Core Insights - The traditional business inquiry platforms face significant challenges, including information silos, operational inefficiencies, high understanding thresholds, and a lack of deep insights, which hinder effective decision-making [1][2][6] Group 1: Traditional Business Inquiry Platform Challenges - Information fragmentation leads to users needing to navigate multiple platforms for data retrieval, resulting in time-consuming processes and potential oversight of critical information [1] - Operational inefficiencies arise from the cumbersome keyword search and filtering processes, which do not meet the demands for rapid responses [1] - High understanding thresholds exist due to the presentation of raw data without sufficient analysis, placing a heavy cognitive burden on users [1] - The lack of insightful analysis limits the ability to derive deeper insights, predict trends, or provide decision-making support, resulting in underutilization of data value [1] Group 2: Waterdrop Credit's MCP Solution - Waterdrop Credit introduces a multi-type enterprise information query MCP that leverages large model technology to transform the business inquiry experience [2][6] - The MCP allows for natural language interaction, enabling users to express queries in everyday language, which the system can accurately interpret and analyze [10] - The platform features a panoramic data architecture that integrates diverse data sources, breaking down information silos and enabling comprehensive enterprise profiling [12] - Dynamic intelligent reports can be generated based on user queries, enhancing efficiency from data retrieval to decision support [14] - The MCP represents a shift from traditional information repositories to intelligent hubs, facilitating proactive insights and decision-making support [16]

Core Insights - The presentation discusses Amazon Web Services' (AWS) practical experiences in intelligent document translation based on large models, focusing on ensuring terminology accuracy and adherence to corporate writing styles [1][21]. - The challenges faced include maintaining terminology accuracy while using large language models and ensuring compliance with corporate writing styles [4][21]. Group 1: Terminology Accuracy - Initially, AWS used a straightforward method of directly inputting hundreds of terms into the model's context, achieving a 90% accuracy rate with 200 term pairs [5][21]. - As the number of terms increased to over 1,000, AWS implemented the Aho-Corasick (AC) algorithm for efficient memory-based key-value matching, addressing limitations in context length and attention mechanisms [6][21]. - For larger datasets, AWS utilized OpenSearch Percolator, which allows for term indexing and retrieval, effectively handling fuzzy matching and special characters in terminology [6][18][21]. Group 2: Corporate Writing Style - To meet corporate writing style requirements, AWS introduced a sample library concept, leveraging historical translation documents to guide new translations [7][21]. - Instead of fine-tuning large models, which can be costly, AWS combined Retrieval Augmented Generation (RAG) and FuseShot to create a web knowledge base, providing a more cost-effective solution [8][21]. - The system allows for the integration of previous translations to ensure consistency in writing style, enhancing the overall translation quality [8][21]. Group 3: Engineering Challenges - AWS faced engineering challenges in translating PDF documents, including differences in information density between languages, which can lead to content expansion of about 30% when translating from Chinese to English [13][21]. - Solutions included dynamic recursive algorithms to optimize rendering and merging of text blocks to prevent translation errors caused by block segmentation [13][21]. - The system architecture supports both offline and online processes, allowing users to upload terminology libraries and translate documents efficiently [10][12][21]. Group 4: Positive Feedback Loop - The professional translation field exhibits a flywheel effect, where the accumulation of internal data assets enhances translation processes and can be applied to other areas such as AI proofreading and smart writing review [15][21]. - AWS's system enables users to upload their terminology and sample libraries, facilitating a continuous improvement cycle in translation quality and efficiency [15][21].

