Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
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arXiv:2607.19378v1 Announce Type: new Abstract: Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while recurrent models require rasterizing data such as images, volumes, and partial differential equation (PDE) into an ad-hoc $1\rm D$ scan order that violates their spatial structure. We introduce \textit{HyenaND}, a subquadratic, global, input-dependent operator that acts…
1Key Takeaways
- We introduce \textit{HyenaND}, a subquadratic, global, input-dependent operator that acts….
- Headline: Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
- Category focus: Research — relevant for AI builders and decision-makers.
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that we introduce \textit{HyenaND}, a subquadratic, global, input-dependent operator that acts…
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