字节的 Agent 产品来了。 4 月 18 日晚间，字节跳动扣子空间开启内测，定位通用 Agent。与其他类似产品如 manus 一样，扣子空间采用了邀请码制。 平台上，用户可以选择精通各项技能的「通用实习生」，也可以选择行业的「领域专家」，通过与 AI 的互动完成工作任务。 就在前一天，火山引擎刚刚面向企业市场推出 OS Agent 解决方案及 AI 云原生推理套件，要帮企业更快、更省地构建和部署 Agent 应用。 扣子有探索和规划两种模式，如果想让它一步到位输出，可以选择探索模式；如果想亲自把控每个步骤，可以选择规划模式。 虽然采用了邀请码制，但扣子显然不是走饥饿营销的路线。用户激活获得的邀请码后，创建并完成一个新任务即可获得 5 个邀请码，邀请码激 活后，还可获得更多邀请资格，多创建多获得多邀请。 上线的第一时间，极客公园就上手实测了扣子空间的 Agent 功能。可以看到，在执行的三个任务中，制定旅游攻略和一周穿搭的任务顺利完 成，但另一个专家助手的任务，却出现了 Python 脚本调用失败、 API 权限异常等 Bug，一个晚上都没能执行成功。 字节迈出了走向 Agent 时代的第一步，但距离完美 ...

ChatGPT 让聊天窗成为大模型应用首选的交互界面。理由是当 AI 足够聪明，似乎就不需要用户学 习，不用熟悉按钮和菜单、只用自然语言下命令就够了。 字节在 2023 年下半年搭起 AI 应用开发平台 "扣子（Coze）"，让开发者不需要熟悉复杂的技术能 力，就能将自己的数据接入最前沿的大模型，做各式各样的应用。到 2024 年中，扣子团队发现，尽 管聊天机器人应用成千上万地出现，从知识问答到情感陪伴，覆盖几乎所有热门的场景，但大多面临 相同的问题：用户增长难，留存更难。 这是一个产品形态与用户真实需求形成偏差的案例。聊天界面简单易用，但对大模型要求极高，导致 不论中美都是一两个通用 AI 聊天应用断层式领先。扣子团队发现，平台上有一类应用的增长和留存 明显更好——嵌入到用户工作流的大模型应用，针对具体工作场景解决具体问题。 这成为他们去年下半年的工作重点。用大模型改造工作流，在工作场景中完成繁琐的任务，正是当下 行业热议的 Agent 初始形态。 大厂团队开发 AI 产品的样本。 4 月 18 日周五晚，字节的 Agent 产品 "扣子空间（space.coze.cn） " 开启内测。团队为此准备不少算 ...

Investment Rating - The report maintains an "Outperform" rating for the computer industry [2][13]. Core Insights - OpenAI has launched two groundbreaking AI models, o3 and o4-mini, enhancing multimodal and tool usage capabilities, marking a shift from language models to task agent models [6]. - The integration of image reasoning and tool usage allows these models to manipulate images during reasoning, significantly broadening AI application scope [6]. - The performance of o3 and o4-mini has set new benchmarks in various fields, including programming and visual perception, with o4-mini achieving a high score of 99.5% in AIME 2025 tests [7]. - The pricing structure for OpenAI's models indicates a competitive market positioning, with o3 priced at $10 per 1M tokens for input and o4-mini at $1.1 per 1M tokens [7]. - The report suggests that 2025 may be a pivotal year for the development of AI agents, with increasing computational demands expected as AI applications deepen [9]. Summary by Sections Market Performance - The computer industry has shown a significant performance trend, with a noted decline of 27% in the past year compared to the Shanghai and Shenzhen 300 index [3]. Related Research - Several related reports have been published, focusing on the recovery of domestic stocks post-external shocks, the resurgence of domestic products, and improvements in supply-demand dynamics within the industry [4]. AI Tools and Agents - The introduction of WeChat's AI assistant "Yuanbao" enhances the capabilities of agents within the WeChat ecosystem, potentially increasing AI application penetration in daily life [9]. - The MCP and A2A protocols are expected to facilitate seamless communication and collaboration among different AI agents, enhancing their functionality and efficiency [9]. Investment Recommendations - The report recommends focusing on various sectors, including AI tools, AI agents, multimodal AI, and AI computing power, highlighting specific companies within each category for potential investment opportunities [9].

