Universal Hypernetworks via Structured Conditioning Across Architectures and Tasks

Conventional hypernetworks are typically designed around a specific target-network parameterization, so changing the target architecture often requires redesigning and retraining the hypernetwork. We introduce the Universal Hypernetwork (UHN), a fixed-architecture generator that predicts weights from deterministic parameter, architecture, and task descriptors. By moving target specificity into these descriptors, UHN decouples the generator design from the structure of the target model. We evaluate UHN across vision, graph, text, and formula-regression tasks, showing that a fixed generator architecture can be applied across different target structures. In addition, a single UHN supports multi-model generalization within a model family and multi-task learning across heterogeneous models. Finally, by treating a UHN itself as a target network, the generation process can be applied recursively.