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首届大模型象棋争霸赛:Grok 4与o3挺进决赛,DeepSeek、Kimi落败
3 6 Ke· 2025-08-07 06:16
Core Insights - The AI chess tournament hosted on Kaggle featured eight large language models (LLMs) competing in a knockout format, with Grok 4 and o3 advancing to the finals after defeating Gemini 2.5 Pro and o4-mini respectively [1][3][8] Group 1: Tournament Structure and Results - The tournament lasted three days and involved eight AI models, including Grok 4 (xAI), Gemini 2.5 Pro (Google), o4-mini (OpenAI), o3 (OpenAI), Claude 4 Opus (Anthropic), Gemini 2.5 Flash (Google), DeepSeek R1 (DeepSeek), and Kimi k2 (Moonshot AI) [1] - The competition utilized a single-elimination format where each AI had up to four attempts to make a legal move; failure to do so resulted in an immediate loss [1] - On the first day, Grok 4, o3, Gemini 2.5 Pro, and o4-mini all achieved 4-0 victories, advancing to the semifinals [3][11][22] Group 2: Semifinal Highlights - In the semifinals, o3 demonstrated a dominant performance, winning 4-0 against o4-mini, showcasing a high level of precision with a perfect accuracy score of 100 in one of the games [5] - The match between Grok 4 and Gemini 2.5 Pro ended in a tie after regular play, leading to an Armageddon tiebreaker where Grok 4 emerged victorious [8] - The semifinals highlighted the strengths and weaknesses of the AI models, with Grok 4 overcoming early mistakes to secure its place in the finals [8][19] Group 3: Performance Analysis - The tournament revealed that while some AI models performed exceptionally well, others struggled with basic tactical sequences and context understanding, indicating areas for improvement in AI chess capabilities [22] - The performance of Grok 4 attracted attention from industry figures, including Elon Musk, who commented on its impressive gameplay [19]
战报:马斯克Grok4笑傲AI象棋大赛,DeepSeek没干过o4-mini,Kimi K2被喊冤
量子位· 2025-08-06 08:14
不圆 奕然 发自 凹非寺 量子位 | 公众号 QbitAI 最新战报最新战报:首届AI国际象棋对战……马斯克家的Grok 4"遥遥领先"了。 是的,谷歌给大模型整了个国际象棋比赛:Kaggle AI象棋竞赛。 在首日对决之后,参赛选手中OpenAI的o3和o4-mini、DeepSeek R1、Kimi K2 Instruct、Gemini 2.5 Pro和2.5 Flash、Claude Opus 4、Grok 4都有了第一轮较量,结果—— Grok 4表现最佳,DeepSeek R1表现强劲,但不敌o4-mini,Kimi K2最惨——都让网友喊冤了。 眼见自家Grok 4表现出色,马斯克当然不会错过PR良机,不过回应略显凡尔赛: 我们没有刻意去训练,这只是一个副作用。 u1s1谁又能为这么个"无厘头"比赛专门刻意训练呢? 当然,让AI对战国际象棋,过程比输赢重要多了,毕竟谷歌发起这次比赛的初衷,就是测试"涌现"能力。 首届Kaggle AI国际象棋竞赛 本次比赛由谷歌发布,作为推广Kaggle游戏竞技场的一个环节。首次比赛以国际象棋开始。 参赛"选手"包括OpenAI的o3和o4-mini、DeepSe ...
