aiqbench analysis
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.
A Hand-Checkable Proof That Two Hidden ReLU Layers Compute the Maximum of Six Numbers
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 193 words
Related coverage

[AINews] not much happened today
Latent Space · Sep 10, 2026

Challenge: Hand coding weights for efficient sequence memorisation
AI Alignment Forum · Jul 23, 2026

Echoverse: Deep, evolving environments for computer-use agents
Microsoft Research · Jul 30, 2026
NP-Hardness of Minimizing Neurons in Two-Hidden-Layer ReLU Neural Networks
arXiv · Oct 9, 2026