Core Viewpoint - The article promotes an event titled "Reinforcement Learning New Paradigm Exploration Night," emphasizing the integration of reinforcement learning (RL) with large model agents, highlighting its significance in the current technological landscape [2][3]. Event Details - The event is scheduled for July 26, 2025, from 19:00 to 21:10, located near the Shanghai Expo Exhibition Center, aiming for an intimate gathering of only 12 participants to facilitate deep discussions [3][4]. - The event will cover three main topics: the synergy between reinforcement learning and large model agents, the dilemma of exploration versus stability in training strategies, and the challenges of aligning and evaluating intelligent agents [4]. Target Audience - The event is designed for individuals from academia, industry, and entrepreneurship, encouraging participants to bring their latest research, practical experiences, and product challenges for collaborative discussions [5][6]. - The focus is on fostering an environment for lively exchanges of ideas rather than formal presentations, aiming for a dynamic and engaging atmosphere [6][7]. Participation Information - Interested participants are encouraged to scan a QR code to express their identity (academic, industry, or entrepreneurial) and the specific RL challenges they wish to discuss, with limited spots available [8]. - The article emphasizes the importance of engaging in meaningful technical discussions and debates, suggesting that the event will provide a unique opportunity for networking and collaboration [9].

推动形成外贸雁阵格局 针对企业"不敢做、不会做"外贸业务难题，将外贸业务实操培训作为最直接的方式，帮助企业迈出 外贸"第一步" "回归一批" 针对近年曾开展外贸业务但"休眠"的企业，"一企一策"制定方案，力争推动存量企业重归外贸赛道 "壮大一批" "破零一批" 加大领航企业培育，对高潜力企业实行"联络员+直通车"服务，建立跨部门服务专班，加速助力企 业向头部梯队跃升 "护航一批" 为企业及时提供各国贸易情况、产业情况、重点潜力贸易商品等信息，同时依托AI大模型智能应 用，全天候响应外贸企业诉求 5月30日，记者从商务厅获悉，为应对复杂严峻国际经贸形势带来的挑战，提升外贸企业拓市场和 抗风险能力，今年全省商务系统将围绕我省六大优势产业和新兴未来产业，分产业开展"百企领航·千企 升级"外贸主体业务培训，力求培训管用好用，让更多企业真正成为出海"精兵强将"和"行家里手"。 管用好用，培训会更加强调实操性 成都市萨尼医疗器械有限公司是一家从事口腔医疗器械生产的外贸企业。当前，该企业在拓展外贸 业务中遇到最大的难题是外贸业务拓展缺乏方向、进展偏慢。在此次培训会上，中国医药保健品进出口 商会信息会培部副主任于盟对全球医药 ...

Core Viewpoint - The main barrier to the usability of large language model agents (LLM Agents) is not the capability of the models but rather the "Agentic ROI" which has not reached a practical threshold for widespread application [1][3][4]. Group 1: Agentic ROI Concept - Agentic ROI (Agentic Return on Investment) is a key metric that measures the ratio of "information yield" to "usage cost" for LLM Agents in real-world scenarios [4]. - Usability is achieved only when the quality of information exceeds a certain threshold and the ratio of time and cost saved by the agent is sufficiently high [4][5]. Group 2: Current Application Landscape - Most LLM Agents are currently applied in high human task time cost scenarios, such as research and programming, where human labor is intensive, thus allowing for significant efficiency improvements [7]. - In everyday applications with high user demand, such as e-commerce and personal assistants, the tasks are simpler, leading to lower marginal value from LLM Agents, which may introduce additional interaction costs and delays, resulting in low Agentic ROI [7]. Group 3: Development Trajectory - The development path of LLM Agents is characterized by a "zigzag" model of first scaling up to enhance information quality, followed by scaling down to reduce time and cost while maintaining quality [9]. - The evolution of foundational models, such as the OpenAI series, illustrates this zigzag trend, with significant performance improvements in larger models and the introduction of smaller models that maintain performance while reducing inference costs and delays [9]. Group 4: Scaling Up Information Quality - Pre-training scaling involves expanding model size, data volume, and computational resources to enhance foundational capabilities in language understanding and reasoning [11]. - Post-training scaling, including supervised fine-tuning and reinforcement learning, aligns the agent's performance with human needs and values, relying on extensive interaction data for continuous learning [12]. - Test-time scaling focuses on building a world model that supports multimodal interactions and can handle complex tasks while reflecting real-world uncertainties [13]. Group 5: Ensuring Robustness and Security - Ensuring the robustness and security of LLM Agents is crucial for enhancing information quality, preventing exploitation of reward mechanisms, and safeguarding against data contamination and feedback manipulation [16]. Group 6: Scaling Down to Reduce Time and Cost - Introducing memory mechanisms allows agents to skip redundant calculations, leveraging past knowledge to enhance processing speed [18]. - Model compression techniques can significantly reduce computational resources and inference delays without compromising performance [18]. - Optimizing reasoning strategies and infrastructure can further enhance the efficiency and responsiveness of LLM Agents [18]. Group 7: Cost Management - Reducing interaction time by enabling agents to proactively understand user intent can lower cognitive burdens and improve user experience [19]. - Managing operational costs effectively is essential, especially in large-scale deployments, by optimizing context management and controlling inference complexity [19]. - Agentic ROI serves as a framework for evaluating the real usability of LLM Agents, shifting focus from mere model performance to practical benefits and comprehensive efficiency [19].

