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星期三

  1. OpenRouter:Announcements

    ElevenLabs 上线 OpenRouter:9 个 TTS 与 2 个 STT 模型

    ElevenLabs 现已登陆 OpenRouter,提供 9 个文本转语音模型和 2 个语音转文本模型,用户无需单独的 ElevenLabs 套餐,直接用 OpenRouter API key 调用 POST /api/v1/audio/speech 和 POST /api/v1/audio/transcriptions。其中 Eleven v4 支持音频标签控制语气,v4 Turbo 面向实时场景且按 v4 半价计费,Scribe v2 支持说话人标注与词级时间戳。

    s instead of speaking them. Tags count toward your billed characters. A full tour script is longer than one request allows (10,000 characters on v4, 5,000 on v3), so split it on paragraph boundaries. ElevenLabs-only settings go under provider.options.elevenlabs. Pass the neighbouring text as previous text and next text so intonation stays consistent across the joins, and fix a seed so reruns are more repeatable: Step 2: Answer questions with a voice agent Now let users ask the tour questions out

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  2. Liquid AI 模型与工程博客精选

    Liquid AI 发布 d1-3B 与 d1-omni-600M 开放权重决策模型

    Liquid AI 发布 d1-3B 和 d1-omni-600M 两款开放权重决策模型,已在 Hugging Face 上线。d1-3B 在 Decision Index v0.2.1 公开集得 48.57,超过所有 10B 以下模型,与 12 倍规模的 Decider 35B-A3B 持平;单次提问在 RTX 4090 上 8 ms、Jetson AGX Thor 上 16 ms、Jetson AGX Orin 上 26 ms、Jetson Orin Nano 上 50 ms。d1-omni-600M 是首个实验性 checkpoint,支持文本与图像或文本与音频输入,同一指数得 15.95。

    Encoder-350M, a bidirectional encoder. It adds vision and audio encoders to handle all three modalities. It accepts either text and image, or text and audio as inputs d1-3B. We averaged the weights of LFM2.5-2.6B and the text backbone of LFM2.5-VL-3B to create a better base model. We then fine-tuned checkpoints with different random seeds and data mixtures before merging them again. Training on long inputs, shuffling answer options, and fixing shortcuts in the data made a bigger difference than

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  3. Artificial Analysis 完整文章

    Anthropic 发布 Claude Haiku 5.5,Artificial Analysis 公布评测结果

    Anthropic 发布 Claude Haiku 5.5,这是首个带 effort 设置与自适应思考的 Haiku 模型,并引入分层定价:100k token 以内为 $0.10/$0.50 每 1M 输入/输出 token,超过 100k 后升至 $0.50/$2.50。Artificial Analysis 评测显示,Haiku 5.5(max)在小型模型中略领先 GLM-5.3 Flash(42)、Gemini 3.8 Flash(41)和 GPT-6 Luna(38),但落后 Claude Sonnet 5.5(max,56)13 分;其上下文窗口从 Claude 4.5 Haiku 的 200k 提升到 1M token。该模型每个 Intelligence Index 任务约消耗 162k 输出 token,约为 GPT-6 Luna(max,约 50k)的 3 倍,Terminal-Bench 4.0 得分 33%(Haiku 4.5 为 0%)。

    does not yet reflect tiered pricing, so provisional cost figures for Haiku 5.5 do not include the step up cost. We are working on support and will follow up with Cost per Task coverage soon. Key takeaways: ➤ Leading small-class model performance: At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38). Its score is comparable to Kimi K3 (44), a 2.8T parameter open weights model, and trails Claude Sonnet 5.5 (max, 56) by 13 point

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  4. OpenRouter:Announcements

    ElevenLabs 上线 OpenRouter:9 个 TTS 与 2 个 STT 模型

    ElevenLabs 正式上线 OpenRouter,带来 9 个文本转语音模型和 2 个语音转文本模型,开发者用 OpenRouter API key 调用 POST /api/v1/audio/speech 和 POST /api/v1/audio/transcriptions 即可,无需单独的 ElevenLabs 套餐。其中 Eleven v4 负责旁白与角色配音,v4 Turbo 面向实时语音智能体且按 v4 半价计费,Scribe v2 支持说话人标签与词级时间戳;Flash v2.5 单次请求最多 40,000 字符,上传上限 25 MB。Realtime Scribe v2 over WebSocket 不在此次发布范围内。

    s instead of speaking them. Tags count toward your billed characters. A full tour script is longer than one request allows (10,000 characters on v4, 5,000 on v3), so split it on paragraph boundaries. ElevenLabs-only settings go under provider.options.elevenlabs. Pass the neighbouring text as previous text and next text so intonation stays consistent across the joins, and fix a seed so reruns are more repeatable: Step 2: Answer questions with a voice agent Now let users ask the tour questions out

