搜索“DeepSeek”

to most of their pass@1 scores: GPT-6 Astra retains 78% of its single-attempt rate, and Claude Opus 5.5 and Claude Opus 5 each retain 71%. At the other end, GLM-5.1, Kimi-K2.6 and DeepSeek-V4-Pro each keep about 8%. The gap between what a model can do once and what it does every time is the whole story. Can you depend on the model behind your agent? Figure 3: Breadth and consistency pull apart. Twelve of the eighteen models are shown; six below 33% pass@1 are omitted for legibility. Kimi-K3 has

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ne, typically backed by Claude 3.5 Sonnet or 3.7 Sonnet. The competing report was generated by feeding ScholarQA's retrieved literature excerpts to a different model: o3, o4-mini, DeepSeek-V3, or DeepSeek-R1, depending on the example. Two judge models – GPT-4.1 and DeepSeek-R1 – compared each pair and picked a winner. We ensured that LLM judges were aligned with human preferences (95% agreement) and only kept pairs where both judges agreed, which gave us a cleaner preference set and cut much of

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Asking a model “land or water?” for 16,200 lat/long coordinates and plotting the answers yields a recognizable world map (@karpathy) Inference, Hardware and Systems Ascend 950 via DeepSeek kernels: An analysis of DeepSeek’s open-sourced DeepGEMM, FlashMLA, TileKernels and DeepEP infers the chip’s layout (@ZhihuFrontier).Estimated specs: The chip has 32 AI cores, each pairing one Cube core with two Vector cores. Estimated peaks are about 432/865/1,730 TFLOPS in BF16/FP8/FP4; Capacity: Supply may

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so available as an NVIDIA NIM. Full BF16 Glimmer needs 55GB+, which leaves almost nothing for context. The 17GB quantized build is the practical choice on 64GB. Larger models like DeepSeek V4 Flash need more than one box. NVIDIA’s own benchmarks run it on 4 64 GB clustered Sparks. 1. An always-on personal agent Install Hermes and point it at Muse Glimmer or Nemotron 3.5 Lightning. Give it tools: your GitHub repos, a test runner, an RSS feed of arXiv categories. Let it run overnight. By morning i

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G edge network orchestration A new arXiv paper used Jev to interpret service contracts at the network edge. At matched correctness, Jev cut median decision latency by 22.4% versus DeepSeek and 61.9% versus Gemini. 5. Real-time game agents TypeSafe’s Doom demo ran Jev at 10 queries per second. The team estimated the cost at roughly $7 per hour. Competitor Landscape: 7 Decision Models Compared The table below covers Jev, its closest commercial rival, and the leading open-weight options. All specs

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的能力持续增强，应用场景覆盖工业生产、医疗手术、生活服务等多个领域。2026年上半年我国人形机器人整机产品超400款，预计全年整机产量将突破10万台。 《报告》显示，我国已构建起全球领先的人工智能产业生态，政策端立体护航、技术端全栈突破、融资端量质齐升。 一是政策体系持续完善。我国生成式人工智能顶层设计持续完善，政策重心从“技术攻关”转向“场景落地”与“规范治理”并重。以《国务院关于深入实施“人工智能+”行动的意见》为总牵引，多部委协同发力，密集出台了一系列配套政策，构建起覆盖宏观战略、行业应用、基础设施与国际合作的立体化政策矩阵。 二是关键技术实现突破。我国人工智能相关底层算力供给持续优化，国产人工智能芯片与算力集群加速突破；核心算法架构加速迭代，模型推理能力与效率大幅提升；开源生态蓬勃发展，以DeepSeek、千问为代表的中国开源模型的影响力逐步提升。 三是融资规模屡创新高。数据显示，2026年上半年我国发生1255起与人工智能相关的投融资事件，已达到2025年全年的78.0%；合计金额约2501.4亿元，已达到2025年全年的182.0%。 来源：中国互联网络信息中心 CNNIC

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的能力持续增强，应用场景覆盖工业生产、医疗手术、生活服务等多个领域。2026年上半年我国人形机器人整机产品超400款，预计全年整机产量将突破10万台。 《报告》显示，我国已构建起全球领先的人工智能产业生态，政策端立体护航、技术端全栈突破、融资端量质齐升。 一是政策体系持续完善。我国生成式人工智能顶层设计持续完善，政策重心从“技术攻关”转向“场景落地”与“规范治理”并重。以《国务院关于深入实施“人工智能+”行动的意见》为总牵引，多部委协同发力，密集出台了一系列配套政策，构建起覆盖宏观战略、行业应用、基础设施与国际合作的立体化政策矩阵。 二是关键技术实现突破。我国人工智能相关底层算力供给持续优化，国产人工智能芯片与算力集群加速突破；核心算法架构加速迭代，模型推理能力与效率大幅提升；开源生态蓬勃发展，以DeepSeek、千问为代表的中国开源模型的影响力逐步提升。 三是融资规模屡创新高。数据显示，2026年上半年我国发生1255起与人工智能相关的投融资事件，已达到2025年全年的78.0%；合计金额约2501.4亿元，已达到2025年全年的182.0%。 来源：中国互联网络信息中心 CNNIC

