# Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens

Authors: Zeyuan Yang, Xueyang Yu, Delin Chen, Maohao Shen, Chuang Gan.

Uses latent visual tokens as an internal imagery channel for multimodal reasoning without rendering every intermediate state.

First public: 2025-06-20.
ArXiv initial submission: 2025-06-20.

[Original paper](https://arxiv.org/abs/2506.17218) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2506.17218/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2506.17218/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2506.17218/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2506.17218/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.