Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is recommended to include a note with the institution/school, name, and research direction [3]

Core Viewpoint - The article discusses the transformation of SenseTime through its "1+X" organizational restructuring, emphasizing the emergence of a new entrepreneurial spirit among its young leaders and the financial accountability they now embrace [6][11][15]. Group 1: Organizational Changes - SenseTime's "1+X" strategy was officially announced on December 3, 2024, marking a significant restructuring aimed at fostering a new entrepreneurial collective to seize opportunities in the AI 2.0 era [6][11]. - The restructuring has led to the establishment of a five-member executive committee, enhancing decision-making efficiency and fostering a collaborative environment [7][11]. - The new structure encourages a focus on core business areas while allowing for flexibility and rapid adaptation in emerging vertical markets [12][14]. Group 2: Financial Accountability and Performance - The restructuring has resulted in a noticeable increase in financial oversight among the CEOs of the "X" businesses, who are now more proactive in managing their financial situations [15][16]. - Each of the six "X" enterprises has successfully raised over 2 billion yuan in cumulative financing, indicating strong investor interest and market validation [17][18]. - The establishment of the "X" businesses has positively impacted the parent company's cash flow, allowing for more resources to be allocated to core operations [17]. Group 3: Strategic Focus and Market Position - SenseTime is focusing on a "three-in-one" strategy that integrates large devices, large models, and applications, while still maintaining its core competency in computer vision (CV) [21][22]. - The company has seen significant growth in its CV business, with increased willingness from clients to invest, particularly in Hong Kong and overseas markets [22][23]. - SenseTime's extensive experience in CV is viewed as a competitive advantage in developing multi-modal large models, which are essential for future AI advancements [24][25]. Group 4: Technological Advancements - The latest model, released on July 27, 2025, showcases significant improvements in multi-modal reasoning capabilities, reflecting the company's commitment to innovation [27]. - SenseTime's strategic focus on integrating visual data with AI applications positions it well for future growth in the rapidly evolving AI landscape [24][25].

WAIC 世界人工智能大会展会上熙熙攘攘，格灵深瞳CEO吴一洲发现会场比往年更热闹，现场的人和产 品的画像更丰富，而且许多大公司展现出的AI单点应用深度也让人印象深刻。AI应用真正走进产业的 脉络更为清晰了。 在钛媒体2025 ITValue Summit前瞻WAIC现场版：AI落地指南系列的直播中，吴一洲与钛媒体联合创始 人刘湘明聚焦视觉智能的进化和AI技术升级下的技术厂商展开对话。 格灵深瞳一直深耕视觉算法和多模态大模型技术研发，经历过上一个技术时代的技术企业在这一波智能 浪潮中有明显不同的感受——产品有了"成长性"。吴一洲在对话中反复强调的一点是：要让产品能用起 来、用得好，而且有持续性的成长性。这不仅是格灵深瞳对产品的要求，也是作为技术厂商与客户共创 的愿景。 "以前，我们会给客户一个通用工具，现在有了智能体Agent之后，变成了个性化、有记忆的工具，相当 于一个搭档、一个执行合伙人，应用上更细化、更成熟了。"吴一洲介绍说，经过近几年的演进，格灵 深瞳构建了由模型、算法、软硬一体的产品和服务形成的端到端的体系。不过，她仍然非常理性，认为 当前AI距离真正的落地应用、在行业里跟专家超融合一样去深化应用， ...

Core Insights - The article discusses the booming field of large models in AI, particularly focusing on various directions such as RAG (Retrieval-Augmented Generation), AI Agents, and multi-modal models [1][2]. Group 1: Large Model RAG - Large model RAG is highlighted as a significant area, with emphasis on understanding components like retrievers, augmenters, and generators, and how knowledge bases can enhance performance [1]. - The article mentions the rapid development of subfields within RAG, including Graph RAG, applications in visual understanding, and various knowledge-oriented methods [1]. Group 2: AI Agents - AI Agents are identified as a hot direction in large models, covering topics such as single-agent and multi-agent systems, reinforcement learning, and efficient communication among agents [1]. - The integration of RAG with agents is also noted as a promising area for exploration [1]. Group 3: Multi-modal Models - The article points out the extensive directions available in multi-modal models, including visual language models, pre-training datasets, and fine-tuning processes [2]. - Deployment, inference, and optimization of these models are also discussed as critical components of the development process [2]. Group 4: Community and Learning - The article encourages engagement with the "Big Model Heart Tech" community for further learning and collaboration in the field of large models [3]. - The community aims to build a significant platform for talent and academic information related to large models [3].

具身智能之心招募科研辅导老师了！如果您是具身智能方向，手里握有多篇顶会、顶刊，欢迎和我们一起带动 学术界的发展。 方向一览 行业资源共享，享有论文署名与现金激励！详细请咨询小助理微信oooops-life了解更多。 要求说明 博士及以上学历（包含在读），2篇A会或一区以上期刊/会议，有辅导经验的优先。 待遇说明 包括但不限于：VLA、VLN、遥操作、Diffusion Policy、强化学习、VLA+RL、sim2real、多模态大模型、仿 真、运动控制、目标导航等方向。 ...

