关键 词 | 底层技术&AI |幻 量科技 "more than Yellow and Orange"到"DeepVerse Blue"，共同拓展材料创新的光谱 【SynBioCon】 获悉，近日，幻量科技（上海）有限公司宣布获得 A 股上市公司 七彩化学（ 300758 ） 战略入股。本次合作是产业资本对材料 信息学与 AI4Science 产业化路径的进一步认可，也为双方在化工与新材料领域探索 "AI 驱动研发 " 提供了更稳健的协同基础 。 幻量科技始终坚持 " 把研发变成可复用的工程系统 " ，让 AI 从概念走向研发一线，真正能在工业场景中持续交付，从一次性项目走向平台化能力 01 产业资本入场，释放材料信息学"从概念到交付"的信号 本次战略投资事件中，七彩化学作为 A 股上市公司产业资本参与方，为材料信息学的产业化落地注入了更强的产业协同势能。对我们而言，这不仅 是一次投资事件，更是一个 AI4Science 正在从 " 被讨论 " 走向 " 被采用 " 的时刻 —— 资本市场与产业链开始更关注 " 可交付的研发效率提升 " ，而非停留在概念叙事 。 回顾融资历程，幻量科技自成立以来持续获得多方关 ...

AI掀起的新一轮智能化浪潮席卷全球，大众所熟悉的场景，是使用Agent协助完成工作任务，乘坐智能驾驶车辆出行，亦或观看机器人表演，静水流深的另 一面，是AI参与和驱动学术、科研，动力路径日益明晰。 12月23日，人才辈出的百年学府上海交通大学有位"特别成员"正式亮相——由上海交大联合华为，共同打造的"致远一号"智算平台，这标志着交大向"AI for Science"科研范式迈出了坚实一步。 其定位全国高校最大的国产智算算力基础设施，自2024年12月启动，用时不到一年，完成了千卡昇腾集群本地部署和云上千卡规模化应用，峰值算力633 PFLOPS、存储容量13P，令千亿参数级的大模型校内训练，落地成为现实。 它为师生提供的，是开箱即用的AI应用服务，为科研训练、教学实训与课程实验，提供了坚实的算力底座。 顺应第五范式的进化 致远一号的出现，恰逢其时。 在全球范围内，AI4Science即"人工智能驱动的科学研究"，正迅速占领高地，成为继经验科学、理论科学、计算科学和数据科学之后，现代科学活动的"第 五范式"。 有两点洞察，值得注意： 其一，越来越多科研人士、学术人士，积极拥抱AI、高效使用AI。 以算促学， 上 ...

