Build specialized multimodal models with LFM3
Build specialized multimodal models with LFM3
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您可能错过的来自theCUBE对Workiva Amplify活动报道的三大洞察
企业级人工智能治理正逐步延伸至数据、工作流以及各项管控机制，这些要素决定了自动化系统能够执行哪些任务。随着智能体在金融、审计、合规与可持续发展等领域承担起日益重要的职责，组织亟需确保其成果既立足于业务情境、可追溯至原始数据来源，又受到明确的问责机制的规范与约束。TheCUBE对Workiva Amplify活动的预览重点介绍了[…]。本文《您可能错过的来自TheCUBE对Workiva Amplify活动报道的三大洞见》首发于SiliconANGLE。
SiliconANGLE AI
用于LLM智能体一致性的递归语义分歧
arXiv:2610.33012v1 发布类型：新 摘要：具备工具调用能力的大型语言模型（LLM）智能体，在从相同初始状态出发的多次独立运行中，表现出行为不一致的现象，例如调用不同的工具、产生相互矛盾的输出，或采取截然不同的策略。这种行为上的波动会随时间累积，从而削弱部署的可靠性。现有的评估指标主要通过基于任务完成度的得分来衡量正确性，而未能捕捉智能体在多次执行中的行为一致性。此外，对智能体行为的分析要么停留在词元层面，借助概率分布进行评估；要么聚焦于单次响应层面，采用语义聚类方法，但两者均无法刻画不一致性如何在轨迹的后续步骤中进一步传播。我们提出了递归语义发散（RSD），一种无
arXiv cs.CL
b11927
metal : add GGML_OP_TOP_K_MASK (#31230) * metal : add GGML_OP_TOP_K_MASK Port the fused top-k mask from the CUDA backend: a grid-stride kernel fills the output with -inf, then one threadgroup per row copies the mask values of the kept cells over it. Function constants drop the score, blocks and extra paths that a given graph does not use. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * metal : clean up top-k mask kernel formatting Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11927/llama-b11927-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](
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llama.cpp 发布
b11907
models : replace ggml_tensor** out-params with output structs (#31142) The hyper-connection and DSA graph builders wrote their extra outputs (post / comb gates, carried top-k selections, inject gates) through ggml_tensor** out-params. Return them in a small struct next to the primary output instead. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11907/llama-b11907-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11907/llama-b11907-bin-macos-x64.tar.gz) - [
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llama.cpp 发布
imec报告：面向人工智能时代的光互连技术规模化
对带宽的空前需求促使人们通过从根本上重新构想光互连路线图，将光学技术更紧密地集成到硅基芯片上。文章《imec报告：面向人工智能时代的光互连技术 scaling》首发于 Semiconductor Engineering。
半工程
OpenAI 推出 GPT-6.1 Terra，将用于代理工作的 GPT-6.1 Sol 的 API 价格下调一半
OpenAI发布了GPT-6.1 Terra，这是一种快速的计算机与视觉模型，每百万个标记的价格为1美元/5美元。其在每项任务上的成本为0.29美元，而GPT-6 Astra的中等努力代理的最后一次测试得分则为4.82美元。该文章《OpenAI推出GPT-6.1 Terra，将GPT-6.1 Sol的代理工作API价格减半》首发于MarkTechPost。
马克科技帖子
[AINews] Anthropic files for $2T IPO in 2 weeks: 1400% growth rate, 48% of revenue through cloud partners, 85% margin, $1B op profit in Q2, $134B cash in bank and $10B lifetime burn
The S-1, the numbers, and the reactions.
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潜在空间
Release: v5.21.0
# Release v5.21.0 ## New Model additions ### MiniCPM-OCR MiniCPM-OCR is a document-understanding model from OpenBMB's MiniCPM-V family. It keeps the MiniCPM-V 4.6 architecture, which pairs a SigLIP vision encoder and a window-attention merger with a Qwen3.5 language model. It is specialized for reading text-dense images such as scanned pages, screenshots, receipts and tables. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/minicpm_ocr) | [Paper](https://huggingface.co/papers/2509.18154) * Feat test (#49920) by @guarin in [#49920](https://github.com/huggingface/transformers/pull/49920) ### Flint Flint is Recursive's mixture-of-experts language model. Compared to a standard SwiGLU MoE transformer such as Qwen3-MoE, it uses hybrid attention (sliding-windo
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变压器发布
Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch
When you’re speaking to a device, you expect text to appear as you talk. That makes streaming automatic speech recognition (ASR) a different challenge from batch transcription, where the model... The post Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch appeared first on PyTorch.
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