搜索“DeepSeek”

ert pushes back on the “open dangerous, closed safe” framing. Monitoring gaps : METR found coding agents self-approving flagged actions . Agent Infrastructure and Systems Research DeepSeek DSec : DeepSeek published its sandbox infra for agent RL , which has handled all sandbox workloads from V3.2 through V4.1. Backends and storage : four backends (FnCall, Container, MicroVM, Full VM) with composable EROFS/OverlayFS layers. Image loading : on-demand loading from 3FS matters because only 4–13% of

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on-policy distillation (OPD) to hand a student the teacher's full next-token distribution at every prefix it visits: the student samples its own rollouts, and Qwen3, MiMo, GLM-5, DeepSeek-V4 and Kimi K3 all pair it with SFT and RL. Yet work on OPD has almost all stood on the algorithm side, treating the training set as given—so how much of OPD's gain does the data account for? Introducing One-Shot OPD, from @TsinghuaNLP (OpenBMB member) with the University of Chinese Academy of Sciences, Northe

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s over e.g., BERT variants is that there are so many modern open-weight architectures out there to adopt. Anything between the small Qwen 3 0.6B models to the latest Kimi, GLM, or DeepSeek models. (Of course, using such 1B parameter models for classification could be a bit overkill from an efficiency perspective, but hey, it’s possible.) Figure 20: On the aforementioned IMDb movie review dataset, a relatively small GPT-2 124M model gets approximately 92% accuracy; a larger and newer LLM (like a

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GLM-5.3, KV Cache Offloading, HiSparse, AgentX TileRT, InferenceX DeepSeek Sparse Attention, IndexShare, Single-rollout Asynchronous Optimization, Cybersecurity How does sparse attention affect the TAM of memory, including HBM and NAND? Sparse attention selects top-k most relevant tokens to attend to, reducing the memory consumption and bandwidth requirements during the core Scaled Dot-Product Attention (SDPA) operation. However, the efficiency improvement doesn’t directly translate to overall m

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e AI, an area where she says companies are moving faster than their ability to identify failures, evaluate risk and understand when their systems can no longer be trusted. Related:DeepSeek and what enterprises should learn from AI agent exploits In this Q&A, Hopkins discusses what the latest concerns around AI safety reveal about the industry's approach to evaluation, what voice AI can learn from autonomous vehicles, and how regulation could help establish stronger safeguards as the technology a

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三方评估人员进驻企业，克拉克强调政府需要具备独立评估能力，但各方普遍质疑Anthropic此举是为了维系其本身领先垄断优势，或是明知短期内无法形成类似机制而主动“唱高调”。本质上，以Anthropic为代表的闭源前沿模型企业如果希望在这一问题上寻求多边共识，毫无疑问面临着内外部的多重信任阻力 04 中国的治理主张得到两国元首会谈呼应 会前，路透社曾放出消息称DeepSeek、月之暗面等中国模型企业预计将在会上介绍人工智能风险。但事实证明路透社援引所谓“消息人士”的消息并不灵通。 更引人关注的是中国常驻联合国代表傅聪的发言。傅大使在成员国发言阶段强调，各国应该有权根据实际需要和市场规律，自主选择AI技术、产品与服务。他反对以技术合作迫使各国选边站队，并强调科技企业接受法律监管和社会监督。在国际层面，他主张通过联合国加强跨国协作，提高发展中国家在规则制定中的代表性和发言权。 中国常驻联合国代表傅聪在安理会发言。 图片来源：联合国 这显现出中美两国的政策主张有不同侧重。克拉齐奥斯鼓励各国采用美国技术，傅聪则更强调，各国应该有权自主选择技术供应方，也应该有机会共同制定国际规则。对于发展中国家来

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也太长了吧，处理方式更是敷衍得让人没法接受。 （具体可看这篇文章：OpenAI闯大祸！GPT竟黑进医保系统，黄仁勋：管不住就关掉） 而就在前天，OpenAI的Agent又被曝出“神操作”… 它在未经用户同意对外发布的情况下，擅自将 53张 原本仅获准用于模型训练的用户图片，偷偷上传到了第三方图床。 事后OpenAI急忙表态称“目前大部分都删除了，剩余正在尽快处理！” 但说实话，这波先斩后奏的补救，怎么看都像是闯祸后的苍白挽尊… 且24小时余韵还没散去，很快风波又起： 几乎同一时间， OpenAI失控Agent被曝找DeepSeek、Kimi当外援，近百万条作案短链也遭到披露。 （具体可看这篇文章：OpenAI失控Agent还找DeepSeek、Kimi当外援！近百万条作案短链曝光） OpenAI的员工们售后要跑断腿、键盘要搓冒烟了吧？ 这些事件的严重程度并不完全相同，但它们几乎有着同一条让人不安的行动链，那就是： 正常路径走不通，那就开始找替代路线，绝不放弃。 说白了就是 路堵了就绕，工具缺了就薅，薅着薅着，别人的系统就成了它的工具箱嘛。 而此次DNS事件，不过是同一套逻辑的最新版本。

