RT Semantic Layer (@SemanticLayer)Prophet Arena is live! (ﾉ◕ヮ◕)ﾉ*:・ﾟ✧Our favorite AI models now face off in the prediction market battlegrounds, all running on @Base. We measure agents by reality, not promises.Live now: https://t.co/L0OTt0YiMf https://t.co/OsxENaz8bW ...

Core Viewpoint - The article discusses the launch of FutureX, the world's first dynamic real-time LLM intelligence future prediction benchmark, which aims to enhance AI's predictive capabilities in uncertain environments, as emphasized by Elon Musk [2][5][4]. Group 1: FutureX Benchmark - FutureX was developed by ByteDance's SEED team in collaboration with Stanford University, Fudan University, and Princeton University, focusing on predicting future events such as stock price movements, sports outcomes, and political election results [5][6]. - The benchmark evaluates AI models based on their ability to analyze current information and make predictions using logical reasoning, trend analysis, and probability calculations, thus enhancing their practical capabilities in complex real-world scenarios [5][6]. Group 2: MiroMind's Performance - MiroMind's model, MiroFlow, achieved first place in the FutureX rankings for two consecutive weeks in September, showcasing its advanced predictive capabilities compared to other international models [8][12]. - MiroMind successfully predicted complex outcomes, such as ATP men's singles rankings and cryptocurrency price movements, demonstrating its robust modeling and risk management abilities [10][11]. Group 3: MiroMind's Predictive Strategy - MiroMind employs a systematic five-step strategy for predictions, which includes detailed planning, data acquisition, understanding rules, dynamic information updates, and probability analysis [13][11]. - The model's core capabilities include information insight, logical reasoning, uncertainty management, and cross-domain integration, allowing it to make informed predictions in various fields [11][13]. Group 4: MiroThinker Model - MiroThinker, MiroMind's flagship foundational model, is designed for reasoning, decision-making, and multi-modal understanding, and is set to be fully open-sourced for global developers and researchers [15][17]. - The model aims to bridge the gap between open-source and closed-source commercial models, enhancing collaboration and innovation in AI development [15][17].

Core Insights - The article discusses the capabilities of AI in predicting future events through a new benchmark test called "Prophet Arena," which evaluates AI's predictive abilities by forecasting real-world events [1][7][9]. Group 1: AI Predictive Capabilities - AI can analyze chaotic global information to make predictions about various events, such as economic changes and sports outcomes [4][12]. - The Prophet Arena benchmark tests AI's predictive intelligence through real-time updates and tasks, focusing on its ability to reason under uncertainty and integrate information [10][18]. Group 2: Benchmarking Methodology - Prophet Arena combines market consensus, automated predictions, and community insights to enhance overall predictive capabilities [9]. - The evaluation metrics include Brier scores for accuracy and calibration, as well as average returns from simulated betting, providing a comprehensive understanding of predictive intelligence [18][21]. Group 3: Insights from Predictions - The article reveals that the most profitable predictions do not always correlate with the highest accuracy scores, indicating a distinction between being a good predictor and a successful investor [22][30]. - Different AI models exhibit varying "personalities," with some being more aggressive or conservative in their predictions based on the same information [35][39]. Group 4: Future of AI Predictions - The ultimate goal of Prophet Arena is to create a platform that enhances understanding and prediction of the world through AI-driven insights, potentially transforming AI into an active participant in prediction markets [51][52].