The calculus of variations of the Transformer on the hyperspherical tangent bundle

• URL: http://arxiv.org/abs/2507.15431v1
• Date: Mon, 21 Jul 2025 09:43:33 GMT
• Title: The calculus of variations of the Transformer on the hyperspherical tangent bundle
• Authors: Andrew Gracyk,
• Abstract summary: We offer a theoretical mathematical background to Transformers through Lagrangian optimization across the token space.<n>The Transformer, as a flow map, exists in the tangent fiber for each token along the high-dimensional unit sphere.<n>We derive the Euler-Lagrange equation for the Transformer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We offer a theoretical mathematical background to Transformers through Lagrangian optimization across the token space. The Transformer, as a flow map, exists in the tangent fiber for each token along the high-dimensional unit sphere. The circumstance of the hypersphere across the latent data is reasonable due to the trained diagonal matrix equal to the identity, which has various empirical justifications. Thus, under the continuum limit of the dynamics, the latent vectors flow among the tangent bundle. Using these facts, we devise a mathematical framework for the Transformer through calculus of variations. We develop a functional and show that the continuous flow map induced by the Transformer satisfies this functional, therefore the Transformer can be viewed as a natural solver of a calculus of variations problem. We invent new scenarios of when our methods are applicable based on loss optimization with respect to path optimality. We derive the Euler-Lagrange equation for the Transformer. The variant of the Euler-Lagrange equation we present has various appearances in literature, but, to our understanding, oftentimes not foundationally proven or under other specialized cases. Our overarching proof is new: our techniques are classical and the use of the flow map object is original. We provide several other relevant results, primarily ones specific to neural scenarios. In particular, much of our analysis will be attempting to quantify Transformer data in variational contexts under neural approximations. Calculus of variations on manifolds is a well-nourished research area, but for the Transformer specifically, it is uncharted: we lay the foundation for this area through an introduction to the Lagrangian for the Transformer.

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