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Microsoft introduces Mage-VL, a codec-native streaming multimodal foundation model for image and video understanding that achieves up to 3.5x inference speedup by using a sparsity pattern inspired by video codecs, cutting visual tokens by over 75%.
This paper introduces a stateful visual encoder for vision-language models that conditions visual representations on prior features, enabling better visual comparison in multi-image and agentic settings. The method shows consistent improvements across tasks such as cross-image spatial aggregation and longitudinal radiology.