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星期二

  1. Mistral AI精选

    Mistral 发布 Mistral Large 4 公开预览，1 万亿参数原生多模态

    Mistral 发布 Mistral Large 4 公开预览，这是一个 1 万亿参数、490 亿激活参数的原生多模态模型，权重将于本月底开放。模型在 Mistral 欧洲自有数据中心的 3800 块 NVIDIA Grace Blackwell GPU 上从零训练，训练数据覆盖 160 多种语言，官方称其在网络安全、金融、法律等企业负载上达到开源模型领先水平。在 Artificial Analysis Cyber Index 上它进入全球前五，在一项复现并修补真实漏洞的测试中得分 82%，Cybench 解题率 93%。

    ted as “Exemplary”). Of particular relevance is the model’s propensity to refuse malicious requests regarding cybersecurity. Despite strong performance on Cyber benchmarks, the average refusal rate of the model on cyber prompts from JailbreakBench, StrongREJECT, and AgentHarm is higher than all OSS models. Human Evaluation We ran an internal evaluation in which expert annotators across coding, computer-aided design (CAD), finance, mathematics and physics compared Mistral Large 4 with GLM-5.3. ML

    匹配字段正文

    #lab#模型发布#多模态#Agent
  2. The Rundown AI

    Reflection AI 发布首款开源权重模型 Beam，主打编码与智能体

    估值 250 亿美元的 Reflection AI 发布首款开源权重模型 Beam，面向编码与智能体任务，公司称其以远低于中国实验室 Z.ai GLM-5.2 的算力达到相近的推理和编码成绩。Reflection 计划本月以 Apache 2.0 许可证放出 Beam 权重，允许企业自行定制和部署，并已在韩国测试由对冲基金或政府用自有芯片和私有数据运行 Beam 的 AI 工厂模式。在自家测试中 Beam 仍落后于 Moonshot 的 Kimi K3，但优于 Thinking Machines 的 Inkling 等美国开源系统。

    hes, with a temporary ban headed to parliament as officials weigh permanent rules. AI video company Runway generated the opening of Sunday’s 60 Minutes, cloning correspondent Jon Wertheim to introduce a segment asking whether AI will take our jobs. Elon Musk hinted at renaming SpaceXAI to ‘SpaceXSI’ following the U.S. push to rename the tech, saying “we will make that change.” Sponsored Listing 🎓 Highlights: News, Guides & Events That's it for today! ⭐️⭐️⭐️⭐️⭐️ Nailed it; ⭐️⭐️⭐️ Average; ⭐️ Fail

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    #newsletter#行业动态#开源生态#Agent
  3. Latent Space精选

    Reflection 发布 Beam：501B 总参数、23B 激活的美国开源 MoE 模型

    Reflection 发布 Beam，一个面向编码、智能体与科研的纯文本 MoE 模型，总参数 501B、激活 23B，从零训练，Apache 2.0 全量权重预计本月发布。团队称预训练用了 23.8T token，并在约 10,500 块 GB300 上各跑四周预训练与 RL，自报 SWE-bench Verified 得分 80.9。观察者认为 Beam 大致处于 GLM-5.2 水平，部分基准低于 DeepSeek V4 Flash。

    round a small task-based workflow engine, so long-running, multiplayer agents can suspend and resume anywhere (Pi).Design: The core is 15K lines of TypeScript with SQLite/JSONL storage and runs on Bun or Cloudflare Durable Objects. Control is separated from execution environments (review); Effect.ts: The authors explain they skipped Effect because it does not provide durability (Zechner); Agent memory: Cognition launched Devin “Dreaming,” which prunes and links a memory graph overnight. It is op

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    #newsletter#模型发布#开源生态#数据/训练
  4. Google Developers Blog

    Google 发布 EmbeddingGemma 2 多模态嵌入模型

    Google 发布基于 Gemma 4 的 EmbeddingGemma 2，采用 Apache 2.0 许可，可将文本、代码、图像、视频和音频映射到统一的 768 维向量空间。模型支持模块化加载，参数量从纯文本与代码的 270M 到全模态的 740M，并借助 MRL 将维度截断至 128 维，在 bfloat16 精度下百万条 768 维向量约占 1.5 GB，截断到 128 维后仅需 250 MB。在 MTEB (Code) 上得分比 EmbeddingGemma 1 高 14%，同时新增图像、视频和音频检索能力。