闹玩呢,首届大模型对抗赛,DeepSeek、Kimi第一轮被淘汰了
3 6 Ke· 2025-08-06 08:01
Group 1 - The core focus of the article is the first international chess competition for large models, where Grok 4 is highlighted as a leading contender for the championship [1][24]. - The competition features various AI models, including Gemini 2.5 Pro, o4-mini, Grok 4, and others, all of which advanced to the semifinals with a 4-0 victory in their initial matches [1][9]. - The event is hosted on the Kaggle Game Arena platform, aiming to evaluate the performance of large language models (LLMs) in dynamic and competitive environments [1]. Group 2 - Kimi k2 faced o3 and lost 0-4, with Kimi k2 struggling to find legal moves after the opening phase, indicating potential technical issues [3][6]. - DeepSeek R1 lost to o4-mini with a score of 0-4, showcasing a pattern of initial strong moves followed by significant errors [10][13]. - Gemini 2.5 Pro achieved a 4-0 victory over Claude 4 Opus, but its true strength remains uncertain due to the opponent's mistakes [14][18]. - Grok 4's performance was particularly impressive, winning 4-0 against Gemini 2.5 Flash, demonstrating a strong ability to capture unprotected pieces [21][27]. Group 3 - The article notes that current AI models in chess exhibit three main weaknesses: insufficient global board visualization, limited understanding of piece interactions, and issues with executing legal moves [27]. - Grok 4's success suggests it may have overcome these limitations, raising questions about the consistency of these models' advantages and shortcomings in future matches [27]. - The article also mentions a poll where 37% of participants favored Gemini 2.5 Pro as the likely winner before the competition began [27].
谷歌约战,DeepSeek、Kimi都要上,首届大模型对抗赛明天开战
机器之心· 2025-08-05 04:09
Core Viewpoint - The upcoming AI chess competition aims to showcase the performance of various advanced AI models in a competitive setting, utilizing a new benchmark testing platform called Kaggle Game Arena [2][12]. Group 1: Competition Overview - The AI chess competition will take place from August 5 to 7, featuring eight cutting-edge AI models [2][3]. - The participating models include notable names such as OpenAI's o4-mini, Google's Gemini 2.5 Pro, and Anthropic's Claude Opus 4 [7]. - The event is organized by Google and aims to provide a transparent and rigorous testing environment for AI models [6][8]. Group 2: Competition Format - The competition will follow a single-elimination format, with each match consisting of four games. The first model to score two points advances [14]. - If a match ends in a tie (2-2), a tiebreaker game will be played, where the white side must win to progress [14]. - Models are restricted from using external tools like Stockfish and must generate legal moves independently [17]. Group 3: Evaluation and Transparency - The competition will ensure transparency by open-sourcing the game execution framework and environment [8]. - The performance of each model will be displayed on the Kaggle Benchmarks leaderboard, allowing real-time tracking of results [12][13]. - The event is designed to address the limitations of current AI benchmark tests, which struggle to keep pace with the rapid development of modern models [12].
1万tokens是检验长文本的新基准,超过后18款大模型集体失智
量子位· 2025-07-17 02:43
Core Insights - The article discusses the performance decline of large language models (LLMs) as the input context length increases, highlighting that the decline is not uniform but occurs at specific token lengths [10][21][44] - A recent study by the Chroma team tested 18 mainstream LLMs, revealing that models like GPT-4.1 and Claude Sonnet 4 experience significant accuracy drops when processing longer inputs [8][9][19] Group 1: Performance Decline - As input length increases, model performance deteriorates, with a notable drop around 10,000 tokens, where accuracy can fall to approximately 50% [4][21] - Different models exhibit varying thresholds for performance decline, with some models losing accuracy earlier than others [6][7][19] - The study indicates that semantic similarity between the "needle" (target information) and the "problem" significantly affects performance, with lower similarity leading to greater declines [19][21] Group 2: Experimental Findings - Four controlled experiments were conducted to assess the impact of input length on model performance, focusing on factors like semantic similarity, interference information, and text structure [17][35][41] - The first experiment showed that as input