A Pathology Generative Vision-Language Model with Native-Preserving Expert Injection

Pathology vision-language reasoning requires fine-grained morphological perception together with flexible multimodal reasoning and generation. General-purpose vision-language models (VLMs) provide broad reasoning capabilities, but their visual representations may miss pathology-specific evidence. Pathology foundation models (PFMs) capture detailed histopathological features but typically lack open-ended reasoning and generation. We introduce PathTIDE (Pathology Token Injection of Domain Experts), an architecture that retains the native VLM pathway while adding a pathology foundation model as a Pathology Expert. PathTIDE aligns Pathology Expert representations with a language-aware feature space and injects spatially structured expert tokens through gated residual fusion. We evaluate PathTIDE on closed-ended pathology VQA, including multiple-choice and Yes/No questions. Across four closed-ended VQA evaluation sets—PathMMU, SlideBench-VQA-TCGA, SlideBench-VQA-CPTAC, and the closed-ended portion of WSI-Bench—PathTIDE (PFM-Full) achieves accuracies of 0.869, 0.793, 0.812, and 0.869, respectively. Architecture ablations show that preserving the native General Expert and using gated expert injection outperforms both General Expert replacement and ungated residual addition. These results support adding pathology-specific evidence to the native visual branch of a general-purpose VLM.