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Jet Functors and Weil Algebras in Automatic Differentiation: A Geometric Analysis

arXiv 2510.14342 ↗First posted 16 Oct 2025cs.LG math.DG stat.MLcited by 0

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Abstract (arXiv)

We present a differential-geometric formulation of automatic differentiation (AD) based on jet functors and Weil algebras. In this framework, forward- and reverse-mode differentiation arise naturally as pushforward and cotangent pullback, while higher-order differentiation corresponds to evaluation in a Weil algebra. This construction provides a unified, coordinate-free view of derivative propagation and clarifies the algebraic structure underlying AD. All results are realized in modern JAX code, where the Weil-mode formulation computes all mixed derivatives in a single forward pass with cost linear in the algebra dimension. The resulting implementation achieves algebraically exact and numerically stable differentiation with predictable scaling, demonstrating that geometric abstraction can yield more efficient and transparent computational differentiation systems. Code is available at https://git.nilu.no/geometric-ad/jet-weil-ad

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References · 3

  1. 2015Automatic differentiation in machine learning: a surveycited by 846
  2. 2017Backprop as Functor: A compositional perspective on supervised learningcited by 44
  3. 2018The simple essence of automatic differentiationcited by 22

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