是Kimi上月发布的首款Agent产品，在HLE测试中超过了Gemini2.5Pro，略高于OpenAI Deep Research，并与Gemini-Pro的Deep Research Agent打平，是目 前已知的最高水平之一。 当地时间9日晚，马斯克旗下公司xAI举办直播发布会，正式发布其最新旗舰模型Grok 4。 直播中提到HLE（Humanities Last Exam，人类最后的考试）进行对比时，分别介绍了OpenAI、谷歌旗下Gemini以及月之暗面Kimi三家公司，而 DeepResearcher正 资料显示，Kimi DeepResearcher功能在执行每个研究任务时，会平均进行23次推理，由模型判断并筛选出信息质量最高的内容后，剔除冗余及低质信息， 自动生成分析结论，拥有文献的严谨性，可有效告别模型幻觉。 在海外社交媒体上，AI从业者纷纷表达着对这款来自中国AI产品的喜爱，有网友表示，Kimi Deep Researcher可能是用过的最好的深度研究模型，视觉效 果出色。也有博主表示，对深度研究的能力和准确性印象深刻。 | February 3. | OpenAl Deep | A ma ...

以下文章来源于光子星球 ，作者郝鑫 光子星球 . 细微之处，看见未来 将投中网设为"星标⭐"，第一时间收获最新推送 处在上市前夜的AI六小虎，却各有各的难。 作者丨 郝鑫 编辑丨 吴先之 来源丨 光子星球 隔壁阿里Qwen3刷新模型记录，字节豆包全家桶打折甩卖。资本和市场就像两个无形的手，让大模 型六小虎们（月之暗面、智谱AI、MiniMax、阶跃星辰、百川智能、零一万物）不敢再沉寂下去。 智谱宣布获得浦东创投集团和张江集团联合10亿元人民币的战略投资，并开源新一代视觉语言大模 型GLM-4.1V-Thinking和全新的生态平台"Agent应用空间"；月之暗面开源代码模型Kimi-Dev， 上市这个选择，既关乎"面子"也关乎"里子"，一方面是靠融资上市续命，缓解"吞金"的压力，另一 方面是争夺行业话语权，撕掉"掉队"的标签。 不过，处在上市前夜的AI六小虎，却各有各的难，能否一举成功上市尚未可知，上市之后仍是挑战重 重。 各有各的难处 首个AI Agent项目Kimi Researcher（深度研究）开启内测；MiniMax"五连更"，涵盖推理模型、 视频生成模型、智能体、音色设计。 截至目前，六小虎中的两 ...

近日，在 36氪WAVES 举办的大会上，一个有趣的环节引发了人们的热议：主办方让Kimi与Minimax两家的投资人进行了对谈。 随着 DeepSeek 的横空出世，整个中国大模型的牌局已天翻地覆。 行业龙头的格局，从原来的大模型六小龙，逐渐演变成了今天的基模五强。 当六小龙不再是市场的焦点时，安静很久的 Kimi 和 Minimax 在 前不久 不约而同有了新动作： Kimi 开源了编程模型 Kimi -Dev，它的第一个Agent Kimi - Researcher深度研究也开启小范围测试。而 Minimax 则开源了首个推理模型 Minimax -M1，并完成连续五天 包含大模型、视频生成、音频生成等多个方向 的更加 。 从产品侧来看，Kimi将重心聚焦到agent，以深度研究为主要方向，似乎有意向金融、学术等方向发力，这条路线虽然已经有了智谱等竞争者，但远离了 以生活服务为主的大厂射程，叠加原本不错的基础模型能力，Kimi似乎找到了自己的舒适区。 而另一边，Minimax则似乎想要弥补自身的遗憾，在没有接入DeepSeek之后，继续发力全方向的布局。 这似乎也意味着，大模型竞争进入下半场之后，更多的 ...

