# Technical Details In this setup, the core components : **instruction** and **data** serve as the primary anchors for **aligning contributions with the goals of the decentralized network**. Instructions represent the specific guidelines or objectives that users must follow to create valuable content. These may be set by the community, AI models, or platform standards, and they define the overarching goals of contributions. By clearly stating what is expected, instructions provide a framework for evaluating user content in alignment with the network's needs. Instructions could range from technical guidelines (e.g., format, quality standards) to thematic directives (e.g., focusing on certain topics) and ethical guidelines (e.g., fairness, relevance). Users respond to instructions by uploading data that they believe satisfies the requirements. This data varies in form (text, images, datasets, etc.) but is assessed uniformly against the instruction’s criteria. At a high level, the reward system is structured around a **collaborative network of specialized AI agents**. Each of these AI agents is initially trained to perform a specific task (often within a particular modality such as image, video, audio, or text), enabling them to effectively process and evaluate data in their area of expertise. By combining the capabilities of these agents, the reward system leverages a diverse skill set across multiple modalities, forming a mechanism for understanding and scoring the user-submitted content. We refer to the resulting scoring agent as Post Quality (PQ) MemAgent. In addition to evaluating contributions, the reward system incorporates a User Quality (UQ) MemAgent that tracks and assesses long-term user behavior to build a reputation score for each user. Unlike individual content assessments, this reputation system enables the reward mechanism to recognize and reward users who consistently demonstrate high standards, originality, and alignment with network goals. The PQ score and UQ score are combined with the task value to compute a final reward score of the uploaded data -- reflecting the contribution of the data point towards DecAI. In the following sections, we break down the components of the point system into key areas that assess content uniqueness, quality, and alignment. --- # 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/technical-details.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.