将一头已灭绝6500万年的远古沧龙做成菜需要几步？ 来自上海的餐厅主理人辛西娅展示了她的步骤：颊肉经历过黄油与香草的低温慢煮，继而在烈火宽油中迎来川菜的试炼；接着，野生鸡油菌和黑松露由黄 油煎制，为这道菜增加了丰富的层次；金黄的脆米粉则作为铺底，颊肉被放置其上，一道"龙吟之心"便被摆在了头戴恐龙头套的"地狱厨师"面前。 这一片段源于B站UP主@黄浦江三文鱼 模仿《地狱厨师》制作的系列视频"把远古沧龙做成六道菜"，每期视频时长都在6分钟以上，且从头到尾都由AI生 成。 这类视频悉数由AI生成，并在信息流中抢占着用户注意。在抖音，"AI美食"话题收获了超2亿次播放；小红书上，AI吃播则与ASMR结合，自成一类内容 模式，动辄收获上万次点赞，且涌现出不少专注于此内容品类的账号。 一面是AI抢入美食赛道，另一面，AI创作的边界问题也在显现。这一切对创作者而言是机遇还是挑战？ AI入侵美食赛道 早前，DS初步爆火互联网时，美食赛道上诞生的一种玩法实际上是"用AI创造料理"，即由AI自由发挥创造菜谱。今年2月，UP主@洛杉矶嬴政W 突发奇 想，要求AI为自己制作一道人类从未见过的料理。在AI的指导下，UP主兢兢业业一步步 ...

" 构建世界模型，为什么不能只靠「炼丹」？ " 作者丨吴彤 编辑丨 林觉民 在香港中文大学（深圳），助理教授韩晓光的实验室名为GAP，意为"像素、点与多边形的生成与分析"。 现在看来，这个名字，也隐喻着他希望弥合真实世界和虚拟世界之间的"鸿沟"的意思。 2018年，韩晓光加入这所大学时，是当时唯一专注于计算机图形学研究的教师。2024年，他尝试从三维 重建拓展至具身智能与世界模型，又一次如入无人之境。 在小红书上，他的账号@韩晓光，简介仅有两行：港中深理工学院助理教授、图形学与三维视觉。他将小 红书视为传播平台，也视为个人思考的整理场所，会公开讨论"显式3D是否还有必要"、"世界模型为何需 要可解释性"等专业问题，也会记录与学生讨论时获得的启发。 这种直接、平实的分享，吸引了一批对技术本质感兴趣的读者，也代表了韩晓光这类青年教师群体打破学 术边界的自觉实践。从某一种角度看，构建世界模型需要理解真实世界的运行逻辑，而他的线上互动，本 身就是一场持续进行的、小规模的"世界模拟"。 在韩晓光的叙述中，他研究演进是自然发生的。从三维重建到动态生成，再到服务于机器人的虚拟环境构 建，核心始终是"三维内容的生成与理解"。 ...

欧盟方面表示，监管机构担心谷歌可能通过对出版商和内容创作者施加不公平条款，或为自身提供对相 关内容的特权访问，从而在训练大型模型时获取竞争者难以复制的数据优势。 外界认为，欧盟正试图在全球科技竞争中巩固对平台行为的规则引导权。 欧盟委员会认为，谷歌可能在创作者无法真正选择的情况下，使用上传至YouTube的视频训练自家的 Gemini与Veo3模型，而创作者在上传内容时被要求授予谷歌广泛的数据使用许可，使得"同意"带有默认 性质，缺乏现实的选择空间。 同时，谷歌禁止第三方公司使用YouTube视频训练模型，除非版权持有人明确授权，这使谷歌可能在训 练数据层面形成天然壁垒，进一步激化外界对其市场支配力的担忧。 对此，谷歌回应称，相关投诉可能抑制本已竞争激烈的市场创新，并强调其已与新闻和创意产业保持合 作，帮助他们适应AI带来的行业变化。 尽管谷歌公司否认有任何滥用市场地位的行为，但欧盟此次行动仍被视为欧洲近年来针对美国科技企业 监管升级的又一次体现。 欧盟委员会近日宣布将对谷歌展开正式调查，重点评估其在训练Gemini等人工智能（AI）模型时，使 用在线出版商内容以及YouTube创作者视频的方式是否违反了欧洲 ...

以下文章来源于锌刻度 ，作者黎炫岐 锌刻度 . 专注科技互联网原创报道 重新定义"吃"的边界。 文 ｜ 黎炫岐 编辑 ｜ 陈邓新 来源｜ 锌刻度（ID： znkedu ） 封面来源 ｜ 小红书 由Veo生成 被咬开时发出清脆声响的玻璃水果、镶嵌着宝石的首饰盒、播放着音乐的水晶球，甚至还有毛绒玩具labubu和金条……各种你能想到或者想不到的，都正成 为AI吃播的"食材"，被AI主播们塞入嘴里，轻松咀嚼。 这是一场风靡国内国外的热潮。在国外，Tiktok上一位叫leilanikovac的博主发了一条AI吃熔浆的视频，点赞数突破81.7万，另一位博主在三天内发了11条切 水果的视频后，粉丝数突破8万；而在国内，各大短视频平台和社交平台上，已有不少相关账号出现，点赞量破万的也不在少数。 当真人吃播面临种种道德和法律困境，猎奇食物逐渐从吃播的饭桌前消失，AI吃播却脑洞大开，主打一个万物皆能吃。 锌刻度了解到，目前大部分AI吃播视频都由Veo3生成。这是今年5月底，Google DeepMind发布的一款视频生成模型。这款模型的最大亮点是AI原生可以一 键直接生成与画面相匹配的声音。而这正是吃播的关键。 AI吃播的流量 ...

