Group 1 - Google has organized an "AI Chess King Championship" featuring top AI models from the US and China, including OpenAI's o4-mini and Google's Gemini 2.5 Pro, to evaluate and promote advancements in AI's reasoning and decision-making capabilities [1][3] - The competition aims to address the limitations of traditional AI benchmark tests, which have failed to keep pace with the rapid development of AI models, by utilizing strategy games as a testing ground [3][11] - The Kaggle Game Arena platform, introduced by Google, serves as a new public benchmark testing platform that allows AI models to compete in a more dynamic and realistic environment compared to conventional tests [3][11] Group 2 - The current investment climate has led to a phenomenon where AI startups can easily achieve valuations exceeding $1 billion, driven by a fear of missing out (FOMO) among investors [4][6] - There is a growing trend of "score manipulation" among AI companies, where high benchmark scores are used as a marketing tool to attract investment, leading to concerns about the integrity of AI performance evaluations [6][9] - Various benchmark tests exist to evaluate AI models, but their lack of flexibility has created opportunities for companies to artificially inflate their scores, undermining the reliability of these assessments [9][11] Group 3 - Google has chosen games as a testing scenario for AI models due to their structured rules and inherent randomness, which effectively measure AI intelligence and capabilities [12][13] - The relationship between gaming and AI is significant, as demonstrated by OpenAI's success in defeating human champions in games like DOTA2, showcasing AI's potential in complex environments [13][15] - The transition to reinforcement learning based on human feedback (RLHF) has been pivotal in enhancing AI's performance, as seen in OpenAI's development of ChatGPT [15]

Core Insights - The article introduces xbench, an open-source AI benchmarking tool aimed at quantifying the effectiveness of AI systems in real-world scenarios and utilizing a long-term evaluation mechanism [1] - The launch of xbench has generated significant interest from both large enterprises and startups, with increasing demand for product testing using the xbench evaluation sets [1] - The initiative aims to foster collaboration within the AI community by providing transparent and open-source resources [1] Group 1: xbench Evaluation Sets - The xbench-ScienceQA evaluation set focuses on high-quality, multi-disciplinary questions sourced from top academic institutions and industry experts, addressing the limitations of existing benchmarks [2] - The average accuracy of the xbench-ScienceQA set is 32%, with one-third of the questions having an accuracy below 20%, indicating a high level of difficulty and differentiation among models [12][10] - The xbench-DeepSearch evaluation set is designed to assess the deep search capabilities of AI agents, emphasizing the need for comprehensive planning, searching, reasoning, and summarization skills [3] Group 2: Evaluation Methodology - The xbench-ScienceQA set includes 77 Q&A questions, 14 multiple-choice questions, and 9 single-choice questions, with a focus on reducing the impact of single-choice questions on scoring [8] - The question construction process for both evaluation sets involves rigorous validation to ensure the uniqueness and correctness of answers, with a focus on avoiding easily searchable content [6][13] - Both evaluation sets will be continuously updated, with monthly performance reports and quarterly updates to maintain relevance and accuracy [2][3] Group 3: Community Engagement - The article encourages AI enthusiasts, model developers, and researchers to participate in the ongoing development and testing of AI technologies through xbench [31] - Contact information is provided for those interested in contributing to the evaluation sets or seeking feedback on their models [32]

Group 1 - The core idea of the article is the introduction of Xbench, an AI benchmarking tool developed by Sequoia China, which emphasizes the importance of evaluating AI systems based on their practical utility in real-world scenarios rather than just the difficulty of the assessment questions [1][5][6] - Xbench was initiated in late 2022 as an internal tool for tracking and evaluating the capabilities of foundational models, evolving through three major updates, with the first public release planned for May 2025 [5][6] - The dual-track evaluation system of Xbench includes AGI Tracking to assess the upper limits of agent capabilities and Profession Aligned to quantify the practical value of AI systems in real-world applications [6][8] Group 2 - The Evergreen Evaluation Mechanism is a dynamic updating evaluation system designed to avoid the pitfalls of static assessments, which can lead to overfitting and rapid obsolescence [10] - The mechanism aims to regularly evaluate mainstream agent products across various sectors such as human resources, marketing, finance, law, and sales, adapting to the fast-paced evolution of agent applications [10] Group 3 - In the initial phase of testing, significant performance differences were observed among various models in recruitment and marketing tasks, with OpenAI's o3 ranking first and GPT-4o scoring the lowest due to its tendency to provide shorter answers [9] - The evaluation revealed that model size is not the sole determinant of task performance, as demonstrated by the comparable results of Google's DeepMind models [9] - Despite DeepSeek R1's strong performance in mathematical and coding benchmarks, its adaptability issues in search-centric tasks led to lower scores in this evaluation [9]