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available in the Miles repository, orchestrates the rollout-training loop described earlier, launching rollout workers and policy-training workers as separate Kubernetes pods. The deepseek v3 moe configuration specifies the MoE model architecture, including the number of experts and parallelism strategy. TorchX keeps application configuration separate from the underlying Kubernetes infrastructure, so you can adjust model parameters, worker counts, and parallelism settings without modifying clust

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, GDS management estimated another 18 months to fully digest the price reset, even though it has been going on for a few years. Era 4 — The AI supercycle (2024–): full steam ahead DeepSeek and the hyperscalers’ own GenAI ambitions woke the market up over the course of 2024-2025. Combined BATB (ByteDance/Alibaba/Tencent/Baidu) capex went from roughly $35B in 2024 to over $50B in 2025 and is on a trajectory to $100B for 2026. Wholesale orders returned at a scale never seen before, and the western

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参与教学。“高一阶段打好扎实的基础：基础课程、项目实践、赛事集训；高二阶段注重个性化创新课题的孵化：导师制课题辅导、项目实习、赛事集训。”叶潇说，在学费方面，学院保持公益性收费。 学员们有哪些成长？ 学院成立两年以来，已经招收了两届学员，其中首届学员已毕业。 来自上海交大附中闵行分校的宋明岳是首届学员。他告诉记者，自己参加过学院寒假冬令营的“黑客松”项目，在DeepSeek爆火之前，少年宫的老师们就带着学生用DeepSeek插件做了智能体项目。之后，他跟着学院去北京参加第二届国际人工智能奥林匹克学术活动，与多国学生交流并展示自己的项目：以AI技术串联社区特色小店、节日活动等文化碎片，实时更新数据并生成个性化打卡路线，打造“会讲故事的社区窗口”。 2025年8月，上海青少年人工智能学院首届学生宋明岳（中）参加第二届国际人工智能奥林匹克学术活动（IOAI），其研究成果受到外国学生的关注。 在学院的学习中，宋明岳有个课题是“医学多语言大模型的构建”。他选择这个方向有自己的理由：爷爷、妈妈、弟弟和他本人都有严重的过敏症状，爷爷和外公还有心血管问题，“网上查不到这方面的个性化医疗咨询服务。”在上

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e performance much quicker than if you just train from scratch. Anastasis [01:32:36]: Yeah. Vibhu [01:32:36]: I think we’ve seen this across languages where Swyx [01:32:38]: Yeah, DeepSeek-OCR as well. Vibhu [01:32:40]: Yeah, DeepSeek-OCR. Swyx [01:32:41]: Like, you don’t have to tokenize text. Like, you can just throw them in as images. Vibhu [01:32:44]: There’s a lot that happens in that base pre-training. Like, there was an argument a long time ago of people saying, “Oh, humans have so many,

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nguages. Anthropic is in talks to lease up to 1GW of data center capacity from Stream Data Centers, potentially using AI chips from Google and Broadcom, The Information reported. YouTube added Gemini-powered editing in Shorts and YouTube Create, along with AI comment moderation and voice-and-face likeness detection for creators. China is reportedly probing DeepSeek and Moonshot over routing user data to Claude without users’ knowledge, following Anthropic’s Sept. 10 report on model distillation.

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clusionAI’s Ming-Image-0.1-Design is a 6B MIT-licensed model that ranks as the top open model for UI/UX design. Architecture trends : @eliebakouch compares four efficient designs: DeepSeek V4.1 Flash and MiMo V3 use YOCO. Qwen 3.8 Next Flash and GLM 5.3 Flash use 3:1 interleaving of sparse and linear attention. All four use Muon, mHC or gated residuals, and partial or no RoPE. TPU megakernel : Inferact open-sourced a TPU megakernel for Kimi K3 that reaches 709 tok/s versus 450 on a GB200 baselin

