MedProb is a lightweight probing framework that predicts multiple-choice medical visual question answering (Med‑VQA) answers directly from frozen vision‑language model (VLM) representations, avoiding free‑text generation. On datasets such as PATH‑VQA, SLAKE, and VQA‑RAD, MedProb extracts more answer‑relevant signal than prompting and outperforms both medical VLMs and agentic systems. The approach also narrows the performance gap between small and large models, shows that medical adaptation does not consistently improve linear decodability, and reveals positional biases in both prompting and generation.
arXiv:2608.22363v1 Announce Type: new Abstract: Medical visual question answering (VQA) is a crucial task in clinical AI, yet its evaluation has so far centered almost exclusively on English, limitin...
arXiv:2609.06419v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-base...
arXiv:2604. 09757v2 Announce Type: replace-cross Abstract: Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded once as static context, and subsequent inference is dominated by language.
arXiv:2607. 12048v1 Announce Type: cross Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge.
arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
MedBenchAgent is a multi‑agent framework that automates the entire process of constructing medical vision‑language model benchmarks, from deriving the benchmark specification to generating and auditing evaluation items. It uses a Benchmark Intermediate Representation (BIR) to encode task definitions, evidence mappings, evaluation protocols, and item specifications, separating planning from instantiation. The system achieves a Task‑Space F1 of 90.9% and demonstrates high audit pass rates, outperforming existing task induction methods and showing portability across specialized medical domains.
arXiv:2608. 12928v1 Announce Type: new Abstract: We introduce a Polish-language medical visual question answering (VQA) benchmark, built from Polish Board Certification Examination questions for licensed physicians and dentists pursuing specialist certification.
BiomedXPro is an evolutionary framework that uses a large language model to automatically generate a diverse set of interpretable, natural‑language prompt pairs for disease diagnosis with biomedical vision‑language models. It addresses the lack of transparency in existing prompt optimization methods by producing prompts that are both understandable and aligned with clinically significant features. Experiments on multiple biomedical benchmarks show that BiomedXPro outperforms state‑of‑the‑art prompt‑tuning techniques, especially in few‑shot, data‑scarce scenarios.
LiteMedCoT-VL is a parameter‑efficient pipeline that transfers chain‑of‑thought reasoning from a 235B teacher model to a 2B student model using LoRA fine‑tuning on explanation‑enriched data. The approach enables a compact vision‑language model to perform medical visual question answering without relying on image captions, achieving 64.9% accuracy on the PMC‑VQA benchmark—an 11‑point improvement over the zero‑shot Qwen3‑VL‑4B baseline. Visual grounding analysis confirms that the model bases its predictions on image content rather than textual priors.
MedFG-VQA is a lightweight medical visual question answering framework that uses a memory bank to enhance low‑frequency DCT features and graph‑enhanced cross‑attention for visual‑textual alignment. It introduces Frequency‑Memory Fusion to retrieve and fuse low‑frequency information from a learnable memory bank, and Graph‑Aware Cross‑Attention to refine cross‑modal features via graph convolution. The authors also create SynMed‑VQA, a synthetic dataset of over 2 million QA pairs across nine imaging modalities, and show that MedFG‑VQA matches or outperforms larger models on several biomedical VQA benchmarks while keeping computational costs low.
arXiv:2607. 04344v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular surface diseases (OSDs) is severely hindered by a paucity of high-fidelity, multimodal instruction-tuning data.
The paper evaluates Vision Language Models (VLMs) on Visual Question Answering tasks where questions violate Grice's maxims. By generating question modifiers that add non-essential, ambiguous, or false information, the authors show that VLMs such as ChatGPT, Claude, Gemini, and Llava exhibit reduced performance. They also compare human pragmatic reasoning to VLM reasoning, noting differences in how each handles human‑induced versus AI‑generated violations, and find that humans spend less time resolving VLM‑induced violations while VLMs are less accurate in those cases.