Reason Through the Latent! Making Latent Visual Reasoning Necessary
Researchers introduce CVRR, forcing multimodal models to rely on recurrent latent computation rather than accessible image tokens, validated via causal interventions and benchmarks.
The paper presents Causal Visual Recurrent Reasoning (CVRR), which makes recurrent hidden-state computation the required image-conditioned path for prediction in vision-language models. Before decoding, visual states and the original multimodal KV cache are removed so only the final recurrent state carries image information to the answer. CVRR retains strong performance on V*, MMVP, BLINK, and MME-RealWorld-Lite while comparable latent reasoners fail under the same constraint. Causal interventions show predictions remain sensitive to recurrent content and that persistent visual evidence causally revises the recurrent trajectory.
- CVRR removes visual states and the multimodal KV cache before decoding so only a recurrent state carries image information
- Causal interventions demonstrate predictions depend on recurrent latent content, not just latent informativeness
- Retains strong performance on V*, MMVP, BLINK, and MME-RealWorld-Lite benchmarks under a strict interface
From the original article
1 min read at huggingface.co
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.
By Suhyeong Park, Junha Jung, Jaewoo Kang · huggingface.co
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