证券日报网讯 天源迪科11月25日在互动平台回答投资者提问时表示，公司结合行业痛点，携手行业客 户，致力于智能化场景应用。目前，公司有自己的大模型智能体平台，运用多智能体协同技术，打造出 自适应、自主决策、智能交互的行业级解决方案，已应用在运营商、金融等行业；在采购供应链智能化 方面，与众多央国企客户共创智能化应用和解决方案，持续升级核心产品。 （编辑 袁冠琳） ...

Core Viewpoint - The company has developed a comprehensive intelligent product matrix centered around the "Jingbeifang Large Model & Intelligent Agent Series," focusing on four key scenarios: intelligent operation and maintenance, intelligent report generation, intelligent anti-fraud, and intelligent compliance [1] Group 1: Product Development - The company has built a large model intelligent agent platform that is compatible with hardware from Baidu Kunlun and Muxi, and integrates with foundational large models such as Alibaba Tongyi Qianwen, DeepSeek, and Wenxin Yiyan [1] - The products support modular embedding into business systems, leveraging natural language processing, time series analysis, and semantic understanding technologies to enhance business automation and intelligence [1] Group 2: Market Application - The intelligent products have already been implemented in various customer scenarios, demonstrating their effectiveness in meeting the digital transformation needs of industries [1] - The company provides private deployment and model optimization services in a fully domestic environment, addressing the core demands of the Xinchuang (信创) scenario [1]

Core Insights - The release of "Renhe Model 2.0" signifies a major leap for the company from pilot applications of AI technology to a comprehensive promotion of "governance productivity" in smart city management [1][6] - The event highlighted the integration of AI agents in various government sectors, showcasing the practical applications of large model technology in urban governance [1][2] Group 1: Product and Technology Development - "Renhe Model 2.0" utilizes a "data-algorithm-application" matrix to transform business operations from "mouse operations" to "natural language interactions," addressing common pain points in government data applications [2][4] - The technology framework supports rapid deployment, allowing for industry model training within 15 days and application launch within 5 days, enhancing operational efficiency [4] Group 2: Industry Applications - The intelligent agents have been implemented in cities like Beijing, Shenzhen, and Chongqing, effectively converting creative potential into tangible governance productivity [3] - Specific applications include efficient regulation of construction waste, a closed-loop supervision model for administrative law enforcement, and proactive safety systems for urban infrastructure [2][3] Group 3: Collaborative Solutions - The company announced four joint solutions with industry leaders like Huawei and Baidu, marking a new phase in smart city ecosystem collaboration [5][6] - These solutions aim to enhance urban governance capabilities, including a comprehensive monitoring system for low-altitude environments and customizable digital government employees [5][6] Group 4: Strategic Implications - The launch of "Renhe Model 2.0" represents a pivotal moment in the company's evolution, integrating extensive industry knowledge with cutting-edge AI technology to drive business model innovation [6] - The core product, the government AI agent, is expected to provide stable subscription revenue, enhancing the company's profitability and market position in the smart city sector [6]

Core Insights - The article discusses the development of LiveTradeBench, a platform designed to evaluate large language models (LLMs) in real-time trading scenarios, marking a shift from static assessments to dynamic decision-making in financial markets [3][11][34] Group 1: Introduction to LiveTradeBench - LiveTradeBench is initiated by a research team from the University of Illinois Urbana-Champaign, focusing on assessing LLMs' capabilities in real-world trading environments [2] - The platform aims to test LLMs' perception, reasoning, and decision-making abilities through real market dynamics, moving beyond traditional static benchmarks [3][8] Group 2: Key Innovations - LiveTradeBench introduces three core innovations: continuous decision-making, portfolio management, and live trading evaluation, which differentiate it from previous benchmarks [12] - The platform connects directly to real-time stock and prediction market data, eliminating information leakage and allowing for genuine market interaction [15] Group 3: Investment Decision Modeling - The investment decision-making process in LiveTradeBench is modeled as a partially observable Markov decision process (POMDP), requiring LLMs to infer and act based on limited information [19] - The model receives observations that include position information, market prices, and news, enabling it to make informed asset allocation decisions [20][21] Group 4: Performance Evaluation - A 50-day real-world test was conducted on 21 mainstream LLMs, revealing that static reasoning does not equate to effective dynamic decision-making in complex market environments [30] - The results indicated a lack of correlation between high scores in static assessments and actual market performance, highlighting the need for a redefinition of "intelligence" in LLMs [31] Group 5: Future Directions - LiveTradeBench opens new dimensions for evaluating intelligent agents, emphasizing the importance of environmental feedback and continuous decision-making in future AI developments [34] - The platform encourages further research and collaboration in the field of large model agents, inviting students and researchers to engage with ongoing projects [36]

Core Viewpoint - Digital Zhengtong (300075.SZ) is set to hold a conference on October 30, 2025, focusing on the launch of the "Renhe Large Model 2.0" industry AI agent, emphasizing the integration of large model technology in smart city governance [1] Group 1: AI Agent Capabilities - The "Renhe Large Model 2.0" industry AI agent includes three foundational AI agents: Intelligent Query Agent, Intelligent Report Agent, and Wukong Brain Agent [1] - The Intelligent Query Agent leverages over 20 years of management experience from more than 5,000 projects, integrating production data and enhancing statistical results through a comprehensive indicator and rule library [1] - The Intelligent Report Agent transforms data into deep insights and decision-making value, creating a knowledge base that covers data indicators, root cause analysis, and management recommendations [2] Group 2: Urban Governance Applications - The Wukong Brain Agent consolidates experience from over 300 city brain projects, enabling intelligent control of urban management through natural language interaction and data insights [2] - The "Renhe Large Model 2.0" industry AI agent drives urban governance from reactive measures to proactive warnings, enhancing efficiency in various sectors such as construction waste management and administrative law enforcement [2] - The initiative aims to develop a proactive protection system for urban lifelines, enabling efficient hazard detection and pipeline service life warnings [2]

Core Insights - The 2025 Cloud Habitat Conference will be held from September 24 to 26, showcasing AI software products, new models, and infrastructure hardware, with a focus on "cloud intelligence and carbon-silicon symbiosis" [1][2] - Alibaba Cloud will present capabilities in large-scale clusters, distributed training, and inference acceleration, including a high-density AI server supporting 144 computing nodes [1][2] - Over a hundred companies will demonstrate AI applications, with more than 200 Agent applications and 300 AI terminal products on display [1] Software Sector Developments - Companies in the Alibaba Cloud ecosystem are actively developing intelligent agent solutions across various sectors, including finance, government, and healthcare [2][3] - Tianfeng Securities reports that Alibaba Cloud has partnered with 18 companies to release multiple intelligent agents covering areas such as credit risk control and intelligent customer service [3] - New Zhi Software has launched various industry-specific robots, focusing on applications in finance, legal services, and automotive sectors [3][4] Hardware Sector Progress - Alibaba's capital expenditure on AI and cloud infrastructure reached 38.6 billion yuan, a 220% year-on-year increase, indicating significant investment in AI capabilities [6] - Companies in the hardware ecosystem are advancing customer onboarding and product delivery, with Chipone collaborating with RISC-V leaders to provide customized chip services [6][7] - Data center developments are ongoing, with companies like Data Port and Runze Technology expanding their infrastructure to meet national data center layout strategies [7]

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].

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].