Reason Through the Latent! Making Latent Visual Reasoning Necessary
Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being present in a latent state does not imply that the model actually relies on that state when producing its answer, especially when alternative image-conditioned paths remain available. We introduce Causal V
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Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However, visual information being present in a latent state does not imply that the model actually relies on that state when producing its answer, especially when alternative image-conditioned paths remain available. We introduce Causal V
This research addresses a critical challenge in multimodal models: ensuring that visual information is genuinely utilized for reasoning rather than being bypassed. For ML engineers, understanding how to enforce and verify visual reasoning is crucial for building robust and interpretable vision-language systems, especially in applications requiring complex visual understanding.