目标驱动导航，赋予机器人自主完成导航目标 具身导航作为具身智能的核心领域，涉及语言理解、环境感知、路径规划三大技术支柱。目标驱动导航（Goal-Oriented Navigation）通过赋予机器人自主决策能 力，是具身导航中最具代表性的方向。 目标驱动导航要求智能体在陌生的三维环境中，仅凭目标描述（如坐标、图片、自然语言）等，即可自主完成环境探索与 路径规划。 与传统视觉语言导航（VLN）依赖显式指令不同，目标驱动导航系统需要实现从"听懂指令走对路"到"看懂世界自己找路"的跃迁：当人类下达"去厨房拿可乐"的指 令时，机器人需自主完成语义解析（识别厨房空间特征与可乐视觉属性）、环境建模（构建家居场景的空间拓扑）以及动态决策（避开移动的人类或宠物），这 背后凝聚着计算机视觉、强化学习与3D语义理解的交叉突破。 目标驱动导航技术已在多个垂直领域实现产业化落地。在终端配送场景中，该技术与社交导航算法结合，使机器人具备应对动态环境和人际交互的能力：美团无 人配送车通过动态路径重规划在复杂城市环境中执行递送任务，Starship Technologies的园区配送机器人已在欧美高校和社区部署。在医疗、酒店及餐饮场景，嘉 ...

Core Viewpoint - The article highlights the intense competition in the AI arms race among traditional tech giants and emerging AI companies, with Meta's aggressive talent acquisition reflecting the urgency of the situation [1][2]. Group 1: Apple - Apple has faced significant setbacks in its AI initiatives, particularly with the Apple Intelligence project, and while it maintains hardware advantages, it needs deeper AI collaborations [4][5]. - The company’s core business remains unaffected by AI threats, as AI applications still rely on Apple devices for access [4]. - Apple should focus on building the best hardware for the AI era and invest in robotics and home automation to maintain its competitive edge [5]. Group 2: Google - Google has a leading position in AI infrastructure, with its Gemini model excelling in media creation, but its core search business faces disruptive threats from conversational AI [6][7]. - The company benefits from vast data resources and distribution channels, particularly through its Android system, which could challenge Apple's dominance in the high-end market [7]. - Google is working to transform AI from a disruptive technology into an enhancement tool for its search capabilities [7]. Group 3: Meta - Meta's strategic positioning is solid, focusing on personalized content and generative advertising, but it faces execution challenges and risks from attention resource competition [8]. - The urgency of Meta's talent recruitment indicates a recognition of significant threats to its core business from AI developments [8]. Group 4: Microsoft - Microsoft remains in a strong position but faces new challenges due to increasing tensions with OpenAI regarding profit-sharing and future collaborations [9][10]. - The company should prioritize maintaining its exclusive access to OpenAI's API through Azure while exploring partnerships with other model providers [10]. Group 5: Amazon - Amazon's outlook has improved, as AI is expected to benefit its business rather than disrupt it, particularly through AWS and product recommendations on Amazon.com [11][12]. - The partnership with Anthropic appears more stable compared to Microsoft's relationship with OpenAI, providing Amazon with a strategic advantage [12]. Group 6: Emerging AI Companies - OpenAI has established dominance in consumer AI, but faces conflicts with companies like Microsoft and Apple over customer relationships [13][14]. - Anthropic has built a strong position among developers, focusing on API revenue streams and maintaining a stable partnership with AWS [14]. - xAI is struggling with its infrastructure strategy and should seek investments to enhance its market position [15].

编辑：张澍楠 广告等商务合作，请点击这里 就发展中国家如何找到适合自身的发展道路相关问题，与会专家给出了自己的看法。约翰斯·霍普金斯 大学艾尔弗雷德·钱德勒政治经济学讲席教授洪源远(Yuen Yuen Ang)认为，对发展中国家来说，工业化 是当务之急，没有任何国家能在不进行工业化的情况下变得富裕。她同时指出，许多发展中国家当前面 临的最大问题是，有了药方，却难以付诸实践，特别是对越南等国来说，当下全球化大门半开半闭，这 让他们无法完全效仿中国的发展路径，必须转而采用未经实践检验过的方法。 香港交易所主席唐家成则称，工业化的未来集中在创新技术上，中国在该领域进行了大量投资。他举例 说，美国在大语言模型领域投入了大量资金，这让外界一度认为，大量的资金投入是研究大语言模型的 前提。不过，中国大语言模型DeepSeek的出现证明，并不需要数百万美元就可以有针对性地研究构建 大语言模型，这对中小型国家来说是一个绝佳的机会。 安哥拉联合电信公司(Unitel)主席阿吉纳尔多·哈伊梅(Aguinaldo Jaime)以本国为例说，技术来源多元化是 未来所在，还要把学生和工人送到其他国家学习，这样才能更好地运用外来技术。同 ...