    Facebook; Twitter; LinkedIn; Mail Modern search and retrieval augmented generation (RAG) applications increasingly need to work across diverse content types, from technical documentation and source code to images, video clips, and audio recordings. The challenge is finding models that deliver strong retrieval accuracy while maintaining low latency across all these formats without requiring massive compute infrastructure to run and index. EmbeddingGemma 2 is designed to provide a single, compact

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    #engineering#产品更新#多模态#RAG
  5. Google Developers Blog

    Google DeepMind 发布 EmbeddingGemma 2 端侧多模态嵌入模型

    Google DeepMind 发布开放权重多模态嵌入模型 EmbeddingGemma 2，可将文本、图像、视频帧和音频映射到统一的向量空间，参数量 740M，文本权重在 Google Pixel 11 Pro 上仅需约 191MB 活跃内存，完整多模态模型约 567MB。该模型无需训练数据或微调即可作为端侧决策引擎，将用户输入直接与分类标签和描述匹配，实现毫秒级零样本意图路由。Google AI Edge Gallery 新增 Instant Media Search 和 Video Moments Finder 两个演示，Google AI Edge Foresight for Mac 也由 EmbeddingGemma 2 与 Gemma 4 驱动；未来数周内该模型将通过 ML Kit 以服务形式登陆 Android，并支持 NPU 加速。

    the power of EmbeddingGemma 2 right away through Google AI Edge’s interactive showcases, Google AI Edge Gallery on mobile and Google AI Edge Foresight on Mac, and how you can leverage the model to build your own private, on-device semantic search, visual keyframe retrieval, and condition-trigger workflows. In the coming weeks, we will also make this model available as a service on Android, accessible through ML Kit, including using NPU acceleration to further optimize performance across a broad

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    #engineering#产品更新#多模态#端侧
  6. MarkTechPost

    Reflection AI 发布 Beam：501B 开源权重 MoE 模型，23B 激活参数面向编码与智能体任务

    Reflection AI 发布首款开源权重模型 Beam，采用稀疏 MoE 架构，总参数 501B、每 token 激活 23B，面向编码、推理与智能体工作负载。该模型预训练使用 23.8 万亿 token，在 6,144 块 NVIDIA GB300 NVL72 GPU 上不到 4 周完成，中训练将有效上下文扩展至 1M token；强化学习阶段用 10.5K 块 GB300 GPU 运行 4 周，生成超 1 亿次 rollout。Reflection 报告 Beam 在 SWE-bench Verified 得分 80.9、Terminal Bench v2.1 得分 80.1，目前处于最终红队测试阶段，通过 Reflection 平台候补名单提供早期访问，Apache 2.0 权重计划于本月晚些时候发布。

    ained routed experts. Load balancing builds on auxiliary-loss-free balancing from DeepSeek-V3, adding cosine decay of expert-bias updates. The busiest expert reached just 1.04x average load at the end of pretraining. Across all 52 layers, residual norms stayed bounded using depth-based scaling, SandwichNorm, attention gating and FP32 residual accumulation. Pretraining finished in under 4 weeks on 6,144 NVIDIA GB300 NVL72 GPUs. Goodput reached 92.3% near the end, with 9 semi-automatic rewinds. Mi

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    #ai-vertical#模型发布#开源生态#Agent

星期一

  1. Liquid AI 模型与工程博客精选

    Liquid AI 发布首个决策模型 d1，支持文本与图像

    Liquid AI 发布首个决策模型 d1，可同时接收文本和图像，一次前向传播直接返回各选项概率而不生成 token，文本决策耗时 200 到 300 毫秒。官方称在六个真实应用中 d1 在四项上追平或超过 GPT-6.1 Sol，成本比 GPT-6.1 Sol 和 Claude Opus 5.5 低 19 到 200 倍，且每项任务响应更快。d1 已在 Liquid AI API 上线，按输入 token 计费，图像按每 32×32 像素块 1.5 token 折算，同时通过 Vercel 和 OpenRouter 提供纯文本版本，视觉能力即将上线。

    ation: In Wordle, d1 reads the board directly from a screenshot. Developers don't need to write a text version of the game, and d1 still solved 12 of 12 games in 3.8 guesses on average d1 also handles purely visual tasks. In Quick, Draw!, it guesses what a player's doodle shows among 62 words, and recognizes 5.2 of 6 drawings (random guessing gets 0.6). Availability and pricing Start building today with the d1 decision model, available on the Liquid AI API as d1. Create an API key at console.liq

