一水 发自 凹非寺 量子位 | 公众号 QbitAI 兵贵神速！ 硅谷Claude Cowork前脚刚出圈，结果国产版立马出现了—— 昆仑天工Skywork桌面版 。 刚刷到时，还以为又是 追热点呢 。滑跪了，结果人家早在去年5月，就推出了网页版的天工超级智能体 （Skywork Super Agents，由多个 智能体组成的打工方队） ，而且还挺火。 好好好，所以兵贵神速更重要的还在前一句"厚积薄发"是吧~ 快速下载安装包后，打眼一瞧产品功能布局就能发现其两大特色： 秉着刷到就是缘分的态度，我们也火速去了解、实测了一番这款桌面版工具，结果发现： 对我等打工人确实友好，平时做个PPT或调研报告、根据本地文档做些数据分析的活儿，基本都是一句话的事儿。 划重点，还都不用你手动上传文件，现在都是智能体自动读取电脑上的海量文件进行work。而且所有文件处理都在本地完成，无需上传云 端，这样一来也能最大程度保证隐私安全。 当然了，更更重要的是，这一次我们 Windows选手 优先拿到了体验权 （P.S. 不像Claude Cowork主要面向macOS） 。 所以话不多说，一手实测这就奉上咯。 实测Skywork桌面版 ...

Core Insights - The article discusses the launch of CL-bench, a benchmark designed to evaluate the ability of large models to learn from context, led by Yao Shunyu, Tencent's Chief AI Scientist [1][2][4] - The research emphasizes that the focus should shift from merely increasing model size to ensuring models can effectively learn and apply knowledge in real-world tasks [5][10] - Current leading models, including GPT-5.1, show disappointing performance, with a task-solving rate of only 23.7%, indicating a significant gap in their contextual learning capabilities [7][29] Summary by Sections Context Learning Importance - The research highlights that while advanced models excel in standardized tests, they struggle in real-world applications where contextual learning is crucial [9][10] - Human learning relies on real-time context rather than static knowledge, which current models fail to replicate [11][14] CL-bench Design and Objectives - CL-bench consists of 500 complex contexts, 1899 tasks, and 31607 validation criteria, designed to require models to learn new knowledge from context [15][19] - The benchmark aims to assess models' abilities to apply knowledge from unfamiliar domains, rule systems, and procedural tasks [18][22] Model Performance Evaluation - Ten leading models were evaluated on CL-bench, with an average task-solving rate of only 17.2%, underscoring their inability to learn from complex contexts [28][29] - The best-performing model, GPT-5.1, achieved a maximum of 23.7%, revealing a widespread issue across models in contextual learning [30] Error Analysis - The analysis identified that ignoring or misusing context is a primary reason for model failures, with many errors stemming from the models' reliance on pre-trained static knowledge [31][32] - Models performed poorly in tasks requiring inductive reasoning from experimental data, often achieving less than 10% success [32] Future Directions - The research team aims to advance contextual learning in AI, moving beyond merely providing context to ensuring models can genuinely learn from it [36][40] - The collaboration between Tencent and Fudan University reflects a commitment to enhancing AI's practical applications in real-world scenarios [39]

Core Insights - Yang Zhiling, founder and CEO of Moon's Dark Side, has been invited as a keynote speaker at NVIDIA's GTC 2026, marking a significant recognition for him and his Kimi models [1][2][27] - The invitation reflects NVIDIA's strategic foresight in identifying emerging trends within the AI industry, as Huang Renxun (NVIDIA's CEO) typically selects speakers who align with future market directions [7][11] Group 1: Yang Zhiling and Kimi's Journey - Yang Zhiling represents a new pain point in AI development, as the industry faces challenges with existing models and the need for innovative solutions [28][30] - Kimi faced significant challenges in 2025 due to the impact of DeepSeek, which threatened its business model and user engagement [33][35] - After a period of silence and strategic retreat, Kimi re-emerged with the launch of Kimi K2, showcasing advanced capabilities and reaffirming its position in the market [38][39] Group 2: Market Position and Financial Developments - Kimi K2.5 was launched in January 2026, demonstrating superior performance in various benchmarks compared to competitors like GPT-5.2 and Claude 4.5 Opus [41][42] - Kimi's successful C-round financing raised $500 million, leading to a post-money valuation of $4.3 billion, indicating strong investor confidence and financial stability [46] - The cash reserves exceeding 10 billion yuan position Kimi well for continued research and development, aiming for leadership in the global SOTA (state-of-the-art) landscape [46]

