What's Holding Back Latent Visual Reasoning?
Humans can approach complex visual problems by mentally simulating intermediate visual steps, rather than reasoning through language alone. Inspired by this, several works on Vision-Language Models have recently explored chain-of-thought reasoning with continuous latent tokens as intermediate visual imagination steps. In this work, we investigate how recent models leverage such latent tokens. Surprisingly, we find that model accuracy is unaffected when latent tokens are replaced by uninformative dummy tokens. This indicates that latent tokens play a minimal causal role in the model's final prediction. To better understand this phenomenon, we analyze both the training signal provided by or...
| Authors | André G. Viveiros, Nuno Gonçalves, André F. T. Martins et al. |
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| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 19 May 2026 |
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