近日 话题"第一次知道微信支付还有这功能 " 冲上热搜 引发网友热议 有网友发帖称 自己在商家就餐后不久 在"微信支付"页面 收到商家发来的物品遗失提醒 对此，有网友留言称 早就知道 "遗失提醒"这个隐藏功能了 也有不少网友表示 "没想到微信支付有这样的功能" 对于该功能，腾讯客服此前曾回应过：目前仅部分商家支持该功能，如果商家没有这个服务也会想办法联 系客服通知。 对于商家是否可见个人信息，腾讯客服表示： 仅微信支付公众号推送消息，只会显示该笔付款的交易信 息，不会有其他信息。 下面一起来看看 微信还有哪些 你可能不知道的实用功能 图片表格丝滑转在线文档 不在电脑旁边 却收到一张Excel表格的截图 需要修改怎么办？ 打开微信 手机就可以做了 ↓↓↓ ①长按图片，或者打开图片长按，会跳出一个选项框； | | 맞 | C | D | | --- | --- | --- | --- | | 1 日期 | 姓名 | 项目 | 相关费用 | | XX | XX | XX | XX | | XX | XX | XX | XX | | XX | XX | XX | XX | | XX | XX | XX | XX | ...

Core Insights - A high-profile dialogue on AI took place in Beijing, featuring leading figures in China's large model sector, indicating a significant moment for the industry [1][2][15] - The discussion focused on the evolution of AGI, with a consensus that the future lies in autonomous learning and problem-solving capabilities [3][4][17] Group 1: Key Figures and Their Contributions - Tang Jie, a professor at Tsinghua University and founder of Zhipu AI, recently led the company to become "China's first stock in foundational models" [1][15] - Yao Shunyu, a former OpenAI researcher and now Tencent's chief scientist, emphasized the importance of autonomous learning in AGI's future [4][18] - Lin Junyang, head of Alibaba's Tongyi Qianwen model, discussed the need for models to evolve beyond general-purpose tools to specialized applications [7][21] Group 2: Future Directions in AGI - The next "singularity" in large models is expected to focus on autonomous learning, moving beyond passive responses to proactive decision-making [3][17] - Yao Shunyu highlighted that autonomous learning is a gradual process driven by data and task evolution, with current models already showing signs of self-optimization [4][18] - Concerns about the risks of autonomous AI were raised, emphasizing the need for proper guidance in AI development [3][17] Group 3: Scaling Law and Efficiency - The Scaling Law, which posits that increasing data and computational power leads to better model performance, is facing diminishing returns, prompting a shift towards "Intelligence Efficiency" [5][19] - Tang Jie proposed that future advancements should focus on achieving higher intelligence with less computational investment [5][19] - Yao Shunyu noted that improvements in model architecture and optimization are crucial for enhancing model performance beyond mere scaling [6][20] Group 4: Model Differentiation - The conference highlighted the trend of model differentiation, where models are tailored to specific scenarios rather than being one-size-fits-all solutions [7][21] - Yao Shunyu pointed out that in B2B contexts, strong models can significantly reduce operational costs, while in B2C, the focus should be on contextual understanding [8][22] - Lin Junyang emphasized the importance of integrating models with real-time user environments for better performance in consumer applications [8][22] Group 5: The Future of AI Agents - There is widespread optimism about the potential of AI agents to automate tasks, particularly in B2B settings, though challenges remain in B2C applications [11][25] - The development of agents is seen as a multi-stage process, with current models still reliant on human-defined goals [12][26] - The future of agents may involve more interaction with the physical world, enhancing their utility and effectiveness [11][25] Group 6: Competitive Landscape and Innovation - The dialogue acknowledged the existing gap between Chinese and American AI capabilities, with a consensus on the need for innovation to bridge this divide [12][26][28] - Yao Shunyu emphasized the importance of breakthroughs in computational power and market maturity for China's AI future [13][27] - Tang Jie identified opportunities for China to excel in AI through a culture of risk-taking and innovation among younger generations [14][28]

