A new study from arXiv (2609.04336v1) introduces MedProb, a lightweight probing framework that predicts multiple-choice medical visual question answering (Med-VQA) answers from frozen vision-language …
A developer's claim that their MadriMed-VL-2B model beats Google's MedGemma 4B on the MedXpertQA-MM benchmark is methodologically unsound because the evaluation likely ran text-only without loading im…
A new study from arXiv (2607.27304v1) introduces CoT-Mediate, a behavioral framework to test whether medical vision-language models (VLMs) causally use their generated chain-of-thought reasoning. Eval…
Researchers introduced PFAdapter, a framework for federated fine-tuning of multimodal large language models on edge devices, that reduces communication costs by nearly 50% while improving accuracy by …