Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens

• URL: http://arxiv.org/abs/2506.17218v1
• Date: Fri, 20 Jun 2025 17:59:31 GMT
• Title: Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens
• Authors: Zeyuan Yang, Xueyang Yu, Delin Chen, Maohao Shen, Chuang Gan,
• Abstract summary: Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning.<n>Recent attempts train VLMs to render explicit images, but the heavy image-generation pre-training often hinders the reasoning ability.<n>We present a Machine Mental Imagery framework, dubbed as Mirage, which augments VLM decoding with latent visual tokens alongside ordinary text.
• Score: 44.19323180593379
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) excel at multimodal understanding, yet their text-only decoding forces them to verbalize visual reasoning, limiting performance on tasks that demand visual imagination. Recent attempts train VLMs to render explicit images, but the heavy image-generation pre-training often hinders the reasoning ability. Inspired by the way humans reason with mental imagery-the internal construction and manipulation of visual cues-we investigate whether VLMs can reason through interleaved multimodal trajectories without producing explicit images. To this end, we present a Machine Mental Imagery framework, dubbed as Mirage, which augments VLM decoding with latent visual tokens alongside ordinary text. Concretely, whenever the model chooses to ``think visually'', it recasts its hidden states as next tokens, thereby continuing a multimodal trajectory without generating pixel-level images. Begin by supervising the latent tokens through distillation from ground-truth image embeddings, we then switch to text-only supervision to make the latent trajectory align tightly with the task objective. A subsequent reinforcement learning stage further enhances the multimodal reasoning capability. Experiments on diverse benchmarks demonstrate that Mirage unlocks stronger multimodal reasoning without explicit image generation.

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