DeepMind's new research paper, HOPE (Hilbert Operator for Progressive Encoding), proposes a novel method for neural network compression by analyzing learned functions in Hilbert space, rather than raw weights. This approach unifies pruning, neuron merging, and residual block removal into a single low-rank projection framework. The technique provides a data-free and hyperparameter-free way to identify redundant functions within a model, potentially exposing its core representation.