New MIT paper introduces a new mathematical framework for representing, manipulating, and compiling deep learning architectures.

Today, engineers mostly describe models with code, equations, and diagrams, but none of those gives one precise language for the whole architecture.

The paper proposes one common language for describing a model, including exactly how its tensor operations connect and behave.

Describe the architecture once, and the same representation can be turned into diagrams, machine-readable graphs, or runnable PyTorch code.

The goal is to eventually let software analyze and optimize model architectures automatically instead of engineers working everything out by hand.

• arxiv. org/abs/2604.07242