Core Insights - The article discusses the emergence and evolution of "Deep Research" systems in the AI Agent exploration wave, highlighting the rapid development and competition among major players like Google, OpenAI, and Anthropic since late 2024 [1][2] - A comprehensive survey from Zhejiang University provides a framework for understanding and evaluating the current landscape of deep research systems, emphasizing the shift from model capability to system architecture and application adaptability as the main competitive focus [1][2] Group 1: Current Landscape and System Comparisons - The ecosystem of deep research systems is characterized by significant diversity, with different systems focusing on various technical implementations, design philosophies, and target applications [3] - Key differences among systems are evident in their foundational models and reasoning efficiency, with commercial giants leveraging proprietary models for superior performance in handling complex reasoning tasks [4] - Systems also differ in tool integration and environmental adaptability, showcasing a spectrum from comprehensive platforms to specialized tools [5] Group 2: Application Scenarios and Performance Metrics - In academic research, systems like OpenAI/DeepResearch excel due to their rigorous citation and methodology analysis capabilities, while in enterprise decision-making, systems like Gemini/DeepResearch thrive on data integration and actionable insights [8] - Performance metrics reveal that leading commercial systems maintain an edge in complex cognitive ability benchmarks, although specialized evaluations highlight the strengths of various systems in specific tasks [9][10] Group 3: Implementation Challenges and Technical Solutions - The implementation of deep research systems involves strategic trade-offs across architecture design, operational efficiency, and functional integration [12] - Core challenges include managing hallucination control, privacy protection, and ensuring interpretability, with solutions focusing on source grounding, data isolation, and transparent reasoning processes [15] Group 4: Evaluation Frameworks - The evaluation of deep research systems is evolving from single metrics to a multi-dimensional framework that assesses functionality, performance, and contextual applicability [16] - Functional evaluations focus on task completion capabilities and information retrieval quality, while non-functional assessments consider performance efficiency and user experience [17][18] Group 5: Future Directions in Reasoning Architecture - Future advancements in deep research systems are expected to address limitations in context window size, enabling more comprehensive analysis of large-scale research materials [22][23] - The integration of causal reasoning capabilities and advanced uncertainty modeling will enhance the systems' applicability in complex fields like medicine and social sciences [27][30] - The development of hybrid architectures that combine neural networks with symbolic reasoning is anticipated to improve reliability and interpretability [25][26]

以下文章来源于暗涌Waves ，作者暗涌 暗涌Waves . 钱的流向，人的沉浮。36氪旗下投资报道账号。 文 ｜ 于丽丽 来源｜ 暗涌Waves（ID:waves36kr） 封面来源 ｜ WAVES2025 活动现场 去年36氪WAVES 2024大会上，我们曾特意设置一个Kimi投资人和MiniMax投资人的对垒环节。彼时，大模型公司的竞争如火如荼。因为两家产品更toc， 更符合美元基金审美，融资也跑得更快，所以经常被放在一起做比较。 但一年后，随着DeepSeek的横空出世，整个中国大模型的牌局已天翻地覆。两家已没有那么针锋相对，他们的未来可能性也成为新的议题。 某种意义上，这是我们重组这个panel的原因之一。在6月11日举办的WAVES 2025大会上，我们重新邀请了当时的部分嘉宾参与讨论。他们是：真格基金 管理合伙人戴雨森、云启资本合伙人陈昱、高榕创投合伙人胡朔和明势资本合伙人夏令。 当时，Kimi和MiniMax已经安静很久。但在上一周，它们则不约而同有了新动作：Kimi开源了编程模型Kimi-Dev，它的第一个Agent kimi-Researcher(深 度研究)也开启小范围测试。而Mini ...

