ROOTROUTE: Hypothesis Formation and Selective Revision for Multi-Label Text Routing

Multi-label text routing must combine evidence distributed across local spans into document-level concept decisions. Aggregating the strongest local matches provides an initial estimate of concept support, but the resulting score compresses the evidence that produced it and offers limited information about whether the initial judgment should be retained or revised. We propose ROOTROUTE, which explicitly separates hypothesis formation from evidence-guided revision. The Core learns reusable concept directions from representative training evidence and sparsely aggregates local matches into an initial support score. A verifier then revisits the current document with target-conditioned attention and predicts a constrained correction from the retrieved evidence and the initial support state. We instantiate ROOTROUTE for anatomical routing from clinical reports using frozen BioMedCLIP representations and 128,627 training reports from five sources. Across four medical-report evaluations and a controlled-language suite of 20,480 examples, ROOTROUTE consistently improves both F1 and exact-set agreement over the Core. On seen-source reports, macro-F1 increases from 0.919 to 0.963 and exact-set agreement from 0.767 to 0.876. On 2,000 independently expert-mapped RadGraph-XL-Stanford reports, it reaches 0.928 macro-F1 and 0.683 exact-set agreement. Decision-level analysis further shows that revision changes only a small fraction of Core decisions and that these changes are predominantly corrective, while controlled diagnostics show that revisiting document evidence can recover concept support missed by the initial aggregation. These results support explicit separation of initial support formation from selective evidence-guided revision for multi-label text routing, while making the resulting decision changes directly inspectable. Code will be released after the review process.