Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents

2026-09-18

AI Summary

本文提出一种公平补偿协议，解决分布式信息检索中数据质量评估与保护的问题，确保在无信任多代理环境中信息安全与合理补偿。

Abstract

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.

Problem

• decentralized information marketplaces
• autonomous AI agents
• data quality and relevance
• compensation
• trustless multi-agent environments

Innovation

• fairly compensated protocol
• distributed information retrieval
• autonomous agent networks
• cryptographic infrastructures