length increased, models struggled more with low semantic similarity, leading to a sharper performance drop [19][21] - The second experiment demonstrated that the presence of interference items significantly reduced model accuracy, with multiple interference items causing a 30%-50% drop compared to baseline performance [26][28] Group 3: Structural Impact - The structure of the background text (haystack) also plays a crucial role in model performance, with coherent structures leading to more significant declines in accuracy compared to disordered structures [40][42] - The experiments revealed that most models performed worse with coherent structures as input length increased, while performance decline was less severe with disordered structures [41][44] - The findings suggest that LLMs face challenges in processing complex logical structures in long texts, indicating a need for improved handling of such inputs [41][44] Group 4: Implications and Future Directions - The results highlight the limitations of current LLMs in managing long-context tasks, prompting suggestions for clearer instructions and context management strategies [44] - Chroma, the team behind the research, aims to address these challenges by developing open-source tools to enhance LLM applications in processing long texts [45][48]
Building a multi-modal researcher with Gemini 2.5
LangChain· 2025-07-01 15:01
Gemini Model Capabilities - Gemini 2.5% Pro and Flash models achieved GA (General Availability) on June 17 [11] - Gemini models feature native reasoning, multimodal processing, million-token context window, native tools (including search), and native video understanding [12] - Gemini models support text-to-speech capabilities with multiple speakers [12] Langraph Integration & Researcher Tool - Langraph Studio facilitates the orchestration of the researcher tool, allowing visualization of inputs and outputs of each node [5] - The researcher tool utilizes Gemini's native search tool, video understanding for YouTube URLs, and text-to-speech capabilities to generate reports and podcasts [2][18] - The researcher tool simplifies research by combining web search and video analysis, and offers alternative ingestion methods like podcast generation [4][5] - The researcher tool can be easily customized and integrated into applications via API [9] Performance & Benchmarks - Gemini 2.5% series models demonstrate state-of-the-art performance on various benchmarks, including LM Marine, excelling in tasks like text, webdev, vision, and search [14] - Gemini 2.5% Pro model was rated the best in generating an SVG image of a pelican riding a bicycle, outperforming other models in a benchmark comparison [16][17] Development & Implementation - The deep researcher template using Langraph serves as a foundation, modified to incorporate native video understanding and text-to-speech [18] - Setting up the researcher tool involves cloning the repository, creating an ENV file with a Gemini API key, and running Langraph Studio locally [19] - The code structure includes nodes for search, optional video analysis, report creation, and podcast creation, all reflected visually in Langraph Studio [20]
小扎千亿挖人名单下一位:硅谷华人AI高管第一人
量子位· 2025-06-28 04:42
Core Insights - Meta, led by Mark Zuckerberg, is aggressively recruiting AI talent, including those previously poached by competitors like OpenAI and Google [1][2] - Zuckerberg is reaching out to former Meta AI executives and researchers to encourage their return to the company [3][4] - The urgency in Meta's recruitment efforts is highlighted by the recent struggles of its AI projects, particularly the Llama 4 model [18][22] Recruitment Strategy - Meta has restructured its AI teams into two main groups: an AI product team and an AGI Foundations team [25][28] - A new superintelligence lab has been established to develop AI systems that surpass human cognitive abilities [29] - The company is willing to offer substantial compensation packages, reportedly reaching up to $100 million for top talent [33][34] Competitive Landscape - Bill Jia, a prominent AI figure who left Meta for Google, has been instrumental in Google's AI advancements, making his return to Meta uncertain [8][10][17] - Google has made significant strides with its Gemini models, contrasting with Meta's recent setbacks [11][18] - Meta's AI department has expanded to over a thousand employees, reflecting its commitment to rebuilding its capabilities [32] Financial Moves - Meta has made substantial investments, including a $14.3 billion acquisition of a stake in Scale AI and attempts to acquire other AI startups [37] - The company is actively pursuing high-profile AI talent, with reports of multiple recruitment efforts targeting OpenAI researchers [38][40] Future Outlook - Despite recent challenges, Meta remains committed to its open-source strategy and plans to continue developing the Llama series [44] - The competitive landscape in AI is intensifying, with both Meta and Google focusing on innovative models and talent acquisition [45]
