Machine learning can be understood as searching for stable points where infinitesimal changes preserve compositional structure—a perspective that unifies deep learning, LLMs, and RL under a single categorical framework.
This paper proposes LINCS, a categorical framework that reformulates machine learning as a problem of fixing compositional failures in abstract mathematical structures called sketches.