Core Viewpoint - The AI large model benchmarking landscape is currently fragmented, prompting Google to introduce a standardized evaluation framework called LMEval to streamline the assessment process for AI models [4][16]. Group 1: Current State of AI Benchmarking - The AI large model benchmarking is characterized by a "hundred schools of thought" scenario, with various institutions and private entities creating their own evaluation tools [3][4]. - Notable benchmarks include C-Eval from Tsinghua University, CMMLU from Shanghai Jiao Tong University, and xbench from Sequoia Capital [3]. Group 2: Introduction of LMEval - Google plans to launch LMEval, an open-source framework designed to provide standardized evaluation tools for large language models and multimodal models [4][17]. - LMEval aims to simplify the benchmarking process by allowing researchers and developers to set benchmarks once and conduct standardized evaluations across major platforms like Azure, AWS, and HuggingFace [6][17]. Group 3: Features of LMEval - LMEval supports not only text evaluation but also image and code assessments, addressing current trends in AI [6]. - The framework includes Giskard safety scoring to evaluate the model's ability to avoid generating harmful content, with higher percentages indicating better safety performance [6]. Group 4: Challenges in AI Benchmarking - The rapid evolution of AI models leads to a situation where the effectiveness of benchmarks diminishes quickly, as models can "cram" for tests by training on specific question sets [8][13]. - The industry faces a challenge in creating a scientific and long-lasting evaluation system that accurately reflects AI capabilities, as current solutions tend to be decentralized and varied [16]. Group 5: Implications of LMEval - By introducing LMEval, Google aims to provide a unified standard for evaluating various capabilities of AI models, reducing the need for developers to switch APIs or integrate different test sets [17].

Core Viewpoint - The traditional AI benchmarking is rapidly losing effectiveness as many models achieve perfect scores, leading to a lack of differentiation and guidance in evaluation [1][2]. Group 1: Introduction of xbench - Sequoia China launched a new AI benchmark test called xbench, aiming to create a more scientific and long-lasting evaluation system that reflects the objective capabilities of AI [2][3]. - xbench is the first benchmark initiated by an investment institution, collaborating with top universities and research institutions, utilizing a dual-track evaluation system and an evergreen evaluation mechanism [2][3]. Group 2: Features of xbench - xbench employs a dual-track evaluation system to track both the theoretical capability limits of models and the practical value of AI systems in real-world applications [3][4]. - The evergreen evaluation mechanism ensures that the testing content is continuously maintained and updated to remain relevant and timely [3][4]. - The initial release includes two core evaluation sets: xbench-ScienceQA and xbench-DeepSearch, along with a ranking of major products in these fields [4][10]. Group 3: Addressing Core Issues - Sequoia China identified two core issues: the relationship between model capabilities and actual AI utility, and the loss of comparability in AI capabilities over time due to frequent updates in the question bank [5][6]. - xbench aims to break away from conventional thinking by developing novel task settings and evaluation methods that align with real-world applications [6][7]. Group 4: Dynamic Evaluation Mechanism - xbench plans to establish a dynamic evaluation mechanism that collects live data from real business scenarios, inviting industry experts to help build and maintain the evaluation sets [9][8]. - The design includes horizontally comparable capability metrics to observe development speed and key breakthroughs over time, aiding in determining when an agent can take over existing business processes [9][8]. Group 5: Community Engagement - xbench encourages community participation, allowing developers and researchers to use the latest evaluation sets to validate their products and contribute to the development of industry-specific standards [11][10].

