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实时 08:20:22
English(EN) Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation

Maverick系统实现私有且可验证的大语言模型推理

一个名为Maverick的新系统已被开发出来,用于实现私有且可验证的大语言模型(LLM)推理。Maverick利用一种新颖的协议将大语言模型中的关键操作——矩阵向量乘法委托出去,以解决隐私和正确性问题,同时没有显著的服务器开销。该系统结合了信息论验证和伪随机掩码以实现输入隐私,并且在Qwen3-4B模型上的评估显示,与本地推理相比,吞吐量有了显著提升。 AI

影响 该系统通过允许用户在不损害隐私或可验证性的情况下委托计算,从而能够更安全、更可信地部署大语言模型。

排序理由 该集群描述了一篇论文中提出的一种新颖的私有且可验证的大语言模型推理方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Maverick系统实现私有且可验证的大语言模型推理

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Tool
该集群描述了一篇论文中提出的一种新颖的私有且可验证的大语言模型推理方法。[lever_c_demoted from research: ic=1 ai=1.0]
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Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, infra
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Story freshness
4 days old
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完整方法见我们的编辑标准

报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Maverick:通过矩阵向量乘法委托实现私有且可验证的大型语言模型推理的实用化

    Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inference without exposing user inputs to a service provider. However, running large-scale models locally requires substantial comput…