Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visual information across spatial dimensions. We introduce Chain-of-Visual-Thought (COVT), a framework that enables VLMs to reason not only in words but also through continuous visual tokens-compact latent representations that encode rich perceptual cues. Within a small budget of roughly 20 tokens, COVT distills knowledge from lightweight vision experts, capturing complementary properties such as 2D appearance, 3D geometry, spatial layout, and edge structure. During training, the VLM with COVT autoregressively predicts these visual tokens to reconstruct dense supervision signals (e.g., depth, segmentation, edges, and DINO features). At inference, the model reasons directly in the continuous visual token space, preserving efficiency while optionally decoding dense predictions for interpretability. Evaluated across more than ten diverse perception benchmarks, including CV-Bench, MMVP, RealWorldQA, MMStar, WorldMedQA, and HRBench, integrating COVT into strong VLMs such as Qwen2.5-VL and LLaVA consistently improves performance by 3% to 16% and demonstrates that compact continuous visual thinking enables more precise, grounded, and interpretable multimodal intelligence.
69252cfc16eb3a9f13103a29
Yiming Qin
claimed_verified
2025-11-30T20:10:30.886Z
6527fe894126af677af1541b
https://cdn-avatars.huggingface.co/v1/production/uploads/6527fe894126af677af1541b/uTjqrkJ92NcstRskuPvhj.png
YM Qin
Wakals
user
Wakals
69252cfc16eb3a9f13103a2a
Bomin Wei
69252cfc16eb3a9f13103a2b
Jiaxin Ge
69252cfc16eb3a9f13103a2c
Konstantinos Kallidromitis
69252cfc16eb3a9f13103a2d
Stephanie Fu
69252cfc16eb3a9f13103a2e
Trevor Darrell
69252cfc16eb3a9f13103a2f
Xudong Wang
Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visual information across spatial dimensions. We introduce Chain-of-Visual-Thought (COVT), a framework that enables VLMs to reason not only in words but also through continuous visual tokens-compact latent representations that encode rich perceptual cues. Within a small budget of roughly 20 tokens, COVT distills knowledge from lightweight vision experts, capturing complementary properties such as 2D appearance, 3D geometry, spatial layout, and edge structure. During training, the VLM with COVT autoregressively predicts these visual tokens to reconstruct dense supervision signals (e.g., depth, segmentation, edges, and DINO features). At inference, the model reasons directly in the continuous visual token space, preserving efficiency while optionally decoding dense predictions for interpretability. Evaluated across more than ten diverse perception benchmarks, including CV-Bench, MMVP, RealWorldQA, MMStar, WorldMedQA, and HRBench, integrating COVT into strong VLMs such as Qwen2.5-VL and LLaVA consistently improves performance by 3% to 16% and demonstrates that compact continuous visual thinking enables more precise, grounded, and interpretable multimodal intelligence.
Chain-of-Visual-Thought (COVT) enables Vision-Language Models to reason through visual tokens, improving their performance on perceptual tasks by capturing dense visual information.
Chain-of-Visual-Thought COVT continuous visual tokens lightweight vision experts dense visual information spatial dimensions 2D appearance 3D geometry spatial layout edge structure autoregressive prediction dense supervision signals depth segmentation edges DINO features CV-Bench MMVP RealWorldQA MMStar WorldMedQA HRBench Qwen2.5-VL LLaVA multimodal intelligence