Author

A. Dayan

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#machine learning Preprint Oct 2026

CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations

Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. Weight-space symmetries such as neuron permutations make equivariance a key design principle. However, existing equivariant weight-space arch...

A. Dayan, Yam Eitan, Haggai Maron · 0 citations

#artificial intelligence Preprint Sep 2026

Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning

This work studies when finite probe-based representations are sufficient for learning neural functionals, establishes general identification and universality results for probing, and shows that using intermediate hidden representations can provide significantly more informative representations than relying only on fina...

Soutrik Sarangi, Yonatan Sverdlov, A. Dayan et al. · 0 citations