EM-OPD: Evidence-Conditioned Multi-Expert On-Policy Distillation for Pathology Report Generation

Multimodal large language models often fail to capture less salient visual evidence required for fine-grained reporting. In contrast, a comprehensive pathology report must integrate heterogeneous evidence from diagnostic patterns, abnormalities, glandular architecture, and cellular morphology. To address this challenge, we propose Evidence-Conditioned Multi-Expert On-Policy Distillation (EM-OPD), a framework that transforms heterogeneous pathological evidence into a unified report generation capability. EM-OPD constructs multiple evidence-conditioned experts equipped with spatial anchors and morphological descriptors. These experts provide complementary perspectives, enabling token-level supervision of student-generated trajectories. By adaptively integrating expert knowledge through balanced joint consolidation, EM-OPD learns to generate pathology reports with improved evidence coverage and diagnostic reliability.