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  5. The Verge AI精选

    OpenAI 发布 722 篇数学手稿,公开未发布前沿模型的解题结果

    OpenAI 公开了由一款未发布的前沿模型产出的 722 篇数学手稿,覆盖 372 个结果族,涉及数百个未解问题。公司称平均每个结果消耗约相当于三小时 ChatGPT Pro 思考的算力,并附上部分推理摘要、算力估算和尝试问题数量等细节。独立顾问组织 AGMAI 此前建议 AI 实验室及时通过学术渠道发布数学结果,并披露模型名称、提示词和算力成本,同时不要将结果发布当作推广模型的营销手段。

    pers and other details also include some summaries of the model’s reasoning, estimates of the compute used, and stats about the number of problems attempted. OpenAI claims the “average result” used the equivalent of three hours of ChatGPT Pro thinking. AGMAI, or the Advisory Group on Mathematics and Artificial Intelligence, published its first recommendations in late September, urging AI labs to release mathematical results promptly and through established academic channels where possible, while

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  6. PyTorch Blog

    PyTorch 用 FBTriton 重写 Table Batched Embedding 前反向算子

    PyTorch 博客介绍了用 Triton 实现的 Table Batched Embedding(TBE)前向与反向 kernel,在推荐系统负载上性能超过原有 CUDA kernel。前向提供通用 gather 与小表 histogram 两条路径,FP16/BF16 权重以 FP32 累加、FP32 权重以 FP64 累加;B200 上 bounds-check 组件最高提速 1.24x。可选 core-module 路径把 index transpose、sort 和 run-length encoding 前移,大 B200 配置下前反向合计延迟从 79.537 ms 降至 66.183 ms(−16.8%)。

    four. Other shapes use one; Index and offset width. TorchRec accepts config-driven int32 indices and offsets when the linearized range fits below 2^31. This halves index/offset storage and the CUB radix-sort key width while keeping int64 as the default; Accumulation. FP16/BF16 weights accumulate in FP32. FP32 weights accumulate in FP64 to preserve accuracy at large D The small-table histogram and tensor-core path The specialized path is selected for one feature with E≤64, 64≤D≤128, L≥64, FP16 we

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  7. Microsoft Research

    Microsoft Research 播客:AI 会犯哪些错,失败又能教给我们什么

    Microsoft Research 播客对话 partner research manager Jennifer Neville,探讨评估如何推动当前 AI 系统突破性能边界以匹配用户需求,以及模型在传统 benchmark 之外测试时出现的“意外失败”。她还分享使用当前 AI 系统的实用建议,并谈到当结果超出预期时为何要仔细审视数据。

    oking at real data, but what does that actually mean? Are you genuinely looking at real data? How is privacy coming in here? What sort of processes do you have for responsibly leveraging data in the wild? NEVILLE: Oh, yeah, that’s a good point. Thanks for bringing it up. We’re not actually looking at the data. We are analyzing the data at scale to extract patterns of failures or successes in a privacy-preserving kind of way. There’s also a lot of, a lot of restrictions that we can’t actually see

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  8. The Decoder

    微软发布诺贝尔经济学奖得主 Daron Acemoglu 的悲观 AI 预测:十年 GDP 仅增 1.5%

    微软发布了诺贝尔经济学奖得主 Daron Acemoglu 对 AI 的悲观预测:AI 将在十年内推动 GDP 增长约 1.5%,最多替代 5% 的工作岗位,远低于部分 AI 实验室的预期。他认为瓶颈在于人性——企业需重新分配任务、提升员工技能并重组流程,这一过程可能比电气化耗时更长,更大的模型无法解决该问题。他还认为,增强人类技能的 AI 在生产力上会胜过完全自动化。

    edict. The bottleneck, Acemoglu says, is human nature. Companies have to reassign tasks, upskill workers, and restructure before productivity gains kick in , a process that could drag on longer than electrification did. Bigger models won't fix that. What's missing are easy-to-deploy apps that change how things get made, he says. AI that extends human skills will beat full automation on productivity, he argues, since even 99 percent accuracy often isn't enough once you factor in last-mile problem

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  9. NVIDIA Developer · Generative AI

