# Skip the Talk, Re-Focus on Vision: Latent Reasoning for Reasoning Segmentation in Multimodal Large Language Models

Authors: Tianhang Guo, Yulin He, Wei Chen, Wenjuan Zhou, Yuhang Li, Xinbiao Gan.

LIRSeg generates a fixed sequence of learned special tokens before a target description and bounding box used for segmentation. Spatial alignment grounds the tokens' hidden states in relevant image regions, followed by training with segmentation rewards.

First public: 2026-09-25.
ArXiv initial submission: 2026-09-25.

[Original paper](https://arxiv.org/abs/2609.30783) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.30783/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.30783/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.30783/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.30783/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.

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.