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Gym Hybrid, the environment used for this experiment. ITSM We also applied AutoSynthData to the ITSM domain of EnterpriseOps Gym, using Gemma-4-26B-A4B-it as the target model and DeepSeek-V4.1-Flash as the teacher. AutoSynthData generated 1,994 synthetic training samples in 66 hours. Generation took longer than in the subsequent Hybrid run described above, primarily because the ITSM run used a larger teacher model and preceded pipeline optimizations that improved throughput. On ITSM, synthetic

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tions center employees found that AI is likely to eliminate some entry-level SOC roles, even as it creates opportunities for workers to move into more strategic positions. Related:DeepSeek and what enterprises should learn from AI agent exploits That creates a challenge for employers: developing workers for more strategic jobs when some of the work that once provided that experience disappears. AI skills are also becoming another dividing line in the workforce. A new PwC survey of nearly 50,000

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核资源。 当地时间10月1日，arXiv正式宣布实施最新投稿限制：每位提交者每自然月最多只能提交2篇论文，适用于计算机科学、数学、物理等全部学科分类。即便论文最终被拒绝，也会占用当月的投稿额度。 作为拥有超过300万篇论文、每月服务数百万用户的科研基础设施，arXiv早已成为AI研究者抢先公开成果的重要渠道。从OpenAI、Google DeepMind，到DeepSeek、阿里千问等大模型团队，研究论文经常通过arXiv率先进入公众视野，接受同行检验与讨论。 如今，这条科研成果快速传播的通道，第一次向所有学科统一设置了明确的月度投稿上限。 1单月涌入4万篇，arXiv被迫限流 arXiv在10月1日发布的官方博客《Fair Moderation,Equitable Access,and AI》中，开篇便指出，科研出版正在经历一场快速变化，研究者交流成果的方式也在发生重大转变。 过去，一名研究者能够独立完成多少篇论文，存在相对清晰的现实上限。因此，arXiv的志愿审核员主要需要关注少数投稿频率明显反常的用户，必要时限制其提交速度。 随着生成式AI工具愈发强大、使用门槛不断降低，研究者能

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ng decisions. In recent months, many routers launched that use the differing strengths across the model landscape to optimize performance. Cost differences: For example, comparing DeepSeek v4 Flash with GPT-6 Astra, GPT-6 Astra’s average price paid per token is over 48x higher, and it’s 21x more expensive to run an average 10 to 49 turn session in Codex. Routers can switch parts of a task to a lower-cost model to save on cost. Task diversity: For example, the model that performs best on legal re

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Altimeter风投的合伙人Freda Duan指出，Meta不需要严格为每位用户配置承诺的硬件。实际上，AI代理沙箱或是虚拟机经常出现资源超配的情况，且当虚拟机处于运行状态并保持其内存和执行状态时，其实际CPU利用率通常接近于零。 Duan假设Muse的日活用户数为1亿，且每个典型用户的平均使用时长为2小时，在这种情况下，需要833万台虚拟机来满足平均工作负载。而在高峰时段，工作负载假设为平均值的2.5倍，这意味着当前情景下的最大虚拟机数量为2080万台。 与此同时，她又探讨了每台虚拟机需要的物理CPU核心数量。她以DeepSeek为例，但考虑到Meta的运行效率不可能比肩DeepSeek，最后得出结论，在2500万台活跃虚拟机的情况下，Meta可能需要1250万个CPU核心。 而随着推理速度的提升，分配给每个虚拟机的物理核心数量也会进一步减少。不过，另一个方面来看，随着一项技术的效率提高，其需求也会增加，因此CPU的总体需求未必会因技术升级而放缓。 总而言之，尽管围绕Meta的Muse等个人AI代理所引发的CPU需求的假设存在诸多变数，但市场都认为CPU制造商已迎来结构性的利好。

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ropic employee. ( CNN ) + They targeted experts in military AI and export controls. ( Reuters $) + OpenAI has accused China’s Moonshot AI of copying its models. ( CNBC ) + China’s DeepSeek and Huawei have teamed up on chip software. ( NYT $) + Could China win the AI race? ( MIT Technology Review ) 3 After months of delays, Google has unveiled Gemini 4 Argon The new model aims to re-establish Google at the frontier of AI. ( Reuters $) + Google says the model beats OpenAI’s Astra on many benchmark