    NVIDIA AICR v1.0 发布:开放、稳定且可验证的 GPU 集群配置

    NVIDIA AI Cluster Runtime(AICR)发布 v1.0,通过版本锁定、经验证的配方固定 GPU 加速 Kubernetes 集群的兼容组件组合,并在 CLI、REST API、Go SDK、bundle 布局和 artifact schema 上建立稳定兼容契约。AICR 提供 Snapshot、Recipe、Bundle、Validation 四项独立能力,可为 Helm、Argo CD、Flux 或 Helmfile 渲染部署产物,并用签名证据校验运行中的集群。目前 AICR 已有超过 100 名贡献者,其中近一半来自 NVIDIA 之外,Pulumi Labs 和 Mirantis k0rdent 已提供生态集成。

    lerated Kubernetes clusters depend on compatible versions across dozens of components, each on its own release cycle: host kernels, GPU drivers, container runtimes, networking, storage, operators, and workload frameworks. A configuration that works for one service, GPU generation, and Kubernetes release may silently fail for another, and tracing version conflicts after deployment is slow and error-prone. NVIDIA AI Cluster Runtime (AICR) addresses this with version-locked, validated recipes for G

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  10. AWS Machine Learning

    如何在 AgentCore 和 OpenClaw 上构建上下文感知 AI 助手

    AWS 介绍如何用开源智能体系统 OpenClaw 搭配 Amazon Bedrock AgentCore runtime 和 AgentCore memory 构建带长期记忆的个人助手,并以园艺助手 Sprout 为例。文本对话路由到 Claude Haiku 4.5,图像理解路由到 Claude Sonnet 4.5,整套系统放在单个 CloudFormation 模板中一键部署,轻量个人使用每月约 1–2 美元。

    Telegram chat where Sprout recalls the user’s full garden inventory, location, and sun exposure in response to a question

    AgentRuntime API on the AgentCore runtime, where a thin server.py process coordinates the OpenClaw gateway, AgentCore memory, and the Amazon Bedrock Converse API. Amazon Simple Storage Service (Amazon S3) provides workspace storage, AWS Key Management Service (AWS KMS) handles encryption, AWS Secrets Manager holds the bot token, and Amazon CloudWatch captures logs and metrics. Prerequisites To deploy your own version using the Launch Stack button or scripts/deploy.sh (described in the Grow your

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  11. TechCrunch AI

    Hark 发布主打隐私的 AI 个人助理 Hark Pro

    由连续创业者 Brett Adcock 创立不到一年的 Hark 正式发布 AI 个人助理 Hark Pro,可免费使用并提供面向重度用户的订阅档。该产品基于专门为电脑操作训练的模型,能接入邮件、日历、硬盘、信用卡等数字生活并代为执行任务,还会在小窗口中展示智能体浏览网页的过程以建立信任。Hark 计划在 2027 年推出配套的 AI 原生设备。

    ight thing. That matters because part of Hark’s pitch is that they are not Meta or OpenAI — “we’re not here to like sell you ads and steal your data,” Chowdhury says. To truly leverage these models, users will need to share their entire digital lives. Beyond very real cybersecurity concerns , will users be comfortable with a company like Meta having its products create detailed profiles of their friends and family? Asked about that kind of profiling, Chowdhury says that “we are doing work to und

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  12. TechCrunch AI

    AI 智能体的下一道门槛:网站是否愿意放行

    TechCrunch 报道称,Meta 的 Muse、Instinct、ChatGPT 的 Dots 等个人 AI 智能体在代用户下单、订票时频繁被网站拦截,最近 Amazon 开始阻止 Meta 的 Muse 浏览和购买其商品。部分拦截是网站主动为之,另一些则来自传统反爬机制,例如 Walmart 的人机验证按钮一旦中断就会把智能体踢出。Meta、Walmart、Stripe、Sierra、Genesys、Rocket、NiCE、Decagon 等已开始制定一套面向电商场景的智能体间通信开放标准,以区分代表用户的好机器人与恶意机器人。

    has gone too far and is essentially blocking actual human from making purchase on their website. I tried to purchase something directly and I can't even sign in. pic.twitter.com/CRAgkrCCa0 In the meantime, consumer confusion abounds. Although Walmart is at least trying to be accessible via AI agents, other companies aren’t so keen on the idea or haven’t fully embraced the technology. For example, flight booking is currently one of the main use cases being positioned for agents, but many people

