I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction. I argue that contemporary deep learning systems often can and do achieve such understanding. However they generally fall short of the ideal of scientific understanding: the understanding is symbolically misaligned with the target system, not explicitly reductive, and only weakly unifying. I label this the Fractured Understanding Hypothesis.
@article{arxiv.2604.04171,
title = {A Model of Understanding in Deep Learning Systems},
author = {David Peter Wallis Freeborn},
journal= {arXiv preprint arXiv:2604.04171},
year = {2026}
}