Latent Implicit Visual Reasoning
While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are limited in their ability to handle reasoning tasks that are predominantly visual. Recent approaches have sought to address this by supervising intermediate visual steps with helper images, depth maps, or image crops. However, these strategies impose restrictive priors on what "useful" visual abstractions look like, add heavy annotation costs, and struggle to generalize across tasks. To address this critical limitation, we propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision. These tokens attend globally and re-encode the image in a task-adaptive way, enabling the model to extract relevant visual information without hand-crafted supervision. Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks -- including those where intermediate abstractions are hard to specify -- while also generalizing to multi-task instruction tuning.
694c9c5f746a34b55dd54018
Kelvin Li
claimed_verified
2026-01-05T13:49:33.315Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/J43zm899YDRFQ2B3i76ZH.png
Kelvin
KZZ01
user
KZZ01
694c9c5f746a34b55dd54019
Chuyi Shang
claimed_verified
2025-12-25T20:43:50.115Z
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Chuyi Shang
chuyishang
user
chuyishang
694c9c5f746a34b55dd5401a
Leonid Karlinsky
694c9c5f746a34b55dd5401b
Rogerio Feris
694c9c5f746a34b55dd5401c
Trevor Darrell
694c9c5f746a34b55dd5401d
Roei Herzig
Latent Implicit Visual Reasoning
While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are limited in their ability to handle reasoning tasks that are predominantly visual. Recent approaches have sought to address this by supervising intermediate visual steps with helper images, depth maps, or image crops. However, these strategies impose restrictive priors on what "useful" visual abstractions look like, add heavy annotation costs, and struggle to generalize across tasks. To address this critical limitation, we propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision. These tokens attend globally and re-encode the image in a task-adaptive way, enabling the model to extract relevant visual information without hand-crafted supervision. Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks -- including those where intermediate abstractions are hard to specify -- while also generalizing to multi-task instruction tuning.