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  13. Wired AI

    OpenAI 计划在 GitHub 一次性发布数百个数学问题解答,数学家不满

    WIRED 报道称,OpenAI 已告知相关人士计划于周二在 GitHub 上一次性发布数百个未解数学问题的解答,此前 8 月该公司曾召集约 40 名数学家讨论 AI 超越人类数学能力后的应对,并暗示模型已解决数百个长期难题。数学家批评 OpenAI 和 Anthropic 通过博客和推文而非论文发布结果,难以验证且常忽略他人先前工作,纽约大学数学家 Tristan Buckmaster 还指控 OpenAI 抢先发布其与 Anthropic 员工 Levent Alpöge 未发表的研究。OpenAI 发言人 Lindsay McCallum 表示公司正依据高等研究院数学与人工智能咨询组的建议负责任地发布结果,尚未确定发布时间。

    digested. The tools include Hexagon, a repository for primarily AI-generated material, and Palomar, a registry of machine-verified mathematics. While OpenAI has been directly encouraged to use these tools, “they haven't changed their behavior,” Kra notes. Nor does she feel the company’s actions are aligned with the Leiden declaration, a call by more than 4,000 mathematicians for AI companies to meet mathematicians’ standards that she helped write. Several employees within OpenAI believe their te

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  14. MarkTechPost精选

    Google DeepMind 发布 EmbeddingGemma 2,740M 开源多模态嵌入模型

    Google DeepMind 发布 EmbeddingGemma 2,一个基于 Gemma 4 架构的 740M 参数开源多模态嵌入模型,可将文本、代码、图像、视频和音频嵌入同一个 768 维空间,采用 Apache 2.0 许可,权重已上线 Hugging Face 和 Kaggle,并提供 Ollama、llama.cpp GGUF 和 LiteRT 版本。模型采用模块化设计,文本与代码主干 270M、视觉编码器 170M、音频编码器 300M 均可选,上下文窗口 8192 token,是上一版的 4 倍。Google 研究团队称其在 MTEB Code 和 MAEB 的 1B 以下多模态嵌入模型中领先,代码检索从 68.76 提升到 78.68;量化后在 Pixel 11 Pro 上纯文本约占 191MB 内存,全多模态约 567MB。

    d audio into one 768-dimensional space. It has 740M parameters, an 8K token context window and an Apache 2.0 license. It targets on-device search, classification and privacy-first RAG. This article analyzes, compares and showcase how EmbeddingGemma 2 fits in the space. Deployable today? Yes. Weights are live on Hugging Face and Kaggle, with Ollama, llama.cpp GGUF and LiteRT builds available now. What an Embedding Model Does An embedding model converts content into a vector of numbers that captur

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星期二

  1. AWS Machine Learning

    用 Amazon Bedrock AgentCore、托管知识库和 Nova Sonic 构建语音旅行助手

    AWS 发布方案,用 Amazon Bedrock AgentCore、Amazon Bedrock Knowledge Bases 和 Amazon Nova 2.5 Sonic 为航空公司应用搭建语音旅行助手,旅客可通过语音查询行程、改座位、更新餐食偏好和咨询政策。方案通过 AgentCore Gateway 以 MCP 连接后端,用 Strands Agents 框架托管在 AgentCore runtime,并支持转接人工客服。前端由 AWS Amplify 托管,后端使用 Lambda、DynamoDB 和 API Gateway。

    End-to-end voice concierge architecture spanning the backend, AgentCore Gateway, AgentCore runtime, and front end

    on C covers the AgentCore runtime. Two CDK stacks provision the runtime infrastructure. Amazon Elastic Container Registry (Amazon ECR) stores the container image, Amazon Simple Storage Service (Amazon S3) holds source uploads, and AWS CodeBuild produces the ARM64 Docker image. The Amazon Bedrock AgentCore runtime runs with WebSocket support using Strands Agents framework with Amazon Nova 2.5 Sonic. Section D covers the front end. One CDK stack deploys the React app on AWS Amplify. User request f

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  2. AWS Machine Learning

    直接在 SageMaker Studio 中管理 Amazon SageMaker HyperPod Spaces

    AWS 推出新功能,数据科学家和 ML 工程师可直接在 Amazon SageMaker Studio UI 中创建、配置、启停和打开 SageMaker HyperPod EKS 集群上的 Spaces,无需再依赖 HyperPod CLI 或 kubectl。新界面位于 HyperPod 集群详情页的 IDE and Notebooks 标签页,支持 JupyterLab、Code Editor 及通过 SSH-over-SSM 连接 VS Code,冷集群启动通常需几分钟,配合超配约 30–40 秒。

    ent, eliminating the need for CLI tools for day-to-day Space operations. Key capabilities available through Studio include: Create Spaces with configurable compute, namespaces, storage, HyperPod Task Governance for compute quota management and image settings through a guided form; View all Spaces in a searchable table showing name, application type, status, access type, storage, GPU, and vCPU allocations; Start and stop Spaces with a single selection to clear up compute resources when Spaces are

