Core Insights - MiniMax has officially open-sourced its latest large language model, MiniMax-M1, marking a significant development in the AI landscape [2][4] - MiniMax-M1 is recognized as the world's first open-weight large-scale hybrid attention inference model, showcasing substantial breakthroughs in performance and inference efficiency [4][6] Model Specifications - MiniMax-M1 features a parameter scale of 456 billion, with each token activating approximately 45.9 billion parameters, and supports a maximum context length of 1 million tokens, which is 8 times longer than that of DeepSeek R1 [7][12] - The model's computational load (FLOPs) for generating 100,000 tokens is only 25% of that required by DeepSeek R1, indicating a significant advantage in long text processing tasks [7][12] Training and Efficiency - The training of MiniMax-M1 utilized a large-scale reinforcement learning (RL) strategy, optimizing performance across various tasks, including mathematical reasoning and software engineering [9][11] - The complete RL training of MiniMax-M1 was accomplished in three weeks using 512 H800 GPUs, with a cost of approximately $534,700, demonstrating high efficiency and cost-effectiveness [11] Performance Comparison - MiniMax-M1 is available in two versions, with maximum generation lengths of 40K and 80K tokens, and has shown superior performance in complex software engineering, tool usage, and long-context tasks compared to leading open-weight models like DeepSeek-R1 and Qwen3-235B [12][19] - In benchmark tests, MiniMax-M1 outperformed other models in various categories, including long-context understanding and tool usage, establishing itself as a strong contender in the AI model landscape [19]

Core Viewpoint - DeepSeek has made significant advancements in the open-source model space with the release of its upgraded R1 inference model (0528), which shows competitive performance against proprietary models [2][4][10]. Performance Summary - The R1-0528 model has improved benchmark performance, enhancing front-end functionality, reducing hallucinations, and supporting JSON output and function calls [3]. - In the latest performance rankings from LMArena, DeepSeek-R1 (0528) achieved an overall ranking of 6th, and it is the top-ranked open model [5][4]. - Specific rankings in various categories include: - 4th in Hard Prompt testing - 2nd in Coding testing - 5th in Math testing - 6th in Creative Writing testing - 9th in Instruction Following testing - 8th in Longer Query testing - 7th in Multi-Turn testing [6][7]. Competitive Landscape - In the WebDev Arena platform, DeepSeek-R1 (0528) is tied for first place with other proprietary models like Gemini-2.5-Pro-Preview-06-05 and Claude Opus 4, surpassing Claude Opus 4 in score [8]. - The performance of DeepSeek-R1 (0528) is seen as a milestone, particularly in the AI programming domain, where it competes closely with established models like Claude [10]. User Engagement - The strong performance of DeepSeek-R1 (0528) has generated increased interest and usage among users, prompting discussions about user experiences [9][11].

Quantitative Models and Construction Methods - **Model Name**: Report/Document Interpretation Workflow - **Model Construction Idea**: Automate the process of interpreting financial reports and extracting key information, including formulas, using AI agents and workflows[28][30] - **Model Construction Process**: 1. Use Coze's official file-reading plugin to extract document content and formula structures[30] 2. Configure prompt logic and output format using LLM nodes in the workflow[30] 3. Test the workflow by inputting a URL of a quantitative research paper, where the AI agent summarizes key information and accurately interprets formulas[31] - **Model Evaluation**: Demonstrates the ability to process complex financial documents and provide accurate formula interpretations, enhancing efficiency in financial research[31] - **Model Name**: Real-Time Financial Data Analysis Workflow - **Model Construction Idea**: Automate the retrieval and analysis of real-time financial data from web sources or plugins[35][38] - **Model Construction Process**: 1. Construct a workflow with a code-processing node to generate complete URLs based on user-input stock codes[38] 2. Use a data-scraping node to retrieve real-time financial data from websites like Sina Finance[35][38] 3. Input the data into the DeepSeek LLM node for comprehensive analysis, focusing on profitability, solvency, and operational efficiency[39] - **Model Evaluation**: Provides timely and structured financial insights, enabling informed decision-making in investment analysis[39] - **Model Name**: Research Report Summarization Workflow - **Model Construction Idea**: Automate the process of extracting and summarizing content from multiple research reports or news articles[52][55] - **Model Construction Process**: 1. Use Coze plugins to scrape HTML content from websites like Eastmoney[55] 2. Employ loop nodes to process multiple reports and extract relevant content[55] 3. Store the extracted data (e.g., titles, content, institution names, links) in Feishu multi-dimensional tables for further analysis[57] - **Model Evaluation**: Effectively consolidates and organizes large volumes of research data, improving accessibility and usability for financial analysts[57] Model Backtesting Results - **Report/Document Interpretation Workflow**: Successfully summarized key information and accurately interpreted formulas from a quantitative research paper[31] - **Real-Time Financial Data Analysis Workflow**: Generated detailed financial analyses based on real-time data, covering multiple financial metrics such as ROE, net profit, and cash flow[39][48] - **Research Report Summarization Workflow**: Efficiently extracted and stored structured data from multiple research reports, enabling streamlined analysis and reporting[57][60] Quantitative Factors and Construction Methods - **Factor Name**: None explicitly mentioned in the report Factor Backtesting Results - **Factor Results**: None explicitly mentioned in the report