谷歌最强大模型Gemini 2.5正式发布,轻量版百万tokens输入价仅0.7元
3 6 Ke· 2025-06-19 11:10
Core Insights - Google has announced a significant update to its Gemini model, introducing Gemini 2.5 Pro and Gemini 2.5 Flash, with the Flash-Lite version in preview [2] Model Performance - Gemini 2.5 Pro is noted for its advanced reasoning and programming capabilities, achieving state-of-the-art (SOTA) performance in long context tasks with a context length of 1 million+ tokens [4] - In various benchmark tests, Gemini 2.5 Pro scored the highest in tasks such as Aider Polyglot programming, Humanity's Last Exam, and GPQA [4] - The model outperformed Gemini 1.5 Pro by over 120 points and surpassed competitors like OpenAI, xAI, and Anthropic, although it lagged behind OpenAI in mathematics and image understanding [4] Model Features - Gemini 2.5 Flash is a hybrid reasoning model designed for complex tasks, balancing quality, cost, and latency effectively [5] - The Flash-Lite version is an economical upgrade, excelling in high-capacity, latency-sensitive tasks like translation and classification, with faster token decoding speeds [5] Pricing Structure - Pricing for Gemini 2.5 Pro is set at $1.25 per million tokens for input and $10.00 for output [6] - Gemini 2.5 Flash has an input price of $0.30 and an output price of $2.50 per million tokens [6] - Gemini 2.5 Flash-Lite offers a significant cost advantage, with input prices at $0.10 and output prices at $0.40 per million tokens, making it 30%-60% cheaper than Gemini 2.5 Flash [7]
刚刚,Gemini 2.5系列模型更新,最新轻量版Flash-Lite竟能实时编写操作系统
机器之心· 2025-06-18 01:24
机器之心报道 编辑:Panda 刚刚,Gemini 系列模型迎来了一波更新: 谷歌 CEO Sundar Pichai 发推表示新推出的 Gemini 2.5 Flash-Lite 是目前性价比最高的 2.5 系列模型。 可以看到,谷歌对 2.5 Flash-Lite 的定位是适合用于「量大且注重成本效率的任务」。相较之下,2.5 Pro 适合编程和高复杂度任务,2.5 Flash 则居中,更适合需要 较快速度的日常任务。 Gemini 2.5 Pro 稳定版发布且已全面可用,其与 6 月 5 日的预览版相比无变化。 Gemini 2.5 Flash 稳定版发布且已全面可用,其与 5 月 20 日的预览版相比无变化,但价格有更新。 新推出了 Gemini 2.5 Flash-Lite 并已开启预览。 | | | 2.5 Flash-Lite | 2.5 Flash | 2.5 Pro | | --- | --- | --- | --- | --- | | | | THINKING OFF | THINKING | THINKING | | Best for | | High volume cost- | Fa ...
奥特曼ChatGPT用法错了!最新研究:要求“直接回答”降低准确率,思维链提示作用也在下降
量子位· 2025-06-09 03:52
Core Viewpoint - The recent research from Wharton School and other institutions reveals that the "direct answer" prompt favored by Ultraman significantly reduces model accuracy [1][9]. Group 1: CoT Prompt Findings - Adding Chain of Thought (CoT) commands in prompts does not enhance reasoning models and increases time and computational costs [2][6]. - For reasoning models, the accuracy improvement from CoT is minimal, with o3-mini showing only a 4.1% increase, while time consumption rose by 80% [6][23]. - Non-reasoning models show mixed results with CoT prompts, necessitating careful consideration of benefits versus costs [7][12]. Group 2: Experimental Setup - The research utilized the GPQA Diamond dataset, which includes graduate-level expert reasoning questions, to test various reasoning and non-reasoning models under different conditions [5][9]. - Each model was tested in three experimental environments: forced reasoning, direct answer, and default [10][11]. Group 3: Performance Metrics - Four metrics were used to evaluate the models: overall results, 100% accuracy, 90% accuracy, and 51% accuracy [12][19]. - For non-reasoning models, CoT prompts improved average scores and the "51% correct" metric, with Gemini Flash 2.0 showing the most significant improvement [12][13]. - However, in the 100% and 90% accuracy metrics, the inclusion of CoT prompts led to declines in performance for some models [14][20]. Group 4: Conclusion on CoT Usage - The study indicates that while CoT can improve overall accuracy, it also increases answer instability [15][22]. - For models like o3-mini and o4-mini, the performance gain from using CoT prompts is minimal, and for Gemini 2.5 Flash, all metrics declined [20][21]. - Default settings of models are suggested to be effective for users, as many advanced models already incorporate reasoning processes internally [25].