在 Text-to-Video / Image-to-Video 技术突飞猛进的今天，我们已经习惯了这样一个常识： 视频生成的第一帧（First Frame）只是时间轴的起点，是后续动画的起始画面 。 但你能想象吗？ 最新研究发现： 第一帧的真正角色完全不是「 起点」。它其实是视频模型的「 概念记忆体 」（conceptual memory buffer）, 所有后续画面引用的视觉实体，都被 它默默储存在这一帧里 。 今天就带大家快速了解这一突破意味着什么。 本研究的出发点，源于该团队对视频生成模型中一个广泛存在但尚未被系统研究的现象的深入思考。 第一帧≠起点， 第一帧 = 大型内容缓存区（Memory Buffer） 论文的核心洞察非常大胆： 视频生成模型会自动把首帧中的角色、物体、纹理、布局等视觉实体，全部「 记住」，并在后续帧中不断复用 。 换句话说，不论你给多少参考物体，模型都会在第一帧悄悄把它们打包成一个「 概念蓝图（blueprint） 」。 这项工作来自 UMD、USC、MIT 的研究团队。 在论文的 Figure 2 中，研究团队用 Veo3、Sora2、Wan2.2 等视频模型测试发现： 这 ...

Core Viewpoint - The competition in AI video generation is intensifying, with Runway's new model Gen-4.5 surpassing Google's Veo3 in benchmark tests, while domestic competitor Kuaishou's new model Keling O1 has also been launched, marking a significant moment in the industry [3][19]. Group 1: Model Performance - Runway's Gen-4.5 achieved a score of 1247 in the Artificial Analysis benchmark, making it the top model in text-to-video generation, followed closely by Google's Veo3 with a score of 1226 and Kuaishou's Keling 2.5 at 1225 [7][9]. - Gen-4.5 demonstrates advancements in understanding and executing complex sequential instructions, allowing users to specify detailed shot scheduling, scene composition, event timing, and subtle atmospheric changes [9][15]. Group 2: Technical Innovations - The model has made breakthroughs in pre-training data efficiency and post-training techniques, achieving unprecedented physical and visual accuracy in generated videos [9][15]. - Runway claims that objects in the generated videos move with realistic weight and dynamics, and liquid flows according to appropriate physical laws, enhancing the realism of the generated content [15][18]. Group 3: Market Position and Future Outlook - Runway, founded in 2018, has reached a valuation of $3.55 billion, with its first video model Gen-1 launched in February 2023, followed by Gen-2 in July, which integrated text-to-video and image-to-video functionalities [18]. - The competitive landscape is expected to become more challenging for Runway starting in 2024, with Google's Veo series solidifying its leading position and other competitors like Kuaishou and MiniMax gaining traction [19].

Core Insights - The competition in AI video generation has intensified with the recent launch of Runway's Gen-4.5 model, which has surpassed Google's Veo3 in benchmark tests [1][3] - Simultaneously, domestic competitor KuaLing AI announced the release of its new model, KuaLing O1, claiming to be the first unified multimodal video model [1][3] Benchmark Performance - Runway's Gen-4.5 achieved a score of 1247, ranking first in the Artificial Analysis leaderboard, followed closely by Google's Veo3 with a score of 1226 and KuaLing's model at 1225 [3][4] - The leaderboard indicates a tight competition, with only a one-point difference between Veo3 and KuaLing 2.5 [3][4] Model Features and Advancements - Gen-4.5 has made significant advancements in pre-training data efficiency and post-training techniques, excelling in understanding and executing complex sequential instructions [5][7] - The model demonstrates improved capabilities in adhering to precise prompts, realistic physical motion effects, style control, and visual consistency [5][7] Physical Realism and Limitations - Runway claims that Gen-4.5 achieves unprecedented physical and visual accuracy, with objects moving realistically and fluid dynamics rendered appropriately [7][11] - However, the model still faces challenges in causal reasoning and object permanence, with occasional discrepancies in the expected behavior of generated objects [11] Company Background and Market Position - Runway, founded in 2018, has reached a valuation of $3.55 billion as of 2023, showcasing rapid growth in the AI video generation sector [11] - The CEO of Runway highlighted the achievement of surpassing a trillion-dollar company with a team of just 100 people, emphasizing focus and hard work [11] Future Outlook - The AI video generation market is expected to become increasingly competitive, particularly with the anticipated release of Google's next-generation model, Veo4, in 2025 [12] - The sustainability of Gen-4.5's leading position is uncertain, especially with KuaLing O1 entering the market as a strong competitor [12]

