对手的安全属性，并通过实现评估了其实际可行性。实验结果表明，所提出的设计在当前密码学基础设施下是实用的，同时保持了机密性、正确性、完整性和公平性。我们相信，此类机制为未来AI智能体生态系统中可信且经济上可持续的去中心化知识市场提供了重要的密码学基础。

英文摘要

The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized information marketplaces: retrieval agents must evaluate the quality and relevance of data before purchase, while data providers must avoid revealing valuable information prior to guaranteed compensation. This paradox becomes particularly critical in trustless multi-agent environments, where no centralized intermediary can enforce fairness between parties. In this paper, we propose a fairly compensated protocol for distributed information retrieval and augmentation in autonomous agent networks. Our framework enables retrieval agents to securely evaluate and rank candidate documents without learning their plaintext contents, while ensuring that data providers are compensated only when valid information is successfully delivered. We further analyze the security properties of the protocol against malicious adversaries and evaluate its practical feasibility through implementations. Experimental results demonstrate that the proposed design is practical with current cryptographic infrastructures while preserving confidentiality, correctness, integrity, and fairness. We believe such mechanisms provide an important cryptographic foundation for trustworthy and economically sustainable decentralized knowledge marketplaces for future AI agent ecosystems.

发表机构

• University of Southern California（南加州大学）

机构由 AI 辅助整理，请以论文原文为准。