BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment

Abstract

Free-text evaluation of multimodal clinical question answering (QA) systems remains a central challenge in medical NLP due to the complexity of medical knowledge, the necessity of integrating visual and textual information, and the limitations of existing automatic evaluation metrics for open-ended outputs. In this work, we present a training-free, agentic evaluation framework that formulates response scoring as evidence-guided orchestration of components rather than a task requiring conventional end-to-end fine-tuning of underlying LLMs/VLMs. Our ReAct-style evaluator combines (i) structured reasoning, (ii) multimodal retrieval of similar encounters, (iii) auxiliary explainable feature-based regression models that provide numeric priors and human-interpretable signals, (iv) VLM-generated visual QA references for comparison, and (v) optional image augmentation tools. Unlike standard LLM-as-a-judge approaches that rely on direct generative scoring, our agent decomposes evaluation into modular stages of evidence acquisition, structured feature modeling, and integrative reasoning. We apply this architecture to the MEDIQA-EVAL shared task - a multimodal, multilingual clinical evaluation challenge that assesses system-generated answers for patient queries paired with images along multiple clinical quality dimensions. We report results across both English and Chinese tracks, comparing against baseline prompting methods, and discuss the feasibility and limitations of lightweight agentic systems for clinical QA evaluation.

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