Group 1 - Google DeepMind's MoR architecture achieves two times inference speed by combining parameter sharing and adaptive computation, resulting in fewer parameters while maintaining large model performance [1] - The dynamic routing mechanism allocates different recursive depths based on token complexity, reducing redundant computations and optimizing KV cache [1] - Experimental results show that MoR improves inference throughput by 2.06 times, reduces training time by 19%, and decreases peak memory usage by 25% [1] Group 2 - Amazon launches Bedrock AgentCore preview, offering seven core AI agent services including runtime, memory, and authentication [2] - The introduction of Nova customization options and Strands Agents V1.0 simplifies agent development and enables multi-agent collaboration [2] - Amazon S3 Vectors cloud object storage is released, reducing vector storage costs by 90%, along with Kiro AI IDE to enhance developer experience [2] Group 3 - Elon Musk is seeking names for the male AI companion Grok, with suggestions like "Draven" that align with characters from "Twilight" and "Fifty Shades of Grey" [3] - A user named Jackywine has created an open-source 3D digital companion "Bella," which retains only the visual aspect without large language model capabilities [3] - The "Bella" project follows an "AI native" development path in three phases: perception core, generative self, and proactive companionship, with plans to incorporate voice recognition and affinity systems [3] Group 4 - Google Search introduces an AI feature that can make phone calls to book local services for users, such as pet grooming [4] - The search integrates the Gemini 2.5 Pro model and Deep Search functionality, capable of handling complex queries and generating in-depth reports [4] - This new feature has launched in the U.S. and will be gradually rolled out globally, sparking discussions about the effectiveness of AI automated calls and merchant experiences [4] Group 5 - The AI programming platform Windsurf reintroduces the Claude Sonnet 4 model, allowing Pro users 250 free calls per month [6] - Claude Sonnet 4 offers advantages such as cross-file intelligent refactoring, a 200,000 token context window, and precise code completion [6] - This renewed partnership follows OpenAI's acquisition failure and executive team changes, representing Windsurf's strategic move to regain user trust [6] Group 6 - Anthropic successfully rehires core programming leaders Boris Cherny and Cat Wu from Cursor within two weeks [7] - Anthropic reveals that direct sales of models and Claude yield a gross margin of 60%, while sales through AWS and Google Cloud result in a negative 30% margin [7] - Claude Code has become a new asset for Anthropic, with weekly downloads increasing sixfold to 3 million since June, contributing over $200 million in annualized revenue [7] Group 7 - CrePal launches the first AI video creation agent, allowing users to produce videos through a single command that orchestrates multiple models [8] - The system can automatically plan scripts, select appropriate models, generate visuals, and add sound effects, addressing high barriers in traditional AI video creation [8] - The innovation lies in transforming the creative process, enabling users to focus on creative expression rather than technical operations by integrating dispersed tools into a unified intelligent task [8] Group 8 - Apple's MLX framework adds CUDA support, enabling developers to train models using NVIDIA GPUs and deploy them back to Apple devices [9] - This move is seen as Apple's concession to the NVIDIA ecosystem, which dominates AI development with 5 million developers [9] - Despite past tensions over NVIDIA support, Apple opts to leverage NVIDIA's ecosystem for compliance and to expand its influence [9] Group 9 - HeShan Technology, founded by alumni from Tsinghua and Beihang University, focuses on AI tactile sensing technology and has developed the world's first AI tactile perception chip [10] - Utilizing capacitive tomography technology, HeShan achieves "sensing and control integration," addressing the tactile feedback needs in robotic precision operations [10] - The company has completed four rounds of financing and serves over 70% of domestic robot manufacturers, transitioning from a hardware provider to a comprehensive tactile solution provider [10] Group 10 - Nobel laureate John Jumper discusses the journey of AlphaFold, highlighting that the value of algorithm research is 100 times that of data [11] - AlphaFold predicts protein structures with atomic-level precision and has been cited 35,000 times, accelerating scientific discoveries [11] - Jumper predicts that AI4Science will become more generalized in the future, with AlphaFold enhancing the pace of structural biology development by 5-10%, leading to widespread advancements across scientific fields [11]

Core Viewpoint - The article discusses the innovative framework DrSR (Dual Reasoning Symbolic Regression) developed by researchers at the Institute of Automation, Chinese Academy of Sciences, which enables large models to analyze data, reflect on failures, and optimize models like scientists do [2][14][56]. Group 1: Framework and Mechanism - DrSR employs a dual-path reasoning mechanism that integrates "data insights" and "experience summaries" to guide large models in scientific equation discovery [16][28]. - The framework consists of three virtual scientists: a data scientist, a theoretical scientist, and an experimental scientist, each contributing to a collaborative mechanism for efficient scientific equation discovery [3][7]. Group 2: Performance and Results - In various interdisciplinary modeling tasks, DrSR has demonstrated superior generalization capabilities, outperforming existing methods in accuracy and efficiency [4][30]. - Experimental results show that DrSR achieved an accuracy of 99.94% in nonlinear damping oscillation system modeling, significantly surpassing all baseline methods [31]. Group 3: Learning and Adaptation - DrSR's process is a closed loop: data analysis → prompt guidance → equation generation → evaluation and scoring → experience summarization, allowing the model to accumulate knowledge and refine its approach [28]. - The framework's experience-driven strategy helps avoid common failure structures, resulting in a higher proportion of valid equations generated compared to other methods [37]. Group 4: Robustness and Generalization - DrSR exhibits strong robustness against noise and out-of-distribution (OOD) data, maintaining low normalized mean square error (NMSE) across various tasks [40][41]. - The model's performance remains stable under different Gaussian noise levels, showcasing its generalization advantages [41]. Group 5: Future Directions - DrSR is integrated into the ScienceOne platform, providing efficient and interpretable scientific modeling services, with plans to enhance its reasoning capabilities and cross-task generalization [57]. - Future improvements will focus on expanding DrSR's capabilities to multi-modal scientific modeling scenarios and incorporating continuous learning mechanisms [61].

