今天，我们推出xbench

Core Viewpoint - The article discusses the launch of a new AI benchmark testing tool called xbench by Sequoia China, aimed at creating a more scientific and effective evaluation system for AI capabilities, particularly in real-world applications [1][2]. Group 1: xbench Overview - xbench employs a dual-track evaluation system that constructs a multidimensional dataset to track both the theoretical limits of AI models and the practical value of AI agents in real-world scenarios [2][3]. - The tool features an Evergreen Evaluation mechanism, ensuring continuous updates to testing content to maintain relevance and timeliness [2][3]. Group 2: Evaluation Methodology - The initial release includes two core assessment sets: xbench-ScienceQA for scientific question answering and xbench-DeepSearch for deep search capabilities, with comprehensive rankings of major products in these fields [3][19]. - The evaluation methodology focuses on aligning assessments with real-world applications, particularly in recruitment and marketing sectors, to establish clear business value [3][12]. Group 3: Historical Context and Development - xbench has been used internally by Sequoia China for over two years to track and evaluate foundational model capabilities, with significant improvements observed in model performance over time [5][7]. - The tool's question bank has undergone multiple updates to reflect increasing complexity and relevance to real-world tasks, demonstrating rapid advancements in AI model capabilities [5][7]. Group 4: Future Directions - The article emphasizes the need for innovative task settings and evaluation methods that align with practical applications, moving beyond traditional assessment frameworks [8][22]. - Future evaluations will focus on dynamic, real-world tasks that reflect the evolving needs of various professional fields, with an emphasis on collaboration with industry experts to refine assessment criteria [24][27]. Group 5: Long-term Evaluation Strategy - The Evergreen Evaluation approach aims to mitigate issues of question leakage and overfitting by maintaining a dynamic and continuously updated assessment pool [11][30]. - The article outlines a vision for ongoing assessments that adapt to the rapid evolution of AI technologies and their applications in diverse professional contexts [30][35].