# S-Space: Exploring Spatial Workspace in Multimodal Language Models

Authors: Zhiyuan Shi, Xiang Li, Xingzhuo Guo, Shangchen Miao, Weiliang Chen, Qiyu Dai, Jinshan Ren, Jiangran Lyu, Fangfu Liu, Mingsheng Long, Jialong Wu.

Identifies spatial-coordinate directions in multimodal model activations and tests them through causal interventions. Separating perception from viewpoint transformation reveals cases where a model represents object locations but fails to perform the required spatial computation.

First public: 2026-10-09.
ArXiv initial submission: 2026-10-09.

[Original paper](https://arxiv.org/abs/2610.13527) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.13527/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.13527/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.13527/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.13527/record.json)

## Approaches

- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.