Finite Clinical Programs: An Inspectable Discrete Interface for Radiology Report Generation

We study radiology report generation with a complete case-dependent conditioning message reconstructible from discrete IDs. Finite Clinical Subspace Learning (FCSL) combines named clinical states, task and prior-study fields, and quantized residual codes, using 202 bits on MIMIC-RG4 and 160 bits on MIMIC-ABN. A 0.8B language backbone accesses this message through a prefix and layer-local writes. Training uses reference-derived states; inference uses predicted IDs. Three-seed system comparisons improve clinical-label agreement but yield lower language and RadGraph scores. A supervision-matched continuous interface attains similar micro-F1 and higher complementary scores. However, matched RG4 interventions on 3,696 shared eligible requests from 245 patients yield 33.05% joint selective success for FCSL versus 18.60% for continuous conditioning: a paired difference of 14.44 percentage points (95% patient-bootstrap interval [11.87, 16.63]). Joint success requires the requested target change with all thirteen non-target labels unchanged. FCSL also exceeds prefix-only's 29.63%, without a demonstrated reduction in non-target changes. These single-seed, exploratory interventions reveal a report-quality–selective-response trade-off on the common eligible subset, rather than universal accuracy gains or clinical safety. FCSL makes complete conditioning reconstructible and its editing benefits and limits measurable at a compact backbone scale.