[2603.20115] Conditioning Protein Generation via Hopfield Pattern Multiplicity

Computer Science > Machine Learning arXiv:2603.20115 (cs) [Submitted on 20 Mar 2026] Title:Conditioning Protein Generation via Hopfield Pattern Multiplicity Authors:Jeffrey D. Varner View a PDF of the paper titled Conditioning Protein Generation via Hopfield Pattern Multiplicity, by Jeffrey D. Varner View PDF HTML (experimental) Abstract:Protein sequence generation via stochastic attention produces plausible family members from small alignments without training, but treats all stored sequences equally and cannot direct generation toward a functional subset of interest. We show that a single scalar parameter, added as a bias to the sampler's attention logits, continuously shifts generation from the full family toward a user-specified subset, with no retraining and no change to the model architecture. A practitioner supplies a small set of sequences (for example, hits from a binding screen) and a multiplicity ratio that controls how strongly generation favors them. The method is agnostic to what the subset represents: binding, stability, specificity, or any other property. We find that the conditioning is exact at the level of the sampler's internal representation, but that the decoded sequence phenotype can fall short because the dimensionality reduction used to encode sequences does not always preserve the residue-level variation that defines the functional split. We term this discrepancy the calibration gap and show that it is predicted by a simple geometric measure of ho...