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    #company#模型发布#多模态#推理

星期日

  1. MarkTechPost

    Aleph Alpha 发布 Kolibri：78.1B 开源英德双语 MoE 模型，每 token 仅激活 3.46B 参数

    Aleph Alpha 发布开源权重英德双语 MoE 模型 Kolibri，总参数 78.1B，每 token 仅激活 3.46B（4.4%），支持最长 1,048,576 token 上下文，并以 Apache 2.0 许可在 Hugging Face 发布。FP8 权重约 78GB，可在单张 B200、B300 或 H200，或 2 张 H100 SXM5 上通过 vLLM 部署，面向公共管理、工业和航空航天等受监管行业的自主部署。模型采用 50 层 Transformer、384 个路由专家中每 token 选 6 个加 1 个共享专家，40 层滑窗注意力配 10 层全注意力，预训练覆盖 20T token。

    y model with the same eval-framework setup. In English, Kolibri leads GPQA Diamond (84.3), AIME 2025 (96.9) and AIME 2026 (96.0). It ties Qwen3.5 35B-A3B on the English agentic average at 63.4. It trails on BFCL v4, scoring 61.4 against 70.5 for Qwen3.5. The dense Qwen3.8 27B scores higher overall (80.2 EN, 79.9 DE), but activates about 8 times more parameters per token. Against its internal predecessor Kolibri Origin, Kolibri decodes about 2.7 times more text per GPU while scoring 21.4 points h

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    #ai-vertical#模型发布#开源生态#端侧

星期六

  1. Ai2 Blog

    Ai2 开源 AstaBrief 8B 科学报告生成模型

    Ai2 开源 AstaBrief 8B，一个把研究问题和检索到的文献片段转成带引用报告的模型，已在 Asta 的 Generate a report 功能中作为 Fast mode 上线，与 Claude 驱动的 Thinking mode 并存。Ai2 称 Fast mode 平均每份报告 51.1 秒，Thinking mode 为 178.5 秒，约快 3.5 倍。模型基于 Qwen3-8B，采用 SFT 加 DPO 训练，训练数据来自 90K 条真实科研查询，权重和训练数据已在 Hugging Face 开放下载。

    y section. The result is nearly an order-of-magnitude reduction in report generation time compared to the proprietary models we tracked—across the full Asta pipeline, Fast mode averages 51.1 seconds per report compared with 178.5 seconds for Thinking mode, about 3.5× faster. Together, those efficiency gains made AstaBrief a useful test case for a broader goal: building open language models that can be adapted to the specific demands of scientific work. Open weights will also let institutions run

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    #lab#开源/仓库#数据/训练#RAG

星期五

  1. Hugging Face

    Ai2 开源 AstaBrief 报告生成模型，基于 Qwen3-8B 训练

    Ai2 开源 AstaBrief，一个基于 Qwen3-8B 训练的科学报告生成模型，已作为 Asta 的 Fast 模式上线，并可从 Hugging Face 下载。训练采用监督微调加直接偏好优化的简单方案，SFT 数据来自 90K 条真实科研查询过滤后的 47K 条样本，DPO 数据约 6K 条，由 GPT-4.1 与 DeepSeek-R1 双评委一致才保留。模型一次生成完整报告，跳过片段摘要与聚类步骤，在 SQABench-CS2 等评测上接近 Claude 驱动的报告管线；374 名试用用户中 29.1% 连续使用两天以上，Fast 模式好评率 84.2%。

    etrics throughout the development of AstaBrief: Rubric score , which measures how much necessary content is covered by the report. Answer precision , which measures whether each paragraph is relevant to the question. Citation precision , which measures whether each citation supports the claim it's attached to. Citation recall , which measures whether the report's claims are fully supported by the citations provided. For our final model, we also ran secondary evaluations: DeepScholarBench , a 63-

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    #lab#开源/仓库#数据/训练#RAG
  2. MarkTechPost

