全球首家 AI原生影视工作室 横空出世，项目收入竟已高达 1.1亿美元 ！ 名叫 Utopai Stu dio s 。 允中 发自 凹非寺 量子位 | 公众号 QbitAI 当前AI热度如日中天，以AI切入电影行业的力量主要分为两派： 一派是以Runway、Pika为代表的 "工具派" ，聚焦AI的工具属性，核心发力点在于提升影视制作环节的效率。 另一派则是 "内容 +AI" 公司 ，其主要在内容的叙事创新与产业化层面推动AI的应用和发行，相当于是把手伸进了影视业最肥沃的利润区"内 容生产+产业落地"。 这两类公司的定位，决定了其不同的天花板。 前者更偏向于工具层的效率提升 ，其特点是，技术门槛高，能不断迭代生成模型的能力，但商业模式也往往会受限于工具类SaaS的逻辑 （即订阅费、API调用费、B端授权费） ，最后很可能会成为影视业的"基础设施型公司"，或容易被后续更强大的通用模型所取代。 后者定位于创造新叙事形式和发行，这让其有机会直接切入到包括IP、版权、分发渠道 ，形成"内容+渠道+AI技术"三位一体的护城河。如若 能够成功突破，天花板将远远高于纯工具派，因为其有机会改变整个影视业的产业链模式，而不仅仅 ...

Core Points - The 2025 China Computer Conference (CNCC2025) will be held from October 22 to 25 in Harbin, Heilongjiang Province, with the theme "Digital Intelligence Empowerment, Infinite Possibilities" [1] - The conference aims to enhance the influence of the computer field in China and promote regional digital economic development [3][5] Event Overview - The conference will feature over 10,000 square meters of exhibition space, open to the public for free, marking a significant expansion in scale and depth [3] - A total of 19 invited reports, 3 main forums, and 154 specialized forums will be organized, focusing on various aspects of digital economy and AI [5][6] Key Participants - Notable speakers include academicians from various prestigious institutions and industry leaders, such as Sumi Helal from the University of Bologna and C. Mohan from Hong Kong Baptist University [5] - The forums will cover themes like "Digital Economy," "Large Model Development," and "Embodied Intelligence," with prominent experts leading discussions [5][6] Organizational Efforts - Harbin Engineering University and Harbin Institute of Technology are collaborating on the event's preparations, including venue setup and volunteer coordination [9][12] - The organizing committee emphasizes high standards in service and emergency management to ensure a successful conference [9][12] Media Engagement - The press conference attracted various media outlets, indicating strong interest and engagement from the media regarding the conference's significance [13][16]

Core Viewpoint - The article discusses the launch of Meituan's AI assistant, Xiaomei, which simplifies daily tasks such as ordering food and making restaurant reservations through natural language commands, eliminating the need for complex graphical interfaces [1][6][49]. Group 1: Functionality and User Experience - Xiaomei serves as a "small and beautiful" life secretary, efficiently handling daily needs and making life simpler [3][6]. - Users can interact with Xiaomei using voice commands, allowing for easy completion of tasks like ordering takeout and finding restaurants without navigating through multiple screens [7][9]. - The assistant can recommend food based on user preferences and past orders, acting as a "wish box" for meal suggestions [29][30]. Group 2: Technology and Data Integration - Xiaomei is powered by Meituan's LongCat model, which excels in natural language processing and can handle complex tasks due to its extensive training on real-world data [51][54]. - The integration of Xiaomei with Meituan's service system allows for seamless execution of tasks, ensuring that user requests are processed accurately and efficiently [58][60]. - The assistant is designed to learn from user interactions, adapting to individual habits and preferences over time, thus enhancing user experience [61][62]. Group 3: Comparison with Traditional Assistants - Unlike traditional AI assistants that require multiple clicks and operations, Xiaomei aims to create a more human-like interaction through natural dialogue [63][64]. - The assistant captures subtle changes in user habits and responds appropriately, fostering a sense of familiarity and understanding [65][66].

