（来源：吴说） 吴说获悉，据 Top Ledger 数据，自 Solana Mobile 空投以来，Seeker 代币 SKR 累计交易量已超过 2 亿美 元；现约 85% 的空投总额已被领取；SKR 的链上交易量主要在 Meteora 中完成，占据了 57.4%。 ...

生意社01月26日讯 1月26日，山东万华化学，华东地区LLDPE7042报7050元/吨，上涨200元/吨；华东地区LLDPE7050报 7100元/吨，上涨200元/吨。 PriceSeek评析 LLDPE，多空评分：2 生意社01月26日讯 1月26日，山东万华化学，华东地区LLDPE7042报7050元/吨，上涨200元/吨；华东地区LLDPE7050报 7100元/吨，上涨200元/吨。 PriceSeek评析 LLDPE，多空评分：2 文章显示华东地区LLDPE现货报价上涨200元/吨，如7042型号报7050元/吨、7050型号报7100元/吨，涨 幅显著。这反映出供应紧张或需求增加，对现货价格构成重大利好。结合大连商品交易所聚乙烯期货数 据，2605合约收盘价6865元/吨，上涨116元/吨，成交量达719797手，持仓量增加3532手，表明市场多 头情绪浓厚。现货价格上涨将强化期货上行预期，支撑未来价格走势。 【大宗商品公式定价原理】生意社基准价是基于价格大数据与生意社价格模型产生的交易指导价，又称 生意社价格。可用于确定以下两种需求的交易结算价： 1、指定日期的结算价 2、指定周期的平均结 ...

但如果把问题继续往深处追问：推理能力的本质，真的只是多算几步吗？ 谷歌、芝加哥大学等机构的研究者最近发表的一篇论文给出了一个更具结构性的答案，推理能力的提升并非仅源于计算步数的增加，而是来自模型在推理 过程中隐式模拟了一种复杂的、类多智能体的交互结构，他们称之为「思维社会」（society of thought）。 过去两年，大模型的推理能力出现了一次明显的跃迁。在数学、逻辑、多步规划等复杂任务上，推理模型如 OpenAI 的 o 系列、DeepSeek-R1、QwQ- 32B，开始稳定拉开与传统指令微调模型的差距。直观来看，它们似乎只是思考得更久了：更长的 Chain-of-Thought、更高的 test-time compute，成为最常 被引用的解释。 简单理解就是，这项研究发现，为了解决难题，推理模型有时会模拟不同角色之间的内部对话，就像他们数字大脑中的辩论队一样。他们争论、纠正对 方、表达惊讶，并调和不同观点以达成正确答案。人类智能很可能是因为社交互动而进化的，而类似的直觉似乎也适用于人工智能！ 通过对推理输出进行分类，以及结合作用于推理轨迹的机制可解释性方法，研究发现，诸如 DeepSeek-R ...

Core Viewpoint - The discussion centers around the competitive landscape in the AI field, particularly focusing on the contrasting approaches of Mistral and DeepSeek in developing sparse mixture of experts (MoE) models, with Mistral's CEO acknowledging China's strong position in AI and the significance of open-source models [1][4]. Group 1: Company Perspectives - Mistral's CEO, Arthur Mensch, claims that open-source models are a strategy for progress rather than competition, highlighting their early release of open-source models [1]. - The recent release of DeepSeek-V3 is built on Mistral's proposed architecture, indicating a collaborative yet competitive environment in AI development [1][4]. - There is skepticism among the audience regarding Mistral's claims, with some suggesting that Mistral's recent models may have borrowed heavily from DeepSeek's architecture [4][13]. Group 2: Technical Comparisons - Both DeepSeek and Mistral's Mixtral focus on sparse MoE systems, aiming to reduce computational costs while enhancing model capabilities, but they differ fundamentally in their approaches [9]. - Mixtral emphasizes engineering principles, showcasing the effectiveness of a robust base model combined with mature MoE technology, while DeepSeek focuses on algorithmic innovation to address issues in traditional MoE systems [9][12]. - DeepSeek introduces a fine-grained expert segmentation approach, allowing for more flexible combinations of experts, which contrasts with Mixtral's flat knowledge distribution among experts [11][12]. Group 3: Community Reactions - The community has reacted critically to Mistral's statements, with some users expressing disbelief and pointing out the similarities between Mistral's and DeepSeek's architectures [2][17]. - There is a sentiment that Mistral, once a pioneer in the open-source AI space, is now perceived as having lost its innovative edge, with DeepSeek gaining more influence in the sparse MoE and MLA technologies [14][17]. - The competitive race for foundational models is expected to continue, with DeepSeek reportedly targeting significant releases in the near future [19].