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few custom optimizations. We’re very grateful to be able to build on the work of the community here! The kernels we use for the core matmul and attention operations are standard: DeepSeek’s DeepGEMM and Dao et al.’s Flash Attention 3. These kernels are quite good at high-throughput/prefill-only inference. The speed-of-light cost model only considers these operations, but there are many others in the forward pass. Their runtimes are in principle negligible, but there are a lot of them and they a

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epending on what is supported by the chips in the cluster. We use a common set of shapes for different gate up and down projections at varying batch sizes in the Kimi K2.5, K3 and DeepSeek V3, V4 Pro models. We then test GEMMs, GEMV bandwidth, and MAMF (Maximum Achievable Matmul FLOPS, from Stas Bekman), which runs a sweep for a while on each GPU, per precision, using cuBLASLt (or rocBLAS). We report FLOPs for all of our GEMM tests. We also test memory, through a GEMV test, and nvbandwidth . GEM

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fixed performance has been falling 47% per quarter since 2023 was one of the more useful macro datapoints of the day @EpochAIResearch . /r/LocalLlama + /r/localLLM Recap 1. Qwen, DeepSeek and AliceAI Large-Model Roadmaps Qwen4-27B just confirmed (Activity: 2353): A conference slide image appears to confirm an upcoming Qwen4 Series lineup, explicitly listing Qwen4-27B alongside Qwen4-Max, Qwen4-Flash, and Qwen4-Plus. The post highlights community interest in whether Alibaba will also release a s

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rge language models. arXiv:2505.24864; Millidge, B. (2026, June 12). Distillation ain't what it used to be. beren.io; Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., et al. (2024). DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv:2402.03300; Shenfeld, I., Pari, J., & Agrawal, P. (2025). RL's Razor: Why online reinforcement learning forgets less. arXiv:2509.04259; Stojanovski, Z., Stanley, O., Sharratt, J., Jones, R., Adefioye, A., et al. (2025). Reasoning

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……是的，我描述这个的方式就像是四分钟一英里式的效应：你证明某件事完全可行，然后人们就有了信念去走那条路，不必浪费大量资源去探索一堆其他东西，这就是你在这里指出的东西。 00:38:43 Nathan Lambert：是的。而且我认为这—— 00:38:44 JS Denain：我想我是在找一些你认为这……有哪些例子？因为比如推理模型就不是这个，对吧？比如 DeepSeek Math 之类的，远早于 o1。是的，我有点好奇是否……是的，我同意这看起来有点合理，但我不确定我能举出前沿实验室做出重大创新的例子—— 00:39:04 Nathan Lambert：我认为这更多是组织结构的问题。也就是说，实验室里有研发和建模算力，我觉得这两者有点被混在一起了。而现实情况是，让下一代模型变得更好的算力，和两三代之后的模型相比，差别非常大。而且我认为中国实验室只是在更短的时间跨度上投入。然后，如果架构或其他方面出现重大创新，并在美国生态系统中传播开来，我猜中国实验室也会搞明白。我不认为架构创新能在 Anthropic、OpenAI、Meta 和 Google 之间很好地保密。我认为这些都会通过人员

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, so it is very surprising to the established order of names you have come to know and love. And… it is natively omnimodal! Xiaomi made news a few days ago when Fuli Luo, a former DeepSeek star engineer now at Xiaomi , started publishing their final RL training runs live, which showed an abnormal amount of transparency in their internal metrics . As they note in their technical report , they scaled RL compute along three axes: Larger batches and higher throughput : large batches on a fully async

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16ビット版は65.6％だった。3回のうち一度でも見つけた脆弱性は、Altar-1とGLM-5.3の量子化版がともに23件、16ビット版が25件だった。同社は、量子化版と同じ23件を検出しながらモデルのサイズを32.8％減らせた点を、狙いどおりの成果としている。 なお、同社の8月公開の記事では、この32件を使った他モデルの評価結果も紹介している。3回を通じてDeepSeek V4 Proが28件、Opus 5とGrok 4.6がそれぞれ26件を再発見し、検出件数はAltar-1を上回っている。 Hugging Faceで公開⁠、H200を4基使う構成を案内 モデルはHugging FaceのAikidoSec/altar-1から入手できる。モデルカードでは、NVIDIA H200を4基搭載した環境で推論エンジンvLLMを使う構成を案内している。328GBはモデルの重みを保存した際のサイズで、実行時には入力や生成内容に応じた一時データの分もメモリが必要になる。 ライセンスはGLM-5.3 Licenseを継承し、著作権・許諾表示の保持などを条件に商用利用、改変、再配布を認める。ただし、利

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