克雷西 发自 凹非寺 量子位 | 公众号 QbitAI 系统性能优化领域顶级专家 Brendan Gregg ，正式官宣加入OpenAI。 入职后，他将加入 ChatGPT性能团队 ，在澳大利亚远程办公，向团队负责人Justin Becker汇报工作。 Brendan被技术圈尊称为 "性能之神" ，他的到来，受到了OpenAI总裁Brockman的亲自欢迎。 Brockman甚至表示，自己就是Brendan多年以来的老粉丝。 同时他还是Linux内核核心技术 eBPF 的主要推动者，一手构建了现代云计算的性能分析工具箱…… 网友们评价，Brendan的这些作品绝对是next level。 | foundrceo @ @foundrceo · 21小时 | | | | | | --- | --- | --- | --- | --- | | that's sick man, brendan's work is next level for sure | | | | | | U | | 3 | ılıl 402 | 1, | | Ton77 ▽ @ @TRJ 77 · 15小时 | | | | | | Greg a ...

Core Viewpoint - The article discusses the introduction of a new type of doctoral degree in China, known as the "practical doctoral degree," which emphasizes practical achievements over traditional thesis requirements, aiming to cultivate top engineers and enhance national innovation capabilities [2][42]. Group 1: Introduction of Practical Doctoral Degree - China has officially launched a practical doctoral degree that does not require a thesis, focusing instead on hard-core practical outputs [1][4]. - This initiative is part of a broader strategy to elevate the country's overall innovation capacity by training top engineers [2][42]. Group 2: Implementation and Examples - Since September 2022, at least 11 individuals have been awarded practical doctoral degrees across various critical technology fields, including energy equipment and aerospace [9][10]. - Notable examples include: - Yuan Xiaohu from Chongqing University, who developed a high-temperature oxidation-resistant coating for turbine valves, achieving over 100 million yuan in economic benefits [12][15]. - Wei Lianfeng from Harbin Institute of Technology, who worked on vacuum laser welding technology, addressing critical issues in nuclear material welding [17][19]. - Nie Hailiang from Tsinghua University, who developed a technology for steel furnace dust treatment, recognized for its industrial application value [21][23]. - Qiu from Xi'an Jiaotong University, who focused on digital hydraulic servo systems for heavy equipment, receiving high praise from industry experts [25][26]. Group 3: Legal and Policy Framework - The core shift in the doctoral degree awarding system is anchored in the implementation of the "Degree Law of the People's Republic of China" on January 1, 2025, which recognizes practical achievements as valid for doctoral graduation [42][43]. - This legal framework marks a significant departure from the traditional emphasis on thesis-based evaluations, promoting a more diversified and legally grounded assessment of doctoral candidates [43][46]. Group 4: Historical Context and Future Outlook - The development of practical doctoral degrees has been a long-term effort, beginning with the establishment of engineering doctoral programs in 2011 and evolving through various policy reforms [44][46]. - The future of doctoral education in engineering is expected to see an increase in practical talent, with a complete reform cycle established from top-level design to nationwide implementation [46].

Core Insights - Claude-Mem addresses the critical issue of session memory loss in AI programming assistants, allowing users to maintain context across sessions [2][5] - The system is completely free and significantly reduces token consumption, with a potential savings of up to 95% during testing phases [4][19] Memory System - Claude-Mem establishes a local memory system that captures user interactions through an event-driven architecture, utilizing five lifecycle hooks [6][11] - It records operations such as file reading, code editing, and command execution, creating "observation records" for future reference [7] Storage and Privacy - The storage solution employs a hybrid approach, combining SQLite for full-text search and Chroma vector database for semantic search [9] - All data is stored locally, ensuring user privacy and control over sensitive information [10][25] Retrieval Efficiency - The "progressive disclosure" retrieval workflow is a standout feature, breaking down the retrieval process into three layers to optimize token usage [14][16] - This method allows for significant reductions in token consumption, with a context that originally required 20,000 tokens potentially reduced to just 3,000 tokens [18] User Experience - Claude-Mem enhances user experience with a built-in mem-search skill for natural language queries about project history [22] - The installation process is simplified to two commands and a restart, avoiding complex configurations [26]

小米Mimo大模型团队投稿 量子位 | 公众号 QbitAI 小米MiMo大模型团队，加入AI拜年战场—— 推出 HySparse，一种面向Agent时代的混合稀疏注意力架构 。 HySparse创新使用极少的全注意力 （Full Attention） 层提供"token选择+KV Cache"，其余稀疏注意力 （Sparse Attention） 层直接复 用这些信息，实现高效精准的长上下文建模。 在总共49层的80B-A3BMoE模型实验中， 仅保留5层Full Attention仍能保持甚至提升模型能力，同时显著降低KVCache存储与计算开销 ，实现效果与效率的兼顾，展示出混合稀疏注意力在超长上下文建模中的巨大潜力。 HySparse的设计灵感来源于学术界已有研究工作的经验和观察之上 。 一部分是显著token在相邻层之间相对稳定。 已有工作如TidalDecode等，观察到连续层的 "重要 token" 会高度重合，因此可以在某层识别重要token并在后续层复用。 HySparse将这一观察提升用于模型结构设计并直接训练。 还有部分受启发于跨层KV Cache共享能显著省显存且不显著伤性能 ，YOC ...

