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About this paper
Jaehoon Lee, Harry Partridge, Mudith Jayasekara, Charles O'Neill, Max Kirkby, Michael Psenka
The authors connect a frozen vision encoder to a frozen text-only language model using a trained roughly 50-million-parameter MLP projector, initialized by aligning reader token embeddings and trained with synthetic supervised examples plus image-based reinforcement learning. GLM-5.2 reaches 56.8% on MMMU-Pro, versus 42.3% without images, a 14.5-percentage-point gain. This multiple-choice result shows selected visual capability, not general grounded vision.
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