Ellis Brown

MLLMs are failing to visually track events in videos, performing only modestly above baseline despite strong results on other benchmarks.

Existing image editing models struggle with precision, achieving only 17.1% accuracy on a new benchmark designed to evaluate fundamental visual editing tasks.

Vision models are far more data-hungry than language models, but Mixture-of-Experts can harmonize this asymmetry for truly unified multimodal models.