    Cohere 发布 Embed 5 嵌入模型家族，Pro 与 Fast 共享同一向量空间

    Cohere 发布 Embed 5 嵌入模型家族，分 Embed 5 Pro 和 Embed 5 Fast 两档，均支持文本、图像及图文融合输入，覆盖 100+ 语言、最长 128K tokens，已在 Cohere API、Model Vault、Microsoft Foundry 和 Amazon SageMaker 上线，私有 VPC 或本地部署走 vLLM。两档共享同一向量空间，可用 Pro 建索引、Fast 查询，Cohere 建议的该组合在归一化评测中得 98.4（Pro+Pro 为 100），全 Fast 为 96.6，但两侧必须使用相同输出维度。模型 ID 为 embed-v5.0-pro 和 embed-v5.0-fast，支持 2048 至 256 六种维度、默认 2048，输出 float、int8 或 binary；Pro 每 1M 文本 token 收费 $0.12，Fast 为 $0.08，图像输入两档均为 $0.40。在 ViDoRe V3 上 Pro 平均 85.8，较 Embed 4 提升 8.8 分，Fast 为 84.5，Voyage 4 Large 为 83.7、Gemini Embedding 2 为 83.2、OpenAI text-embedding-3-large 为 75.5；多语言方面 Pro 在欧洲语言平均分 77 领先，但在 Cohere 自测表中 10 种其他语言里有 9 种落后于 Gemini Embedding 2。多数成绩使用 Cohere 新指标 RCP-nDCG@10，评测代码已公开但独立复现尚未完成。

    Cohere has released Embed 5 , a new embedding model family. It targets enterprise search, RAG, and agentic retrieval. The model family ships in 2 tiers. Embed 5 Pro targets maximum retrieval quality. Embed 5 Fast targets latency and cost on the live query path. Both accept text, images, and fused text plus image inputs. Both cover 100+ languages and read up to 128K tokens. The key design choice: Pro and Fast share 1 embedding space. You can index with one and query with the other. Is it deployab

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    #ai-vertical#产品更新#RAG#多模态
  3. Microsoft AI Blog精选

    微软发布 MAI-Transcribe-2-Streaming 流式转写模型及两款语音模型

    微软发布首款流式转写模型 MAI-Transcribe-2-Streaming，支持 60 种语言和自动连续语言检测，在 Artificial Analysis 的最终与部分转写准确率上均排名第一，收到音频后 100ms 出头即可给出首个部分结果，年内按每小时音频 0.54 美元计价。同时发布多语言文本转语音模型 MAI-Voice-2.1，支持 23 种语言和 26 个地区，单一音色可用各语言的原生口音，定价每 100 万字符 22 美元；以及面向高并发低延迟场景的 MAI-Voice-2.1-Flash，端到端延迟 150ms、可生成 45 秒音频，推理速度提升 55%，定价每 100 万字符 15 美元。三款模型可通过 Microsoft Foundry 等渠道使用，MAI-Voice-2.1 系列已上线 OpenRouter。

    Pink semicolons arranged in a swirling pattern against a blue background with soft, blurred light streaks.

    a truly native accent. Just ask it to speak English… then Mandarin… then German… and the speaker stays unmistakably the same, naturally picking up the local parlance, rather than dragging one accent across languages. That means your brand can keep a single voice everywhere: a tutoring app can switch languages mid‑lesson without swapping teachers, and a multilingual assistant can reply in whatever language it’s addressed in, all while still sounding like the same voice. And it’s priced at $22 per

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    #company#模型发布#语音#Microsoft

星期四

  1. The Rundown AI

    Google 发布 Gemini 4 Argon，19 项基准中 13 项超过 GPT-6 Astra 与 Claude Opus 5.5

    Google 发布新前沿模型 Gemini 4 Argon，在 Google 自测的 19 项基准中有 13 项超过 GPT-6 Astra 和 Claude Opus 5.5，目前仅向经过审核的特定网络安全团队开放。该模型在 Arena 文本榜登顶，AA 智能指数得 53 分，仅次于 Opus 5.5，与 Fable 5.1 和 Astra 持平，并在真实编码测试 DeepSWE 上取得 77.9% 的领先成绩。Google 尚未公布更大范围上线时间，API 定价为每百万 token 输入 2 美元、输出 10 美元，促销期结束后升至 4 美元和 20 美元；彭博社报道称内部对 Argon 的编码能力存疑，Google 否认了这一说法。

    an the Flash line. Argon’s numbers put Google back in the frontier talks, but we need to see how it runs in a broader rollout before declaring this a comeback story. The Rundown: Fragmented tools and slow handoffs cost more than most teams realize. A Forrester Consulting study quantified it, and for a composite enterprise, the result was 312% ROI over three years, $38.6M net present value, and payback in under 6 months. This ROI calculator lets you: Plug in your team size and tool stack to see p