Core Insights - DeepSeek has released a significant paper on Manifold-Constrained Hyper-Connections (mHC), focusing on the fundamental issue of how information flows stably through ultra-deep networks in large models, rather than on model parameters, data volume, or computational power [2] Group 1: Residual Connections and Their Limitations - The concept of residual connections, introduced by Kaiming He’s team in 2015, is a milestone in AI development, allowing deeper neural networks by addressing the vanishing gradient problem [3] - Prior to residual connections, neural networks were limited to depths of 20-30 layers due to the exponential decay of gradients, which hindered effective feature learning [3][4] - Residual connections introduced a "shortcut" for signal transmission, enabling the depth of trainable networks to increase from tens to hundreds or thousands of layers, forming the structural foundation of modern deep learning [4] Group 2: Introduction of Hyper-Connections - Hyper-Connections emerged as a solution to the limitations of residual connections, allowing multiple pathways for information transfer within a model, akin to a relay race with multiple runners [6][7] - This approach enables information to be distributed across multiple parallel channels, allowing for dynamic weight allocation during training, enhancing the model's ability to handle complex, multi-source information [6][7] Group 3: Challenges with Hyper-Connections - Hyper-Connections face a critical flaw: instability due to excessive freedom in information flow, which can lead to imbalances in the model's internal information flow [9] - The training process of models using Hyper-Connections can exhibit high volatility and loss divergence, indicating a lack of stability in information transmission [9] Group 4: The Solution - mHC - mHC, or Manifold-Constrained Hyper-Connections, introduces a crucial constraint to Hyper-Connections by employing a double stochastic matrix, ensuring that information is redistributed without amplification [11] - This constraint prevents both signal explosion and signal decay, maintaining a stable flow of information throughout the network [13] - The implementation of mHC enhances training stability and performance, with only a 6.7% increase in training time, which is negligible compared to the significant cost savings in computational resources and debugging time [13][14] Group 5: Implications for Future AI Development - mHC strikes a new balance between stability and efficiency, reducing computational costs by approximately 30% and shortening product iteration cycles [14] - It supports the development of larger models, addressing the stability bottleneck in scaling to models with hundreds of billions or trillions of parameters [16] - The framework of mHC demonstrates that "constrained freedom" is more valuable than "complete freedom," suggesting a shift in AI architecture design from experience-driven to theory-driven approaches [16]

January 23, 2026 07:57 AM GMT 科技脉动 | Asia Pacific DeepSeek——少即是多 DeepSeek 最新推出的Engram模块通过将存储与计算解耦，减 少对HBM的依赖并降低基础设施成本。这有望缓解中国在AI 计算方面的瓶颈，并表明下一阶段的AI竞争焦点可能不再是更 大的模型，而是更高效的混合式架构。 从稀缺的GPU资源中挖掘更高的效率。DeepSeek将"条件式记忆"从计算 （Engram）中解耦，将大语言模型的效率提ⶍ至一个全新的水平。Engram旨在缓 解 AI 基础设施中的存储瓶颈，通过高效"查找"关键信息，避免过度ⶭ用 HBM， 从而释放更大容量用于更复杂的推理任ⱷ。在现有 GPU 与系统存储架构下提ⶍ效 率也意味着未来可能⬵少昂贵的HBMⶍ级。对HBM获取受限的中国市场而言，这 项技术可缓解对昂贵存储硬件的ⷭⱱ。 影响。要在基础设施成本更低的情⬅下获得更强大的推理能ⱱ，就意味着最低需 要约200GB的系统DRAM，而 Vera Rubin系统中每颗CPU已配备1.5TB的DRAM，ⷊ 每个系统使用的通用DRAM将约提ⶍ 13%。DeepSeek 的结 ...

Group 1 - DeepSeek has revealed a new model identifier "MODEL1" in its FlashMLA code, suggesting it may be nearing completion or deployment, potentially as a new architecture distinct from existing models [1] - Elon Musk criticized ChatGPT for being linked to multiple suicide cases, while OpenAI's Sam Altman acknowledged the complexities of operating a large AI platform and highlighted the safety concerns surrounding AI technologies [2] - Wang Xiaochuan responded to concerns about AI in healthcare, advocating for a model where AI assists doctors rather than replacing them, emphasizing the importance of patient benefits [3] Group 2 - OpenAI's API business generated over $1 billion in annual recurring revenue last month, with projections indicating a significant increase in annual revenue to over $20 billion by 2025 [4] - Baidu has established a new personal superintelligence business group, merging its document and cloud storage divisions, which is expected to enhance AI application capabilities [6] - NVIDIA's CEO highlighted three major breakthroughs in AI models over the past year, including the emergence of agentic AI and advancements in open-source models [7] Group 3 - Sequoia Capital is reportedly investing in AI unicorn Anthropic, which is raising over $25 billion in funding, potentially doubling its valuation to around $350 billion [8] - Meta's new AI lab has delivered its first key models, although significant work remains before these technologies are fully operational for internal and consumer use [9] - Musk's X platform has open-sourced its recommendation algorithm, which relies heavily on AI to customize user content [10][11] Group 4 - Suiruan Technology reported significant losses exceeding 4 billion yuan over three years, with a high dependency on sales to Tencent [12] - Moore Threads anticipates a narrowing of losses in the upcoming year, projecting revenues of 1.45 to 1.52 billion yuan for 2025 [13] - Yushu Technology announced that it shipped over 5,500 humanoid robots last year, surpassing previous market estimates [14] Group 5 - The "Qiming Plan" project has been launched to establish global consensus on AI safety measures, aiming to balance opportunities and risks associated with rapid AI development [15]

