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(CA) Liquid AI LFM2.5: Small Model, Big Agent Power

Liquid AI 发布 LFM2.5-24B-A2B,用于高效智能体任务

Liquid AI 发布了 LFM2.5-24B-A2B,这是一款专为智能体任务设计的开放权重模型。该模型采用混合专家(mixture-of-experts)方法,在总计 240 亿参数中,每个 token 激活约 20 亿参数,旨在平衡能力与成本效益。主要升级包括显著扩展的 131,072 token 上下文窗口、翻倍的词汇量以增强多语言支持,以及针对 OpenClaw 和 Hermes Agent 等智能体框架的增强调优。虽然该模型在自身基准测试中擅长工具使用和指令遵循,但在知识密集型和编码任务方面,大型模型仍表现更优。 AI

影响 支持更具成本效益的本地智能体部署和私有数据处理。

排序理由 前沿实验室模型发布,附带系统卡。[lever_c 从 frontier_release 降级:ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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

Liquid AI 发布 LFM2.5-24B-A2B,用于高效智能体任务

本文如何被排名

Signal score
55 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
前沿实验室模型发布,附带系统卡。[lever_c 从 frontier_release 降级:ic=1 ai=1.0]
Source corroboration
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
model release, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 (CA) · NeurAITool ·

    Liquid AI LFM2.5:小型模型,强大智能体能力

    <p>Most agent models ask you to choose between capability and cost. Liquid AI's LFM2.5-24B-A2B tries to remove that trade-off: it is a large open-weight model that only wakes up a small slice of itself for each token.</p> <h2> The idea in plain terms </h2> <p>The headline is simp…