不圆 发自 凹非寺 量子位 | 公众号 QbitAI 网络搜索Agent效果不好，猛猛投喂一波数据，表现还那样，咋回事？ 港科大&MiniMax团队指出问题核心：不是模型参数不够多，而是缺乏足够有挑战性的训练数据。 换句话说，别死记硬背了，来做点"真题"吧。 他们提出了一种构建高质量QA对的方法 WebExplorer 。 用该方法构建的数据集去训练，即使是较小的模型，也可以在复杂、长程的搜索任务上超越更大的模型。 训练后的8B模型支持高达 128K的上下文长度 和 100次工具调用轮次 的长期推理，能在参数量低于10B的模型中取得顶尖结果。 网友评价：用模型驱动的方式做探索，确实比传统图谱方法更能让智能体的浏览行为变灵活。 模型及数据集均已开源，链接可见文末。 优质训练数据稀缺 随着大语言模型（LLM）的快速发展，智能体的能力边界不断扩展。 网络搜索智能体作为这一发展的重要组成部分，能够自主地从广泛的在线资源中检索信息；长视野（Long-Horizon）网络智能体更是需要在 多个网站间进行复杂的推理和搜索。 可是呢， 现有的开源网络智能体在处理复杂搜索任务时往往表现有限，更强大的商业模型又缺乏透明的训练细节 ...

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI AI生图再进化！图像分辨率直接卷到 2K 。 腾讯开源 混元图像2.1 （HunyuanImage2.1） ，画质直接拉满的同时，还能读懂千字长文本，甚至中英文混搭渲染。 或者搞个美洲驼的概念图，也是轻轻松松～ 新一代模型在技术上全面升级，不仅显著提升图文语义一致性和跨场景泛化能力，还能够精细控制场景、角色姿态甚至多物体描述，达成开源 生图模型中的SOTA。 模型开源之后，在Hugging Face趋势榜上一路飙升，目前已拿下第一名的宝座。 话不多说，先来看几个网友试玩感受一下。 首先康康真实场景下的表现，细腻的手部和脸部纹理，处理细节过关 海报制作上，文本渲染也相当干净。 还有每次必不可少的动漫风环节：魔女宅急便 （圆润猪咪版） 可以说，混元图像2.1更懂语义、更擅图文、更多风格、更高清画质…… 所以咱们这不赶紧上手体验一波。 四大亮点 打开官网，操作界面是酱紫的～选择需要生成的图像尺寸和数量，填写prompt （上限2048） ，就能秒获取超高分辨率图像。 我们体验了一下，总结下来这个模型有四大亮点。 亮点1：复杂语义生成能力强 得益于多样化的大规模图 ...

Core Viewpoint - The article discusses a paradigm shift in control theory from model-driven control to data-driven control, emphasizing the need for a standardized data representation in the latter to enhance efficiency and reduce redundancy in algorithm design [1][7][12]. Group 1: Introduction to Data-Driven Control - The rise of big data has led to a new turning point in control theory, transitioning from reliance on models to reliance on data [1]. - There is a lack of standardized data representation in the data-driven control field, prompting research from Tsinghua University to introduce a new data-based system description format [2][12]. Group 2: Standard Form in Data-Driven Control - Each sample in the proposed standard form consists of necessary transfer and pluggable attributes, which describe system dynamics and user-defined features, respectively [3][19]. - The new data standard form can be customized according to algorithm requirements, significantly accelerating controller design and improving the efficiency of data-driven algorithms [4][32]. Group 3: Challenges and Solutions - The transition to data-driven control faces challenges due to the vast amounts of complex interaction data generated by systems like robotics and autonomous driving [12]. - Efficiently organizing and describing data to minimize redundant calculations and speed up algorithm execution is a core challenge in data-driven control [16]. Group 4: Application of Data Standard Form - A typical application example demonstrates that many reinforcement learning algorithms require nearest neighbor searches to ensure reliable controller design [20]. - By pre-defining spatial attributes for each sample, the proposed data standard form can significantly accelerate the nearest neighbor search process [22][28]. Group 5: Experimental Results - Experiments conducted in the D4RL dataset's Hopper environment showed that using the spatial standard form reduced training time from approximately 20 hours to just 7 hours, achieving a threefold efficiency improvement [29][31].