Core Insights - Kimi, once a prominent player in the AI space, has seen a decline in attention as newer models from companies like Quark, Tencent, and Alibaba gain traction [1][2] - The initial hype around Kimi was driven by its technological scarcity, particularly its long-text processing capabilities, which were unmatched at the time [2][3] - Kimi's early valuation of $3 billion was supported by its unique technology, the founder's impressive background, and the capital's anxiety to find a domestic alternative to leading AI models [4][5] Technology and Market Position - Kimi's long-text processing ability, which expanded from 200,000 to 2 million words, was a significant technological breakthrough that positioned it as a leader in the AI field [2][3] - The founder, Yang Zhilin, had a strong academic and entrepreneurial background, which enhanced investor confidence in Kimi's potential [3][4] - The competitive landscape was characterized by a rush to find alternatives to ChatGPT, leading to Kimi's rapid user acquisition through aggressive marketing strategies [4][5] Financial Strategy and User Acquisition - Kimi faced challenges in managing its newfound capital, leading to excessive spending on user acquisition, with monthly advertising costs peaking at 220 million RMB [6][7] - Despite a significant increase in daily active users (DAU) from 508,300 to 5,897,000, this growth was primarily driven by financial investment rather than product quality [8][9] - The pressure from investors to demonstrate commercial viability led Kimi to prioritize user numbers over technological development, resulting in a loss of strategic direction [8][9] Challenges and Strategic Missteps - Kimi's marketing strategy shifted focus from its core user base in academia and professional fields to entertainment sectors, diluting its brand identity [11][12] - The company struggled with maintaining its technological edge as competitors began to catch up, particularly with the emergence of open-source models [12][13] - Kimi's reliance on user growth without a solid feedback loop or data quality management led to a false sense of security regarding its market position [13] Future Opportunities - Kimi has potential avenues for recovery, including enhancing the value density of its products and focusing on deep search capabilities for specific industries [15][17] - The company could benefit from developing comprehensive tools for developers, improving its API offerings to facilitate easier integration for enterprise clients [18][19] - Emphasizing quality over quantity in user engagement and product offerings could help Kimi regain trust and market relevance [20][21] Strategic Recommendations - Kimi needs to establish a clear commercial strategy from the outset, ensuring that its products meet genuine market demands and have viable monetization paths [29][30] - The focus should shift towards building a sustainable revenue model based on user payments rather than relying on external funding for growth [31] - A strategic approach that prioritizes understanding and fulfilling real user needs will be crucial for Kimi's long-term success in the competitive AI landscape [31][32]

「 最疯狂的可能是人类不会是这个星球上唯一的智能物种。 」 文 ｜ 于丽丽 去年36氪WAVES 2024大会上，我们曾特意设置一个Kimi投资人和MiniMax投资人的对垒环节。彼时，大模型公司的竞争如 火如荼。因为两家产品更toc，更符合美元基金审美，融资也跑得更快，所以经常被放在一起做比较。 但一年后，随着DeepSeek的横空出世，整个中国大模型的牌局已天翻地覆。两家已没有那么针锋相对，他们的未来可能性也 成为新的议题。 某种意义上，这是我们重组这个panel的原因之一。在6月11日举办的WAVES 2025大会上，我们重新邀请了当时的部分嘉宾 参与讨论。他们是：真格基金管理合伙人戴雨森、云启资本合伙人陈昱、高榕创投合伙人胡朔和明势资本合伙人夏令。 当时，Kimi和MiniMax已经安静很久。但在上一周，它们则不约而同有了新动作：Kimi开源了编程模型Kimi-Dev，它的第一 个Agent kimi-Researcher(深度研究)也开启小范围测试。而MiniMax则开源了首个推理模型MiniMax-M1，并完成连续五天 的更新。 这些信号也都指向本场panel中所总结的：尽管所有中国AI公司都从D ...

