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cs.AI2026
MIRAGE: The Illusion of Visual Understanding
Mohammad Asadi, Jack W. O'Sullivan, Fang Cao +5
Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual-language reasoning remain surprisingly poo…
cs.AI2026
MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management
Jack W O'Sullivan, Mohammad Asadi, Lennart Elbe +8
Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Current AI vision-language models are…
cs.AI2026
Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain
Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan +2
Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating res…