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    #newsletter#行业动态#Google#OpenAI
  2. Latent Space精选

    Google DeepMind 发布 Gemini 4 Argon，输出上限提至 1M token

    Google DeepMind 发布前沿模型 Gemini 4 Argon，面向编码、企业知识工作和网络安全防御，今日起通过 Fairwind Program 向受信任测试者开放，并承诺尽快扩大访问。Google 称其输出上限达 1M token，较此前的 64K 大幅提升，标准定价为每 1M 输入/输出 token 4 美元/20 美元，另有 50% 的限时折扣。Google 称 Argon 在 19 项公开基准中的 13 项排名第一，DeepSWE 得分 77.9%，高于 Opus 5.5 的 74.2% 和 Astra 的 74.1%；Artificial Analysis 测得其在 Intelligence Index 上得 53 分，与 GPT-6 Astra 持平。

    At discounted pricing it costs $1.99 per task versus $3.26 for Astra; standard pricing would raise this to $3.98. Token use : The savings come from price, not efficiency. Argon averages 62K output tokens per task against Astra’s 27K. Agentic work : It ranks 1 on AutomationBench-AA at 77.5% and scores 57% on Terminal Bench 4, behind Sonnet 5.5, Opus 5.5 and Astra. Hallucination : Its 15% rate on AA-Omniscience compares with 51% for Astra. The tradeoff is lower accuracy: 50% versus Astra’s 63% ( @

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    #newsletter#行业动态#Google#OpenAI
  3. A6 a6-070 · 字节 Seed：Research Papers精选

    字节 Seed 发布 SeedRealtime 原生音视频全双工 LLM

    字节 Seed 正式发布 SeedRealtime，一个原生音视频全双工 LLM，用统一架构原生融合音频、视频和文本，支持连续多模态流上的实时交互。官方称其实现音视频联合理解、主动交互和自然对话节奏三项突破，端到端人工评测显示相比级联模型，音视频对话节奏问题减少一半。目前 SeedRealtime 已全面上线，官方称这是音视频全双工技术的大规模部署。

    -only menu. SeedRealtime identifies the dishes straight from the scene, recommends them in English, and even draws on cultural background to explain "why there is no fish in fish-fragrant pork (yuxiang rousi)" and "how century eggs are made." As the server sets down a dish and casually says it "goes great with rice," the model understands the line in the context of the dish on screen and translates it for the guest. Visual information does not need to be turned into text first; it is aligned wit

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    #a6-editorial#模型发布#多模态#语音
  4. A6 a6-070 · 字节 Seed：Research Papers精选

    字节 Seed 发布视频生成模型 Seedance 2.5

    字节 Seed 团队正式发布新一代视频创作模型 Seedance 2.5，单次可生成 30 秒音视频片段并支持多轮续写，单次最多可输入 30 张图片、10 段视频和 10 段音频作为参考素材。模型新增时间戳级编辑能力，并强化绿幕、镜头视角和参考编辑等功能，已在即梦 AI、豆包 Pro 等平台上线，API 将通过 BytePlus ModelArk 提供。

    erate the video, ensuring that the composition and blocking of complex shots closely match the creator's expectations. Additionally, Seedance 2.5 improves lighting control. By leveraging the spatial information from the clay render, it generates realistic lighting effects that follow physical laws, such as light source direction, color temperature, intensity, and shadow projection. This results in more natural light and shadow in the final output. R2V prompt: Refer to @Clay Render 1 for camera m

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    #a6-editorial#模型发布#视频#多模态
  5. A6 a6-070 · 字节 Seed：Research Papers精选

