The Token Replacement Test shows VLMs keep most accuracy gains even after corrupting or replacing continuous thought token content, indicating the tokens are not used as information bottlenecks.
ReferItGame: Referring to objects in pho- tographs of natural scenes
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Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?
The Token Replacement Test shows VLMs keep most accuracy gains even after corrupting or replacing continuous thought token content, indicating the tokens are not used as information bottlenecks.