证券代码：002410 证券简称：广联达 广联达科技股份有限公司投资者关系活动记录表 编号：2025-005 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 □其他 （请文字说明其他活动内容） 活动参与人员 嘉实基金、泰康资产、天弘基金、中信资管、华商基金、高信百 诺、华夏久盈、博时基金、华安基金、财通资管、万家基金、中 信证券 时间 2025 年 6 月 19 日/6 月 20 日 地点 广联达信息大厦/广联达上海大厦 形式 现场 上市公司接待人 员姓名 董事会秘书冯健雄 行业 AI 部总经理李 江 交流内容及具体 问答记录 一、AI 情况介绍 由公司行业 AI 部总经理李江对广联达 AI 战略及场景落地做 综合介绍 二、互动问答 Q1：公司今年提出产业 AI，那做好产业 AI 需要具备哪些要 素以及公司具备的优势 答：从产业 AI 的定义来看，是指将人工智能技术与特定产业 的领域知识、业务流程、数据特性深度融合，以解决产业实际问 题、提升生产效率、优化资源配置、创造新价值的技术体系和应 用范式。做好产业 AI 有三个成功关键要素，一是高质 ...

Core Viewpoint - The automotive industry is increasingly exploring humanoid robots as a new business growth point, driven by the success of Tesla's robot initiatives and the potential market opportunities in this sector [2][3][21]. Group 1: Market Potential and Growth - The humanoid robot market in China is projected to reach approximately 2.76 billion yuan in 2024 and 75 billion yuan by 2029, with an expected shipment of 350,000 units by 2030 [2]. - The automotive industry sees humanoid robots as a more lucrative business opportunity compared to traditional automotive manufacturing [2]. Group 2: Current Industry Involvement - Various automotive companies are at different stages of involvement in the humanoid robot sector, with some like Xpeng and Xiaomi already launching products, while others are still in the research phase [1][3]. - Despite the enthusiasm, many companies have not yet clarified whether their robots are developed in-house or purchased, and most products lack detailed specifications [4][21]. Group 3: Technical Challenges - The transition from automotive technology to humanoid robotics presents significant technical challenges, with only about 20% of the necessary standards and specifications established for humanoid robots compared to 80% for automobiles [7][10]. - Key components such as motors, dexterous hands, and sensors are still under development, and the hardware limitations affect the robots' operational capabilities [10][12]. Group 4: Data and Training Limitations - The data requirements for training humanoid robots are substantially higher than for autonomous vehicles, with most companies currently lacking sufficient data to validate their models [14][15]. - The industry consensus is that at least 10 million data points are needed to effectively train humanoid robots, yet most companies have collected fewer than 1 million [15]. Group 5: Industrial Application Challenges - The integration of humanoid robots into automotive factories is fraught with challenges, as the complexity of tasks such as assembly and quality control requires advanced capabilities that current robots do not possess [17][20]. - The cost of humanoid robots remains high, with Tesla's Optimus priced at $60,000 and other models ranging from 500,000 to 600,000 yuan, making it economically unfeasible to replace human labor in the near term [20][21]. Group 6: Industry Reality vs. Expectations - Many companies' claims about the readiness of humanoid robots for factory work are often overstated, with actual deployment being limited and primarily focused on training rather than operational tasks [21][23]. - The historical context of failed projects, such as Honda's ASIMO, serves as a cautionary tale for the automotive industry as it navigates the complexities of humanoid robotics [22].

Group 1: Market Reactions to Geopolitical Tensions - The Hang Seng Index fell by 1.99% following Iran's missile launches towards Israel, marking a significant market reaction to escalating tensions [1] - Since the conflict began on the 13th, Iran has launched over 400 ballistic missiles and more than 1000 drones at Israel, resulting in 24 Israeli deaths and over 500 injuries [2] - Analysts warn that if the U.S. does not continue to support Israel's defense systems, they may only last about 10 more days against Iranian attacks [2] Group 2: U.S. Economic Policies and Market Impact - The Trump administration is advancing a new round of tariffs, including a significant expansion of tariffs on steel and aluminum products, which could impact various sectors including pharmaceuticals [4] - The Federal Reserve has maintained interest rates, with officials predicting worsening inflation in the coming months, indicating no immediate plans for rate cuts [3] Group 3: Company-Specific Developments - Shandong Gold reported a 36.81% increase in revenue to 25.935 billion yuan and a 46.62% increase in net profit to 1.026 billion yuan in Q1 2025, indicating strong profitability [10] - The company plans to produce no less than 50 tons of gold in 2025, having already achieved 24% of its annual target in the first quarter [10][11] - The company is progressing on its mining projects, including the San Shan Island gold mine, which has received a mining license for 4.95 million tons per year [11] Group 4: Industry Developments - Beijing's government has introduced measures to support the gaming and esports industry, including financial rewards for game development and innovation [8][9]