Core Viewpoint - The article discusses the launch of Keling 3.0, a multimodal video generation tool that enhances AI capabilities in video production, particularly in smart scene segmentation and character consistency. Group 1: Features of Keling 3.0 - Keling 3.0 introduces intelligent scene segmentation, allowing users to input complex prompts for multi-shot videos, which the AI can automatically divide into different scenes [11][14][21]. - The tool demonstrates high performance in maintaining character consistency across different angles and actions, significantly improving the user experience in video generation [21][22][30]. - The text consistency feature ensures that text remains clear and unaltered even during dynamic scene changes, achieving near commercial quality in outputs [28][30]. Group 2: User Experience and Performance - Users have reported that the intelligent scene segmentation effectively replicates the intended shots and dialogues, although minor bugs in audio and dialogue matching were noted [18][20]. - The platform allows for the upload of reference images to enhance character recognition, although some inconsistencies in character appearance were observed [25][26]. - The new O3 OMNI model offers enhanced capabilities for complex prompts and allows for custom scene segmentation, although it may require more expertise from users to achieve optimal results [34][38]. Group 3: Market Implications - The advancements in Keling 3.0 suggest a shift in video production dynamics, potentially reducing the need for human actors and traditional filming methods [8][10]. - The ability to generate videos in multiple languages and dialects, including regional accents, indicates a broader market appeal and usability for diverse audiences [32][33]. - The article hints at the potential for rapid adoption of AI tools in creative industries, as evidenced by user engagement and the sharing of generated content on social media platforms [6][8].

Core Viewpoint - Wang Huiwen is actively seeking to engage with entrepreneurs and projects related to OpenClaw, indicating a strong interest in the AI sector and local agent development [1][2][30]. Group 1: OpenClaw and Its Impact - OpenClaw has gained significant traction, with its GitHub stars increasing from 100,000 to 171,000, nearly double that of PyTorch [10][11]. - The platform represents a new product approach in the AI era, focusing on integrating APIs into user workflows rather than just model development [8][9]. - OpenClaw's community engagement is evident, with over 1,000 fans attending its first offline gathering in San Francisco, showcasing strong user loyalty and brand presence [42][46]. Group 2: Wang Huiwen's Investments - Wang Huiwen has invested approximately $70 million in Kimi, which recently announced free access to its Kimi K2.5 model, marking a significant development for users and investors alike [36][37]. - His investment strategy spans various AI sectors, including foundational infrastructure, model development, and application layers, indicating a comprehensive approach to the AI landscape [68]. - Wang's previous ventures include the acquisition of OneFlow and investments in Trooly.AI, reflecting his ongoing commitment to AI innovation and entrepreneurship [60][67]. Group 3: Market Dynamics and Trends - The AI market is witnessing a surge in interest, with products like Moltbook and rentahuman.ai gaining popularity, although some skepticism exists regarding their user metrics and authenticity [20][25]. - The local agent sector is emerging as a hot trend, with significant potential for commercialization and user engagement, as evidenced by the sustained interest in OpenClaw [46][69]. - The rapid evolution of AI products and platforms suggests a dynamic market environment, where user engagement and community building are becoming increasingly important for success [41][45].

Core Insights - The article introduces LaST₀, a novel VLA model that utilizes Latent Spatio-Temporal CoT for efficient reasoning in robotics, achieving state-of-the-art performance in various tasks [1][2][4]. Group 1: Model Overview - LaST₀ integrates high-efficiency latent space reasoning into embodied large models, surpassing previous methods like Pi0.5 in dual-arm and humanoid dexterous hand tasks [2][4]. - The model employs a Mixture-of-Transformers (MoT) architecture, featuring a slow reasoning expert for low-frequency latent space reasoning and a fast action expert for high-frequency action generation [5][11]. Group 2: Technical Innovations - LaST₀ introduces a compact latent space to model future visual dynamics, 3D structural information, and robot proprioceptive states, enabling a coherent temporal reasoning process [4][10]. - The model's architecture allows for asynchronous frequency coordination between the slow reasoning expert and the fast execution expert, optimizing real-time robotic operations [23]. Group 3: Performance Metrics - In simulations, LaST₀ achieved an average success rate of 82% across 10 RLBench tasks, outperforming existing state-of-the-art methods by 8% to 21% [24]. - In real-world tasks, LaST₀ demonstrated a 72% average success rate on the Franka platform, significantly exceeding competitors like SpatialVLA (41%) and CoT-VLA (50%) [27]. Group 4: Implications for Robotics - The model's ability to capture intricate physical and dynamic features through latent space reasoning enhances its performance in complex robotic tasks, indicating its potential for broader applications in dynamic environments [9][28]. - LaST₀'s design allows for effective interaction with the physical world, crucial for robust robotic operations in various settings [9][12].