Core Viewpoint - The article discusses the recent surge in gold prices and predicts that several other commodities, including silver, copper, natural gas, coffee, and cocoa, will also experience price increases due to various market factors [1][2][4][5][7]. Group 1: Gold Market Analysis - Gold prices have risen significantly, reaching over $4,000, with a year-to-date increase of 52%, marking the largest annual gain since 1979 [1][2]. - Key drivers for gold's price increase include geopolitical tensions, such as the Middle East conflicts and the ongoing Russia-Ukraine war, which have heightened market risk aversion [2]. - The expectation of two rate cuts by the Federal Reserve in 2025 is anticipated to weaken the dollar's appeal, further boosting gold prices [2]. Group 2: Other Commodities Expected to Rise - Silver is expected to rise due to strong industrial demand, particularly in the photovoltaic sector, where it accounts for 65% of industrial usage [4]. - Copper demand is projected to grow over 60% by 2030, driven by energy transition initiatives and infrastructure upgrades, with supply constraints from mining accidents [4]. - Natural gas prices are forecasted to increase by approximately 10% in Europe and 60% in the U.S. in 2025, influenced by geopolitical factors and weather conditions [5]. - Coffee prices are rising due to drought conditions in Brazil, which produces nearly half of the world's Arabica coffee [7]. - Cocoa prices are also increasing due to similar supply issues, with drought affecting production [7]. Group 3: Investment Considerations - Investment in commodities can be approached through physical assets like gold bars or coins, ETFs, or futures contracts for other commodities [10]. - The potential impact of rising commodity prices on everyday costs is acknowledged, particularly for coffee and cocoa, while natural gas price increases may affect heating costs [10]. - The article emphasizes the importance of risk management in commodity investments, suggesting that investors should allocate a reasonable portion of their assets to commodities [12].

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ （来源：智通财经） 1月23日，轻舟智航CEO于骞在年度品牌活动上宣布，截至2026年1月，其辅助驾驶系统（NOA）搭载 车辆突破100万辆。这是继华为乾崑智驾之后，业内少有的公开宣布达到这一规模的智驾供应商。 百万辆NOA上车历来被视为一个重要的里程碑。在此前与智通财经的专访中，于骞表示百万级NOA上 车交付量将成为智驾竞争的分水岭，因其数据将服务于端到端技术泛化能力、性能体验的提升。 而在2026年智驾行业的背景之下，这一数字则显得尤为意味深长。大卓智能、毫末智行等智驾公司一度 风光无两，却在2025年底相继驶入死胡同，技术路线加速收敛；而元戎启行此前也多次公开表示要跨越 百万辆交付的硬门槛，认为这是留在智驾牌桌上的"安全区"。 对轻舟智航而言，百万辆NOA上车究竟意味着什么？于骞在媒体沟通会上对智通财经等媒体回应称， 100万辆和我国汽车3000万左右的年产销量相比仍是较小的数字，还有很大增长空间。而在被问及行业 格局变化时，他认为产业上下游存在竞争关系，主机厂不会希望供应商只剩一两家，短期内不会出现高 度集中，"但长期发展过程中 ...

报告由兰德公司（RAND）发布，聚焦 2024 年 4 月至 2025 年 8 月美中大型语言模型（LLM）的全球竞争格局，通过分析 135 个国家的网站流量数据，探究 市场动态、DeepSeek R1 的突围影响及 adoption 驱动因素，为理解中美 AI 霸权争夺提供关键洞察。 报告核心发现显示，全球 LLM 市场增长迅猛，期间主要平台月访问量从 24 亿次增至 82 亿次，美国模型持续占据主导地位，2025 年 8 月全球市场份额达 93%。然而，2025 年 1 月中国 LLM 模型 DeepSeek R1 的推出打破了市场格局，引发 "DeepSeek 颠覆效应"：两个月内中国 LLM 平台访问量激增 460%，全 球市场份额从 3% 跃升至 13%，且未分流其他中国模型流量，反而带动整体市场扩张。截至 2025 年 8 月，中国模型在 30 个国家的渗透率超 10%，11 个国 家市场份额达 20%，增长主要集中在发展中国家及与中国政治经济联系紧密的国家。 Markets 在 adoption 驱动因素研究中，报告分析了定价、多语言支持和 AI 外交三大维度。定价方面，中国模型 API 费用仅 ...