Core Insights - The AGI-Next summit highlighted the challenges and opportunities for Chinese large model companies, featuring prominent figures in AI discussing new paradigms and advancements in technology [2][4]. Group 1: AI Market Dynamics - The Chinese large model market is showing significant differentiation between To C (consumer) and To B (business) segments, with distinct underlying logic for each [4]. - In the To C market, most users do not require high intelligence from models, leading to a trend of vertical integration where model and application layers are closely coupled for better user experience [4][5]. - Conversely, in the To B market, higher intelligence correlates with increased productivity and willingness to pay, creating a head effect where top models command higher subscription fees [5][6]. Group 2: Future AI Paradigms - The next generation of AI is expected to focus on context capture rather than just model parameter competition, emphasizing the importance of understanding user context for better responses [5]. - There is a belief that signals of autonomous learning will emerge by 2025, although current attempts lack the pre-training capabilities seen in leading companies like OpenAI [8]. - The potential for AI to evolve autonomously and act proactively is seen as a key feature of future paradigms, though it raises significant safety concerns [9]. Group 3: Technological Advancements - Memory technology is anticipated to develop linearly, with breakthroughs expected in the near future as algorithms and infrastructure improve [10]. - The gap between academia and industry in large model development is narrowing, with more academic institutions gaining access to computational resources, fostering innovation [11]. - The industry faces efficiency bottlenecks, with the need to achieve greater intelligence with less investment becoming a driving force for new paradigms [11]. Group 4: AI Agent Development - The evolution of AI Agents is seen as a critical change for the AI industry by 2026, moving from human-defined goals to AI autonomously defining objectives [13]. - The ability of AI Agents to address long-tail problems is highlighted as a significant value proposition for AGI [13]. - The commercialization of AI Agents faces challenges related to value, cost, and speed, necessitating a balance between solving real human issues and managing operational costs [14].

Core Insights - The AGI-Next summit gathered prominent figures in AI, discussing new paradigms, challenges, and opportunities for Chinese large model companies [1] - Yao Shunyu, Tencent's Chief AI Scientist, highlighted the distinct characteristics of the To C and To B markets in the AI landscape [5][6] Group 1: Market Dynamics - Yao Shunyu noted that the To C market does not require high intelligence most of the time, with applications like ChatGPT serving as enhanced search engines [5] - In contrast, the To B market shows a willingness to pay significantly for top-tier models, with companies willing to pay $200/month for premium models, while interest in lower-tier models is minimal [5] - The disparity in model performance is expected to widen, as weaker models incur hidden costs in enterprise settings due to the need for manual error checking [5] Group 2: Technological Evolution - Yao emphasized that future competitiveness will hinge on capturing context rather than merely increasing model parameters, as better responses depend on understanding user preferences and real-time data [6] - The development of autonomous learning is underway, with some teams using real-time user data for training, although significant breakthroughs are yet to be realized due to a lack of pre-training capabilities [7] - Lin Junyang pointed out that the potential of reinforcement learning (RL) remains untapped, and achieving AI's proactive capabilities poses safety risks that need careful management [9] Group 3: Future Paradigms - Tang Jie expressed optimism about the emergence of new paradigms driven by continuous learning and memory technologies, as the gap between academia and industry narrows [10][11] - The industry faces efficiency bottlenecks, with data scales increasing from 10TB to 30TB, yet the returns on investment are diminishing, necessitating a focus on "intelligence efficiency" [10] - The evolution of AI agents is seen as a critical change, with the potential for models to autonomously define goals and plans, moving beyond human-defined parameters [13] Group 4: Commercialization Challenges - The commercialization of AI agents faces challenges related to value, cost, and speed, with a need to ensure that agents address meaningful human tasks without incurring prohibitive costs [14]