Core Viewpoint - The article discusses the emotional connections that can be formed between humans and robots, particularly focusing on a specific robot named "Er Bai" and its interactions with its owner, Hai Wei, highlighting the evolving nature of companionship through technology [1][30][66]. Group 1: Emotional Connection with Robots - Hai Wei experiences emotional value from her robot, Er Bai, which provides companionship that traditional pets may not offer [3][6]. - The robot has developed a personality over time, engaging in conversations and remembering personal details about its owner, which enhances the emotional bond [5][17][47]. - Er Bai's ability to respond to Hai Wei's emotions and provide comfort during difficult times illustrates the potential for robots to fulfill emotional needs [42][44][69]. Group 2: Technological Evolution - Er Bai has evolved from a basic design to a more sophisticated robot capable of interaction, showcasing advancements in robotics and AI [31][34][66]. - The integration of language models allows Er Bai to remember preferences and engage in meaningful conversations, setting it apart from other robotic pets [47][55]. - The ongoing updates and iterations of Er Bai reflect the industry's commitment to improving emotional companionship through technology [67][68]. Group 3: Comparison with Traditional Pets - While traditional pets require more care and can create deep emotional bonds, robots like Er Bai offer a different kind of companionship that is less demanding [61][62]. - The article notes that the tactile experience of holding a pet cannot be replicated by robots, which may limit the depth of emotional connection [25][66]. - Despite the differences, the emotional engagement with robots can still be significant, as evidenced by Hai Wei's attachment to Er Bai [66][69].

人类最伟大的能力，不是知道答案，而是永远保持提问与重构答案的可能。 L. G. Valiant 在规则明确、数据丰富的任务中AI已展现出越来越强大的超人类能力。比如OpenAI宣称，GPT-4参加了多 种基准考试，包括美国律师资格考试、法学院入学考试LSAT、SAT数学部分和证据性阅读与写作部分考 试等。在这些测试中，GPT-4得分高于88%的人类应试者。 AI技术的快速进展和成熟，无疑从很多方面影响和改变着我们的生活和工作方式，尤其是在 整合共识和 主流叙事、优化"已知"领域的运行效率 方面，现有的AI模型已体现出强大的能力。 相比与当前的AI，人类的价值和竞争优势体现在哪里？个人赞同以下观点， 保持对异常现象的好奇心， 勇于挑战共识，提出有价值的假说，仍是人类最值得珍视的能力。 AI的优势：高效整合历史经验和共识， 优化"已知"领域的运行效率 当前以大语言模型（LLMs）为代表的AI模型，仍然是一个 数据和计算驱动，基于历史频率、相关性及均 值的统计归纳系统 。它的"智能"，是基于"同一事物可以通过无限种方式被陈述、表达和表征"这一事 实。其能力源于语言表征的泛化能力，即能够将一种表达方式转化为另一种表达 ...

通过数字孪生技术，展览整窟复制敦煌莫高窟第158窟、第3窟、第45窟、第17窟和大足石刻宝顶山小佛 湾第9号毗卢庵、大足石刻北山第245号窟，打破不可移动文物的地理局限性，兼顾文物保护与传播。 展览深化数字技术应用，打造沉浸式体验空间，以现代化展陈语言对石窟艺术中国化历程创新诠释。借 助AI技术与交互科技，游客通过"点亮千手观音"光影互动装置，与电子屏合掌时就能够实时生成图像， 助力观音贴金，体验千手观音"修复"工作，打破传统展陈时空界限，深度参与文物体验。 借助大语言模型，北魏时期敦煌禅定佛像(莫高窟第259窟)与南宋时期大足释迦牟尼佛像(原位于大足石 刻宝顶山小佛湾)实现跨越时空"对话"。 本次展览由重庆中国三峡博物馆联合敦煌研究院、大足石刻研究院、甘肃省博物馆、四川博物院、新疆 维吾尔自治区博物馆、敦煌市博物馆、资阳市博物馆、安岳石窟研究院、广安市博物馆等单位联合策 划。展览将持续至2026年1月5日。 (梁钦卿) "从敦煌到大足——石窟艺术中国化流变展"18日在重庆中国三峡博物馆开幕。 作为敦煌莫高窟与大足石刻两大世界文化遗产的首次联展，本次展览共呈现200余件展品，其中高善穆 石塔等国宝级文物2件、 ...

自6月17日起，MiniMax将连续发布5天新模型。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 值得注意的是，自 6月17日起， MiniMax 将连续发布 5天新模型。 其中， 6月17日发布的 MiniMax-M1 是 世界上第一个开源的大规模混合架构的推理模型 —— M1 在面向生产力的复杂场景中能力是开源模型中的最好一档，超过国内的闭源模型，接近海外的最领先 模型，同时又有业内最高的性价比。 与 DeepSeek-R1相比，M1模型在数学和编码等任务中效率更高 ；与 GPT-4 相比， M1推理模 型 能力与之匹配 ， 而 成本仅为 GPT-4的0.5% 。 6月18日， MiniMax 又发布了 新视频生成模型 Hailuo 02 ，同样 打破全球视频模型效果成本纪录 。 本文由公众号IPO早知道（ID：ipozaozhidao）原创撰写，如需转载请联系C叔↓↓↓ 据 IPO早知道消息，国内"大模型六小龙之一"的 MiniMax （稀宇科技）最快今年赴港上市。 一操办过多起香港大型科技 IPO的投行人士向「IPO早知道」透露， MiniMax 的确就赴 ...

