AI Model Performance Prediction @Everlyn_ai, @intodotspace, @0xPolygon As artificial intelligence technology rapidly advances, evaluating and comparing the performance of models has become an important technical challenge. The AI model performance prediction discussed in this article describes the concept of using the benchmark scores of an AI model called Everlyn, developed on the Polygon network, as a trading target in the prediction market Space. This approach does not evaluate after results are produced as in the past, but rather creates verifiable data from the evaluation results themselves and connects this to market mechanisms. The starting point of this structure is the evaluation record method provided by Everlyn. Everlyn records the execution process and results of the AI model in an immutable form using hash values and metadata, documenting what outputs were produced from which inputs and settings. This means that benchmark scores are treated not just as simple numbers but as reproducible technical records under the same conditions. This recording method helps prevent the performance of the model from being arbitrarily modified or interpreted. Based on the performance data recorded in this way, Space performs the function of a prediction market. In Space, whether a specific AI model has exceeded a predetermined benchmark or reached a score range is set as a single outcome, and participants express their judgments through trading. In this process, prices are formed as a result reflecting the judgments and information of the participants, and once the performance results are confirmed, settlements are made according to market rules. This is described as a way of quantifying technical performance through collective judgment. The Polygon network serves as the foundation for all these transactions and settlements. Polygon is a blockchain infrastructure with low transaction costs and high throughput, making it suitable for the prediction market environment that requires small transactions and frequent settlements. The fact that various payments and stablecoin transfers are already being actively conducted shows that there is an environment where these performance prediction transactions can be processed technically. Overall, the concept of AI model performance prediction explains a structure where Everlyn's verifiable evaluation records, Space's prediction market structure, and Polygon's payment infrastructure each play their roles and connect. This is not a claim that a specific system is actually integrated and operated, but rather an objective explanation of a technical structure that addresses AI performance evaluation through data verification and market mechanisms. This explanation serves as a case study showing how AI evaluation methods can be combined with other technical areas to be treated in new forms. $LYN $SPACE $POL $USDC $USDT $SOL