FLARE: Selectively Routing Lesion Evidence for Comprehensive Volumetric Radiology Reporting

Recent multimodal large language models have enabled radiology report generation from full 3D CT volumes, but clinically important focal lesions can remain underrepresented within global volumetric context. A natural remedy is to augment the global input with lesion-focused visual tokens. However, our experiments show that this naive global-plus-local approach does not yield consistent improvements, suggesting that simply providing local evidence does not ensure its effective use. We introduce FLARE, a post-training framework that explicitly controls when local lesion evidence influences report generation. FLARE pairs a frozen global report generator with a crop expert that extracts lesion-specific evidence from local crops. A router decides whether this evidence should influence the global prediction at each decoding step of full-report generation. On the ReXGroundingCT held-out test set, FLARE shows overall improvements across clinically oriented metrics over both full-report supervised fine-tuning and naive local augmentation, while maintaining comparable textual similarity to the global-only baseline. These results support selective lesion-evidence routing as an effective approach for strengthening focal finding descriptions within comprehensive volumetric reports.