AI RESEARCH
Seizure-Semiology-Suite (S3): A Clinically Multimodal Dataset, Benchmark, and Models for Seizure Semiology Understanding
arXiv CS.CV
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ArXi:2605.21852v1 Announce Type: new While Multimodal Large Language Models (MLLMs) have nstrated remarkable proficiency in general video understanding, their capacity to interpret involuntary, and spatio-temporally evolving pathologic motor behaviors such as seizure semiology remains largely untested. To address this gap, we