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  3. AWS Machine Learning

    Amazon SageMaker HyperPod 管理与治理最佳实践

    AWS 发布 Amazon SageMaker HyperPod 管理与治理最佳实践,提出组织、项目、集群、工作负载四层控制模型,用于在 SageMaker Unified Studio 中向 ML 团队提供已批准的加速计算资源。文章建议将集群、调度器和稀缺加速器容量集中在一个指定容量账户中,并通过 IAM、Amazon EKS RBAC 或 Slurm 控制、任务治理配额与抢占策略来管理共享容量。

    Figure 1: The four layers of control—organization, project, cluster, and workload. Each layer answers a different question and uses a different control, so review each at its own boundary.

    rator. For Amazon EKS, use a namespace per tenant together with RBAC, per-tenant service accounts and EKS Pod Identity roles, default-deny network policies, and tenant-specific storage and AWS KMS permissions. For Slurm, use Slurm accounting with hierarchical accounts and associations, quality of service (QoS), priority and fair-share policies, and partitions. Also use operating-system identity and file permissions, network controls, and tenant-specific data paths; Use IAM roles and resource pol

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  4. AWS Machine Learning

    AWS 如何帮助客户对齐 ISO/IEC 42005:2025 负责任 AI 治理

    AWS 发文解读国际标准 ISO/IEC 42005:2025,说明其如何指导企业将 AI 系统影响评估整合进现有治理体系。该标准 Annex D 提供简化流程以避免重复评估,Annex E 提供独立实施模板,并覆盖评估全生命周期与重新评估触发条件。AWS 已为 Amazon Bedrock、Amazon Q Business、Amazon Transcribe、Amazon Textract 取得 ISO 认证,并提供 ISO/IEC 42001:2023 AIMS 实施指南与 Well-Architected Framework 负责任 AI Lens。

    l find ISO/IEC 42005 specifies the specific information that should be documented in an AI system impact assessment via a templated assessment approach, explaining how to also leverage other risk assessments the enterprise may already be conducting to avoid duplication. Documentation should include items such as: A description of the AI system and its intended uses; How it could foreseeably be misused; The data used to develop the AI system, its underlying components and algorithms; The environm

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  5. Google DeepMind

    Google DeepMind 发布 EmbeddingGemma 2 多模态嵌入模型

    Google DeepMind 发布 EmbeddingGemma 2,一个 740M 参数、Apache 2.0 许可的端侧多模态嵌入模型,原生将文本、图像、音频和视频映射到统一嵌入空间。它在 MTEB Code 上从 68.76 提升到 78.68,支持 8K token 上下文窗口,可处理约 5.5 分钟音频、29 张图像或 58 帧视频,并可通过 MRL 将向量从 768 维截断至 128 维。模型权重已在 Hugging Face 和 Kaggle 开放下载,支持 LiteRT、MediaPipe、transformers.js、vLLM、Ollama 等部署方式。

    EmbeddingGemma 2

    se blew past our expectations. With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines. Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-dev

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  6. Google Blog

    Google DeepMind 发布 EmbeddingGemma 2 多模态嵌入模型

    Google DeepMind 发布 EmbeddingGemma 2,这是面向端侧的多模态嵌入模型,可将文本、图像、音频和视频映射到统一嵌入空间。模型基于 Gemma 4 架构,采用 Apache 2.0 许可,参数量 7.4 亿,文本任务最低 2.7 亿参数,可选视觉(1.7 亿)和音频(3 亿)编码器。它支持 8K token 上下文窗口,是 EmbeddingGemma 1 的 4 倍,可处理约 5.5 分钟音频、29 张图片或 58 帧视频;借助 MRL 可将输出向量从 768 维截断至 512、256 或 128 维,最多减少 6 倍存储占用。在 MTEB Code 上得分从 68.76 提升至 78.68,模型权重已在 Hugging Face 和 Kaggle 开放下载。

    EmbeddingGemma 2

    se blew past our expectations. With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines. Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-dev

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