Core Insights - The article provides a comprehensive overview of large language models (LLMs), their definitions, architectures, capabilities, and notable developments in the field [3][6][12]. Group 1: Definition and Characteristics of LLMs - Large Language Models (LLMs) are deep learning models trained on vast amounts of text data, capable of understanding and generating natural language [3][6]. - Key features of modern LLMs include large-scale parameters (e.g., GPT-3 with 175 billion parameters), Transformer architecture, pre-training followed by fine-tuning, and multi-task adaptability [6][12]. Group 2: LLM Development and Architecture - The Transformer architecture, introduced by Google in 2017, is the foundational technology for LLMs, consisting of an encoder and decoder [9]. - Encoder-only architectures, like BERT, excel in text understanding tasks, while decoder-only architectures, such as GPT, are optimized for text generation [10][11]. Group 3: Core Capabilities of LLMs - LLMs can generate coherent text, assist in coding, answer factual questions, and perform multi-step reasoning [12][13]. - They also excel in text understanding and conversion tasks, such as summarization and sentiment analysis [13]. Group 4: Notable LLMs and Their Features - The GPT series by OpenAI is a key player in LLM development, known for its strong general capabilities and continuous innovation [15][16]. - Meta's Llama series emphasizes open-source development and multi-modal capabilities, significantly impacting the AI community [17][18]. - Alibaba's Qwen series focuses on comprehensive open-source models with strong support for Chinese and multi-language tasks [18]. Group 5: Visual Foundation Models - Visual Foundation Models are essential for processing visual inputs, enabling the connection between visual data and LLMs [25]. - They utilize architectures like Vision Transformers (ViT) and hybrid models combining CNNs and Transformers for various tasks, including image classification and cross-modal understanding [26][27]. Group 6: Speech Large Models - Speech large models are designed to handle various speech-related tasks, leveraging large-scale speech data for training [31]. - They primarily use Transformer architectures to capture long-range dependencies in speech data, facilitating tasks like speech recognition and translation [32][36]. Group 7: Multi-Modal Large Models (MLLMs) - Multi-modal large models can process and understand multiple types of data, such as text, images, and audio, enabling complex interactions [39]. - Their architecture typically includes pre-trained modal encoders, a large language model, and a modal decoder for generating outputs [40]. Group 8: Reasoning Large Models - Reasoning large models enhance the reasoning capabilities of LLMs through optimized prompting and external knowledge integration [43][44]. - They focus on improving the accuracy and controllability of complex tasks without fundamentally altering the model structure [45].

Core Insights - The article discusses the advancements in Discrete Tokenization for Multimodal Large Language Models (LLMs), emphasizing its role in transforming various modalities into discrete representations that LLMs can process effectively [2][39]. - A comprehensive survey has been released, detailing the technical landscape, challenges, and future research directions in the field of Discrete Tokenization for Multimodal LLMs [2][39]. Multimodal LLMs and Discrete Tokenization - Recent breakthroughs in Large Language Models (LLMs) have led to their application in various text tasks, prompting interest in extending their capabilities to non-text modalities such as images, audio, and video [2]. - Discrete Tokenization has emerged as a key solution, utilizing techniques like Vector Quantization (VQ) to compress high-dimensional continuous inputs into compact discrete tokens, enhancing cross-modal understanding and generation [2][39]. Systematic Review and Methodologies - The article presents the first systematic review of Discrete Tokenization for Multimodal LLMs, organizing content based on input data modalities and combinations, from early single-modal to multi-modal tokenization methods [2][39]. - Eight core categories of Vector Quantization methods are identified, including VQ, RVQ, PQ, AQ, FSQ, LFQ, BSQ, and Graph Anchor-Relation Tokenization, each with unique characteristics suitable for different modalities and tasks [8][9][14]. Challenges and Future Directions - Key challenges in Discrete Tokenization include codebook collapse, information loss during quantization, difficulties in gradient propagation, and issues with granularity and semantic alignment [12][36]. - Future research directions may focus on adaptive quantization, unified frameworks, biologically inspired codebooks, cross-modal generalization, and enhancing interpretability [37][36]. Applications in Single and Multimodal Tasks - Discrete Tokenization has been widely applied in single-modal tasks such as image retrieval, audio encoding, and video representation, allowing LLMs to process non-text modalities effectively [20][22]. - In multimodal tasks, it serves as a semantic bridge, enabling models to handle complex inputs across different modalities, facilitating tasks like cross-modal retrieval and generation [27][30].

