Core Insights - The article discusses the current state of "intelligent" sports systems, highlighting that most remain at the "scoring + visualization" stage, lacking actionable insights for athletes and coaches [1] - It introduces the SportsGPT framework, which aims to provide a complete intelligent loop from "motion assessment" to "professional diagnosis" and "training prescription" [5][37] Group 1: Limitations of Current Models - General large models like GPT-5 struggle with specialized sports biomechanics analysis due to their lack of fine-grained visual perception, leading to generic and sometimes physically infeasible suggestions [3][9] - A comparative evaluation shows that SportsGPT outperforms other models in accuracy (3.80) and feasibility (3.77), indicating its unique advantages in generating precise, actionable training guidance [8][9] Group 2: Motion Analysis Techniques - MotionDTW is a two-stage time series alignment algorithm designed for sports motion analysis, addressing traditional DTW's limitations by constructing a high-dimensional feature space [10][21] - The algorithm employs a weighted multi-modal feature space to eliminate errors caused by athlete body differences and incorporates dynamic features like angular velocity to enhance motion phase representation [12][18] Group 3: Diagnostic Capabilities - KISMAM serves as a bridge between raw biomechanical data and interpretable diagnostics, establishing a quantitative benchmark based on data from 100 youth sprinters [25][26] - The model quantifies deviations from standard thresholds and constructs a high-dimensional mapping matrix to understand complex relationships between motion anomalies and technical issues [28][30] Group 4: Training Guidance - SportsRAG, built on a large external knowledge base, enhances the generation of training guidance by integrating domain knowledge with diagnostic results, ensuring actionable recommendations [33][34] - The absence of the RAG module significantly reduces the feasibility of the model's outputs, demonstrating its critical role in transforming diagnostic insights into professional training prescriptions [34] Group 5: Conclusion - The SportsGPT framework represents a significant advancement in intelligent sports training, moving from mere data presentation to providing executable, expert-level guidance [37] - It establishes a new standard in smart sports by effectively addressing the challenges of motion analysis, diagnosis, and training instruction [37]

Core Viewpoint - MiniMax, a leading AI model unicorn, has successfully passed the Hong Kong Stock Exchange hearing, signaling its IPO ambitions amidst discussions about the bubble in large AI models like OpenAI [1][3]. Group 1: Company Overview - MiniMax has raised over $1.5 billion in funding within four years, attracting investments from notable firms such as MiHoYo, Alibaba, Tencent, and others [3][62]. - The company has a global presence, serving over 200 countries, with 70% of its revenue coming from international markets [6][42]. - MiniMax aims to achieve Artificial General Intelligence (AGI) and views scalability as a core driver towards this goal [8][7]. Group 2: Technological Advancements - MiniMax is one of the few companies that invested in multimodal model development from its inception [10]. - The company has released several models, including the M1 and M2 text models, with M2 achieving top rankings in performance and cost efficiency [16][17]. - MiniMax has also developed leading models in voice, music, and video, with its video model Hailuo ranking in the top tier of international tests [20][25][26]. Group 3: Financial Performance - MiniMax's revenue surged from $346,000 in 2023 to $30.52 million in 2024, marking a 782.2% increase [39]. - By the first nine months of 2025, revenue reached $53.44 million, significantly surpassing the previous year's total [40]. - The company has achieved a gross margin improvement from -24.7% in 2023 to 23.3% in the first nine months of 2025 [45][46]. Group 4: Operational Efficiency - MiniMax's R&D expenses have increased significantly, but the efficiency of these investments has improved, with training-related cloud computing costs as a percentage of revenue decreasing from over 1365% in 2023 to 266.5% in 2025 [52][54]. - The company has a cash reserve of $1.102 billion, sufficient to sustain operations for over 53 months without additional fundraising [58][59]. - MiniMax's team is young, with an average age of 29, and a high proportion of R&D personnel, which contributes to its innovative and efficient operational model [70][71].

