Conditional Rank Allocation for Taxonomy-Aware Medical Language Model Adaptation

Excerpt

arXiv:2610.06765v1 Announce Type: new Abstract: Medical question answering spans specialties and clinical operations that may benefit from different adaptation directions. We propose ARBOR, a parameter-efficient method that selects rank-one components from a shared low-rank basis for each question. An additive gate combines question representations, specialty tags, operation tags, and their interaction; a learned coefficient scales the adapter residual. An illustrative separation under orthogona