来源：市场资讯 （来源：美股情报站） 近期，野村分析师发声力挺阿里巴巴，维持其"买入"评级，并将目标价从170美元上调至215美元。虽然 分析师对其盈利预测下调了4.7%，主要归因于对大型语言模型的投资增加会导致部分业务部门亏损扩 大，但阿里云的估值提升为目标价上调提供了支持。野村对阿里未来扩展大模型用户基础的战略持积极 态度，尽管短期内货币化效果尚未明显。 此外，中金公司也对阿里巴巴进行了重新评估，虽然下调了收入预测，但依旧认为阿里有超过11%的股 价上涨空间，并维持"跑赢行业"的评级。由于闪购及其他业务亏损的扩大，中金对非通用准则下的净利 润预测进行了调整。估值方面，中金采用了SOTP估值法，给予电商业务和云业务不同的估值倍数，显 示出其对阿里未来增长的信心。 与此同时，阿里巴巴在技术优化领域的战略布局也不断推进。阿里的SEO团队，通过对旗下平台的搜索 引擎优化，提高了自然流量的获取能力。这一优化不止步于传统站内方法，还扩展到全域SEO，以更好 地适应搜索引擎算法和用户行为的变化。这样的技术推动不仅降低了获客成本，同时也加强了用户体 验，显示出阿里在流量管理中的深谋远虑。 从投资角度来看，阿里巴巴的财务和 ...

本文共同第一作者为李鸿宇（布朗大学博士生）和孙凌峰（Robotics and AI Institute 研究员，博士毕业于加州大学伯克利分校）。通讯作者付佳慧在 Robotics and AI Institute 任研究员，博士毕业于麻省理工学院。George Konidaris 为布朗大学副教授。 构建能够在新环境中、无需任何针对性训练就能执行多样化任务的通用机器人，是机器人学领域一个长期追逐的圣杯。近年来，随着大型语言模型（LLMs）和视 觉语言模型（VLMs）的飞速发展，许多研究者将希望寄托于视觉 - 语言 - 动作（VLA）模型，期望它们能复刻 LLM 和 VLM 在泛化性上取得的辉煌。然而，理想 很丰满，现实却很骨感。VLA 模型的端到端训练范式，要求海量与特定机器人相关的 "视觉 - 语言 - 动作" 数据。与 LLM 和 VLM 可以轻易获取的网络规模数据不 同，机器人数据的采集成本极高、难度极大，这形成了一个巨大的 "数据瓶颈"。有没有可能绕过这个瓶颈，让机器人不依赖于昂贵的 "亲身经历" 数据，也能学会 新技能呢？ 最近，来自布朗大学（Brown University）和机器人与人工智能研究 ...

IBM(IBM.US)盘中涨4%，报300.79美元创历史新高。消息上，IBM Corp宣布与Anthropic达成深度合作，将后者大型语言模型Claude系列集成至精选内部及 外部开发工具与企业产品中，旨在为IBM客户提升生产力。此外，IBM计划通过即将推出的watsonx Assistant for Z将功能扩展至大型机，专用Z代理将通过理 解对话上下文与自动化流程，在保障安全合规的前提下推动系统管理从被动故障排除向主动模式转型。 ...

机器之心报道 编辑：Panda 对于大型语言模型而言，生成更长、更复杂的推理链，往往意味着巨大的计算成本。为了解决这一难题，田渊栋团队在 2024 年提出的「连续 思维链」 (Coconut) 提供了一种全新的范式，它将推理轨迹保留在连续的隐空间中，而非离散的文字符号。现在，他们与 Stuart Russell 团队的 最新合作研究则从理论上回答了一个核心问题：这种高效的推理范式是如何在训练中自发产生的？答案指向了一种关键机制——叠加的涌现 大型语言模型（LLM）在许多复杂任务上展现出了强大的推理能力，尤其是在引入思维链（CoT）之后。然而，长思维链在复杂任务中的推理成本极高，因此，近 期有不少研究在尝试寻找更高效的测试时扩展方法，以期望更高效地提升模型的推理能力。 一种前景较为可观的方法是田渊栋团队在 2024 年提出的「 连续思维链 」（Chain-of-Continuous-Thought，简称 Coconut）。与传统的 CoT 不同，连续思维链是将模 型的推理轨迹保存在连续隐空间中，而非回投到离散的 token 空间。这种做法不仅在理论上具有多项优势，在实验中也带来了显著性能提升。参阅我们之前的报道 ...

