构建全球模型图谱，让海量未文档化模型可被发现与分析。
We Should Chart an Atlas of All the World's Models
- 用图结构记录模型及其权重变换关系，形成统一模型地图。
- 从模型权重直接推断功能、性能和来源，无需依赖文档。
- 适合关注模型发现、元学习与模型溯源的研究者。
公开模型仓库如今包含数百万个模型，但大多数缺乏文档，几乎等同于丢失。本文倡导构建一个名为模型图谱（Model Atlas）的统一结构：一种记录模型、属性及其权重变换关系的图数据。该图谱可支持模型溯源、元机器学习研究与模型发现，而这些任务在当前非结构化仓库中难以实现。然而，由于多数模型无文档，图谱的大部分区域仍处于未测绘状态。为此，需发展新机器学习方法，将模型本身作为数据，直接从权重中推断其功能、性能和谱系。我们主张绕过模型权重中的独特参数对称性，以实现可扩展的建图路径。全面绘制全球模型图谱需社区协作，其广泛用途有望激励研究者共同参与。
原文摘要 · Abstract (English)
Public model repositories now contain millions of models, yet most models remain undocumented and effectively lost. In this position paper, we advocate for charting the world's model population in a unified structure we call the Model Atlas: a graph that captures models, their attributes, and the weight transformations that connect them. The Model Atlas enables applications in model forensics, meta-ML research, and model discovery, challenging tasks given today's unstructured model repositories. However, because most models lack documentation, large atlas regions remain uncharted. Addressing this gap motivates new machine learning methods that treat models themselves as data, inferring properties such as functionality, performance, and lineage directly from their weights. We argue that a scalable path forward is to bypass the unique parameter symmetries that plague model weights. Charting all the world's models will require a community effort, and we hope its broad utility will rally researchers toward this goal.
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