Perceptron 发布 35B 具身智能模型 Mk1.5，请求完成速度最高提升 4.7 倍

7 倍。该模型专门用第一人称（egocentric）视频训练，针对机器人与智能眼镜中手部频繁入镜、遮挡和相机移动的场景。

在 MMSearch 基准上，启用工具后 F1 从 18.2 提升到 54.

3，提升 36.1 分，可用能力包括网页搜索、页面阅读、反向图搜索和图像放大。

Perceptron released Mk1.5, a 35B embodied-agent model with up to 4.7× faster request completion.

4. First-person video is a very different problem from ordinary video understanding.

For a robot or a pair of smart glasses, hands constantly enter the frame, manipulate objects, disappear, return, and partially occlude what matters. The camera itself is moving too.

Perceptron trained Mk1.5 specifically on egocentric video, and very interestingly the model gets a 36.1-point jump on MMSearch when tools are enabled. Moves from 18.2 to 54.3 F1 when it can use web search, page reading, reverse-image search, and zoom.

This is a useful way to think about embodied intelligence too.

A robot does not need every possible capability encoded into its weights. It needs to recognize when its current information is insufficient, pick the right external capability, use it, then continue the task.

本条情报归属 AITOP「模型」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Perceptron、Mk1.5、多模态、具身智能，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

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