Core Viewpoint - The semiconductor storage industry is expected to experience steady growth driven by the maturation of generative AI and large language models, alongside sustained demand for core hardware, potentially leading to a price and volume increase from 2025 onwards, maintaining a rating of outperforming the market [1][2]. Group 1: Industry Trends - The NAND price sentiment is rising due to enterprise-level stocking and new smartphone demands, with significant capital expenditures from domestic internet companies, such as Alibaba's investment of 38.6 billion yuan in AI and cloud infrastructure in Q2 2025, and Tencent's capital expenditure doubling to 19.107 billion yuan in the same period [3]. - The DRAM market is experiencing a significant price increase due to the EOL notifications from manufacturers, with expectations of a 20%-50% quarter-on-quarter price rise in Q4 2025, following a 70% increase in contract prices for Nanya Technology in Q3 2025 [4]. Group 2: Market Dynamics - The NOR Flash market is expected to see a healthy supply-demand balance, with price increases projected to reach double-digit percentages in Q4 2025, driven by rising AI data center demands and a recovering automotive market [5]. - The niche DRAM market is facing a supply shortage as major overseas manufacturers exit, leading to price increases, with expectations of continued price hikes throughout the year [5]. Group 3: Investment Recommendations - Companies to focus on include: for niche storage - Zhaoyi Innovation, Puran, Juchen, and Dongxin; for module manufacturers - Kaipu Cloud, Jiangbolong, Demingli, Baiwei Storage, and Shannon Chip Creation; for storage supporting chips - Lanke Technology and Lianyun Technology [6].

Group 1: Semiconductor Storage Industry - The semiconductor storage industry is steadily rising due to the maturation of business models related to generative AI and large language models, along with sustained demand for core hardware, potentially leading to simultaneous price and volume increases [2][5] - Major domestic internet companies are significantly increasing capital expenditures for AI investments, with Alibaba's capital expenditure reaching 38.6 billion yuan in Q2 2025, and Tencent's capital expenditure doubling to 191.07 billion yuan in the same period [5] - The NAND flash market is expected to see a price increase, particularly in enterprise-level and mobile markets, with a projected single-digit percentage increase in enterprise storage prices in Q4 2025 [5] Group 2: DRAM Market - The DRAM market is experiencing significant price increases due to the discontinuation of older process DRAM products, with prices for DDR4 and LPDDR4X expected to rise by 20%-50% quarter-on-quarter in Q4 2025 [6] - Notable price increases have been reported, with Nanya Technology's contract price rising by 70% in Q3 2025 and expected to increase by another 50% in Q4 2025 [6] Group 3: Agricultural Chemicals - Lier Chemical - Lier Chemical reported a total revenue of 4.507 billion yuan in H1 2025, a year-on-year increase of 35.36%, with net profit rising by 191.21% to 271 million yuan [9][10] - The company plans to distribute a cash dividend of 2 yuan per 10 shares, corresponding to a dividend payout ratio of 59.17% for the first half of the year [9] - The agricultural chemicals sector remains at a low overall market sentiment, but some product prices are beginning to recover, leading to improved performance for Lier Chemical [10]

Investment Rating - The industry investment rating is "Outperform the Market," indicating that the semiconductor storage industry is expected to perform better than the benchmark index over the next 6-12 months [1][34]. Core Insights - The semiconductor storage industry is experiencing steady growth driven by the maturation of business models related to generative AI and large language models, alongside sustained demand for core hardware. This demand is likely to lead to a simultaneous increase in both price and volume [1]. - The NAND market is expected to see a price increase due to rising demand from enterprise-level storage and mobile devices, with projections indicating a modest price rise in Q4 2025 [7][14]. - The DRAM market is anticipated to experience significant price increases, with quarterly growth rates projected between 20% to 50% in Q4 2025, driven by supply constraints and increased demand [15][18]. - The niche storage market is witnessing price increases due to structural shortages, with NOR Flash and niche DRAM products expected to see price adjustments in the coming quarters [20][24]. Summary by Sections Industry Overview - The semiconductor storage industry is on an upward trajectory, supported by increased capital expenditures from major internet companies focusing on AI and cloud infrastructure [10][13]. - Major players like Alibaba, Baidu, and Tencent are significantly increasing their capital expenditures, which is expected to drive demand for storage solutions [10][13]. Market Trends - The NAND flash market is currently facing downward price adjustments but is expected to rebound with a price increase in Q4 2025, particularly in enterprise and mobile sectors [7][14]. - The DRAM market is experiencing a shift due to the discontinuation of older process technologies, leading to substantial price increases for DDR4 and LPDDR4X products [15][18]. Investment Recommendations - Recommended companies to watch include: - Niche Storage: Zhaoyi Innovation, Puran, Jucheng, Dongxin - Module Manufacturers: Kaipu Cloud, Jiangbo Long, Deming Li, Baiwei Storage, Shannon Chip Creation - Storage Supporting Chips: Lanke Technology, Lianyun Technology [3][28].

