Article

IG-VLA: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination

The authors propose the IG-VLA framework, which lets a vision-language-action model imagine task-relevant future scene evolution in visual representation space and internalize the reasoning results into a compact Scene Gist Token via Scene Gist Memory, thereby skipping explicit future imagination at inference time.

Part of the event: IG-VLA: A VLA Framework That Internalizes Future Scene Imagination into Compact Tokens

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