HOW MUCH SCHEMA CAN A READER USE? CAPACITY-ADAPTIVE TEST-TIME EXPOSURE FOR OUT-OF-DOMAIN MULTI-INTENT DETECTION

When a frozen language model, or reader, predicts intents from label descriptions, showing more labels can help it find the correct ones but also introduce distractions. How many labels to show depends on both the query and the reader. We introduce Capacity-Adaptive Pool Exposure (CAPE) for multi-intent detection in new domains, without labeled target examples or access to reader internals. A source-trained selector estimates whether a candidate pool contains all required labels. Tests built from target label descriptions measure how well each reader handles additional labels, and prediction agreement under reordered descriptions refines this estimate for each query. Across 72 settings spanning nine local readers and four domains, CAPE improves macro exact-set accuracy (Exact) by 12.2% relative to fixed top-20 exposure, displaying 11.3 labels on average for the final prediction. With matched calibration and per-query call budgets, it improves Exact by 2.0% overall and 2.2% on held-out readers relative to reader-calibrated coverage thresholding. These results show that measuring reader responses to additional labels improves exposure decisions beyond estimating label coverage alone.