Core Viewpoint - The development direction of the industry is to create Agents with advanced reflection, planning, and autonomous decision-making capabilities, moving beyond the current basic level of many Agents [1] Group 1: Agent Development - The foundation for building Agents is deep thinking models, which must possess the ability to think, plan, and reflect [1] - Agents should support multimodal capabilities, similar to human visual and auditory functions, to better handle complex tasks [1] Group 2: Product Launch - The Doubao 1.5 deep thinking model was officially launched, showcasing strong performance in general tasks such as mathematics, programming, scientific reasoning, and creative writing [1] - A visual version of the deep thinking model was introduced, which has visual reasoning capabilities, allowing it to associate and think about what it sees like a human [1]

将投中网设为"星标⭐"，第一时间收获最新推送 这一天，投资人期待已久。 作者丨 刘燕秋 来源丨 投中网 2022 年底， ChatGPT 席卷世界，由此点燃了国内轰轰烈烈的大模型创业投资热潮。两年过去，一度跑出大模型"六小虎"，如今这些公司各自有了不 同的走向。 其中，李开复创立的零一万物已与阿里云成立"产业大模型联合实验室"，不再追求训练超级大模型，转而训练参数适中的更快、更便宜的模型，基于后 者打造可以赚钱的应用。 MiniMax 转向多模态模型，百川深耕医疗业务，月之暗面 Kimi 则将推出首个内容社区产品。众多国资加持的智谱，将成为 率先走向资本市场的那个。 无论未来如何，眼下二级市场的热情已经被点燃，和智谱有关的概念股行情眼看又要起势。 4 月 15 日，与智谱在多个项目上展开战略合作的思美传媒 收获了一个涨停，尽管这家公司此前在回答投资者提问时表示，"该合作对公司财务状况及经营成果影响很小"。 清华学霸组团创业 在大模型"六小虎"中，出身清华的创业者占据了一半席位。 智谱由清华大学计算机系的技术成果转化而来，源自成立于 1996 年的清华大学知识工程（ KEG ）实验室。该实验室专注研究网络环境下 ...

曲凯： 今天我们请来了国内强化学习 (RL) 领域的专家吴翼，吴翼目前是清华大学交叉信息研究院助理教授，他曾经在 OpenAI 工作过，算是国内最早研究强化学 习的人之一，我们今天就争取一起把 RL 这个话题给大家聊透。 首先吴翼能不能简单解释一下，到底什么是 RL？ 因此，RL 其实更通用一些，它的逻辑和我们在真实生活中解决问题的逻辑非常接近。比如我要去美国出差，只要最后能顺利往返，中间怎么去机场、选什么航 司、具体坐哪个航班都是开放的。 但 RL 很不一样。 RL 最早是用来打游戏的，而游戏的特点和分类问题有两大区别。 第一，游戏过程中有非常多的动作和决策。比如我们玩一个打乒乓球的游戏，发球、接球、回球，每一个动作都是非标的，而且不同的选择会直接影响最终的结 果。 第二，赢得一场游戏的方式可能有上万种，并没有唯一的标准答案。 所以 RL 是一套用于解决多步决策问题的算法框架。它要解决的问题没有标准答案，每一步的具体决策也不受约束，但当完成所有决策后，会有一个反馈机制来评 判它最终做得好还是不好。 吴翼： RL 是机器学习这个大概念下一类比较特殊的问题。 传统机器学习的本质是记住大量标注过正确答案的数据对。 ...

Core Viewpoint - The future of marketing will shift from being human-centric to model-centric, as AI agents like Manus may become integral to everyday operations [1][2]. Group 1: Opportunities from Large Models - The capabilities of large models, such as DeepSeek-R1, have improved tenfold, presenting significant opportunities for businesses [2]. - Many companies struggle to utilize these models effectively, primarily due to issues like hallucination, which can be turned into entrepreneurial opportunities by leveraging proprietary data [2]. Group 2: Understanding Agents - An "Agent" in the business context can be seen as a representative that serves either supply-side or demand-side interests, with both paths offering substantial opportunities [3][4]. - Manus exemplifies a successful agent by connecting various tools and resources to enhance operational efficiency [5]. Group 3: Building a Company as an Agent - Companies can be deconstructed into four components: perception system, cognitive system, action system, and goals, to transform into an effective agent [6]. - The perception system should incorporate feedback from frontline employees to enhance decision-making and operational efficiency [8][10]. Group 4: Cognitive System - The cognitive system should focus on high-frequency decision-making and leverage AI to improve organizational efficiency [12][14]. - High leverage in decision-making is crucial and should be based on real-time data from frontline interactions [15][17]. Group 5: Action System - In knowledge-intensive industries, the action system is represented by AI, which can automate processes through APIs and RPA [18][19]. - Manus serves as a complex agent that can execute commands and streamline operations across the organization [19]. Group 6: Goals and Feedback Loops - The primary goal of any company is to understand and meet customer needs, creating a feedback loop that enhances responsiveness [20][21]. - Companies should continuously iterate and upgrade their systems to adapt to changing demands and improve efficiency [23]. Group 7: Strategic Recommendations for Entrepreneurs - Entrepreneurs should upgrade their teams and protect core data while focusing on marketing towards large models rather than just individuals [25][26]. - Product design should anticipate a future where robots and smart devices may replace human interaction, necessitating a shift in design philosophy [26].