Core Insights - The article introduces "Immersive Translate," a bilingual translation browser extension created by Owen in late 2022, which has gained millions of users globally and won Google's Best Extension award in 2024 [2][3]. Group 1: Product Features - Immersive Translate significantly enhances reading and information acquisition, especially for foreign materials, by providing a dual-language translation interface [3][4]. - The tool allows for real-time bilingual subtitle translation for videos, improving comprehension of English grammar and context [10][11]. - It offers a PDF translation feature called BabelDOC, which maintains the layout and data visualization of academic papers and reports [16][18]. Group 2: User Experience - Users can quickly browse various foreign information sources, including financial news and social media, with the tool enhancing efficiency in information retrieval [3][4]. - The extension supports translating local documents and books that lack translations, promoting knowledge accessibility [25][23]. - The product includes features like hover translation and word translation, which can also read aloud, enhancing user interaction [29][26]. Group 3: Technology and Integration - Immersive Translate integrates multiple translation engines, including DeepSeek, ChatGPT, and DeepL, along with specialized AI terminology databases for various fields [29][34]. - The company aims to democratize information access by allowing users to surf the internet in their native language, thus broadening their perspectives [34][36].

Group 1 - Sequoia China has launched a new AI benchmarking tool called xbench, developed in collaboration with over ten domestic and international universities and research institutions [3] - The dual-track evaluation system of xbench includes a multi-dimensional assessment dataset that tracks both the theoretical capabilities of models and the practical value of AI agents [3] - The long-term evaluation mechanism of xbench is designed to be dynamic and continuously updated, addressing concerns about static assessments and potential score manipulation [3][4] Group 2 - The rapid advancements in AI capabilities, particularly in long text processing, multi-modality, tool usage, and reasoning, have led to explosive growth in AI agents [4] - There is a consensus that valuable AI agent evaluations must be closely related to actual tasks, necessitating the construction of specific domain assessment sets that align with productivity and commercial value [4] - The characteristics of agents, including their rapid iteration and integration of new features, require testing tools to track the continuous growth of agent capabilities [4][5] Group 3 - xbench-DeepSearch will focus on evaluating multi-modal models with reasoning chains for their ability to generate commercially viable videos, the credibility of widely used MCP tools, and the effectiveness of GUI agents in utilizing dynamically updated or untrained applications [5]

Core Viewpoint - The rapid development of foundational models and the scaling application of AI agents have led to challenges in accurately reflecting the objective capabilities of AI systems through benchmark tests, necessitating the creation of a more scientific and sustainable evaluation system to guide AI technology breakthroughs and product iterations [1][2]. Group 1: Introduction of xbench - Sequoia China announced the launch of a new AI benchmark test called xbench, which is the first benchmark initiated by an investment institution in collaboration with top universities and research institutions, utilizing a dual-track evaluation system and evergreen evaluation mechanism [2][4]. - xbench aims to assess and enhance the capabilities of AI systems while quantifying their utility value in real-world scenarios, capturing key breakthroughs in agent products over time [2][4]. Group 2: Features of xbench - xbench employs a dual-track evaluation system that constructs a multidimensional dataset to track both the theoretical capability limits of models and the practical value of agents [4][5]. - The evaluation tasks are divided into two complementary main lines: assessing the upper limits of AI system capabilities and quantifying their utility value in real-world applications [4][6]. - An evergreen evaluation mechanism is adopted to ensure the timeliness and relevance of the testing content by continuously maintaining and dynamically updating the test materials [4][10]. Group 3: Addressing Core Issues - Sequoia China identified two core issues with existing evaluation methods: the relationship between model capabilities and actual AI utility, and the loss of comparability in AI capabilities over time due to frequent updates of test materials [6][7]. - To address these issues, xbench proposes innovative task settings and evaluation methods aligned with real-world applications, introducing a dual-track system that includes AGI tracking and profession-aligned assessments [7][8]. Group 4: Initial Assessment Sets - The first release of xbench includes two core assessment sets: xbench-ScienceQA for scientific question answering and xbench-DeepSearch for deep search capabilities, along with a comprehensive ranking of major products in these fields [8][11]. - xbench has been used internally by Sequoia China for tracking and evaluating foundational model capabilities over the past two years and is now publicly available for the AI community [8][11]. Group 5: Community Collaboration - Sequoia China encourages community collaboration in building and publishing specific industry standards for profession-aligned xbench, inviting developers and researchers to contribute to the ongoing development and maintenance of evaluation updates [11][13].

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].