Core Insights - The FFGo method revolutionizes the understanding of the first frame in video generation models, identifying it as a "conceptual memory buffer" rather than just a starting point [1][26] - This research highlights that the first frame retains visual elements for subsequent frames, enabling high-quality video customization with minimal data [1][6] Methodology - FFGo does not require structural changes to existing models and can operate effectively with only 20-50 examples, contrasting with traditional methods that need thousands of samples [6][24] - The method leverages Few-shot LoRA to activate the model's memory mechanism, allowing it to recall and integrate multiple reference objects seamlessly [16][22] Experimental Findings - Tests with various video models (Veo3, Sora2, Wan2.2) demonstrate that FFGo significantly outperforms existing methods in multi-object scenarios, maintaining object identity and scene consistency [4][17] - The research indicates that the true mixing of content begins after the fifth frame, suggesting that the first four frames can be discarded [16] Applications - FFGo has broad applications across multiple fields, including robot manipulation, driving simulation, aerial and underwater simulations, product showcases, and film production [12][24] - Users can provide a single first frame with multiple objects and a text prompt, allowing FFGo to generate coherent interactive videos with high fidelity [9][24] Conclusion - The study emphasizes that the potential of video generation models has been underutilized, and FFGo provides a framework for effectively harnessing this potential without extensive retraining [23][24] - By treating the first frame as a conceptual memory, FFGo opens new avenues for video generation, making it a significant breakthrough in the industry [24][26]

Summary of Conference Call on Kuaishou and the Video Generative AI Sector Industry Overview - **Industry**: China Internet Sector, specifically focusing on Video Generative AI - **Key Players**: Kuaishou, Bytedance, Google, OpenAI Core Insights 1. **Kuaishou's Leadership in Video Generative AI** Kuaishou's Kling platform is recognized for its superior performance in video generative AI, outperforming competitors like Sora 2, Veo3, and Seedance. The platform excels in prompt learning, video duration, and detail control, supported by Kuaishou's commitment to resource allocation in this area [2][2][2] 2. **Technical Advantages of Kling** Kling utilizes a hybrid architecture that allows 80% of generation workloads to be processed on-device, significantly reducing costs and latency. Its deep-learning engine is optimized for mid- and low-end hardware, expanding its user base [2][2][2] 3. **Market Positioning** Kling targets professional consumers (to-C), while Bytedance's Seedance focuses on business monetization (to-B) through subscription and private deployment models. This distinction highlights Kuaishou's strategic positioning in the market [2][2][2] 4. **Unit Economics Challenges** Current unit economics for video generative AI operators are low or negative due to high R&D and training costs. Operators are prioritizing market share over profitability, with expectations of declining model pricing in the near future [3][3][3] 5. **Application Scenarios** Video generative AI is primarily applied in advertising and e-commerce, enhancing productivity by over 60% through AIGC-assisted workflows. Digital humans in e-commerce can reduce labor costs and provide personalized content around the clock [4][4][4] Investment Outlook 1. **Positive Outlook for Kuaishou** Kuaishou is viewed as a top pick in the video generative AI space due to its reasonable valuation and growth potential, with projected EPS CAGR of 20% from 2024 to 2026 [5][5][5] 2. **Valuation Metrics** The company is currently trading at a PE ratio of 13x for 2025 and 11x for 2026, with a potential upside in valuation as video generative AI progresses [5][5][5] 3. **Investor Positioning** There is still relatively low investor positioning in Kuaishou, indicating potential for growth as the market recognizes its value [5][5][5] Risks and Considerations 1. **Competitive Landscape** Key risks include intensifying competition, fast-evolving technology trends, and uncertain monetization strategies within the internet sector [7][7][7] 2. **Regulatory Environment** Kuaishou faces risks from tightening regulations in online videos, livestreaming, and gaming, which could impact user growth and monetization [8][8][8] 3. **Economic Factors** A slowing Chinese economy may lead to reduced growth in online advertising revenues, posing a risk to Kuaishou's financial performance [8][8][8]

Group 1 - Google released its latest AI model, Gemini 3, which scored 1501 in the LMArena large model arena, ranking first [1] - Gemini has over 650 million monthly active users, with more than 70% of cloud customers utilizing its AI capabilities, and 13 million developers are leveraging its generative models [1] - Berkshire Hathaway's first investment in Alphabet indicates strong recognition of Google's product ecosystem and AI strategy, boosting global market expectations for AI companies [1] Group 2 - Chinese AI company Wondershare Technology has integrated Google's Veo3 and Nano Banana model capabilities into its products, showcasing its AI-powered video editing tool at the 2025 Google Developer Conference [2] - Wondershare Technology operates in over 200 countries and regions, with a cumulative active user base exceeding 2 billion, offering popular products like Wondershare Filmora and others [2] - In the first three quarters of 2025, Wondershare's AI server call volume surpassed 800 million, reflecting increased user enthusiasm for AI [2]