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Forcing VLMs to "think" visually with panoramic renderings, rather than relying on language alone, unlocks surprisingly robust spatial reasoning.
DINOv2's representation space is so statistically well-behaved that you can train a vanilla diffusion transformer on it and beat specialized architectures with fewer parameters.
Forget hand-crafted prompts: RL can automatically unearth 36 new failure modes in VLMs that humans miss, revealing surprising blind spots in counting, spatial reasoning, and viewpoint understanding.
Even the best MLLMs still fall far short of human performance in collaborative spatial reasoning, revealing a critical gap in their ability to build and maintain shared mental models through dialogue.
