全部 AI 动态

结合 Agentforce 与 AWS MCP 扩展公共部门智能数据处理能力

公共部门机构日常需处理大量如执法记录仪视频和扫描文档等非结构化证据。本文介绍了如何将 Amazon Bedrock 数据自动化与模型上下文协议（MCP）相结合，将这些非结构化多模态数据转化为结构化洞察，并通过自然语言在 Salesforce Agentforce 智能体平台中实现无缝查询与交互分析。

大模型推理框架发布 v0.30.0 版本：新增 DeepSeek-V4.1、GLM-5.3 等多款模型及优化支持

开源大模型推理框架发布 v0.30.0 版本更新，包含来自 315 位贡献者的 762 次提交。新版本支持了多款前沿大模型及架构优化，包括支持 MXFP8 格式 KV 缓存与 FlashMLA 的 DeepSeek-V4.1-Flash、支持 ROCm 与 LoRA 的 DeepSeek-V4-Flash-Vision-Exp、支持 EPLB 的 GLM-5.3-Flash，以及 K2-Horizon、Cohere Compass、Bailing V3 VL 等，并引入了基于 AVX512/AMX 的 CPU 后端支持。

Higgsfield AI 借助 GPT-6 Astra 单日交付视频新功能

AI 创企 Higgsfield AI 借助名为 GPT-6 Astra 的模型，据称在一天之内便上线了全新视频功能。该功能主要面向小型企业，旨在简化视频广告制作流程，并加速创新创意工具的推向市场。原素材信息较为有限，未披露该模型的技术细节或更深入的实现机制。

Moonshot AI 的 Kimi K3 模型正式上线 Amazon Bedrock

Moonshot AI（月之暗面）开发的 Kimi K3 模型现已在 Amazon Bedrock 上线。该模型为开放权重架构，专为编程与知识工作场景优化。Kimi K3 具备原生视觉理解能力，支持高达 100 万 token 的上下文窗口，并提供显式提示词缓存（Prompt Caching）功能，可有效降低推理延迟与输入成本。

利用 PyNvVideoCodec 与 vLLM 实现多 GPU 视频字幕生成扩展

本文介绍了如何结合使用英伟达硬件视频解码库 PyNvVideoCodec 与大模型推理引擎 vLLM，在多 GPU 环境下实现视频字幕生成与视频描述任务的高效扩展。该方案通过硬件级视频解码加速数据预处理流程，有效解决了多模态视频理解任务中的数据吞吐瓶颈，提升了多卡并行推理的整体效率与性能表现。

如何利用AI智能体构建与验证用于物理仿真的3D场景

文章介绍了如何将AI智能体工作流应用于物理AI系统的数字孪生准备与验证中。通过智能体工作流，AI能够自动巡检并解析3D场景，生成和编辑仿真所需的关键数据，从而大幅提升物理仿真环境的搭建效率，为具身智能与机器人等物理AI系统的训练与测试提供高质量的数字孪生支撑。

Google推出Gemini 3.8 Live语音模型，支持实时双向语音交互

Google发布了Gemini 3.8 Live与Gemini 3.8 Live Extended Thinking两款新型语音到语音（speech-to-speech）大模型，对标OpenAI的GPT-Live系列。开发者通过其WebSocket API构建了轻量级Web端体验工具，用户可在浏览器中选择模型、预设声音及系统提示词，体验支持随时打断的低延迟实时双向语音对话。

Gemini 3.8 Live 与 3.8 Live Extended Thinking 模型正式发布

该动态宣布推出 Gemini 3.8 Live 以及具备扩展思考能力的 Gemini 3.8 Live Extended Thinking 新模型。由于输入素材仅包含标题，具体的技术架构细节、性能评测基准以及实际功能特性等详细信息尚待进一步披露。

面向多语言环境的普及化人工智能愿景展示

该素材内容极其简略，仅包含一段展示多种不同语言词汇缓缓放大掠过的动画描述，标题表达了将人工智能普及至全球各种语言使用者的愿景。由于缺乏具体技术细节、发布产品或实施方案的描述，实际有效信息严重不足。

When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill...

NeoMME: an efficient Multimodal-native and Multilingual Encoder

Release: v5.16.0

Release v5.16.0 New Model additions Qwen4-Exp Qwen4-Exp builds on Qwen3.5's hybrid text and multimodal architecture with three key components: GatedResidual (GR), Qwen Sparse Attention (QSA), and Per-Layer Embedding (PLE). GR is a Qwen-developed residual architecture that combines Hyper-Connection with GatedNorm. It mixes multiple residual streams with fine-grained elementwise gating before each attention and Mixture-of-Experts (MoE) block, then controls how much of the block output is injected back into each stream. QSA uses multiple query heads to score compressed key blocks, selects the most relevant contiguous token blocks, and keeps the

Release v5.16.1

Release v5.16.1 This is a special release as we include GLM! (and a few small fixes) GLM-5.3-Flash GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply r

Release: v5.15.0

Release v5.15.0 New Model additions Meta Muse Glimmer Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups. Muse Glimmer is a dense 30B parameter model consisting of: 2B ViT-style encoder for vision (Perception Encoder) 28B parameter text decoder We're covering it in the following blogpost: http://hf.co/blog/muse-glimmer GraniteMoeSWA & GraniteSWA Links: Documenta

Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

Release v5.14.0

Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI- powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-foll

Release v5.13.0

Release v5.13.0 New Model additions KimiK 2.5, 2.6, and 2.7 This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7: Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in Kimi K2.5: Visual Agentic Intelligence and further improved in [Kimi K2.6: Advancing Open-Source Coding](Kimi K2.5: Visual Agentic Intelligence). Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming langua

Introducing Gemma 4 12B: a unified, encoder-free multimodal model