arXiv — Machine Learning · · 3 min read

Attention Is All You Need (to Avoid Spurious Oscillations)

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Computer Science > Machine Learning

arXiv:2609.13531 (cs)
[Submitted on 11 Sep 2026]

Title:Attention Is All You Need (to Avoid Spurious Oscillations)

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Abstract:Can attention move a shock across several cells in one update without breaking it? We develop a conservative, fixed grid finite-volume scheme in which a CFL-conditioned attention flux selects upstream information according to the transport required by the current time step. One-dimensional inviscid Burgers transport is used as the central mechanism test: the same learned flux remains reliable in the conventional small-step regime and, with a time step four times larger, preserves sharp shocks while using one stage per update. A standard fifth-order WENO scheme with third-order strong-stability-preserving Runge-Kutta time integration (WENO-5+SSP-RK3) is included alongside controlled Forward Euler comparisons to separate flux selection from time integration. The learned attention shifts upstream with the local transport reach and becomes more selective near shocks; inference-time interventions and retrained ablations show that transport-scale information and state-dependent selection contribute directly to performance. Directional two-dimensional scalar Burgers transport and the one-dimensional shallow-water system then test whether the conservation-scale-selection principle transfers beyond the original scalar setting. The results support attention as a learnable information stencil for conservative large-step shock transport, while identifying finite candidate reach and problem-dependent robustness as the present limits.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Fluid Dynamics (physics.flu-dyn)
Cite as: arXiv:2609.13531 [cs.LG]
  (or arXiv:2609.13531v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.13531
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jinyoung Jeong [view email]
[v1] Fri, 11 Sep 2026 20:59:26 UTC (1,536 KB)
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