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见 。 examples/audio/. 🗂️ 仓库结构 Ming-3.0-audio-flash/ ├── cookbook.ipynb Interactive walkthrough ├── deploy/ vLLM-Omni deployment profiles ├── examples/ │ ├── audio/ Sample audio and metadata │ ├── sglang audio chat.py SGLang text-output client │ ├── vllm omni audio chat.py Text and non-streaming speech client │ ├── vllm omni audio streaming.py Streaming speech and latency metrics │ ├── transformers thinker talker.py Direct Thinker-to-Talker inference │ └── transformers audio.py Direct Transformers

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erates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford. Jean-marc MommessinMeta, OpenAI and Uber Just Taught AI Agents to Talk First. What About When to Stay Quiet? RELATED ARTICLESMORE FROM AUTHOR Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks Meet Drex Max: Nace AI’s Multimodal Decision Model for Computer-Use Agents with a Score 67.59 MiniMax Just Launched M3.1 Flash

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et 4.5 didn’t exploit any of the shortcuts, but DeepSeek-R1-Zero exploited 13.9%, with higher rates associated with reinforcement learning post-training. Blocking access to answer metadata and preventing changes to verification functions reduced exploit rates by nearly 88% without affecting task success. A separate team went looking for weaknesses in the benchmarks themselves. Their BenchJack audit uncovered 219 flaws across 10 popular agent benchmarks, with generated exploits achieving near-per

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Nvidia has released since it agreed to acquire the open source AI platform last month. The release highlights how the open model paradigm has evolved over the past few years since Meta first released its Llama open-weight model family. Related:IBM: Agentic AI is reshaping governance, controls and trust In recent years, open models have grown markedly larger, highlighted by Meta's 405-billion-parameter Llama model, introduced in 2024. "What we think of as small models has gotten quite bigger,” sa

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构建的一些作品。 嘿 Beam，构建一个原生本地 MacOS 应用程序！ 在 Hugging Face，我们喜欢能在其硬件上原生运行的开放本地模型，但在 Mac 上构建原生应用可能很难！让我们看看 Beam 能否帮到我们。 我们让 Beam 构建一个原生 MacOS 应用程序，将语音转换为文本，并方便地将其放到光标处。我们希望使用 MLX 充分发挥 Mac Metal 的性能，并使用 Parakeet 模型实现出色的语音转文本。 我们使用 Beam 配合 Pi 编码工具构建了初始的原生 macOS 菜单栏应用，通过 MLX 运行 Parakeet。保存的运行记录包含后续提示；最终源代码还包含后来的 Codex 修复。上面的提示是构建需求的精简版本。 你可以在该 trace 中查看 Beam 如何在 pi 中构建此应用的智能体轨迹。如果你想试用该演示，请下载源代码或阅读设置说明。 嘿 Beam，如何在多个智能体上扩展？ 我们想看看 Beam 能否通过将编程任务拆分给多个智能体来更快取得进展。我们将 Beam 与开源 Pi 编码智能体配对，并比较了三种设置：1. 一个智能体独自工作；2.

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utation and communication for each expert group. This reduces kernel-launch overhead and tail effects. Second, we reduce host-device synchronization by using device-resident token metadata and initiating all-to-all communication directly from the GPU. Third, we further optimize all-to-all with symmetric memory to exchange tokens directly over NVLink without staging copies, improving communication throughput and reducing HBM contention when running GEMMs concurrently. Lastly, we batch load-balanc

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撑，持续领跑国产大模型极速适配赛道。 据介绍，自 2025 年 12 月以来，沐曦股份已完成 43 个主流旗舰模型的 Day 0 适配。范围涵盖智谱、阿里千问、MiniMax、DeepSeek、阶跃星辰、腾讯混元等头部模型厂商，适配速度、数量、生态广度均位居行业前列。 硬件层面，曦云 C 系列 GPU 基于沐曦自研 XCORE 架构，搭载大容量高带宽显存与 MetaXLink 高速片间互联，具备混合精度算力与集群线性扩展能力，天然适配 MoE 模型专家调度、百万 Token 长上下文推理的算力与显存需求，单机多卡、大规模智算集群均可稳定承载。 软件层面，核心依托沐曦自研 MXMACA 全栈软件栈，覆盖底层驱动、MXCC 编译器、深度优化算子库、主流 AI 框架适配层，原生兼容主流 AI 开发生态，可将传统大模型适配周期从数周压缩至小时级，大幅降低模型迁移成本。 壁仞科技表示，本次 Day0 适配中，针对 Step 5 Preview 模型的文本理解和生成以及高效推理能力，壁仞科技基于 AI 推理框架 vLLM 与成熟的 BIRENSUPA 软件栈，在壁砺系列通用 GPU 上快速完成模型

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models is self-serving. The more companies run their own open models, the greater the demand for compute hardware, and open models will likely stay optimized for Nvidia for the foreseeable future. Meanwhile, companies like Meta, Google, Anthropic, and OpenAI are building their own chips and optimizing models for those instead. To push the spread of open AI models further, Nvidia recently invested $12.9 billion in Hugging Face , one of the most important platforms for developing and sharing them.

