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Forget unimodal tasks—UniM throws down the gauntlet for truly unified multimodal AI, demanding models juggle any combination of text, image, audio, video, code, documents, and 3D inputs and outputs in a single, interleaved stream.
Overcome the scarcity of 4D training data by cleverly borrowing spatial understanding from 3D models and temporal dynamics from video models.
Finally, interpretable medical text embeddings that rival black-box models in performance, thanks to ontology-grounded question generation and a training-free approach.
