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The Head Complexity of Boolean Functions in Single-Layer Attention

Paper recorded by Signals 4 on 2026-09-03 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.LG · 机器学习 · first seen 2026-09-04

Abstract

What can a single layer of self-attention compute? We study head complexity: the minimum number of attention heads required to compute a function in a one-layer attention-only model. We establish an exact hierarchy under this measure: $k$ heads compute $k$-bit parity but cannot compute $(k+1)$-bit parity. The lower bound is unconditional in the two resources a transformer might otherwise exploit

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#136 most recent of 215 cs.LG papers we have recorded · ↑ newer: Subspace Inference Enables Efficient Active Reward Learning from Prefe · ↓ older: Influence of Extruded Filament Shape on Buildability in 3D Concrete Pr
Cite this page: The Head Complexity of Boolean Functions in Single-Layer Attention: the #136 most recent of 215 cs.LG papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/the-head-complexity-of-boolean-functions-in-single-layer-attention.html
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