Core Insights - The article discusses the release of Qwen3-Next, a next-generation model architecture, which is a preview of Qwen3.5 [1] - The Qwen team has open-sourced the Qwen3-Next-80B-A3B-Base model, which has 80 billion parameters but costs less than one-tenth of the training cost of Qwen3-32B [2][3] - The new model demonstrates significant improvements in context processing and inference efficiency, achieving up to ten times the throughput of its predecessor in long context scenarios [3][24] Model Improvements - **Hybrid Attention Mechanism**: The Qwen3-Next model incorporates a Gated DeltaNet for better context learning, using a 3:1 hybrid strategy to balance performance and efficiency [10] - **High Sparsity MoE Structure**: The model features a high sparsity MoE architecture with 80 billion total parameters, activating only about 3 billion during inference [13] - **Stability Optimization**: The model employs Zero-Centered RMSNorm and weight decay to enhance training stability and mitigate weight growth issues [16][17] - **Multi-Token Prediction Mechanism**: The introduction of a native Multi-Token Prediction mechanism improves overall model performance and speculative decoding acceptance rates [18] Performance Metrics - Qwen3-Next-80B-A3B-Base outperforms Qwen3-32B-Base in most benchmark tests while using only 9.3% of the GPU resources required by Qwen3-32B [22][28] - In various benchmarks, Qwen3-Next-80B-A3B-Instruct shows superior performance compared to Qwen3-30B-A3B-Instruct-2507 and approaches the performance of Qwen3-235B-A22B-Instruct-2507 [31][34] - Qwen3-Next-80B-A3B-Thinking surpasses the closed-source model Gemini-2.5-Flash-Thinking in multiple benchmark tests [35] Practical Applications - The model supports multimodal capabilities, allowing for quick and accurate responses to complex tasks, such as solving math problems and generating code [39][43] - Users can access the new model through various platforms, including Qwen Chat and API services provided by Alibaba Cloud [48]

Core Viewpoint - The article discusses the launch of Gaode's new feature, "Gaode Street Ranking," which aims to provide a more authentic ranking system for offline services based on real user behavior rather than manipulated ratings [1][3][4]. Summary by Sections Introduction of Gaode Street Ranking - Gaode has introduced a new credit system called "Gaode Street Ranking," which promises to bring 10.8 million consumers to offline service businesses daily [2]. - The ranking system is designed to combat the existing issues of fake reviews and manipulated ratings in the restaurant and service industry [3][4]. Unique Features of the Ranking System - The ranking is based on real user behaviors, such as navigation and visits, rather than artificial manipulation [3][4]. - Gaode's data includes 514.3 million users and 1.3 billion navigation instances over a year, covering a distance equivalent to 57 times around the Earth [12][68]. Data and AI Integration - The success of the Gaode Street Ranking is attributed to the integration of AI with Gaode's extensive historical data, which has been accumulated over 20 years [6][67]. - AI helps in processing user behavior data to ensure the authenticity of the rankings, making it difficult for users to manipulate the system [48][65]. Ranking Dynamics - The ranking system includes various categories such as "Top Performers," "Street Rankings," and "Popular Check-ins," which are updated daily to reflect real-time user preferences [14][15]. - The "Top Performers" list is updated annually, while the "Street Rankings" are refreshed daily, allowing for more dynamic and responsive rankings [15]. User Engagement and Experience - Users can only leave reviews after visiting a location, which helps maintain the integrity of the ratings [47][65]. - The system encourages genuine user engagement, allowing for a more accurate representation of popular venues [39][70]. Conclusion and Implications - The Gaode Street Ranking represents a significant shift in how offline services are evaluated, emphasizing authenticity and user experience [72]. - This initiative could set a precedent for other industries facing similar challenges with fake reviews and ratings [73].

