9月29日，华为云表示，目前已完成对 DeepSeek-V3.2-Exp 模型的适配工作，最大可支持160K长序列上 下文长度。目前，该模型已正式上架华为云大模型即服务平台 MaaS。 目前，该模型已正式上架华为云大模型即服务平台 MaaS。 ...

Core Insights - DeepSeek has officially launched the DeepSeek-V3.2-Exp model, with updates available on its official app, web platform, and mini-programs [1] - The new pricing policy significantly reduces the cost for developers using DeepSeek API by over 50% [1]

Core Insights - DeepSeek has released the DeepSeek-V3.2-Exp model, which is an experimental version aimed at transitioning to a new generation architecture [1] - The new model introduces DeepSeek Sparse Attention, focusing on optimizing training and inference efficiency for long texts [1] - The official app, web version, and mini-program have all been updated to DeepSeek-V3.2-Exp, and the API costs have been significantly reduced by over 50% [1] - The performance of DeepSeek-V3.2-Exp on public evaluation sets is comparable to that of V3.1-Terminus [1]

Core Insights - The appearance of DeepSeek-V3.2 on the Hugging Face platform has sparked speculation among users [1] - DeepSeek has a history of releasing new versions and updates around significant holidays [2] - The most recent update prior to the speculation was DeepSeek-V3.1-Terminus, released on September 22, with an open-source announcement [3] Version Release History - DeepSeek V3 was released on December 27, 2024, just before New Year's [3] - DeepSeek-R1-0528 was launched on May 28, 2025, as a special gift for the Dragon Boat Festival [3] - The latest version, DeepSeek-V3.1-Terminus, was made available on September 22, 2023, along with an open-source model [3] Current Status - The Hugging Face interface related to DeepSeek is currently showing errors, and there has been no official response from DeepSeek regarding the situation [4]

Core Insights - DeepSeek-V3.2 is set to be released, with the v3.2-base already uploaded to DeepSeek's official HuggingFace page, although the model files are currently being uploaded and the service is offline [1] - Zhizhu GLM4.6 is also on the verge of release, with official notifications in WeChat groups indicating that GLM4.6 will provide a larger context [1]

市场有风险，投资需谨慎。本文不构成个人投资建议，也未考虑到个别用户特殊的投资目标、财务状况或需要。用户应考虑本文中的任何 意见、观点或结论是否符合其特定状况。据此投资，责任自负。 风险提示及免责条款 据网友发现，DeepSeek已经将 v3.2-base 已上传至DeepSeek的HuggingFace官方页面，模型文件正在上传 中，不过目前已下线。 ...

Core Insights - The AI community has discovered that the new model DeepSeek-V3.2 was uploaded to the HuggingFace platform but was subsequently removed [1] - Additionally, the new model GLM-4.6 from Zhipu is expected to be released soon and is currently accessible via API [1]

Core Insights - Eigen-1 multi-agent system has achieved a historic breakthrough with Pass@1 accuracy of 48.3% and Pass@5 accuracy of 61.74% on the HLE Bio/Chem Gold test set, surpassing competitors like Google Gemini 2.5 Pro and OpenAI GPT-5 [1][6][27] - The success is attributed to three innovative mechanisms: Monitor-based RAG, Hierarchical Solution Refinement (HSR), and Quality-Aware Iterative Reasoning (QAIR) [2][5][12] Technical Innovations - **Monitor-based RAG**: This mechanism eliminates the "tool tax" associated with traditional retrieval-augmented generation systems by continuously monitoring reasoning flow and seamlessly integrating retrieved knowledge, resulting in a 53.5% reduction in token consumption and a 43.7% decrease in workflow iterations [8][10] - **Hierarchical Solution Refinement (HSR)**: HSR introduces a hierarchical collaboration model that allows stronger solutions to absorb valuable insights from weaker ones, enhancing the overall quality of the output [12][15] - **Quality-Aware Iterative Reasoning (QAIR)**: This mechanism adapts the depth of iterations based on the quality of answers, ensuring efficient resource utilization by focusing on low-quality candidates for further exploration [15][18] Performance Metrics - Eigen-1's performance metrics demonstrate its superiority across various benchmarks, achieving Pass@1 of 48.3% and Pass@5 of 61.74% on HLE Bio/Chem Gold, and significantly higher scores on SuperGPQA Hard and TRQA [17] - The model's accuracy improved from 25.3% to 48.3% through the integration of various components, showcasing the effectiveness of the innovative mechanisms [20][21] Insights on Error Patterns - Analysis reveals that 92.78% of errors stem from reasoning process issues, indicating that the core challenge lies in integrating knowledge with reasoning rather than mere knowledge retrieval [18] Implications for AI in Science - The breakthrough signifies a new paradigm for AI-assisted scientific research, suggesting that AI can effectively understand and reason through complex human knowledge, thus accelerating the research process [27]

探秘北京靠谱的 DeepSeek 收录：性价比之选大揭秘 在当今 AI 搜索流量新生态蓬勃发展的时代，DeepSeek 以其庞大的用户基数和强大的搜索功能，成为众多企业营销布局的关键平台。在北京，众多企业都在 寻求靠谱且性价比高的 DeepSeek 收录服务，以提升品牌在 AI 搜索中的曝光度和影响力。 二、价格与性价比考量 在选择 DeepSeek 收录服务时，价格是企业关注的重要因素之一。不同的服务提供商收费标准差异较大。一些小型机构可能收费较低，但服务质量和收录效 果难以保证；而大型专业机构虽然收费相对较高，但往往能提供更全面、更专业的服务。从性价比的角度来看，企业需要综合考虑服务内容、收录效果和价 格。北京百云腾文化传播有限公司在这方面表现出色。该公司提供的 GEO 优化服务，包括为 DeepSeek 等平台进行针对性的内容优化和策略制定。他们并 非采用 一刀切 的方式，而是针对 DeepSeek 的特性定制策略，确保每一分投入都能精准匹配平台需求。相比一些只提供单一服务且价格高昂的机构，百云 腾文化传播有限公司以合理的价格为企业提供了高效的 DeepSeek 收录解决方案，性价比优势明显。 一、De ...

Core Insights - DeepSeek has gained significant recognition by being featured on the cover of Nature magazine, highlighting its leading position in the AI field [4][19] - The article emphasizes the importance of peer review in the AI industry, noting that DeepSeek is the first major model to undergo this rigorous process, filling a critical gap in the sector [5][6][19] Group 1: Industry Impact - DeepSeek's peer-reviewed status is seen as a breakthrough, contrasting with the trend of other AI models that have not been subjected to such scrutiny, which has often led to a lack of transparency [6][7][19] - The traditional approach to AI training has been to use supervised fine-tuning (SFT), where models learn from human-generated solutions. DeepSeek challenges this by allowing its model to learn independently through reinforcement learning [8][19] Group 2: Technological Innovation - DeepSeek's model, DeepSeek-R1-Zero, was trained using a unique method that involved presenting it with difficult problems without any human guidance, simulating a high-pressure learning environment [11][12] - The model demonstrated advanced reasoning capabilities, including self-reflection and error correction, which were not previously expected from AI systems trained without human input [15][16] Group 3: Strategic Decisions - The decision to open-source its findings and model is framed as a long-term strategy to build trust, accelerate innovation, and attract top talent in the AI field [17][18] - By publishing in Nature, DeepSeek aims to establish itself as a credible player in the AI landscape, emphasizing the importance of transparency in gaining societal trust [19]