Large vision-language models (LVLMs) are gaining traction in clinical tasks such as diagnostic support, report generation, and medical question answering. Among post-training techniques, Direct Preference Optimization (DPO) has shown promise in aligning model outputs with human preferences, yet its effectiveness in high-stakes medical contexts remains underexplored. In this work, we present the first systematic evaluation of nine DPO variants applied to two leading medical LVLMs, LLaVA-Med and HuatuoGPT-Vision. We benchmark these models on five curated datasets covering diverse clinical tasks. Evaluations include both automated metrics and expert assessments. Our results show that while DPO improves alignment and reduces severe hallucinations, it yields inconsistent gains over supervised fine-tuning. We further introduce DPO variant that better handles visual misinterpretations and enhances clinical understanding. These findings reveal both the potential and limitations of DPO in medical AI. To support future research, we will release all DPO training data, model checkpoints, and expert annotations upon acceptance.

Data-sovereignty regulations increasingly require public institutions to deploy open-source, on-premise LLM agents that chain multiple tool-calls across live government APIs. However, open-source models consistently underperform in this multi-step setting, and no existing benchmark measures the gap. We introduce the Korean Open Public API Benchmark KOPA-Bench, comprising 145 real-world tasks on which open-source models underperform. To close this gap, we present EDGE, an Execution-grounded Dynamic Graph for tool-calling data synthEsis driven by live execution. EDGE builds a graph of how each tool’s output can feed another’s input, keeps only the links that succeed when actually called against the live APIs, and traverses these verified links to synthesize executable multi-step trajectories. Fine-tuned via GRPO on the resulting dataset, our 9B model nearly matches the untuned 27B model from the same family, improving substantially not only on KOPA-Bench but also on the BFCL benchmark.

The increasing utilization of patient portals has amplified clinicians’ workloads, primarily due to the necessity of addressing detailed patient inquiries related to their health concerns. The ArchEHR-QA 2025 shared task aims to alleviate this burden by automatically generating accurate, evidence-grounded responses to patients’ questions based on their Electronic Health Records (EHRs). This paper presents a six-stage multi-agent framework specifically developed to identify essential clinical sentences for answering patient questions, leveraging large language models (LLMs). Our approach begins with OpenAI’s o3 model generating focused medical context to guide downstream reasoning. In the subsequent stages, GPT-4.1-based agents assess the relevance of individual sentences, recruit domain experts, and consolidate their judgments to identify essential information for constructing coherent, evidence-grounded responses. Our framework achieved an Overall Factuality score of 62.0 and an Overall Relevance Score of 52.9 on the development set, and corresponding scores of 58.6 and 48.8, respectively, on the test set.