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个领域开展推广工作，另一企业形成了细分解决方案。 【玩转ETF】该AI细分行业正迈入成熟深化阶段，“政策支持+头部公司加大采购投入+大模型能力持续跃迁”背景下，产业加速发展 ①该AI细分行业正迈入成熟深化阶段，“政策支持+头部公司加大采购投入+大模型能力持续跃迁”背景下，产业加速发展；②T+0 ETF是什么？③机构：核心通胀再抬头，10月美联储是否会出手。 Meta发布开放权重大模型，并与推动落地苹果电脑，机构称AI办公有望成为AI应用商业化的下一主战场，这家公司推出了全新版AI原生办公产品，另一家多个领域市场占有率均为国内第一。 ①本周，随着由债务驱动的AI狂热遭遇了残酷的现实打击，正促使投资者纷纷抛售股票、重新涌入政府债券市场； ②国债似乎正在重新确立其昔日避风港地位，再次成为抵御股债市场波动的对冲利器。 AI风向标生变！Anthropic估值预期据称缩水 高增长故事讲不下去了？ 财联社 刘蕊 2026-10-16 08:41 ①据悉，Anthropic部分现有股东以及有意参与其IPO的潜在投资方，正在重新评估这家AI独角兽的价值，下调对其上市估值预期； ②作为AI板块具有风向标意

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iPhone百度智东西大模型苹果腾讯微软华为5GIDx通用高通360三星小米英特尔谷歌 个人智能体爆火！蚂蚁发新模型接招，实测编辑部小助理上线 智东西10月15日报道，近一周，硅谷AI大厂轮番放出新进展，都指向让AI接手真实工作。 10月12日，Meta发布Muse Wonder系列，包括Muse Wonder和Muse Wonder Full两个型号，重点面向个人与专业智能体任务。 10月9日，谷歌云也在Gemini at Work 2026大会上推出企业通用智能体Gemini Agent，能够自主规划任务、调用工具，在办公文档、邮件和开发环境中完成多步骤工作。昨天，谷歌开放其最新旗舰模型Gemini 4 Argon，重点面向编程、金融、法律、网络安全和科学等领域的复杂现实任务。 围绕真实任务需求，蚂蚁百灵于9月30日发布Ling-3.1-flash。今天，Ling-3.1-flash正式开源，该模型总参数量约为560B，每个Token激活参数约25B，上下文窗口上限为 1M，重点面向日常办公、长周期软件研发和复杂Agent任务。 在第三方评测机构Artificial Analysi

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ender中自主构建了一组高度精细的寺庙建筑群，包括多座建筑、植被和复杂光照，呈现出接近照片的视觉效果。 Gemini 4 Argon生成的宝塔模型 X用户@mweinbach分享的《心灵杀手2》移植案例，则展示了Argon在跨平台工程中的应用。他称，模型协助将游戏从x86 Windows环境移植到原生macOS Arm64环境，涉及指令转换、Rust绑定和Metal着色器转换，项目在M3 Ultra上达到了每秒140多帧。 从真实机器人到科学研究 大型代码库检验的是Argon处理复杂软件系统的能力，机器人项目则考验模型能否理解真实世界中的空间关系和物理交互，并将这种理解转化为可执行的程序。 谷歌展示的Real2Sim项目，正是围绕这一问题展开。 Argon可以观看真实机器人遥操作视频，构建对应的数字孪生环境，并获取或生成仿真资产，使物体不仅外观接近真实对象，在与灵巧机械手接触时也能呈现相似的物理行为。随后，模型通过反复视觉推理调整物体属性和场景设置，让仿真中的视觉效果与接触过程尽可能贴近真实记录。 左（真实视图），同步的真实机器人摄像头画面；右（模拟视图），由Argon重建并校准的3

