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Support Topology and Gradient Mixing in Sinkhorn Layers

arXiv 2609.07954 ↗First posted 7 Sep 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

Sparse Sinkhorn layers use a fixed support graph to restrict transport between tokens. How does this graph control gradient propagation through the scaling iterations. We develop a fixed-support calculus showing that each row-column cycle induces a row-stochastic operator on column-potential perturbations modulo constants. Its transpose propagates zero-mass reverse-mode cotangents. The finite-cycle operator uses two distinct half-step transport plans; at a balanced fixed point it reduces to a two-step walk determined by a single plan. We derive the accompanying score and marginal source terms and use Dobrushin contraction and minorization to bound homogeneous and source-driven tail cotangents. Our main result characterizes when support and marginals guarantee one-step contraction uniformly over finite scores: every feasible face of the transportation polytope must have pairwise two-hop column overlap. Otherwise, suitable score directions make the contraction coefficient arbitrarily close to one. We extend this analysis to ordered support schedules and derive certificates for partition heat-bath layers, coordinate sweeps, forced shared mass, and register-augmented supports. These results provide mathematical criteria for support design in differentiable transport layers, with guarantees restricted to the fixed-support quotient-gradient component.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 7 Sep 2026.

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5 of 15 references.

References · 5

  1. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  2. 2022A Unified Framework for Implicit Sinkhorn Differentiationcited by 28
  3. 2018An explicit analysis of the entropic penalty in linear programmingcited by 24
  4. 2022The derivatives of Sinkhorn-Knopp convergecited by 9
  5. 2013Dobrushin ergodicity coefficient for Markov operators on cones, and beyondcited by 1

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