石海东告诉钛媒体App："DeepSeek不仅在大模型推理成本和推理能力实现了极大优化。更重要的是， 对于各行业客户而言，过去对大模型存在负面印象，包括幻觉、训练数据等偏见和缺陷性问题， DeepSeek正在抹除这部分担心。这进一步推动了深度垂直智能体的落地。" 未来会有大量专业Agent，而不是只有一个超级Agent 相较于通用型Agent，面向B端垂直场景的Agent其机会窗口正在扩大。春节过后，DeepSeek的出圈，中 国有至少60家银行相继宣布对接了DeepSeek，但基本面向投资者的投研报告、财报分析、客户资料分 析报告等非核心业务场景，亦或者是OA、办公自动化，IT代码开发等非业务场景。 与一些企业或厂商先高调发声再选择行动不同的是，飞虎互动深入金融行业的Agent这件事情已经一年 有余。目前围绕金融银行三大关键环节：营销-风控-交易，飞虎互动构建了三款大模型驱动的Agent用 例，包括对客营销机器人，风控合规机器人，交易服务机器人。 飞虎互动公司创始人董事长石海东及团队与钛媒体交流中指出，AI大模型在银行业务价值和落地优先 级高的其实是在营销、风控领域，目前DeepSeek还没有接入到这些领域 ...

Core Viewpoint - The company reported a decline in total operating revenue and an increase in net loss for 2024, indicating ongoing challenges during its transformation phase [1][2]. Group 1: Financial Performance - In 2024, the company achieved total operating revenue of 9.153 billion, a year-on-year decrease of 6.6% [1]. - The net profit attributable to shareholders was a loss of 2.061 billion, which is an increase in loss by 1.09 billion compared to 2023 [1]. - The actual revenue was in line with the forecast median, while the actual loss slightly exceeded the forecasted net loss range of 1.72-1.92 billion [1]. Group 2: Revenue Drivers - The decline in operating revenue was primarily due to a temporary delay in customer demand and a decrease in signed amounts [1]. - The transition to a subscription business model has impacted short-term overall revenue [1]. - The increase in losses was attributed to higher amortization of capitalized intangible assets, increased employee compensation due to layoffs, and higher goodwill impairment losses [1]. Group 3: Cloud Transformation and AI Strategy - In 2024, the cloud service business generated revenue of 6.85 billion, a year-on-year decrease of 3.4%, while subscription revenue grew by 26.0% [2]. - The company reported contract liabilities of 3.05 billion, an increase of 8.8% from the end of 2023, with cloud-related contract liabilities growing by 13.0% [2]. - The company launched the enterprise service model YonGPT2.0, focusing on AI and agent technologies to bridge complex enterprise needs with general models [2]. Group 4: Future Outlook - The company maintains an "overweight" rating despite adjusting profit forecasts for 2025-2026 due to lower-than-expected client investment and ongoing transformation impacts [3]. - Projected revenues for 2025-2027 are 10.51 billion, 12.19 billion, and 14.18 billion, respectively, with net profits of 0.1 billion, 0.41 billion, and 0.84 billion [3]. - The company is expected to benefit from the successful advancement of its cloud and AI initiatives, with a clear industry position and potential for long-term growth [3].

行业点评 | 计算机 从 Copilot 到 Agent：AI 编程的范式革新 AI Coding 正在成为 Agent 商业化的突破口。我们认为编程领域的规则明确 性为 Agent 应用提供了天然约束框架，编程环境的技术特性为 Agent 自纠错 提供了理想试验场，同时编程原子化任务与大模型链式推理机制深度契合。 而在需求端，企业开发效率的刚需则创造了明确付费意愿，AI 编程领域已逐 步形成"技术验证-产品迭代-商业变现"的完整闭环。 AI 大模型在编程中的应用发展分为"Copilot→Agent→Multi-Agent"三个 阶段，目前各大厂商 AI coding 产品多处于第一阶段向第二阶段迈进的关键 节点。1）第一阶段：LLM as Copilot。大模型作为 Copilot，辅助程序员完 成任务，但并不改变软件工程的专业分工。2）第二阶段：LLM as Agent。 Agent 能够自主完成一部分任务，成为一个单一职能专家，能够自主使用工 具完成预定的任务。人在这个阶段的作用是给定上下文完成知识对齐。3） 第三阶段：LLM as Multi-Agent。多智能体互相协作完成复杂任务，人类则 负责创意 ...