most citedMIRAGE: The Illusion of Visual Understanding

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

math.DG2026

On the Gage-Hamilton-Grayson Theorem for free boundary curves

Yueheng Bao, Lorenzo Sarnataro

We provide an alternative and independent proof of the analogue of the Gage-Hamilton-Grayson Theorem for embedded free boundary curve shortening flows in a convex planar domain est…

cs.LG2026

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…

cs.AI2026

Towards Conversational Medical AI with Eyes, Ears and a Voice

Meet Shah, Jason Gusdorf, Anil Palepu +50

The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues between doctors and patients. B…

cs.AI2026★ 1 cited

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 +10

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…