1 citations · 1 across the 2 of their papers we have counts for
4 papers
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6
Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods typically rely on learned fusi…
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…
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…
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…