Core Insights - The 2025 China International Financial Expo successfully took place in Shanghai, focusing on "Open Innovation, Technology Empowerment, and Co-creating a New Future for Finance" with over 400 financial institutions, technology companies, and industry organizations participating [1] Group 1: Company Innovations - Zandong Technology showcased a multi-purpose business light terminal solution driven by advanced large language models, which allows seamless switching between tablet and terminal modes, revolutionizing traditional service models in financial institutions [1] - The introduction of the Zandong Intelligent Banking Solution enhances operational efficiency and intelligence by allowing various business processes to be autonomously connected and assisted by large language models, positioning AI as a "co-pilot" for professionals [1] - Zandong Technology also presented a mobile banking product based on HarmonyOS 5.0, integrating native capabilities for a personalized and intuitive user experience, enabling users to complete business transactions efficiently through simple verbal requests [3] Group 2: Industry Reception and Future Plans - The innovative achievements of Zandong Technology received high praise from industry peers, with many attendees expressing strong interest in the products and experiencing the convenience and efficiency they offer [4] - Zandong Technology aims to continue leading industry trends and collaborate with more partners to advance the fintech sector, focusing on providing safer, more convenient, and intelligent financial services [4] - The company is committed to its development philosophy of "Persistence, Innovation, Trust, and Respect," planning to launch more financial technology products with independent intellectual property rights and core competitiveness [4]

Core Insights - ByteDance recently disclosed a serious violation involving senior members of the Seed team, resulting in the dismissal of the head of the Seed large language model, Qiao Mu [1] - The violation involved an inappropriate personal relationship between Qiao Mu and an HRBP, which breached the company's conflict of interest policy [1] - Qiao Mu's total earnings at ByteDance over 11 years are estimated to exceed 500 million RMB, with significant income from stock options [2] Group 1 - The violation included failure to declare a personal relationship that violated company policy regarding conflicts of interest [1] - Qiao Mu and the HRBP provided false statements during the investigation, leading to severe disciplinary actions including termination and forfeiture of year-end bonuses [1] - Qiao Mu's estimated annual salary is over 10 million RMB, based on industry comparisons [1][2] Group 2 - The company's stock options have significantly appreciated, with the repurchase price rising from approximately 5 USD per share in 2014 to 189 USD, a 38-fold increase [2] - If Qiao Mu's compensation included 1 million RMB in cash and 1 million RMB in options, the value of the options would have surged to about 39 million RMB today [2] - The Seed team has recently released the Seed1.5-VL model, which demonstrates advanced multimodal understanding and reasoning capabilities [3]

Seed 某前员工（即乔木）与支持其团队的某前 HRBP 存在亲密关系，属于利益冲突的禁止场景（如存 在上下级关系、拥有共同直属上级、一方是另一方的 HRBP 等情形）。二人均未进行利益冲突申报并在 接受调查过程中多次作虚假陈述，公司已将二人辞退，并扣罚全部年终奖。 Seed 是字节跳动豆包大模型团队名称，乔木作为负责人在字节跳动内部拥有较高职级，曾是直接向字节跳动 CEO 梁汝波汇报的核心团队成员之一。 早在今年 3 月 27 日，网上传出乔木的妻子罗某在网上实名举报丈夫婚内出轨同部门 HRBP 程某，晒出亲密 消费记录、聊天录音及财产隐瞒证据，进而引发全网热议。详情请看这篇旧文：《 婚内出轨 | 字节技术大佬 乔某，他身价大概是多少？ 》。 2025 年 6 月 23 日，字节跳动发布新一期廉政通报，Seed 大语言模型负责人乔木被公司辞退。 - EOF - 推荐阅读 点击标题可跳转 1、 中国工程师携硬盘海外训练 AI，这波神操作引全球关注，外交部正式回应 2、 10 句话让 Cursor 的编程水平提... 3、 41 岁程序员连续 4 年住车里，被质疑占用公共资源。网友一边倒 据网友称，辞退乔木这事 ...

编辑丨王多鱼 排版丨水成文 新冠大流行期间，一份实时更新 的" 全球新冠疫情数据可视化地图 "火遍全球，该疫情地图 通过结合自动化数据采集与人工审核机制，成为全球多个国家政府、 媒体引用最广泛的疫情追踪系统之一，单日访问量一度高达 20 亿。 这一地图的开发者是 约翰·霍普金斯大学的两位中国留学生 —— 董恩盛 、 杜鸿儒 。 近日 ， 杜鸿儒 作为第一作者，在 Nature 子刊 Nature Computational Science 上发表了题为 ： Advancing real-time infectious disease forecasting using large language models 的研究论文。 该研究开发了一款 多模态大型语言模型—— PandemicLLM ，通过融合多模态信息 （包括 文本形式的公共卫生政策以及基因组监测、空间和流行病学时间序列 数据 ） ，采用 人工智能与人类协作的提示词设计，来实时预测疾病传播。研究团队将该模型应用于美国的 COVID-19 疫情，预测性能显著优于现有模型。 该研究让 大语言模型 （LLM） 化身 疫情预报员 ，成功突破传统模型瓶颈， 不 ...

