The authors fine-tune LLaVA-Med (language model and multimodal adapter, vision encoder frozen) on 84 training pairs from MediConfusion for up to 1000 epochs. The model fails to reach 100% set accuracy on the training set even after extensive training, which the authors interpret as evidence that the BiomedCLIP vision encoder produces embeddings too similar for the confusing pairs to be distinguished by any language model. On the held-out test split, set accuracy improves from 0% to approximately 20%, but this is still well below the 25% random baseline, showing the learned format does not generalize. The authors conclude that MediConfusion can only be solved by improving the vision encoder directly.