Lost in Aggregation: A General Perspective on Zero-Cost Neural Architecture Search

Selecting neural network architectures typically requires training and comparing many candidates. Even at the same parameter scale, different connectivity patterns and operations can lead to substantial performance differences. Zero-cost neural architecture search (NAS) evaluates candidates before training and thus offers a low-cost route to architecture screening. Existing zero-cost proxies, however, use different computational forms and their common structure remains insufficiently understood. We provide a general perspective grounded in the initialization state of a network. Although existing proxies use different information, they can be understood through the local effect of the network on output changes. We show that conventional global scores lose differences between layers when aggregating layer information, while other readouts retain only local responses or intermediate features. We therefore construct architecture scores that preserve layer-wise information and aggregate it after layer-wise readout. On a strictly parameter-matched candidate subset, the proposed method provides strong architecture screening performance across several evaluations. Experiments further show that both layer-wise information and its aggregation rule materially affect zero-cost proxy rankings.