消息面上，英矽智能今日宣布，其用于炎症及神经退行性疾病治疗的口服NLRP3抑制剂ISM8969临床试 验新药(IND)申请获得美国食品药品监督管理局(FDA)批准，用于帕金森病治疗。即将开展的这项I期临 床研究旨在评估ISM8969在健康人群中的安全性、耐受性及药代动力学表现，并找到临床推荐最佳剂量 以供后续的进一步研究。值得一提的是，为加速ISM8969的全球开发，英矽智能已与衡泰生物达成共同 开发合作协议。双方各持有该项目50%的全球权益，同时英矽智能有权获得最高逾5亿港币的预付款和 里程碑付款。 此外，英矽智能日前发布了大语言模型训练框架Science MMAI Gym，旨在将具有因果推理能力的LLM 转化为具备在真实世界处理药物发现与开发任务能力的高性能引擎。经过训练后，原本在专业任务领域 失败率高达75%–95%的LLM，可在关键药物发现基准测试中实现最高10倍的性能提升。此次发布将进 一步推进制药超级智能(PSI)愿景。 英矽智能(03696)涨超6%，高见62.9港元创上市新高。截至发稿，涨6.11%，报62.5港元，成交额2637.99 万港元。 ...

Core Insights - DeepSeek has released a new model "MODEL1" in the open-source community, coinciding with the one-year anniversary of the DeepSeek-R1 model launch [1] - The company plans to gradually unveil five code repositories during the "Open Source Week" starting in February 2025, with Flash MLA being the first project [3] - Industry analysts suggest that "MODEL1" may represent a new architecture distinct from the existing "V32" model, potentially indicating the next-generation model (R2 or V4) that has not yet been publicly released [4] Group 1 - Flash MLA optimizes memory access and computation processes on Hopper GPUs, significantly enhancing the efficiency of variable-length sequence processing [3] - The core design of Flash MLA includes a dynamic memory allocation mechanism and parallel decoding strategy, which reduces redundant computations and increases throughput, particularly for large language model inference tasks [3] - DeepSeek has been active since January 2026, releasing two technical papers on a new training method called "optimized residual connections (mHC)" and a biologically inspired "AI memory module (Engram)" [4] Group 2 - On January 12, DeepSeek published a new paper in collaboration with Peking University, introducing a conditional memory mechanism to address the inefficiencies of the Transformer architecture in knowledge retrieval [5] - The Engram module proposed by DeepSeek is said to enhance knowledge retrieval and improve performance in reasoning and code/mathematics tasks [5] - The private equity firm managed by Liang Wenfeng, known for high returns, has provided substantial support for DeepSeek's research and development efforts [5]

东软集团表示，面向未来，公司将继续秉持"开放融合、生态共赢"的理念，与更多顶尖技术伙伴在汽车 智能化、AI化趋势下深度协同，共同助力车企在市场中破局，为全球用户打造更安全、更具温度的智 能出行体验。 随着汽车技术向智能化、情感化不断演进，用户对座舱交互的期待进一步提升。人们不再满足于基础的 语音响应，更需要一个能真正理解自然语义、交流顺畅且具备情感共鸣的出行伙伴。东软集团与 Cerence AI的强强联合，正是瞄准这一行业趋势，希望让"听得懂、答得准、有温度"的交互体验成为智 能汽车"新标配"。 Cerence AI是全球领先的对话式AI用户体验解决方案提供商。此次合作，东软集团将以先进的NAGIC智 能座舱软件平台为核心载体，深度融合Cerence AI在语音技术、生成式AI以及大语言模型方面的先进技 术与专业经验，围绕高频出行场景，共同探索智能语音交互的创新应用。 依托东软集团广泛的全球产品研发与交付网络，以及Cerence AI的技术优势与品牌影响力，双方将携手 开拓全球目标市场。 "东软集团"微信公众号1月22日消息，近日，东软集团与Cerence AI正式签署合作谅解备忘录。双方宣 布，将围绕智能语 ...

人民财讯1月22日电，近日，东软集团（600718）与Cerence AI正式签署合作谅解备忘录。双方宣布， 将围绕智能语音、大语言模型等前沿领域开展深度合作。通过技术共创、生态融合，共同为全球汽车伙 伴打造预集成、场景化、高体验的智能交互解决方案。 ...