Core Insights - Beijing is emerging as a leading hub for AI innovation, with nearly 40% of the country's registered large models and over 2,400 AI companies, contributing to a core industry scale of approximately 350 billion yuan [1] - The development of AI in Beijing is supported by technological breakthroughs, abundant computing power, and significant policy backing, creating a closed-loop system of "technological breakthroughs - industrial applications - innovative consumption" [1][12] - The shift from traditional production to intelligent production is exemplified by the rapid content creation capabilities of AI, significantly reducing production costs and time [4][5] AI Industry Landscape - The AI industry in Beijing is characterized by a diverse range of applications, from high-end manufacturing to creative content generation and smart city governance [1] - The multi-modal large models have become standard for AI companies, enabling the generation of images and videos, thus enhancing user interaction and experience [1][7] - The AI-driven production process is transforming traditional media, as seen in the case of the short film "Mountain Sea Mirror," which was produced in just two months using AI technology [3][4] Technological Advancements - The rapid iteration of AI models, such as the Keling AI, has led to significant improvements in content generation, with over 450 million images and 200 million videos produced since its launch [3][5] - AI technology is being applied in various sectors, including e-commerce, where virtual fitting rooms allow consumers to try on clothes digitally, enhancing the shopping experience [7][8] - The AI hospital established by Tsinghua University is capable of completing the diagnostic workload of a top-tier hospital in just two days, showcasing the efficiency of AI in healthcare [6] Economic Impact - The cost reduction in video marketing materials is estimated to be between 60% to 70% due to AI technologies, significantly impacting the marketing strategies of businesses [4] - The AI-driven marketing solutions have led to substantial revenue growth for companies like Keling AI, with reported earnings exceeding 150 million yuan in the first quarter of 2025 [9][15] - The integration of AI in consumer experiences is reshaping retail dynamics, moving from standardized offerings to personalized solutions, thus driving new consumption patterns [8][9] Policy and Infrastructure - Beijing's government is actively promoting AI applications through various policies, creating a supportive environment for the development and implementation of AI technologies [12] - The city's computing power supply is projected to exceed 450,000 P by the end of 2025, providing a robust infrastructure for AI development [11] - The establishment of research institutions like the Zhiyuan Research Institute is crucial for fostering innovation and talent in the AI sector [11][12]

Core Viewpoint - Hikvision has shown a strong performance in the first half of 2025, with revenue and net profit growth, indicating a successful transition towards AI and IoT solutions [1][3][6] Financial Performance - In the first half of 2025, Hikvision achieved revenue of 41.818 billion yuan, a year-on-year increase of 1.48% [1][3] - The net profit attributable to shareholders was 5.657 billion yuan, reflecting a significant year-on-year growth of 11.71% [1][3] - The operating cash flow improved dramatically from -190 million yuan in the same period last year to 5.34 billion yuan, marking a 2917.5% increase [3] Business Structure - Traditional security business remains the core, but innovative business has emerged as a "second growth curve," contributing 11.766 billion yuan in revenue, a 13.92% increase, accounting for 28.14% of total revenue [3] - Key innovative segments include Hikrobot, Ezviz, Hikvision Automotive Electronics, and Hikvision Microfilm, which have established leading positions in their respective fields [3] Strategic Transition - Hikvision is transitioning from a "security equipment leader" to an "AIoT solution provider," with a focus on leveraging AI breakthroughs for business growth [1][6] - The company has invested over 50 billion yuan in R&D since 2020, with R&D expenses accounting for 13.56% of revenue in the first half of 2025 [6][8] Market Challenges - The traditional security business faces challenges due to shrinking market demand and increased government fiscal pressure, leading to a decline in domestic revenue contribution [4] - Internationally, Hikvision's business has been impacted by being placed on the U.S. entity list and restrictions in key markets like Canada, although the overall revenue impact remains limited [5] AI Innovations - Hikvision has launched hundreds of AI model products across various sectors, including industrial manufacturing and traffic management, enhancing operational efficiency and safety [7][8] - The company’s AI innovations are seen as a key driver for its market valuation, with a target market capitalization approaching 300 billion yuan [8]

1. 大家都知道数数据是提升智能燃料，然后传感器又是采集数据的关键，想问一下智元在传感器的研发采 购上有什么规划？如何增加产品数据的使用性？ 罗剑岚：我们已与多家传感器供应商展开合作，重点聚焦视觉触觉与高密度传感器的联合研发。同时，我 们正在构建跨平台的数据采集 API，实现任务语义的统一映射，为模型训练提供标准化、可训练的数据输 入。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 具身智能之心受邀参加WAIC 2025智启具身论坛，并有幸采访到了智元机器人首席科学家罗剑岚博 士。以下为采访过程中罗博重点提到和探讨的问题。 具身智能数据讨论 2. 因为你刚才说的世界模型挺有用的，加入世界模型以后，加一些采集数据可以让它变好了，我想知道完 成这一步之后距离应用还有多远，从采集完数据到应用之间还有什么门槛？ 罗剑岚：还有性能，机器人的性能要很高，真正变得有用，在你家里，给一个机器人扫地也好，或者装洗 碗机的机器人，要有95%的成功率，在100万家庭里面，这是很难的问题。 3. Sergey Levine他有发过最新的一篇文章，提出了一个Sporks of AGI观点。仿真会阻碍具身智能的scale。 我想知 ...