# Reward Mechanism > *A cornerstone of this approach is a fair and transparent reward mechanism, which not only incentivize user participation but also promotes high-quality contributions essential for training robust AI models.* To this end, in this work we develop a **multi-agent dynamic point-based system** aimed at allocating rewards in proportion to the quality and relevance of user-generated content. This enables the users to receive rewards that reflect the practical value of their contributions across several dimensions, including content originality, alignment with AI objectives, data quality, and adherence to community standards. This framework allows for the flexible assignment of points based on the evolving needs of the decentralized network, providing a foundational mechanism for future token distributions and monetary rewards as the network matures. ## **Core Principles:** Eidon's reward system is rooted in the ideals of fair compensation and radical transparency. Users are rewarded for contributions that enhance the network's value. As we progress on our [decentralization path](/the-network/path-to-decentralization.md) towards full trustlessness, we want to be radically transparent about every parameter that goes into calculating user rewards for data submissions — from underlying functions and models used to overarching rationale and guiding principles. We hope that this radical transparency can foster community-directed governance as the network evolves towards becoming fully decentralized. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.eidon.ai/the-network/reward-mechanism.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.