Core Viewpoint - The "Exploration Plan 2024" aims to integrate culture and technology to promote the digital preservation of cultural heritage, with a focus on the "In Kowloon City, Witness Hong Kong" project, which highlights the historical significance of Kowloon City and its cultural narratives [3][10]. Group 1: Project Overview - The "In Kowloon City, Witness Hong Kong" project is a collaboration between Hong Kong United Publishing Group, Electronic Publishing Co., and Huacui Starlight (Beijing) Intelligent Technology Co., utilizing advanced technologies like large model agents and 3D virtual spaces to recreate the cultural essence of Kowloon City [3][4]. - The project was selected from 81 cultural demand scenarios as one of the six key cultural co-creation scenes under the "Exploration Plan 2024" [4]. Group 2: Technological Innovations - The project team is developing a multimodal knowledge intelligent agent that supports bilingual and trilingual interactions, enhancing user engagement with Kowloon City's historical culture [4]. - An AI interactive narrative game is being designed to create immersive learning experiences, encouraging public interest in Kowloon City's history [4]. - A 3D virtual space of Kowloon City will be constructed to allow users to experience different historical periods and cultural customs [4]. Group 3: Expert Insights and Discussions - Experts from various sectors, including cultural institutions and universities, discussed the importance of technology and culture working together to enhance cultural dissemination and user engagement [11]. - The discussions emphasized the need for a shift from one-way cultural output to a collaborative and shared approach, utilizing gamification and user-generated content to stimulate cultural transmission [11]. - The project aims to create sustainable development models by integrating educational and cultural tourism resources, focusing on local schools and Kowloon City Park as pilot sites [11]. Group 4: Future Events and Exhibitions - The results of the "In Kowloon City, Witness Hong Kong" project will be showcased at the Shenzhen Cultural Expo from May 22 to 26 and at the Hong Kong Book Fair from July 16 to 22 [13].

【编者按】 大模型的快速发展催生了智能体系统（agentic system），为人类打开了语义处理的大门，彻底改变了人类与计算机的交互方式。而以大模型为 关键组件的智能体系统，集成了传统编程语言编写的源代码，使相关研究变得复杂且充满新的挑战。 近年来，微软亚洲研究院首席研发经理杨玉庆及其团队致力于大模型智能体系统的研究与优化。在本文中，杨玉庆将分享他对提升大模型智能体系统效率和 性能的关键方向的见解，以及对其未来广泛应用的展望。 作者 | 杨玉庆， 微软亚洲研究院 首席研发经理 出品丨AI 科技大本营（ID：rgznai100） " 大模型的快速发展，驱动了全新软件系统——智能体系统的诞生。由于其混合架构所带来的高度互联与动态的特性，我们需要关注智能体系统 的整体设计与运行，从不同方向提升效率、可靠性和适应性，充分释放智能体系统的巨大潜力。" —— 杨玉庆 微软亚洲研究院 首席研发经理 大模型的发展已成为推动软件形态巨变的关键因素之一，它正在催生一种全新的软件形态，即目前我们所理解的智能体（AI agent）或智能体系统（agentic system）。 不难想象，智能体系统将会是大模型应用的普遍形式。它的性 ...