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arXiv · 2510.06035

Universal Neural Architecture Space: Covering ConvNets, Transformers and Everything in Between

Ondřej Týbl·Lukáš Neumann
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Abstract

We introduce Universal Neural Architecture Space (UniNAS), a generic search space for neural architecture search (NAS) which unifies convolutional networks, transformers, and their hybrid architectures under a single, flexible framework. Our approach enables discovery of novel architectures as well as analyzing existing architectures in a common framework. We also propose a new search algorithm that allows traversing the proposed search space, and demonstrate that the space contains interesting architectures, which, when using identical training setup, outperform state-of-the-art hand-crafted architectures. Finally, a unified toolkit including a standardized training and evaluation protocol is introduced to foster reproducibility and enable fair comparison in NAS research. Overall, this work opens a pathway towards systematically exploring the full spectrum of neural architectures with a unified graph-based NAS perspective.

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Ondřej Týbl, Lukáš Neumann. 2025-10-07. Universal Neural Architecture Space: Covering ConvNets, Transformers and Everything in Between. https://arxiv.org/abs/2510.06035

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