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财联社10月14日（编辑 史正丞）紧挨着周一发布前沿模型的Meta， 科技巨头谷歌周二宣布，公司备受期待的旗舰大模型Gemini 4 Argon正式上架。 （来源：谷歌掌门皮查伊） 这也意味着，现在能提供“比肩”Anthropic和OpenAI旗舰大模型服务的公司，在本周头两天里就增加了两家，再一次体现出AI领域的实力格局能以多快的速度发生变化。 作为背景，Argon是谷歌去年推出Gemini 3以来的首个重大前沿模型。 此前谷歌披露的性能数据显示，与主要竞品对比，Argon更擅长金融、法律、长资料分析等专业知识工作。同时，谷歌把单次输出上限从6.4万提高到100万词元（注：GPT-6 Astra和Claude Opus 5.5常规输出上限均为12.8万词元），给长时间推理、多轮验证和大型代码修改留出更多空间。 与此同时，Argon首发优惠价只有Astra和Fable的五分之一，即百万词元的输入/输出价格仅为2/10美元，11月底优惠结束后的常规定价（4/20美元）则与Opus 5.5持平。 目前Argon模型只向每月至少付费20美元的订阅用户以及API用户开放。谷歌宣布，该模型周二起

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nted incursions — Big Tech leaders have pivoted to speak about AI’s ability to cure cancer, for example. Gemini 4 Argon competes with frontier models like OpenAI’s GPT-6 Astra and Meta’s new Muse Wonder. Google had the edge among AI companies for most of 2025, but advancements from other labs left it mostly behind for this year. Also throughout the past year, concerns about AI safety have risen significantly, particularly around “AI doomsday” scenarios. Google says that it engaged with the White

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ot on the Vals Index, but it still trails Anthropic's Claude Opus 5.5 in independent overall rankings. And because Google waited roughly two weeks to ship, it now also sits behind Meta's Muse Wonder Full , which launched just yesterday. Google doesn't state this figure explicitly but says the cache is 95 percent cheaper than the regular input token price. Google says Argon turned months of internal engineering into weeks Google shared some internal numbers with the launch, though they're self-re

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equests with a scheduled task. Watch Argon synthesize data from 3 open-source repositories into a medical learning tool where you can peel back anatomical layers, inspect clinical metadata, and trigger biomechanics in a 3D exploration of the human body. Argon using Antigravity in the Gemini macOS app generates design iterations for new legs for a sofa using Nano Banana then generates a 3d printable file for one. Starting from a sketch of the measurements and a photo of the couch Argon builds the

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equests with a scheduled task. Watch Argon synthesize data from 3 open-source repositories into a medical learning tool where you can peel back anatomical layers, inspect clinical metadata, and trigger biomechanics in a 3D exploration of the human body. Argon using Antigravity in the Gemini macOS app generates design iterations for new legs for a sofa using Nano Banana then generates a 3d printable file for one. Starting from a sketch of the measurements and a photo of the couch Argon builds the

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Language Recognition (Optional) FireRedTTS3-Base relies on explicit language tags for best performance. However, if you don't know the exact language of the text, you can download Meta's FastText language-id model and let it detect the language automatically. Text Normalization (TN) TN converts written numbers, dates, units, currencies, acronyms, etc. into their spoken form (e.g. 19:30 → nineteen thirty). By default, FireRedTTS3 uses the wetext TN tool, which supports Chinese and English, other

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pendent, we’ve continued to invest in and refine our provenance tools. For general availability, our provenance safety tooling for our First-party (1P) products will include: C2PA metadata on all assets, providing verifiable origin through an industry standard; Visible moving watermark on videos downloaded from sora.com or the Sora app; Internal detection tools to help assess whether a certain video or audio was created by our products We recognize that there is not a single solution to provenan

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istry workflows, evaluating multimodal chemical structure understanding; structure-activity relationship (SAR); prediction of drug potency, toxicity, and absorption, distribution, metabolism, excretion (ADME); multiparameter lead-optimization decision-making, and retrosynthesis. Table 1 3.2 Genomics and quantitative biology To evaluate GPT-Rosalind-5.5 on genomics understanding and quantitative biology, we designed an agentic evaluation on long horizon, end-to-end genomics analysis tasks. This e

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e interpreted alongside remaining failures, evaluation awareness findings, and monitoring limitations; September 9, 2026: Naming and substance updates to the section on Verbalized Metagaming and Oversight Gaming. Verbalized Metagaming is when a model reasons in its Chain of Thought about how it will be graded, rewarded, or monitored, rather than only engaging the intended task. We revised this section to more prominently emphasize that we are currently measuring metagaming as verbalized in chain

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