Core Viewpoint - Adobe has announced the discontinuation of Adobe Animate, a 2D animation software that has been in use for over 25 years, primarily due to a shift in focus towards AI technologies [10][38]. Group 1: Announcement and User Reactions - Adobe officially notified users that sales of Adobe Animate will cease on March 1, 2026, with varying support timelines for enterprise and individual users [10][19]. - The announcement has led to widespread disbelief and frustration among users, particularly those who have invested time in learning the software [3][5]. - Many users feel abandoned, citing a lack of communication and a suitable alternative from Adobe [28][29]. Group 2: Impact on Users and Industry - Despite its decline, Adobe Animate remains essential for many web animators, game developers, and content creators, with some users stating it is irreplaceable [11][13]. - The transition to alternative software, such as Toon Boom, is complicated by high migration costs and the need to relearn workflows [16][17]. - Users express concerns that Adobe's decision will negatively impact their work quality and existing projects [12][46]. Group 3: Adobe's Strategic Shift - Adobe's rationale for discontinuing Animate centers around the advancement of technology and a strategic pivot towards AI-driven tools [37][38]. - The company has been focusing on integrating AI features across its applications, which has led to the neglect of Animate [39][41]. - Critics argue that the decision to shut down Animate reflects a broader trend of prioritizing new technologies over established products, even when those products still have a dedicated user base [44][46]. Group 4: Historical Context and Legacy - Adobe Animate, originally launched as FutureSplash Animator in 1996, played a significant role in transforming the internet by enabling rich multimedia content [48][50]. - At its peak, Flash Player was installed on over 98% of computers, making it a cornerstone of web animation and independent game development [52][54]. - Despite its historical significance, Animate has struggled to adapt to modern demands, leading to its eventual phase-out [62][67].

Core Insights - The article discusses the launch of the new open-source agent model Step 3.5 Flash, which features a total of 196 billion parameters and 11 billion active parameters, supporting a context window of 256K [2][36]. Model Performance - The model achieves a peak inference rate of 350 TPS, comparable to closed-source models in agent scenarios and mathematical tasks, capable of handling complex, long-chain tasks [5][41]. - In benchmark tests, Step 3.5 Flash scored 97.3 in the AIME 2025 benchmark, 74.4% in the SWE-bench Verified coding tasks, and 88.2 in the τ²-Bench for agent tasks, indicating strong performance across various applications [7][6]. Technical Architecture - Step 3.5 Flash employs a MoE sparse mixture of experts architecture, activating approximately 11 billion parameters during inference to control computational and deployment costs effectively [36]. - The model incorporates a 3:1 sliding window attention mechanism to address long context issues, enhancing its ability to manage lengthy texts [37]. - It features a self-developed MIS-PO reinforcement learning framework to improve inference and agent execution capabilities, reducing data noise and gradient variance for stable optimization in long-sequence tasks [42]. Ecosystem Integration - The model is designed to work seamlessly with major AI acceleration chip platforms from various manufacturers, including Ascend, Mu Xi, and Alibaba's T-head, ensuring compatibility with current mainstream domestic AI hardware [4]. - Step 3.5 Flash emphasizes a cloud-edge collaboration approach, where the cloud handles complex planning and reasoning while the edge focuses on secure data retrieval and local execution [30][32]. Future Developments - The development team is already working on Step 4, indicating ongoing advancements in the model's capabilities [43].

编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 AI产业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个人影响力 ：通过撰写独家原创内 ...