Core Viewpoint - The article highlights the significant advancements made by Moore Threads in the GPU sector, particularly through the launch of the MUSA architecture and its associated products, which aim to enhance the developer ecosystem and position domestic GPUs at a competitive level in the global market [1][4][19]. Group 1: MUSA Architecture and Innovations - MUSA stands for Meta-computing Unified System Architecture, representing a comprehensive framework that encompasses chip architecture, instruction sets, programming models, and software libraries [6][7]. - The latest GPU architecture, Huagang, boasts a 50% increase in density and a 10-fold improvement in efficiency, with three new chips focusing on AI training, graphics rendering, and intelligent SoC [8][10]. - The MUSA architecture has been iteratively developed over five years, culminating in the latest iteration that optimizes low-precision computing for AI applications [11][13]. Group 2: New Product Launches - Moore Threads introduced three new chips: Huashan, Lushan, and Yangtze, along with two hardware products, AIBOOK and AICube, and the KUAE 2.0 AI Foundry cluster [20][21]. - The Huashan chip targets AI training and high-performance computing, supporting full precision from FP4 to FP64 and significantly enhancing Transformer throughput [22][25][27]. - The Lushan chip focuses on graphics computing, achieving a 64-fold increase in AI performance and a 15-fold improvement in 3A game rendering performance [28][30][31]. - The Yangtze chip is designed for edge computing, providing 50 TOPS of heterogeneous AI computing power for various applications [32][34]. Group 3: Software Ecosystem and Developer Engagement - The MUSA software stack 5.0 was launched, offering a complete toolchain from compilers to AI frameworks, with plans to open-source key components to foster community engagement [15][16]. - Moore Threads aims to build a robust developer ecosystem through the establishment of the Moore Academy, targeting a community of 1 million developers by 2025 [59][61]. - The company emphasizes the importance of a comprehensive ecosystem that integrates software, hardware, and developer trust to create a sustainable competitive advantage in the GPU market [56][58].

非羊 整理自 凹非寺 量子位 | 公众号 QbitAI 还记得《流浪地球2》里的那台 550W量子计算机 吗？ 电影里，MOSS最让人印象深刻的点，除了其强大算力，还有它可以根据需求，实时生成底层操作系统的能力。 如果现在告诉你，我们已经在从"人类需求"生成"底层系统"这件事上迈出了关键一步呢？ 来自上海交大IPADS实验室的研究团队，面对自动生成操作系统核心组件的难题，做出了全新的尝试。这项研究成果也即将亮相文件系统与 存储领域顶级学术会议 USENIX FAST'26 。 操作系统：与时俱进的沉重负担 操作系统 （OS） ，是整个数字世界的基石。 向下，它要管理和调度硬件资源 （CPU、内存、硬盘等） ；向上，它要为应用软件提供稳定可靠的运行环境。无论是你手机上的App，还 是云端强大的AI模型，都构建在这块基石之上。 然而，OS必须与时俱进，来满足硬件和应用的双重需求： 一方面，硬件的发展日新月异，例如存储设备，在短短数年内，就从机械硬盘演进到闪存甚至非易失性内存，OS必须快速迭代，才能榨干 这些新硬件的性能； 另一方面，新应用也层出不穷，例如大数据分析、AI训练等，每一个新型应用的出现，都可能对OS的 ...

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 ...

henry 发自 凹非寺 量子位 | 公众号 QbitAI 当Agent在应用侧不断加速，推理系统能否承受随之而来的真实负载，正在成为行业关注的焦点。 这是12月20日在杭州收官的 SGLang AI 金融 π 对 上，被反复提及的一个背景。 在这场聚焦大模型推理效率的"π对"上—— Agent的Vibe被暂时搁到一边，真正摆上桌面的，是推理系统在真实负载中的工程问题： 高并发请求 、 长上下文窗口 、 多轮推理 、 内存 管理， 以及在具体金融agent场景下的 一致性生成 问题。 同时，在活动讨论中，昇腾作为算力平台也被多次提及。 当前，昇腾已作为SGLang原生支持的后端之一进入主仓库，随着 SGLang推理引擎的更新，DeepSeek、Qwen、GLM等模型可以在不调整 模型参数、不引入额外插件的情况下直接运行，HiCache、Mooncake等系统能力也在对应版本中引入。 可以说，这次SGLang AI金融π对呈现的，并非零散技术点，而是一条清晰的推理工程演进路径——从缓存与内存体系，到权重更新、强化学 习效率，再到算力与模型生态的协同。 接下来，我们具体来看。 而在特定的部署场景，如 金融Agen ...

Core Viewpoint - The embodiment intelligence model is considered an independent foundational model parallel to language and multimodal models, specifically designed for the physical world [6][12][61] Group 1: Differences Between Physical and Virtual Worlds - The fundamental differences between the physical and virtual worlds are recognized, with the physical world characterized by continuity, randomness, and processes related to force, contact, and timing [2][10] - Existing models based on language and visual paradigms are structurally misaligned with the complexities of the physical world [3][21] Group 2: Need for a Separate Foundational Model - A separate foundational model is necessary due to the significant randomness in the physical world, which existing models struggle to accurately represent [10][17] - The current reliance on multimodal models for embodiment intelligence is seen as inadequate, necessitating a complete rethinking of model architecture and training methods [9][21] Group 3: Future of Multimodal Models - Shifting perspectives on embodiment intelligence will lead to new insights in model architecture and data utilization [24][30] - The learning processes in the physical world differ fundamentally from those in the virtual world, suggesting that future multimodal models must adapt to these differences [25][28] Group 4: Scaling Laws and Data Utilization - The concept of Scaling Law is crucial in the development of large models, particularly in robotics, where data sourcing remains a significant challenge [47][49] - A phased approach to training and data collection is recommended, emphasizing the importance of real-world data for effective learning [52][53] Group 5: Hardware and AI Integration - A new learning paradigm necessitates the redesign of hardware in the physical world, advocating for AI to define hardware rather than the other way around [54][55] - The potential for embodiment intelligence to drive exponential growth in resources and capabilities is highlighted, drawing parallels to historical industrial advancements [60][61]

