证券代码：001225 证券简称：和泰机电 杭州和泰机电股份有限公司 投资者关系活动记录表 编号：2025-002 投资者关系活动 类别 ☑ 特定对象调研 □分析师会议 □媒体采访 □业绩说明会 □新闻发布会 □路演活动 □现场参观 □电话会议 □其他（请文字说明其他活动内容） 参与单位名称及 人员姓名 招商证券 朱艺晴、方嘉敏 时间 2025 年 8 月 27 日 （周三） 上午 10:00-11:00 地点 线上腾讯会议 上市公司接待 人员姓名 董事会秘书 方青女士 董秘办 徐若然女士 投资者关系活动 主要内容介绍 一、公司基本情况介绍 二、交流环节 1、公司 2025 年上半年订单及营业收入情况如何？ 2025 年上半年，公司大力开拓市场，积极拓展产品应用领域，持续 深化多领域、多市场战略，订单同比实现增长，其中第一季度销售订单同 比增长超过 30%。公司上半年整体实现营业收入 12,351.68 万元，同比增 长 0.44%。 2、公司 2025 年上半年毛利率同比下降的原因？ 受物料输送设备行业市场竞争加剧，以及和泰链运智能化工厂投产 后，厂房、设备等折旧增加推升产品成本等因素影响，公司 2025 ...

Core Viewpoint - The article discusses the current state and future directions of LLM (Large Language Model) Agents, emphasizing the need for multi-modal integration and the challenges faced in various application areas, particularly in gaming and simulation [1][14]. Group 1: Types of LLM Agents - The first type is referred to as game-theoretic or MALLM agents, primarily derived from MARL (Multi-Agent Reinforcement Learning) methods, focusing on matrix games and environments like Overcooked [2]. - The second type is game-oriented agents, which can be further divided into text-based environments and traditional games like chess and poker, highlighting the importance of understanding game mechanics [4][5]. - The third type involves embodied intelligence, particularly in robotics, which requires more substantial real-world applications rather than pure simulations [5]. Group 2: Challenges in Development - Key challenges include the creation of effective simulators, ensuring personalized and intelligent responses from models, and managing interactions among potentially millions of agents [8]. - The lack of front-end rendering in some projects is noted as a disadvantage, as compelling demos are crucial for attracting attention and investment [9]. - The article emphasizes that the most commercially viable agents are those used in customer service and retrieval-augmented generation (RAG) applications, which are currently in high demand [9]. Group 3: Specific Applications - Minecraft is highlighted as a competitive area with three main approaches: pure reinforcement learning, pure LLM, and a combination of both, with a caution against entering this saturated market without significant confidence [11][12][13]. - The article concludes that the initial opportunities in the agent field have largely been exhausted, and future endeavors must be strategically planned to leverage existing strengths and commercial support [14].