Core Insights - xAI has launched Grok Imagine 1.0, described as the most powerful video and audio generation model to date [1] - The model supports text-to-video and image-to-video generation, with a maximum duration of 10 seconds and a resolution of 720P, significantly enhancing audio quality [2] Group 1: Model Capabilities - Grok Imagine 1.0 can accurately capture user creative concepts, producing rich and coherent visuals, such as an AI version of "How to Train Your Dragon" [4] - The model excels in generating interactive sound effects and expressions, enhancing the overall user experience [5] - Users can create short videos quickly by stringing together generated clips [6] Group 2: Performance Metrics - In the past 30 days, Grok Imagine has generated 1.245 billion videos [8] - The core capabilities of Grok Imagine are divided into video generation and video editing [9] - The model demonstrates cinematic-level understanding of camera movements and smooth scene transitions [11][13] Group 3: Editing Features - Grok Imagine allows users to replace objects and modify scenes, including changing colors and details of objects [25][29] - Users can apply different visual styles to existing video materials and animate static black-and-white line drawings [33] - The model has undergone iterative optimizations focusing on latency and cost control [35] Group 4: Benchmarking and Rankings - According to Artificial Analysis, Grok Imagine ranks first in text-to-video generation, excelling in cost and latency metrics [36] - Comparative evaluations from Artificial Analysis and LMArena confirm Grok Imagine's leading position in both latency and cost [39] - In a blind evaluation of video editing capabilities, Grok Imagine outperformed competitors in overall performance, instruction adherence, and effect consistency [43]

Core Viewpoint - Clawdbot, now known as OpenClaw, has gained significant popularity, reaching 120,000 stars on GitHub within a week, with its Mac mini accessories sold out and rapid integration by major companies like Alibaba and Tencent [1][4]. Group 1: Clawdbot Features and Functionality - Clawdbot transforms AI from a standard chatbot into a 24/7 AI employee, capable of performing tasks while users are occupied or asleep [5]. - It can respond to messages on mobile devices and proactively notify users upon task completion [6]. - Users have reported high costs associated with using Clawdbot, as it can quickly consume hundreds of dollars in token fees for minimal output [10]. Group 2: Introduction of Xuanwu CLI - Xuanwu CLI is a new open-source framework that allows users to run Clawdbot locally without needing to purchase a Mac mini or incur API costs, making it more accessible [13][14]. - It simplifies the local deployment of models, providing an "app store-like" experience for users to select and use models without complex configurations [18]. - The command system of Xuanwu CLI is highly compatible with Ollama, allowing for easy transition for users familiar with that platform [20]. Group 3: Technical Advantages of Xuanwu CLI - Xuanwu CLI supports local AI engines, enabling integration with Clawdbot for continuous operation and interaction [25]. - It is designed to be user-friendly, requiring minimal setup and allowing for quick service startup, often within one minute [29]. - The framework is compatible with OpenAI API standards, facilitating easy integration with existing applications and reducing the cost of switching from cloud to local models [30]. Group 4: Adaptation to Domestic Chips - Xuanwu CLI is uniquely adapted to domestic chips, providing a cost-effective solution for running models locally, unlike other solutions that primarily rely on NVIDIA hardware [34]. - It addresses common issues faced with domestic chips, such as configuration complexity and performance variability, by encapsulating hardware differences and providing a unified resource pool [39]. - The architecture of Xuanwu CLI allows for intelligent scheduling and optimal resource allocation, ensuring stability and performance across different hardware setups [46]. Group 5: Company Background - Qingmiao Intelligent, founded in 2022, focuses on chip adaptation and the optimization of models, frameworks, and operators [48]. - The company has received significant investment and aims to create a comprehensive optimization system from hardware to intelligent agents [51]. - Qingmiao has successfully developed various domestic integrated machine solutions, achieving high performance and adaptability across multiple chip platforms [52].

