We Should Chart an Atlas of All the World's Models
This addresses the challenge of unstructured model repositories for researchers and practitioners, though it is a position paper advocating for future work rather than presenting incremental results.
The paper tackles the problem of millions of undocumented models in public repositories by proposing a unified Model Atlas graph to capture models, their attributes, and connections, enabling applications in model forensics, meta-ML research, and discovery.
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.