MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf{MedProb}, a lightweight probing framework that predicts multiple-choice Med-VQA answers from frozen VLM representations without free-text generation. Across PATH-VQA, SLAKE, and VQA-RAD, MedProb recovers substantially more answer-relevant signal than prompting and performs stronger than medical VLMs and agentic systems. Probing also reduces the apparent gap between small and large models compared to prompting, suggesting that smaller VLMs contain more recoverable Med-VQA signal than generation-based evaluation ...
| Authors | Erfan Nourbakhsh, Ke Yang, Anthony Rios |
|---|---|
| Primary category | cs.CL |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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