Core Viewpoint - Meta is negotiating with several media companies to obtain content licenses for its AI product development, indicating a strategic shift towards integrating news content into its AI-driven offerings [1][2] Group 1: Negotiations and Partnerships - Meta has engaged in discussions with media entities such as Axel Springer, Fox Corporation, and News Corp to secure article licenses for its AI products [1] - The negotiations are still in preliminary stages, and there is no guarantee that new agreements will be reached [1] - Meta's past collaborations with publishers have been mixed, having previously invested millions in partnerships but ceased payments for content in 2022 [1] Group 2: Impact on Publishers - Many publishers have experienced a significant decline in traffic from Facebook due to Meta deprioritizing news content on its platform [2] - Recently, some publishers have reported a resurgence in traffic from Facebook, suggesting a potential recovery [2] - Publishers are taking measures to restrict unpaid AI crawlers from accessing their websites, reflecting the ongoing tension between tech companies and the publishing industry [2] Group 3: Competitive Landscape - Meta's competitors, such as OpenAI and Amazon, have already established content licensing agreements with various publishers, highlighting a competitive race in the AI content space [2] - OpenAI, supported by Microsoft, has signed licensing agreements with News Corp, Axel Springer, and Dotdash Meredith, while Amazon has partnered with The New York Times [2]

Core Viewpoint - The article discusses the evolution of Bird's Eye View (BEV) perception as a foundational technology for autonomous driving, highlighting its importance in ensuring safety and reliability in complex driving environments [2][4]. Group 1: Essence of BEV Perception - BEV perception is an efficient spatial representation paradigm that projects heterogeneous data from various sensors (like cameras, LiDAR, and radar) into a unified BEV coordinate system, facilitating a consistent structured spatial semantic map [6][12]. - This top-down view significantly reduces the complexity of multi-view and multi-modal data fusion, aiding in the accurate perception and understanding of spatial relationships between objects [6][12]. Group 2: Importance of BEV Perception - With a unified and interpretable spatial representation, BEV perception serves as an ideal foundation for multi-modal fusion and multi-agent collaborative perception in autonomous driving [8][12]. - The integration of heterogeneous sensor data into a common BEV plane allows for seamless alignment and integration, enhancing the efficiency of information sharing between vehicles and infrastructure [8][12]. Group 3: Implementation of BEV Perception - The evolution of safety-oriented BEV perception (SafeBEV) is categorized into three main stages: SafeBEV 1.0 (single-modal vehicle perception), SafeBEV 2.0 (multi-modal vehicle perception), and SafeBEV 3.0 (multi-agent collaborative perception) [12][17]. - Each stage represents advancements in technology and features, addressing the increasing complexity of dynamic traffic scenarios [12][17]. Group 4: SafeBEV 1.0 - Single-Modal Vehicle Perception - This stage utilizes a single sensor (like a camera or LiDAR) for BEV scene understanding, with methods evolving from homography transformations to data-driven BEV modeling [13][19]. - The performance of camera-based methods is sensitive to lighting changes and occlusions, while LiDAR methods face challenges with point cloud sparsity and performance degradation in adverse weather [19][41]. Group 5: SafeBEV 2.0 - Multi-Modal Vehicle Perception - Multi-modal BEV perception integrates data from cameras, LiDAR, and radar to enhance performance and robustness in challenging conditions [42][45]. - Fusion strategies are categorized into five types, including camera-radar, camera-LiDAR, radar-LiDAR, camera-LiDAR-radar, and temporal fusion, each leveraging the complementary characteristics of different sensors [42][45]. Group 6: SafeBEV 3.0 - Multi-Agent Collaborative Perception - The development of Vehicle-to-Everything (V2X) technology enables autonomous vehicles to exchange information and perform joint reasoning, overcoming the limitations of single-agent perception [15][16]. - Collaborative perception aggregates multi-source sensor data in a unified BEV space, facilitating global environmental modeling and enhancing safety navigation in dynamic traffic [15][16]. Group 7: Challenges and Future Directions - The article identifies key challenges in open-world scenarios, such as open-set recognition, large-scale unlabeled data, sensor performance degradation, and communication delays among agents [17]. - Future research directions include the integration of BEV perception with end-to-end autonomous driving systems, embodied intelligence, and large language models [17].

Core Insights - OpenAI is accelerating its investment in robotics, focusing on humanoid robots as a key step towards achieving Artificial General Intelligence (AGI) [1][2] - The company is actively recruiting experts in humanoid robot control algorithms and related technologies, indicating a strategic shift back to robotics after disbanding its previous robotics department in 2021 [1][2] - OpenAI's move comes at a time when the industry is reassessing the development path of large language models, suggesting a need to engage with the physical world for breakthroughs [1] Recruitment and Team Building - OpenAI's recruitment efforts are intensifying, with notable hires from Stanford University and other robotics labs, emphasizing the goal of unlocking general robotic technology [2] - Job postings indicate a clear focus on developing AGI-level intelligence in dynamic real-world environments through robotics [2] Hardware Development and Collaboration - It remains unclear whether OpenAI plans to develop its own robotic hardware, utilize existing hardware, or collaborate with other robotics companies [3] - A recent job listing for a mechanical engineer suggests potential plans for creating proprietary robots or developing remote operation systems, with an emphasis on large-scale production capabilities [3] Competitive Landscape - OpenAI's re-entry into the robotics field places it in a highly competitive market, facing established companies like Tesla and Google, as well as emerging startups [4] - Despite the competitive environment, the humanoid robotics sector is experiencing significant investment, with over $5 billion from venture capitalists since early 2024, and Morgan Stanley predicts a market value of $5 trillion by 2050 [4] - Current humanoid robots struggle with complex environments, but increased capital and talent influx may accelerate technological advancements [4]