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This paper provides a hand-verifiable analytical proof demonstrating that two hidden layers of ReLU networks are sufficient to compute the maximum function for six real inputs. By deriving an explicit hexagon identity with rational weights, the authors offer a self-contained construction that eliminates the need for external verification programs in this specific depth-bound scenario. This work advances theoretical understanding of network expressivity and the minimal architectural requirements for fundamental computational tasks in deep learning.
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A Hand-Checkable Proof That Two Hidden ReLU Layers Compute the Maximum of Six Numbers

arXiv1 min read

arXiv:2610.04256v1 Announce Type: new Abstract: Exactly computing the maximum function is a standard test case for studying depth in ReLU networks. Two hidden layers are known to suffice for up to twelve inputs through computer-assisted constructions. For six real inputs, we give an explicit hexagon identity whose local structure yields a self-contained analytical proof of this depth bound. The identity was found by computer-assisted search; we prove it through explicit cancellations that can be checked entirely by hand, without executing a verification program. The identity also yields an explicit network with hidden widths $17$ and $41$, zero biases, and rational weights.

Abstract:Exactly computing the maximum function is a standard test case for studying depth in ReLU networks. Two hidden layers are known to suffice for up to twelve inputs through computer-assisted constructions. For six real inputs, we give an explicit hexagon identity whose local structure yields a self-contained analytical proof of this depth bound. The identity was found by computer-assisted search; we prove it through explicit cancellations that can be checked entirely by hand, without executing a verification program. The identity also yields an explicit network with hidden widths $17$ and $41$, zero biases, and rational weights.

Machine Learning (cs.LG); Computational Complexity (cs.CC)

arXiv:2610.04256 [cs.LG]

(or arXiv:2610.04256v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.04256 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 193 words

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