"现在我们去陌拜医生，花10秒钟介绍演示，基本上100%都会下载这个产品。" 1月中旬，在位于杭州城西的阿里健康总部，极少在媒体前露面的CTO祥志接受了《健闻咨询》的独家 专访。 大概半年前，阿里健康在手机应用商城悄无声息地上线了一款名为"氢离子"的AI原生APP，主要面向医 生群体，功能定位类似于美国的医疗AI独角兽——OpenEvidence。 那时候，大洋彼岸的OpenEvidence刚完成B轮融资，估值35亿美元，"医生版ChatGPT"的故事尚未完全 展开。 对于以医药电商和C端用户为基本盘的阿里健康来说，这更像是一次未知的冒险——为医生提供循证医 学下的决策支持，并不在其主营业务的雷达范围之内。相比之下，预问诊、医生数字分身，乃至院外场 景中的AI健康管家，都能为传统的线上药品交易带来更多的商业机会和增长空间。 此后的半年时间里，阿里健康不做产品宣发，婉拒媒体采访，把"氢离子"的曝光度降到最低。他们只做 了一件事——邀请不同层级的医生进行产品内测，再根据反馈迭代优化。 如果我们把视线再拉长一点，从2022年底ChatGPT引爆大语言模型的技术浪潮以来，阿里健康就似乎一 直潜伏于水面之下：不搞自研大 ...

Core Viewpoint - Elon Musk has open-sourced the X recommendation algorithm, which combines in-network content from followed accounts and out-of-network content discovered through machine learning, using a Grok-based Transformer model for ranking [3][12][18]. Summary by Sections Algorithm Overview - The open-sourced algorithm supports the "For You" feed on X, integrating content from both followed accounts and broader network sources, ranked by a Grok-based Transformer model [3][5]. - The algorithm fetches candidate posts from two main sources: in-network content (from accounts users follow) and out-of-network content (discovered through machine learning) [9][10]. Algorithm Functionality - The system filters out low-quality, duplicate, or inappropriate content to ensure only valuable candidates are processed [7]. - A Grok-based Transformer model scores each candidate post based on user interactions (likes, replies, shares, clicks), predicting the probability of various user actions [7][8]. Historical Context - This is not the first time Musk has open-sourced the X recommendation algorithm; a previous release occurred on March 31, 2023, which garnered over 10,000 stars on GitHub [12][14]. - Musk aims to enhance transparency in the algorithm to address criticisms regarding bias in content distribution on the platform [18][19]. User Reactions - Users on the X platform have summarized key insights about the recommendation algorithm, emphasizing the importance of engagement metrics like replies and watch time for content visibility [22][23]. Importance of Recommendation Systems - Recommendation systems are crucial to the business models of major tech companies, with significant percentages of user engagement driven by these algorithms (e.g., 35% for Amazon, 80% for Netflix) [25][27]. - The complexity of traditional recommendation systems often leads to high maintenance costs and challenges in cross-task collaboration [28]. Future Implications - The introduction of large language models (LLMs) presents new opportunities for recommendation systems, potentially simplifying engineering and enhancing cross-task learning [29][30]. - The open-sourcing of the X algorithm may not lead to immediate changes across other platforms, as they may lack the resources to implement similar systems [39].

Investment Rating - The report maintains a "Buy" rating for the following companies: Weike Technology (301196.SZ), Nengke Technology (603859.SH), Hehe Information (688615.SH), and Maixinlin (688685.SH) [6][50]. Core Insights - The AI application landscape is evolving, with the launch of the new "Task Assistant" feature in the Qianwen app, which integrates over 400 services from Alibaba's ecosystem, marking a significant shift from information processing to task execution [3][27]. - DeepSeek has released an open-source Engram module that enhances memory retrieval and reasoning efficiency in large models, addressing traditional architecture challenges [2][20]. - SkildAI has completed a $1.4 billion Series C funding round, indicating strong market potential for general AI models in robotics, with a valuation exceeding $14 billion [36][38]. Summary by Sections Computing Power Dynamics - The rental prices for computing power remain stable, with specific configurations priced at 28.64 CNY/hour for Tencent Cloud and 31.58 CNY/hour for Alibaba Cloud [17][19]. - DeepSeek's Engram module introduces a "lookup-computation separation" mechanism, significantly improving model efficiency in knowledge retrieval and reasoning tasks [2][20]. AI Application Dynamics - QuillBot's weekly traffic increased by 13.20%, indicating growing user engagement in AI applications [25][26]. - The Qianwen app's upgrade allows users to complete complex tasks like ordering food and booking travel through natural language commands, showcasing the practical application of AI in daily life [3][28]. AI Financing Trends - SkildAI's recent funding round attracted major investors, including SoftBank and Bezos Expeditions, highlighting the increasing interest in AI robotics and its potential across various industries [36][39]. Investment Recommendations - The report suggests focusing on companies like Maixinlin (688685.SH), Weike Technology (301196.SZ), Hehe Information (688615.SH), and Nengke Technology (603859.SH) for their growth potential in AI applications and computing power [48].

