Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents
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...
| Authors | Yixiang Yao, Pasha Barahimi, Srivatsan Ravi |
|---|---|
| Primary category | cs.ET |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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