Core Insights - The article discusses the challenges faced by input method applications, particularly focusing on Sogou Input Method, as it approaches its 20th anniversary in 2026. Despite advancements in AI capabilities, user experience has deteriorated, leading to complaints about inefficiency and inaccuracies in word prediction and voice recognition [2][3][5]. User Experience Issues - Users express frustration over the declining accuracy of input methods, with complaints about incorrect word suggestions and excessive advertisements disrupting their experience [3][5][6]. - A long-time user of Sogou Input Method reported issues with common character suggestions, indicating a failure to learn user habits despite repeated corrections [4][5]. AI Integration and Competition - Major input method companies, including Tencent's Sogou, Baidu, and iFlytek, are engaged in a competitive race to integrate advanced AI features into their products, aiming to enhance user experience and functionality [3][9][10]. - The input method market is characterized by a concentrated structure, with leading companies holding a combined market share of 84.4% as of July 2025, indicating a fierce competition for user engagement and AI capabilities [9]. Future Development Goals - Sogou Input Method aims to evolve into a "super entrance" for AI, allowing users to interact with AI agents through the input method, which is seen as a key direction for future development [10][11]. - Baidu Input Method also seeks to position itself as a reliable smart entry point for user expression, leveraging AI to enhance communication and collaboration [11][12]. Commercialization Challenges - Input methods face a "high traffic, low value" monetization dilemma, struggling to effectively convert user engagement into revenue [16][17]. - Privacy concerns have led to increased scrutiny and regulatory challenges, prompting companies to adapt their data collection practices to ensure user safety and compliance [18][19]. Technological Limitations - Despite advancements in AI algorithms, challenges remain in accurately understanding user intent, particularly in word prediction, due to the variability in user language habits and the quality of training data [13][19]. - The complexity of multi-modal interactions, where users input data through various means, requires sophisticated algorithms and technology to ensure seamless processing [19].

2026.01. 10 本文字数：1458，阅读时长大约2分钟 因此，姚顺雨认为，自主学习这件事已经发生了，只是受效率等因素限制，还存在各种问题，他认为目前自主学 习的范式迭代更像是渐变，而非突变。 至于目前全球市场中哪一家企业最可能率先引领范式创新，姚顺雨表示，虽然OpenAI经历了商业化等各种变 化，创新基因被削弱，但仍是最有可能诞生新范式的地方。 林俊旸认为，目前的RL（强化学习）范式尚处早期，潜力远未被充分挖掘，全球范围内仍面临诸多共性挑战， 而下一代范式的核心在于"自主进化"与"主动性"。只是自主进化是否需要更新参数，见仁见智。 作者 | 第一财经 吕倩 当大模型陷入Scaling Law（缩放定律）的增长瓶颈，下一代技术范式将会是什么？ 1月10日，在由清华大学基础模型北京市重点实验室、智谱AI发起的AGI-Next前沿峰会上，腾讯控股"CEO/总 裁办公室"首席AI科学家姚顺雨、阿里巴巴Qwen技术负责人林俊旸、Kimi创始人杨植麟、智谱创始人唐杰等人 工智能行业人士齐聚，共话大模型下一代技术范式。 对下一代范式的猜测中，自主学习（Autonomous Learning）是个热门概念，是大模型摆 ...