当下内需新消费与 AI 应用如何看？20250622 摘要 近期市场回调受地缘政治及新消费股解禁影响，但暑期档电影和潮玩等 内需型消费领域仍具潜力。人民日报对盲盒经济的关注预示着潜在监管 风险。 暑期档短剧市场受广电总局分级管理政策影响，进入精细化阶段。腾讯 短剧小程序和字节跳动红果增长迅速，预示着市场竞争加剧。 院线公司如万达电影、上海电影、横店影视等积极布局泛娱乐市场，通 过 IP 运营和线下体验拓展业务，预计 2029 年中国泛娱乐市场规模将超 3,000 亿人民币。 短剧行业在政策引导下向优质内容转型，与游戏、线下展览等娱乐形式 融合，推动衍生品和潮玩经济发展。关注腾讯小程序的数据表现及《长 安荔枝》系列。 传媒板块中，出版阅读和游戏公司业绩稳定，如南方传媒、中南传媒等。 中报业绩改善或增长预期，叠加 AI 应用的公司有望获得估值提升。 AI 应用板块在经历年初高涨后进入冷静期，但港股 AI 企业 IPO 及 ARCS 科幻短剧的推出显示行业新动向。关注 AI 生成内容在影视动画领域的潜 力。 AI 生成内容市场规模预计达千亿美元级别。关注 AI 加教育（天舟文化、 荣信文化）、AI 加陪伴（奥飞娱乐 ...

E-commerce and Retail - Vipshop's Vice President of Marketing, Feng Jialu, is under investigation for personal economic issues, but the company maintains that its operations are normal and has a zero-tolerance policy for corruption [1] - Tmall's "618" event saw 453 brands surpassing 100 million yuan in sales, a 24% increase year-on-year, indicating a successful simplification of the event that boosted user engagement [2] - JD.com reported over 2.2 billion orders during its "618" event, with a more than 100% increase in active users, highlighting the effectiveness of its online and offline integration strategy [3] Logistics and Supply Chain - JD Logistics launched a new B2C express delivery brand, "JoyExpress," in Saudi Arabia, offering fast delivery services and local customer support, aiming to capture market share in the region [4] - Cainiao introduced a new affordable unmanned delivery vehicle priced at 21,800 yuan, designed to reduce costs for delivery points while maintaining high-quality autonomous driving features [5][6] Life Services - Ele.me launched the "Yuexiang Membership" program aimed at frequent users, offering personalized services and benefits to enhance user experience in the increasingly competitive food delivery market [7] Innovation and Investment - MiniMax released the MiniMax-M1 series, the world's first open-source large-scale hybrid architecture inference model, achieving significant breakthroughs in processing long texts and reducing reinforcement learning costs to $530,000 [9] - AI startup "Memory Tensor" secured nearly 100 million yuan in angel funding, focusing on low-cost, high-generalization AI models, aligning with industry demands for improved performance and practicality [10] - Kimi's first agent, Kimi-Researcher, began gray testing, utilizing end-to-end reinforcement learning technology, with plans for gradual open-sourcing to foster developer engagement [11]

Group 1 - ByteDance founder Zhang Yiming is not returning to the front line, still based in Singapore, focusing on AI and technology discussions [1][2] - Microsoft plans to cut thousands of jobs, following a previous layoff of 6,000 employees, as part of its AI investment strategy [2][3] - Amazon's CEO indicated that generative AI will replace a significant portion of jobs in the coming years, making layoffs inevitable [3] Group 2 - Yushu Technology has completed its C round financing, with a valuation exceeding 10 billion RMB, backed by major investors including China Mobile and Tencent [4] - MiniMax is preparing for an IPO in Hong Kong, with its valuation reportedly exceeding 2.5 billion USD after recent funding rounds [5][6] - MiniMax has launched several AI models, including the MiniMax-M1, which can handle long text inputs and has significantly reduced training costs [5][6] Group 3 - Luo Yonghao has invested heavily in AR technology but acknowledges the challenges in commercialization, shifting focus to AI solutions [7][8] - JD.com's Liu Qiangdong discussed the company's supply chain strategy in the food delivery sector and expressed a desire to innovate after a stagnant five years [9][10][11] Group 4 - 58.com is undergoing significant layoffs, affecting 20-30% of its workforce, with compensation packages offered [12] - Meta attempted to acquire Ilya Sutskever's company but shifted to hiring its CEO after the acquisition was declined [13][14] Group 5 - Google apologized for a major cloud service outage that lasted several hours, affecting numerous services and caused disruptions for third-party applications [18][19] - Harvard University has released an open dataset for AI training, encompassing 983,000 books across 245 languages, supported by Microsoft and OpenAI [26][27]