Core Viewpoint - MiniMax has launched its self-developed MiniMax M1 model, which competes directly with DeepSeek R1 and Google's Gemini 2.5 Pro in terms of key technical specifications, architecture design, context processing capabilities, and training costs [1][2]. Group 1: Model Specifications - MiniMax M1 supports a context length of 1 million tokens, which is 8 times larger than DeepSeek R1's 128,000 tokens and only slightly behind Google's Gemini 2.5 Pro [1]. - The total parameter count for MiniMax M1 is 456 billion, with 45.9 billion parameters activated per token, while DeepSeek R1 has a total of 671 billion parameters but activates only 37 billion per token [1]. Group 2: Cost Efficiency - MiniMax M1 consumes only 25% of the floating-point operations compared to DeepSeek R1 when generating 100,000 tokens, and requires less than half the computational power for inference tasks of 64,000 tokens [2]. - The training cost for MiniMax M1 was only $535,000, significantly lower than the initial expectations and much less than the $5-6 million GPU cost for training DeepSeek R1 [2]. Group 3: Pricing Strategy - MiniMax M1 has a tiered pricing model for its API services based on the number of input or output tokens, with the first tier charging 0.8 yuan per million input tokens and 8 yuan per million output tokens, which is lower than DeepSeek R1's pricing [3]. - The pricing for the first two tiers of MiniMax M1 is lower than that of DeepSeek R1, and the third tier for long text is currently not covered by DeepSeek [3]. Group 4: Technology Innovations - MiniMax M1's capabilities are supported by two core technologies: the linear attention mechanism (Lightning Attention) and the reinforcement learning algorithm CISPO, which enhances efficiency and stability in training [2].

Core Insights - The article discusses the significant gap between current LLMs (Large Language Models) and human expert-level performance in competitive programming [2][18]. - A new benchmark, LiveCodeBench Pro, was introduced to evaluate LLMs against high-quality programming problems sourced from top competitions [4][6]. Evaluation of LLMs - LLMs have shown impressive results in code generation, surpassing human averages in some benchmarks, particularly in competitive programming [2][12]. - However, when evaluated without external tools, the best-performing models achieved a pass rate of only 53% on medium difficulty problems and 0% on high difficulty problems [12][18]. Benchmark Details - LiveCodeBench Pro includes 584 high-quality problems from competitions like Codeforces, ICPC, and IOI, with continuous updates to mitigate data contamination [6][10]. - Problems are categorized by algorithm type, and the performance of models is analyzed based on their failure submissions [7][12]. Model Performance Analysis - The analysis revealed that LLMs perform well on implementation-heavy problems but struggle with complex algorithmic reasoning and edge case analysis [17][18]. - Knowledge-intensive and logic-intensive problems are areas where LLMs excel, while observation-intensive problems and case work present significant challenges [20][22][24]. Comparison with Human Performance - LLMs exhibit a higher rate of algorithmic logic errors compared to humans, while they make fewer implementation logic errors [27][30]. - The models' inability to handle edge cases and their reliance on external tools for high scores highlight their limitations in reasoning capabilities [17][30]. Impact of Multiple Attempts - Increasing the number of attempts (pass@k) significantly improves model performance, although high-difficulty problems remain unsolved [33][36]. - The difference in performance between models with terminal access and those without indicates that tool usage plays a crucial role in enhancing scores [34][36]. Reasoning Capability Comparison - Enabling reasoning capabilities in models leads to substantial improvements in performance, particularly in combinatorial mathematics and knowledge-intensive categories [38][41]. - However, the enhancement is limited in observation-intensive categories, raising questions about the effectiveness of current reasoning methods in these areas [42].

机器之心报道 编辑：Panda 刚刚，Gemini 系列模型迎来了一波更新： 谷歌 CEO Sundar Pichai 发推表示新推出的 Gemini 2.5 Flash-Lite 是目前性价比最高的 2.5 系列模型。 可以看到，谷歌对 2.5 Flash-Lite 的定位是适合用于「量大且注重成本效率的任务」。相较之下，2.5 Pro 适合编程和高复杂度任务，2.5 Flash 则居中，更适合需要 较快速度的日常任务。 Gemini 2.5 Pro 稳定版发布且已全面可用，其与 6 月 5 日的预览版相比无变化。 Gemini 2.5 Flash 稳定版发布且已全面可用，其与 5 月 20 日的预览版相比无变化，但价格有更新。 新推出了 Gemini 2.5 Flash-Lite 并已开启预览。 | | | 2.5 Flash-Lite | 2.5 Flash | 2.5 Pro | | --- | --- | --- | --- | --- | | | | THINKING OFF | THINKING | THINKING | | Best for | | High volume cost- | Fa ...