Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual
  Tokens
Authors: name: Zeyuan Yang, name: Xueyang Yu, name: Delin Chen, name: Maohao Shen, name: Chuang Gan

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.