Core Viewpoint - The article emphasizes the ongoing AI boom and invites individuals to join the company "Quantum Bit," which focuses on tracking AI advancements and has established itself as a leading content platform in the industry [1]. Group 1: Job Opportunities - The company is hiring for three main directions: AI Industry, AI Finance, and AI Product, with positions available for both experienced professionals and fresh graduates [2][4]. - Positions are open for various levels, including editors, lead writers, and chief editors, with a focus on matching roles to individual capabilities [6]. Group 2: Job Responsibilities - **AI Industry Direction**: Responsibilities include tracking innovations in infrastructure, such as chips, AI infrastructure, and cloud computing, as well as interpreting technical reports from conferences [6][7]. - **AI Finance Direction**: Focuses on venture capital, financial reports, and capital movements within the AI industry, requiring sensitivity to data and strong logical structuring [11]. - **AI Product Direction**: Involves monitoring AI applications and hardware developments, conducting product evaluations, and engaging with entrepreneurs and product experts [11]. Group 3: Benefits and Growth - Employees can expect to gain exposure to the latest AI technologies, enhance their work efficiency through new tools, and build personal influence in the AI field [6]. - The company offers competitive salaries, comprehensive benefits, and a supportive team environment that encourages growth and mentorship [6][11]. Group 4: Company Impact - By 2025, Quantum Bit aims to have over 2.4 million subscribers on WeChat and more than 7 million users across platforms, with a daily reading volume exceeding 2 million [12].

Core Viewpoint - The article discusses the increasing integration of AI in transportation safety, particularly through the "Eagle Eye" warning system developed by Gaode, which enhances driver awareness and reduces accident risks during the Spring Festival travel season [2][4][6]. Group 1: Spring Festival Travel - The Spring Festival travel volume is expected to reach a record high, with an estimated 9.5 billion trips over 40 days, and 80% of travelers opting for self-driving [1]. - The article highlights the unique aspects of this year's travel, emphasizing the role of AI in enhancing safety during journeys [1]. Group 2: AI's Role in Safety - The "Eagle Eye" system, developed in collaboration with the China Safety Production Science Research Institute, provides real-time risk awareness by detecting 24 types of potential hazards, including sudden braking and adverse weather conditions [4][6]. - The system has been upgraded to offer broader coverage and faster alerts, ensuring that it is accessible across various vehicles and road types [7]. Group 3: Technical Implementation - The core of the "Eagle Eye" system is the TrafficVLM model, which utilizes real-world traffic data to create a digital twin for training purposes [8][10]. - TrafficVLM enhances the system's ability to predict traffic conditions and provide timely warnings to drivers, thereby improving overall road safety [13][15]. Group 4: Performance Metrics - As of February 1, 2026, the "Eagle Eye" system has issued 11.2 billion warnings, averaging 88 million warnings per day, contributing to a 10% reduction in daily accident rates during peak travel times [16][18]. - The system's effectiveness is validated by real-world data, demonstrating its ability to help drivers avoid potential accidents [16][19].

Core Viewpoint - The article discusses the launch of OpenScholar, an AI system developed by the Allen Institute for AI and the University of Washington, which aims to eliminate the issue of false citations in academic writing by leveraging a vast database of 45 million scientific papers [2][5]. Group 1: OpenScholar's Features - OpenScholar connects to a large database called ScholarStore, which contains full texts and abstracts of 45 million papers, significantly reducing the false citation rate of traditional large language models (LLMs) [9][11]. - The system employs Retrieval-Augmented Generation (RAG) technology to ensure that each knowledge point is backed by a real paper, enhancing the accuracy of citations [12][13]. - OpenScholar's feedback loop allows it to refine its outputs by searching, generating, self-reviewing, and revising, which helps confirm the existence of supporting literature [12][13]. Group 2: Performance Comparison - In a benchmark test called Scholar QABench, OpenScholar-8B outperformed GPT-4o by 5% in correctness and matched human expert citation accuracy [16]. - A double-blind experiment showed that 51% of OpenScholar's answers were rated better than those written by human researchers, with an upgraded version achieving a 70% success rate [18]. - Experts noted that OpenScholar's strengths lie in its comprehensive information coverage, clearer structure, and stronger logical coherence compared to traditional models [19].