欢迎大家 点击【预约】 按钮 预约 我 下一场直播 本文重点 观点来自： 6 月 9 日本周一直播 谷歌未来的目标是实现通用人工智能（AGI），即让机器具备与人脑同等的通用智能能力。DeepMind 团队对AGI有清晰定义，认为通用智能即机器能像人脑一样处理各种任务。尽管现阶段AI在某些简单任 务仍有不足，但正在不断弥补"认知漏洞"，逐步向真正的通用智能靠近。 【 强 烈建议直接看】 本段视频精华，逻辑更完整 谷歌与特斯拉被认为是最接近实现"世界模型"的两家公司，谷歌依托YouTube海量视频数据，特斯拉则 依靠车辆摄像头采集的现实世界数据。这些多维度的现实数据对训练通用智能极为关键，远超单一文本 数据的深度。 文字版速览 总的来说，谷歌的AI技术不仅扎实，更具备创新和超越的潜力。未来几年，谷歌AI有望在智能发现、 模型完善以及通用智能方向实现突破，继续保持其在AI领域的领先地位。作为关注AI发展的朋友，我 认为谷歌值得持续跟踪和关注。 谷歌作为AI领域的重要玩家，其发展历程和技术积累值得深入分析。谷歌母公司Alphabet的架构设计十 分巧妙，它将多个创新子公司独立运营，如Google、DeepMind、I ...

该工作第一作者为蚂蚁技术研究院副研究员胡翔，蚂蚁技术研究院高级研究员武威为通讯作者。 在大语言模型如火如荼的当下，长文本建模仍然是一个极具挑战的问题。纠其根源，一方面在于主流 LLMs 的架构 Transformers 中平方复杂度及随序列长度线性增 长的推理阶段显存开销；另一方面在于 full-attention 有限的外推能力，难以泛化到远超预训练阶段长度的输入。 而高效处理长上下文能力，除了简单的工业界降本增效的需求外，还涉及通用人工智能 (AGI) 的核心问题：具有永久记忆的智能体。如果将人类从出生开始接收 到的信息视作长上下文，人类拥有记忆无非是访问这些上下文。因此记忆可以看作是超长上下文访问能力，而拥有与用户所有对话记忆的智能体，很可能为大语 言模型公司构建数据护城河 (事实上，OpenAI 已经开放了类似能力)。 近日，蚂蚁的研究团队为这个问题带来了一个新思路。就像人类开卷考试只会挑和当前问题相关的关键页作为参考，语言模型也可以只关注与当前上下文相关的 过去片段。以此为出发点，他们提出一种 基于因果检索的注意力机制 GCA (Grouped Cross Attention)，完全端到端地学习如何 ...

日前，全球最大的上市对冲基金集团——英仕曼集团宣布，其全资子公司英仕曼（上海）投资管理有限公司于中国市场推出首只自主管理的股票指数增强 策略产品——英仕曼美量中证500指数增强策略。该产品已于中国证券投资基金业协会（简称协会）备案，面向合格投资者发行。 自2017年在境内登记为证券私募管理人以来，英仕曼集团发展节奏历经波动。英仕曼集团于6月12日发布的新闻稿中表示，该产品的发行标志着集团在中 国投资市场的重要战略布局进入新阶段。 于中国市场推出自主管理指增产品 英仕曼进一步表示，该产品将集团旗下Numeric团队的全球长期实盘经验的系统化量化投资方法用于中国A股市场投资。据了解，Numeric团队拥有超过30 年的量化投资经验。截至2025年3月31日，其管理的全球股票策略资产规模超过400亿美元。 英仕曼Numeric高级投资经理方子昂表示，随着中国经济的稳健增长，作为全球第二大股票市场，A股市场不仅拥有显著的配置潜力，而且为量化策略提 供了丰富的Alpha来源。 英仕曼Numeric投资经理、英仕曼美量中证500指数增强策略首席基金经理杨海翔表示，投资策略在量化模型基础上，整合了包括公司基本面、行业另类数 ...