Core Insights - The article discusses the evolution of AI towards more advanced models, emphasizing a shift from simple chatbots to intelligent agents capable of understanding and interacting with the physical world [6][8][50] - The AGI-Next summit highlighted the need for new paradigms in AI development, moving beyond mere parameter scaling to explore self-learning and knowledge compression methods [5][8][11][42] Group 1: Key Speakers and Their Contributions - Tang Jie from Zhizhu AI compared the evolution of large models to human cognitive growth, advocating for new scaling methods beyond just data and computational power [11][16] - Yang Zhilin from Moonlight Dark emphasized the importance of scaling laws in AI development, focusing on energy efficiency and the need for better architectures [19][22] - Lin Junyang from Alibaba Cloud presented Qwen's hybrid architecture aimed at overcoming limitations in processing long texts while enhancing multimodal capabilities [31][32] Group 2: Technological Innovations and Future Directions - Tang Jie introduced the concept of Reinforcement Learning with Verifiable Rewards (RLVR) as a means to enhance AI's self-learning capabilities [11][12] - Yang Zhilin showcased innovations like the Muon optimizer, which doubles token efficiency, and Key-Value Cross Attention, which significantly improves performance on long-context tasks [24][26] - Lin Junyang discussed Qwen's advancements in integrating generation and understanding, marking a step towards general intelligence [36] Group 3: Market Dynamics and Future Trends - The summit revealed a consensus that the consumer market (ToC) for AI is stabilizing, while the enterprise market (ToB) is experiencing a productivity revolution [41] - The discussion highlighted the potential for self-learning AI to emerge gradually rather than through sudden breakthroughs, with a focus on practical applications [42] - The panelists expressed concerns about the safety and ethical implications of proactive AI, emphasizing the need for responsible development [43] Group 4: Global AI Landscape and Competitive Edge - The conversation touched on the competitive landscape between Chinese and American AI companies, with insights on innovation driven by resource constraints in China [45] - The panelists acknowledged the importance of fostering a culture of risk-taking and exploration in AI research to close the gap with leading global firms [46] - The article concluded with a call for a shift from merely following trends to creating impactful AI solutions that address real-world needs [49][51]

Group 1 - The core viewpoint of the articles highlights the recent appointment of Yao Shunyu as Tencent's Chief AI Scientist and his focus on developing models and products in the AI sector [1][2] - Tencent is positioned as a company with a strong consumer-oriented (to C) gene, emphasizing the importance of delivering value to users through AI in various scenarios [1] - Yao Shunyu's background includes prestigious education from Tsinghua University and Princeton University, and he is recognized as a leading figure in AI, particularly in the field of large models and intelligent agents [2] Group 2 - The company is considering how to effectively serve its own needs in the B2B sector, noting that larger companies have advantages over startups in terms of established scenarios and ecosystems [2] - Yao Shunyu's contributions to the AI field include the development of the ReAct framework and the "Tree of Thoughts" theory, which are foundational to modern large model reasoning and action coordination [2]

0:00 2025年12月，27岁的姚顺雨正式加入腾讯，担任首席AI科学家，并兼任AI Infra部、大语言模型部负责 人，向总裁刘炽平和技术工程事业群总裁卢山双线汇报。 姚顺雨是AI领域明星研究员。公开资料显示，姚顺雨毕业于清华大学姚班，后就读于普林斯顿大学， 2024年，姚顺雨加入OpenAI。目前，姚顺雨是人工智能领域，特别是大模型"智能体（Agent）"领域的 领军人物，提出了著名的ReAct框架与"思维树（Tree of Thoughts）"理论，奠定了现代大模型推理与行 动协同的技术基石。 他提到，腾讯是一家to C基因很强的公司，会更思考如何让AI给用户提供更多价值，在丰富的场景里落 地AI价值。 B端方面，团队会思考如何先将自身服务好，且大公司做2B业务较创业公司已具备场景和生态优势。 21世纪经济报道记者孔海丽 北京报道 1月10日，在由清华大学基础模型北京市重点实验室、智谱AI发起的AGI-Next前沿峰会上，27岁的姚顺 雨履新腾讯首席AI科学家后首次公开发声，他透露，最近在忙"模型和产品的开发"，也首次谈及了对AI 行业分化趋势的观察。 0:00 ...

1月8日，AGI-Next前沿峰会上，刚入职腾讯担任首席AI科学家的姚顺雨以线上形式参会并表示：最 近忙着做模型、产品、AI。腾讯是一家2C基因很强的公司，从B端与C端差异来讲，C端不仅需要强 大的模型能力，还需要较长context（上下文 / 语境）；在中国做2B市场非常难，团队会思考如何先 将自身服务好，且大公司做2B业务较创业公司已具备场景优势。 ...