Leveraging Latent Visual Reasoning in Silence
Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation. However, the necessity of these latent tokens at inference remains ambiguous. We show that replacing latent tokens with random noise or removing them completely causes little performance degradation across spatial reasoning benchmarks. Reinforcement learning further diminishes the latent generation behavior after post-training. These observations raise a central question: \textit{Is latent visual reasoning still meaningful}? We argue that its value should be measured by how effectively latent tokens guide learning, rather than whether they...
| Authors | Dongyao Zhu, Zhen Wang, Xi Xiao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 28 September 2026 |
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