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cs.CV2026
Decoding the Pulse of Reasoning VLMs in Multi-Image Understanding Tasks
Chenjun Li
Multi-image reasoning remains a significant challenge for vision-language models (VLMs). We investigate a previously overlooked phenomenon: during chain-of-thought (CoT) generation…
cs.CV2025
Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning
Chenjun Li, Cheng Wan, Laurin Lux +4
Vision-Language Models (VLMs) offer a promising path toward interpretable medical diagnosis by allowing users to ask about clinical explanations alongside predictions and across di…
cs.CV2025
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis
Chenjun Li, Laurin Lux, Alexander H. Berger +3
Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging models are hardly interpretable, an…