Title: Understanding Transformer Architecture through Continuous Dynamics: A Partial Differential Equation Perspective
Primary area: foundation or frontier models, including LLMs
Keywords: Transformer Architecture，Information Bottleneck，Partial Differential Equation (PDE)

Abstract: The Transformer architecture has revolutionized artificial intelligence, yet a principled theoretical
understanding of its internal mechanisms remains elusive. This paper introduces a novel analytical
framework that reconceptualizes the Transformer’s discrete, layered structure as a continuous spa-
tiotemporal dynamical system governed by a master Partial Differential Equation (PDE). Within this
paradigm, we map core architectural components to distinct mathematical operators: self-attention
as a non-local interaction, the feed-forward network as a local reaction, and, critically, residual con-
nections and layer normalization as indispensable stabilization mechanisms. We do not propose
a new model, but rather employ the PDE system as a theoretical probe to analyze the mathemat-
ical necessity of these components. By comparing a standard Transformer with a PDE simulator
that lacks explicit stabilizers, our experiments provide compelling empirical evidence for our cen-
tral thesis. We demonstrate that without residual connections, the system suffers from catastrophic
representational drift, while the absence of layer normalization leads to unstable, explosive train-
ing dynamics. Our findings reveal that these seemingly heuristic ”tricks” are, in fact, fundamental
mathematical stabilizers required to tame an otherwise powerful but inherently unstable continuous
system. This work offers a first-principles explanation for the Transformer’s design and establishes
a new paradigm for analyzing deep neural networks through the lens of continuous dynamics.