Core Viewpoint - LeCun expresses skepticism about the potential of large language models (LLMs) to achieve artificial general intelligence (AGI), arguing that the path to superintelligence through LLMs is fundamentally flawed [2][78]. Group 1: Departure from Meta - LeCun is leaving Meta after nearly 12 years, criticizing the company's increasingly closed approach to research and its focus on short-term projects [3][11][26]. - He plans to establish a new company named Advanced Machine Intelligence (AMI), which will prioritize open research and focus on world models [10][19]. Group 2: World Models vs. LLMs - LeCun believes that world models, which handle high-dimensional and continuous data, are fundamentally different from LLMs, which excel at discrete text data [28][29]. - He argues that relying solely on text data will never allow AI to reach human intelligence levels, as the complexity of real-world data is far greater than that of text [31][32]. Group 3: Research Philosophy - LeCun emphasizes the importance of open research and publication, stating that without sharing results, research lacks validity [15][17]. - He critiques Meta's shift towards short-term projects, suggesting that true breakthroughs require long-term, open-ended research [18][26]. Group 4: Future of AI - LeCun envisions that the development of world models and planning capabilities could lead to significant advancements in AI, but achieving human-level intelligence will require substantial foundational work and theoretical innovation [84][85]. - He asserts that the most challenging aspect of AI development is not reaching human intelligence but rather achieving the intelligence level of dogs, as this requires a deep understanding of foundational theories [88][89]. Group 5: Personal Mission - At 65, LeCun remains committed to enhancing human intelligence, viewing it as the most scarce resource and a key driver for societal progress [92][94]. - He reflects on his career, expressing a desire to continue contributing to the field and emphasizing the importance of open collaboration in scientific advancement [103].

Core Insights - The article discusses a groundbreaking research paper by Google titled "Nested Learning: The Illusion of Deep Learning Architectures," which is being referred to as "Attention is All You Need" V2, emphasizing a new perspective on AI's learning capabilities [1][5]. Group 1: AI Limitations - Current large language models (LLMs) suffer from a condition termed "digital amnesia," where they forget recently learned information shortly after it is taught [2][3]. - The industry has focused on making models deeper and larger, believing that increasing scale would lead to emergent memory capabilities, but this approach has significant limitations [3][4]. Group 2: Nested Learning Paradigm - The research introduces the concept of "nested learning," which posits that effective intelligent learning requires two orthogonal dimensions: depth (model layers and capacity) and frequency (the rhythm and speed of internal component updates) [9][10]. - The paper argues that mainstream optimizers, traditionally viewed as mere training engines, actually function as associative memory systems that continuously record gradient changes [6]. Group 3: HOPE Architecture - The new architecture proposed, named HOPE, features a continuous memory system with multiple MLP modules arranged like a spectrum, each updating at different frequencies [14]. - This architecture mimics the human brain's memory processes, allowing new knowledge to be integrated without causing systemic collapse or forgetting [17][16]. Group 4: Future Implications - The value of "nested learning" lies not in immediately replacing existing models like Transformers but in providing a new design logic and framework for AI development [18]. - The exploration of memory and learning processes is still in its early stages, suggesting that future AI advancements may require systems capable of learning and evolving rather than being static repositories of knowledge [18].

RePro团队 投稿 量子位 | 公众号 QbitAI 这篇论文将推理的过程视为模型内部状态的优化过程，从而对如何重塑大模型的CoT提供了一个全新视角： 核心观察：推理即优化 RePro 基于这样一个核心思想：将模型的推理轨迹 （Trajectory） 看作是在损失曲面上寻找最优解的路径。 然而，"长思考"并非总是完美的。我们常发现模型会陷入 "过度思考" （Overthinking） 的陷阱：为了得出一个简单的结论，模型可能会生成 数千个冗余Token，甚至在错误的路径上反复横跳 （Backtracking） 。这不仅浪费了宝贵的算力，还增加了推理延迟。 RePro的三大"矫正"机制 近年来，随着o1、DeepSeek-R1等模型的爆发，Long Chain-of-Thought （Long CoT） 已成为提升LLM复杂推理能力的标配。 如何让模型在"深思熟虑"的同时，保持"思维敏捷"？ 基于上述视角，RePro设计了一套过程奖励机制，直接嵌入到RLVR （如PPO，GRPO） 流程中。 近日，上海人工智能实验室的研究团队提出了一种全新的后训练范式—— RePro （Rectifying Process- ...