functional-attention

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#functional-attention

@Phoenixyin13: I think this is a top-notch work in ICML 2026. The attention mechanism of traditional Transformers is essentially point-to-point matching: it cuts input into a bunch of tokens (discrete points), computes similarity between Query and Key, and then weights the Value. In NLP...

X AI KOLs Timeline ↗ · 2026-06-25 Cached

Introduces the ICML 2026 paper Functional Attention, which treats functions as first-class citizens and replaces softmax point-to-point similarity with structured linear operators. It addresses issues of discretization, resolution sensitivity, and high computational complexity in traditional Transformers when handling continuous functions. Achieves or surpasses SOTA in tasks like PDE solving and 3D segmentation, and exhibits strong OOD generalization.

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