Core Insights - Muxi Co., Ltd. identifies government and enterprise clients as one of its main customer segments [2] - The Xisi N100 product, launched in 2022, is the company's first product designed for various traditional AI application scenarios, providing strong inference computing power and video encoding/decoding capabilities [2] - Subsequent products in the Xisi N series, such as Xisi N260 and Xisi N300 (under development), are primarily aimed at cloud-based AI inference scenarios under generative artificial intelligence, featuring powerful mixed-precision computing power and large memory capacity [2] Product Details - The Xisi N100 product is applicable in smart city, smart transportation, smart education, and intelligent video processing sectors [2] - The upcoming Xisi N series products support mainstream deep learning development frameworks and are designed to provide end-to-end acceleration services for content generation applications and large language models [2] - The company commits to timely information disclosure in accordance with relevant regulations to ensure investors' right to know [2]

Core Viewpoint - ICML 2026 has introduced a new review type selection mechanism allowing authors to decide whether to permit the use of large language models (LLMs) in the review process [3][9]. Group 1: Review Policy Changes - Two policies have been established: Policy A strictly prohibits the use of any LLMs during the review process, while Policy B allows their use with specific restrictions [4]. - Allowed actions under Policy B include using LLMs to assist in understanding the paper, language polishing of review comments, and querying LLMs for strengths or weaknesses of the paper [7][9]. - The choice of whether to allow LLMs in the review process is now in the hands of the authors, marking a significant shift from previous practices where the decision was primarily up to reviewers [9]. Group 2: Implementation Challenges - There are concerns regarding the enforcement of the new regulations on LLM usage, as past experiences have shown a prevalence of AI-generated reviews [11][13]. - A study on ICLR 2026 revealed that 21% of review comments were entirely generated by AI, indicating a widespread reliance on AI tools in the review process [11]. - The effectiveness of ICML's new rules may be limited, as compliance by reviewers cannot be guaranteed, raising questions about the integrity of the review process [14][15]. Group 3: Author Control and Options - Authors now have the option to refuse LLM-assisted reviews, providing a "one-size-fits-all" choice that may address concerns about trust in the review process [16].

Core Insights - The World Economic Forum (WEF) and McKinsey have recognized EVE Energy Co., Ltd. as the world's first "lighthouse factory" for cylindrical battery manufacturing, highlighting its integration of advanced technologies such as AIoT, physical simulation, and large language models [1] Group 1: Smart Manufacturing - EVE Energy has established a highly efficient digital system that spans the entire research, production, and sales chain, featuring a domestic first 300ppm high-speed production line capable of producing 300 cylindrical battery cells per minute, averaging nearly 27 cells per second [2] - The integration of physical simulation and AI process models has led to a 75% reduction in the number of R&D experiments, significantly shortening the time from R&D to mass production [2] - The automation rate in key production processes has reached 100%, with an AIoT-driven equipment health prediction system enhancing overall equipment efficiency to 95% [2] - EVE Energy's quality control system boasts a first-pass yield rate of over 97%, with AI production quality prediction models improving voltage consistency by 70% [2] Group 2: Green Innovation - EVE Energy aims to reduce unit carbon emissions by over 60% and unit energy consumption by over 55% from 2022 to 2025, leveraging digital technologies for sustainable development [3] - The company has implemented an "electricity passport" system, assigning a unique digital ID to each battery, which supports accurate recycling and reuse across over 200,000 data nodes in the supply chain [3] - EVE Energy is committed to reducing the carbon footprint of its products by 15% through renewable energy utilization, recycled material application, and energy-saving technology upgrades [3] Group 3: Future Outlook - The practices of EVE Energy demonstrate a viable path for achieving breakthroughs in manufacturing efficiency and green performance through the integration of AIoT, physical simulation, and other advanced technologies [4] - The company's innovations in automation, AI optimization, and battery passports serve as replicable models for high-quality and low-carbon development in the new energy sector [4] - As digital technologies and clean energy manufacturing continue to converge, Chinese new energy companies are expected to play a crucial role in global energy transition and contribute to a zero-carbon future [4]