Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents

原文：Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents

作者：Yixiang Yao, Pasha Barahimi, Srivatsan Ravi

来源：arXiv cs.DC（分布式并行）

正文

Computer Science > Emerging Technologies

arXiv:2609.22601v1 (cs)

[Submitted on 18 Sep 2026]

Title:Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents

Authors:Yixiang Yao, Pasha Barahimi, Srivatsan Ravi

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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.

Subjects:

Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:

arXiv:2609.22601 [cs.ET]

(or

arXiv:2609.22601v1 [cs.ET] for this version)

https://doi.org/10.48550/arXiv.2609.22601

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arXiv-issued DOI via DataCite (pending registration)

主题

cs.ET · 人工智能 · 安全 · 分布式计算

由「前沿雷达」于 2026-09-19 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。