    字节 Seed 发布多模态图像创作模型 Seedream 5.0 Pro

    字节 Seed 正式发布多模态图像创作模型 Seedream 5.0 Pro，在图文对齐、结构一致性、文字渲染和视觉美感等基础能力上全面提升。该模型带来四项核心突破：复杂信息可视化、交互式精准编辑、写实影像与人物质感，以及原生多语言输入与生成，支持法语、德语、俄语、日语、韩语、西班牙语、阿拉伯语等十余种语言。官方称其在复杂信息图生成和交互式精准编辑上取得突破，但更细粒度的文字渲染与像素级编辑一致性仍有提升空间。

    tal decorations. The background areas previously obscured by the main subject are also seamlessly inpainted and restored. These layers retain their transparency and can be freely dragged or scaled. Creators can even replace the core subject with a new element (such as swapping the parrot for a peacock). Multi-image fusion: combining multi-source materials for complex scenes By simultaneously inputting multiple reference materials and a target base image, the model fuses the different elements in

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    #a6-editorial#模型发布#图像生成#多模态
  6. The Decoder精选

    Google 发布 Gemini 4 Argon，评测追平 OpenAI 与 Anthropic 但未明显领先

    Google 发布新前沿模型 Gemini 4 Argon，在 Artificial Analysis 智能指数上以 53 分追平 OpenAI GPT-6 Astra（max）和 Claude Fable 5.1，但仍落后 Claude Opus 5.5 的 58 分。该模型先向 Fairwind 项目的“可信网络防御者”和 Google 内部团队开放，无网络安全护栏，介绍性价格为每百万输入 token 2 美元、输出 10 美元，缓存输入便宜 95%。输出上限从 64,000 提升到 100 万 token，Gemini API 新增 Long Decode Continuation 功能以支持长响应续写；在 Arena.ai 文本竞技场以 1,525 分排名第一。

    1 Sol. Once the discount ends, the cost rises to $3.98, about 20 percent above GPT-6 Astra. The price advantage comes from lower token rates, not from efficiency. Argon uses an average of 62,000 output tokens per task, while GPT-6 Astra needs only 27,000. Argon also made significant gains on agentic tasks, which according to Artificial Analysis have been a weak spot for Gemini models. On AutomationBench-AA, the Artificial Analysis variant, it takes first place at 77.5 percent, six points ahead o

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    #ai-vertical#模型发布#Google#评测/基准

星期三

  1. Cohere 产品与研究博客

    Cohere 发布 Embed 5 系列嵌入模型，Pro 与 Fast 共享同一嵌入空间

    Cohere 发布 Embed 5 系列嵌入模型，包含面向最高质量的 Embed 5 Pro 和面向延迟与成本敏感场景的 Embed 5 Fast，两者共享同一嵌入空间，可用 Pro 建索引后用任一模型查询而无需重建索引。Embed 5 支持多模态输入与检索、100 多种语言和 128K token 上下文窗口，Pro 在 ViDoRe V3 上平均得分 85.8，Fast 为 84.5。模型已在 Cohere API、Model Vault、Microsoft Foundry 和 Amazon SageMaker 上线，Pro 定价为每百万 token 0.12 美元，Fast 为 0.08 美元。

    Key takeaways State-of-the-art enterprise retrieval: Embed 5 Pro achieves the highest average score of any model we tested, particularly across financial datasets, parsed PDFs, and visually rich documents; A new Fast tier: Embed 5 Fast brings strong retrieval quality to latency, and cost-sensitive workloads, at $0.08 per million tokens; One index, two models: Pro and Fast share an embedding space, so teams can index with Pro and query with either model without re-indexing; Built for complex ente

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    #company#产品更新#RAG#多模态
  2. Google DeepMind精选

    Google DeepMind 发布 Gemini 4 Argon 前沿模型

    Google DeepMind 发布新前沿模型 Gemini 4 Argon，先通过 Fairwind Program 向受信任的网络安全防御方开放，后续再逐步扩展到开发者、企业和消费者。该模型输出 token 上限从 64K 提升到 1M，在 DeepSWE v1.1 上取得 77.9%，在 LVBench 上取得 91.7%，并在 Vals Index、AutomationBench 等评测中领先。Google 称 Argon 已用于内部工作流，并针对提示注入、失准监控和沙箱环境等方向加强了安全措施。

    alidate, and patch critical software vulnerabilities. For trusted defenders and our own internal teams at Google, we’ll be releasing Argon without cyber guardrails so they can leverage its full frontier-level cybersecurity defense capabilities. Wiz is already using Argon for cybersecurity defense through its Scan for Good initiative – a program dedicated to protecting critical public infrastructure for free by finding and remediating high-risk exposures. In an early demonstration of its impact,

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    #lab#模型发布#推理#编码