Core Viewpoint - The article emphasizes the need for not just "talkative" large language models (LLMs) but also "explainable" ones, highlighting the emergence of Sparse Autoencoder (SAE) as a leading method for mechanistic interpretability in understanding LLMs [2][10]. Group 1: Introduction to Sparse Autoencoder (SAE) - SAE is a technique that helps interpret the internal mechanisms of LLMs by decomposing high-dimensional representations into sparse, semantically meaningful features [7][10]. - The activation of specific features by SAE allows for insights into the model's "thought process," enabling a better understanding of how LLMs process information [8][10]. Group 2: Technical Framework of SAEs - The technical framework of SAE includes an encoder that decomposes LLM's high-dimensional vectors into sparse feature vectors, and a decoder that attempts to reconstruct the original LLM information [14]. - Various architectural variants and improvement strategies of SAE are discussed, such as Gated SAE and TopK SAE, which address specific challenges like shrinkage bias [15]. Group 3: Explainability Analysis of SAEs - SAE facilitates concept discovery by automatically mining semantically meaningful features from the model, enabling better understanding of aspects like temporal awareness and emotional inclination [16]. - It allows for model steering by activating or suppressing specific features to guide model outputs, and aids in anomaly detection to identify potential biases or safety risks [16]. Group 4: Evaluation Metrics and Methods - Evaluation of SAE involves both structural assessment (e.g., reconstruction accuracy and sparsity) and functional assessment (e.g., understanding LLM and feature stability) [18]. Group 5: Applications in Large Language Models - SAE is applied in various practical scenarios, including model manipulation, behavior analysis, hallucination control, and emotional steering, showcasing its versatility [19]. Group 6: Comparison with Probing Methods - The article compares SAE with traditional probing methods, highlighting SAE's unique potential in model manipulation and feature extraction, while acknowledging its limitations in complex scenarios [20]. Group 7: Current Research Challenges and Future Directions - Despite its promise, SAE faces challenges such as unstable semantic explanations and high training costs, with future breakthroughs anticipated in cross-modal expansion and automated explanation generation [21]. Conclusion - The article concludes that future explainable AI systems should not only visualize model behavior but also provide structured understanding and operational capabilities, with SAE offering a promising pathway [23].

Core Insights - The article discusses the challenges and innovations in formalizing mathematical proofs, particularly focusing on inequality problems and the limitations of current large language models (LLMs) in providing rigorous reasoning [1][27][38]. Group 1: Inequality Proofs and Formalization - Inequality problems serve as ideal subjects for testing the rigor of mathematical reasoning due to their clear structure and logical simplicity [1]. - Current formal systems like Lean and Coq require high precision in expression, making them difficult to apply at scale, especially for middle and high school level problems [1][5]. - A new approach proposed by research teams from Stanford, UC Berkeley, and MIT involves breaking down inequality proof tasks into two non-formal but verifiable sub-tasks: Bound Estimation and Relation Prediction [2][7]. Group 2: IneqMath Dataset - The IneqMath dataset is the first benchmark for Olympic-level inequality proofs, consisting of 1,252 training problems, 200 test problems, and 100 validation problems [12]. - The training set includes 83 theorem types and 29 theorem categories, allowing for model fine-tuning [12][13]. - Each problem in the dataset has a unique correct answer, facilitating the verification of results [10]. Group 3: Evaluation Framework - The research team developed a framework called LLM-as-Judge, which includes five automated reviewers to assess the logical rigor of the reasoning process in LLMs [20][23]. - The framework evaluates whether models merely guessed the correct answer or followed a logical reasoning chain at each step [23][24]. - The evaluation system has shown high alignment with human annotations, achieving an F1 score of 0.93, indicating its reliability and scalability [24]. Group 4: Findings on LLM Performance - The study found that while LLMs like GPT-4 and others can guess answers accurately, they often fail to maintain logical rigor in their reasoning processes [27][30]. - The accuracy of final answers can be high, but the overall reasoning correctness remains low, with some models dropping from 71.5% to 6% when evaluated for logical rigor [29]. - Increasing model size or reasoning time does not significantly improve the quality of reasoning, suggesting that simply scaling models is insufficient for enhancing logical closure [30][32]. Group 5: Improvement Strategies - The research identified effective strategies for improving LLM performance, such as self-improvement via critic and theorem augmentation, which have shown to enhance accuracy by approximately 5% and 10% respectively [42]. - The IneqMath leaderboard encourages community participation, allowing researchers to submit their models for evaluation based on both final answer accuracy and reasoning rigor [36][37].

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 ...