arXiv AI September 7, 2026 By Taegyun Kim, Youngwook Ham, Jungwook Rhim, Ju-Hyun An, Sungkyu Park, Kunwoo Park

PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning

PetQA is a Korean long‑form question‑answering benchmark designed to assess veterinary knowledge and clinical reasoning in large language and vision‑language models. It comprises 10,076 text‑only and 8,751 multimodal QA pairs about dogs and cats, with expert veterinarian answers, and a test split called PetQA‑Bench that includes question type and clinical condition annotations. The study evaluates 18 models across zero‑shot, retrieval‑augmented generation, and supervised fine‑tuning settings using ROUGE, BERTScore, and LLM‑as‑a‑judge metrics, revealing current models’ strengths and limitations and underscoring the need for better adaptation methods for clinically reliable veterinary AI; translated versions in five languages are also provided.

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OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

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