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而这种情况确实存在。 不过，看来真正的监管目标是“角色聊天机器人”这类服务。这些产品专门被设计成一种拟真的关系形式。目前有数以百计的此类应用程式，允许用户创建AI男友、女友、朋友，甚至家人。 对于未成年人而言，法规要求则更为明确。14岁以下人士若要使用任何以人类方式进行对话的AI，都必须获得父母同意，其中可能包括类似ChatGPT的中国AI助手，如深度求索（DeepSeek）。 所有18岁以下人士均被禁止接触虚拟伴侣、虚拟配偶，甚至虚拟亲属，以及任何其他形式的虚拟“亲密关系”。 图像来源， AFP via Getty Images 对成年人而言，这并非全面禁止，但限制相当严格，尽管部分措辞仍有模糊之处。AI助手必须向所有年龄层用户明确表示自己并非人类，并且每隔两小时发送通知，提醒用户已在线多久。 聊天机械人不得造成“情感依赖或成瘾”，也不得诱导人们作出“不合理决定”。 它们不得鼓励、甚至不得暗示自残或自杀，也不得使用“言语暴力”。 企业若违反规定，可能面临20万元人民币（2.2万英镑）罚款，或被政府“通报批评”，实际上即是遭到公开点名谴责。 敏（Min）与她的AI女友之间的关系，或许正

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ction) at equal batch size and throughput ( @ekzhang1 , @cHHillee ). Implication : It helps prefill-heavy workloads, not decode-bound low-latency serving. Compilers and hardware : DeepSeek on Huawei : DeepSeek released an open-source Ascend toolkit with TileLang optimized for Ascend 950 ( @kimmonismus ). AI as compiler : A model translates Triton directly to PTX, with a verifier checking correctness, races and deadlocks. Speedups on B200 reach 1.37x on FlashAttention ( @Azaliamirh ). Vera Rubin

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r second on its public endpoints: For dedicated enterprise endpoints, Cerebras also supports Kimi K2.6 , GLM 5.1 , MiniMax M2.5 , Qwen3 Coder 480B , Llama 4 Maverick , Mistral Large 3 , DeepSeek V3.2 , and many additional model families . Performance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested. 1237 E. Arques Ave Sunnyvale, CA 94085

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博客 推理工程 模型性能 Baseten 如何让 pyannote 的说话人分离模型提速 9.6 倍 Matte Lim 另有 1 人 模型性能 为托管式 RL rollout 优化 delta 权重同步 Paras Stefanopoulos 模型性能 Baseten 在 Coval 语音 AI 基准测试中领先 Ansel Erol 另有 1 人 AI 工程 供编码智能体使用 Baseten 的新 MCP 与技能 Marius Killinger 另有 1 人 AI 模型 最适合后训练的开源模型 Chloe Florit 模型性能 LLM 推理的高效前沿 Philip Kiely 模型性能 生产环境中的智能体内核 Brian Li 另有 2 人 AI 模型 GLM-5.3：以直观方式解读后训练扩展 Chloe Florit 另有 1 人 AI 工程 如何在 DeepSeek Harness 中运行任意开放模型 Alex Ker 1 2 3 ... 27 requestAnimationFrame(function(){$RT=performance.now()}); 热门模型

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I's James Landay says it's the right conversation framed the wrong way. Chinese open-weight models have made stunning progress in recent months, with offerings from companies like DeepSeek and Alibaba closing the performance gap with American counterparts. Stanford HAI’s own AI Index found the U.S.-China performance gap narrowing to a few percentage points last year. Two new Chinese models narrow this gap even more. Moonshot AI’s Kimi K3, a 2.8-trillion-parameter model with a million-token conte

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ull control of the target program in 4 percent of tasks, compared to 6 percent for Mythos Preview. Older models like GLM-5.2 and Claude Opus 4.6 failed both tests, and Kimi K3 and DeepSeek V4.1-Flash barely got off zero. Anthropic also paired GLM-5.3 with a human expert. Within a single day and with little human attention, the model found several previously unknown vulnerabilities in the JavaScript engine of a widely used browser. It then chained them into a web page that can read any file on a

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hat we’re stuck. Take that as a premise: we’re in a low-growth universe, and it’s unclear where the jobs come from. Logan, is AGI going to give us the jobs? Can humanoid robots or DeepSeek V7 save China’s economy? Logan Wright: You’re asking rhetorical questions. The entire purpose would be to displace manufacturing labor. Jordan Schneider: Fewer jobs as robots take over, AI doing more work for fewer people. Logan Wright: Marginal labor productivity can increase while overall labor force partici

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