Core Insights - AI4Science is recognized as the "fifth paradigm" of scientific discovery, integrating artificial intelligence into key research processes to accelerate innovation and transform scientific research [1] - The Suzhou Industrial Park has developed a trillion-level AI industry cluster with over 1,800 AI-related companies, leading in generative AI services and algorithms [1] - WuShi Technology is leveraging AI to revolutionize chemical synthesis, moving from traditional experimental methods to data-driven predictive designs [2][3] Group 1: Industry Overview - AI technology is reshaping the chemical research paradigm, particularly in chemical synthesis, significantly improving research efficiency and enhancing experimental accuracy and safety [2] - The global registered substances exceed 250 million, highlighting the urgent need for AI to enhance the speed of chemical exploration [3] - Traditional chemical synthesis relies heavily on expert judgment and existing knowledge, which limits efficiency and innovation [3] Group 2: Company Profile - WuShi Technology - WuShi Technology focuses on integrated design for synthesis processes, utilizing a combination of AI computing and laboratory automation to create an intelligent closed-loop ecosystem [2][4] - The company has achieved significant milestones, including the commercialization of China's first AI hardware and software automated synthesis platform and the development of a standardized product matrix [5] - WuShi Technology has received multiple qualifications, including national high-tech enterprise status and has successfully completed four rounds of financing from notable investment firms [5] Group 3: Technological Innovations - The ChemPro.AI platform serves as the core hub connecting intelligent computing and laboratory automation, driving a revolution in chemical synthesis and drug development efficiency [6][10] - The platform features four core functional modules: material information retrieval, reaction literature retrieval, reaction condition recommendation, and retrosynthesis [6] - WuShi Technology's automated laboratory solutions have demonstrated impressive market performance, significantly improving experimental success rates and reducing search times [12][16] Group 4: Future Prospects - WuShi Technology aims to expand its global application landscape by collaborating with leading enterprises and top research institutions [11][16] - The company is projected to achieve breakeven in 2024, with an expected revenue growth of 200% in 2025 [11] - The Suzhou Industrial Park is committed to becoming a national hub for AI industry development, focusing on deep integration of AI with the real economy [16]

Core Insights - The Chinese Academy of Sciences' Automation Research Institute launched the ScienceOne intelligent research platform based on a scientific foundational model at the 8th Digital China Construction Summit [1] - ScienceOne aims to facilitate interdisciplinary collaboration and enhance scientific research processes through a platform that supports the entire research workflow from hypothesis generation to discovery [1] Group 1 - ScienceOne is developed in collaboration with various institutes and industrial platforms, focusing on a scientific foundational model that integrates architecture solutions [1] - The platform addresses common scientific research needs across disciplines, achieving breakthroughs in data understanding, computational optimization, and reasoning evaluation [1] Group 2 - Two tools were launched with ScienceOne: S1-Literature literature assistant and S1-ToolChain scientific tool scheduling platform [2] - S1-Literature is designed to generate high-level literature reviews and understand scientific data types, with current adaptations in mathematics, physics, and materials, and plans for future expansion [2] - S1-ToolChain enables autonomous collaboration of scientific tools across disciplines, integrating nearly 300 tools for data analysis, differential equation solving, and cross-scale simulation [2]