CrossMax: Continual Model Routing with Preferences and Zero-Shot Registration

Public model hubs expand continually as new models are published, each requiring discoverability well before evaluation on any real query. Selecting the right model for a query in this setting faces two obstacles. First, users already express preferences in language—a preferred size, language, or publisher—yet routing methods typically ignore it, being trained only to satisfy the functional need, or apply that preference as a filter after scoring. Second, a router must select a model without executing it. Routers rely on past queries, but newly registered models have none. We propose CrossMax, a preference-guided router that supports zero-shot routing. CrossMax relies on two components: a prototype router that trains a small encoder on preference-augmented queries, letting stated preferences shape the ranking directly rather than filter it afterward, and a frozen embedding of the whole model card for models that have never been used. Each component outputs a winner and a margin rule compares the two by their model cards. The margin, set at inference time, chooses how readily a newly registered model is returned. Extending CMRBench, the reference continual routing benchmark, with preferences drawn from model cards, we show that training on the preference text makes the ranking follow the preference, while the same architecture trained without preferences and tested with them does not catch up even when paired with a parsed filter, and that a conservative margin keeps most of the accuracy on models the hub already knows while still returning models that have never been used.