以下文章来源于智能车参考 ，作者有车无人 站上新台阶的Waymo明确，当下的业务重心是面向全球扩张，放话"大规模自动驾驶时代已经到来"。 出海，这也是中国Robotaxi玩家萝卜快跑、文远知行和小马智行正在开拓的方向，今年有望看到中美Robotaxi双雄同台竞技。 全球自动驾驶玩家，也将同步Waymo迎来新一轮重估。 Waymo官宣完成160亿美元融资 Waymo通过社交平台官宣，最新一轮融资融了 160亿美元 ，约合人民币 1112.4亿元 ，超越此前三轮融资之和。 智能车参考 . 追踪AI+汽车新进展。量子位旗下汽车频道 一凡 发自 副驾寺 智能车参考 | 公众号 AI4Auto Waymo官宣完成1112亿融资，估值超8760亿元。 特斯拉攻上门的当口，资本市场用真金白银，表达了对Waymo和「Waymo路线」的认可与坚信，托举Waymo一轮融了160亿美元。其中谷 歌被曝豪掷900亿，红杉资本押注，将Waymo估值推上新的高度， 19个月翻了3倍 。 Waymo透露， 最新一轮融资的主要投资者仍然是谷歌 ，领投方还包括红杉资本、Dragoneer Investment Group和DST Global ...

Core Insights - Kimi K2.5 has achieved significant recognition, topping the Trending chart on Hugging Face with over 53,000 downloads [2] - The model excels in agent capabilities, outperforming flagship closed-source models like GPT-5.2 and Claude 4.5 Opus in various benchmark tests [3] - Kimi K2.5's technical report reveals its development process and innovative features [5] Group 1: Model Architecture and Training - Kimi K2.5 is built on the K2 architecture and has undergone continuous pre-training with 15 trillion mixed visual and text tokens [6] - The model adopts a native multimodal approach, allowing it to process visual signals and text logic within the same parameter space [7] - This extensive data training has led to synchronized enhancements in visual understanding and text reasoning, breaking the previous trade-off between the two [8] - Kimi K2.5 demonstrates high cost-effectiveness, achieving better performance than GPT-5.2 while consuming less than 5% of its resources [9] Group 2: Visual Programming and Debugging - The model has unlocked "visual programming" capabilities, enabling it to infer code directly from video streams [11] - Kimi K2.5 can accurately capture the dynamics of visual elements in videos and translate them into executable front-end code [12] - To address issues with code execution and styling, K2.5 integrates a self-visual debugging mechanism that verifies the rendered interface against expected outcomes [14] - If discrepancies are found, the model can autonomously query documentation to identify and correct issues [15] - This "generate-observe-query-fix" automated loop simulates a senior engineer's debugging process, allowing the model to independently complete end-to-end software engineering tasks [16] Group 3: Agent Swarm Architecture - Kimi K2.5 features an Agent Swarm architecture, capable of autonomously constructing digital teams of up to 100 agents for parallel task execution [17] - This system breaks down complex tasks into numerous concurrent subtasks, significantly reducing processing time [18] - The operation of this large team is managed by the PARL (Parallel Agent Reinforcement Learning) framework, which includes a core scheduler and multiple sub-agents [20][21] - The scheduler oversees task distribution, while sub-agents focus on efficiently executing specific instructions [22] - The design balances flexibility in planning with the logical rigor required for large-scale parallel operations [23] Group 4: Training and Efficiency - The training process employs a phased reward shaping strategy to encourage efficient division of labor among agents [25] - Initially, the focus is on incentivizing the scheduler for parallel exploration, gradually shifting to the success rate of tasks as training progresses [26] - This gradual approach fosters a mindset in the model to maximize concurrency while ensuring result accuracy [27] - Efficiency evaluation incorporates critical steps as a core metric, emphasizing the reduction of end-to-end wait times [28] Group 5: Future Developments and Community Engagement - Following the launch of K2.5, the founders of Moonlight appeared on Reddit for a 3-hour AMA, discussing the model's development and future plans [29] - The team hinted at the next-generation Kimi K3, which may be based on a linear attention mechanism, promising significant advancements [31] - They acknowledged that while they cannot guarantee a tenfold improvement, K3 will likely represent a qualitative leap over K2.5 [32] - The team also addressed the model's occasional misidentification as Claude, attributing it to the high-quality programming training data that included Claude's name [34] - The laboratory emphasizes that achieving AGI is not solely about increasing computational power but also about developing more efficient algorithms and smarter architectures [38]