Core Viewpoint - The article discusses the latest advancements and discussions in AI technology, highlighting key insights from industry leaders at the Inclusion Bund Conference, emphasizing the transition to an "experience era" in AI development and the importance of open-source strategies in AI competition [6][10][20]. Group 1: AI Development Insights - Richard Sutton, the Turing Award winner, states that the human data dividend is nearing its limit, and AI is entering an "experience era" focused on continuous learning, which will unlock greater potential [9][10][19]. - Sutton emphasizes the need for new data sources generated through direct interaction between intelligent agents and the world, moving beyond static knowledge transfer [12][14]. - The concept of "experience" in AI involves the exchange of observation, action, and reward signals, which is crucial for developing intelligence [16][17]. Group 2: Open Source and AI Competition - Wang Jian, founder of Alibaba Cloud, highlights that the choice between open-source and closed-source models has become a critical variable in AI competition, especially after the U.S. imposed export controls on closed-source models [20][25]. - He discusses the evolution of open-source from code sharing to resource sharing, which now includes data, computing power, and model weights, significantly lowering the barriers to entry in AI development [26][30]. - Wang Jian's recent project involves deploying a complete AI model into space, showcasing the potential of collaborative resource sharing in advancing AI capabilities [31][35]. Group 3: Future of AI Applications - Yuval Noah Harari, a historian and philosopher, warns that the pace of technological change should not overshadow the need for understanding and governance, emphasizing that true progress is measured by cooperation and empathy rather than speed [60][67][70]. - Zhu Xiaohu from Sinovation Ventures predicts a significant explosion in AI applications next year, particularly in low-code and no-code software, and encourages entrepreneurs to seize opportunities in the AI space [54][57]. - Wang Xingxing from Yushutech believes that the integration of AI and robotics is on the verge of a breakthrough, enabling robots to perform tasks autonomously, although challenges remain in data quality and model alignment [46][50].

Core Viewpoint - The article highlights the severe impact of funding cuts on scientific research in the U.S., particularly at UCLA, where even top mathematicians like Terence Tao are struggling to secure funding for their research and students [2][4][5]. Funding Cuts and Their Impact - On July 25, 2023, the U.S. government abruptly suspended funding from the National Science Foundation (NSF) and the National Institutes of Health (NIH) to UCLA, amounting to $500 million [4]. - Although a federal court restored some funding on August 12, the funds had not yet been disbursed by early September [5]. - The funding cuts have led to significant disruptions in ongoing research projects and have severely affected graduate students' opportunities for academic development [12][21]. Personal Experiences and Concerns - Terence Tao expressed greater concern for his students than for himself, emphasizing that the funding is crucial for their participation in academic conferences and career development [6][12]. - Tao has had to shift his focus from research to fundraising activities due to the funding crisis [7][19]. Broader Implications for the Research Ecosystem - The article discusses how the cuts have eroded the independence of the research ecosystem, with many ongoing projects being abruptly halted [12][15]. - Tao noted that the NSF's funding has historically facilitated collaboration between universities, laboratories, and industry, which is essential for scientific advancement [14]. Psychological Impact on Students - The funding instability creates an environment of uncertainty, negatively affecting students' mental health and their ability to focus on research [23]. - Tao highlighted that the loss of funding directly impacts students' scholarships and research assistant positions, which are vital for their livelihoods [21][22]. Future Considerations - In an interview, Tao expressed uncertainty about his future in the U.S. academic landscape, indicating a potential shift in his perspective on remaining in the country due to the funding issues [24].