Core Insights - OpenAI has launched o3-pro, an enhanced version of its reasoning model, following a 9-hour outage of ChatGPT, aiming to provide more reliable responses and extended thinking time [1][2][4]. Model Performance - o3-pro has been made available to all ChatGPT and API Pro users, with usage limits for Plus users increased from 100 to 200 times per week [2]. - In expert evaluations, o3-pro outperformed its predecessor o3 in all tested categories, particularly in science, education, programming, business, and writing assistance [2][6]. - The model supports both text and image inputs, with a context window size of 200k and a maximum output token count of 100k [11]. Competitive Landscape - OpenAI's performance is under scrutiny, especially with Google’s Gemini 2.5 Pro entering the market, which has been noted for its competitive pricing and capabilities [4][24]. - In internal tests, o3-pro surpassed Gemini 2.5 Pro in mathematical benchmarks and outperformed Anthropic's Claude 4 Opus in doctoral-level science tests [27]. Pricing Strategy - o3-pro is priced at $20 per million tokens for input and $80 for output, significantly lower than its predecessor o1-pro, which is expected to be phased out [24][27]. - Following the launch of o3-pro, OpenAI announced an 80% price reduction for o3, making it more competitive against Gemini 2.5 Pro [27]. User Experience - Users have reported that o3-pro is slower in response times compared to other models, taking several minutes for simple queries, which has raised concerns about its efficiency [15][17]. - Despite the slower response, o3-pro has demonstrated strong analytical capabilities and proficiency in using tools for complex problem-solving [19][22].

摘要 万马科技通过收购有方科技切入车联网领域，车联网业务收入从 2021 年的 5,000 万元增长到 2024 年的 2.6 亿元，利润也显著提升，并已建 立完整的数据闭环工具链和智驾算力中心。 国内车联网行业渗透率约为 80%，海外市场渗透率不足 30%，随着智 能驾驶对数据需求的增加，国内外市场均有较大的发展空间，尤其 Robotaxi 对实时数据监控和技术要求更高，单车价值提升显著。 优卡科技提供蓝海全球车联和云自动驾驶数据闭环两大解决方案，支持 1,400 万辆车辆，客户包括吉利、上汽、东风和理想等，并在全球范围 内支持 Robotaxi 企业的业务布局。 Robotaxi 被视为车联网行业发展的"皇冠上的明珠"，高盛预测中国 Robotaxi 市场年化增长率将达到 96%。目前已在北京、武汉、广州以 及香港、迪拜等地进行常态化运营，特斯拉也即将推出相关业务。 Robotaxi 运营对网络质量有极高要求，包括运行安全、用户交互、合 规性、自动驾驶数据采集和运维等方面，需要高清地图、车路协同、远 程脱困以及海量数据支持。 万马科技 20250612 据监控需求高，对技术和数据量要求也更高，从单车价值上 ...

文 | 中关村储能产业技术联盟 6月1 1 日，双登股份发布了 Power Warden3.0 — 6.25MWh半固态液冷储能系统 产品及 解决方案 ，标准 2 0 呎高柜，单舱 6 .25MW h ， 兼容组串、构网及长时储能场景 。 强大内 "芯" 持久供能 Power Warden3.0 系统首次 搭载双登 5 87A h半固态电芯 ，重新定义储能行业安全与能量标准。该款电芯体积能 量密度达到 416Wh/L， 20年超长循环寿命和9 5% 超高能量效率， 赋能全生命周期成本 更低 ，为储能场景提供 更具竞争力的价值解决方案。 双登 5 87A h半固态电芯，通过创新原位聚合技术实现液态电解液向半固态电解质的革命性突破，极端条件下产热功 率更低， 大幅降低热失控风险， 同时具备较高的 高机械强度与稳定性 ，能有效抑制电极材料体积变化， 从根本上 减少膨胀力的产生 ，重塑电池安全机制 。电芯日历存储 2年， 额定 能量 零衰减 ，耐久性表现领跑行业。 极致防护 盾构安全 安全是储能系统的生命线， Power Warden3.0系统 构建了从电芯到系统的全方位安全防护设计。 PACK 安全层面，基于半固态 ...

证券代码：300830 证券简称：金现代 金现代信息产业股份有限公司 投资者关系活动记录表 编号：2025-003 |  | 特定对象调研 分析师会议 | | --- | --- | | 投资者关系活动 | □媒体采访 □业绩说明会 | | 类别 | □新闻发布会 □路演活动 | | | ☑现场参观 | | | □其他 （请文字说明其他活动内容） | | 参与单位名称及 人员姓名 | 华创证券张文星、太平洋证券汪奇立、翊安投资张益锋 | | 时间 | 2025 年 6 月 11 日（星期三） | | 地点 | 公司展厅及会议室 | | 上市公司接待人 | 董事会秘书、财务总监 鲁效停 | | 员姓名 | 证券事务部助理 张慧丽 | | | 一、参观公司展厅、介绍公司的基本情况 二、公司董事会秘书介绍公司 2024 年度及 2025 年第一季度业 | | | 绩情况 | | | 三、问答环节 | | 投资者关系活动 | 1、问：公司 2024 年度营业收入按产品分类情况如何？ | | 主要内容介绍 | 答：公司 2024 年度营业收入中，定制化软件开发及服务 | | | 占比约为 78%，标准化软件产品开发 ...