---
configs:
- config_name: anchor_recognition
  data_files:
  - split: test
    path: anchor_recognition/test-00000.parquet
- config_name: global_counting
  data_files:
  - split: test
    path: global_counting/test-00000.parquet
- config_name: relative_distance
  data_files:
  - split: test
    path: relative_distance/test-00000.parquet
- config_name: relative_direction
  data_files:
  - split: test
    path: relative_direction/test-00000.parquet
- config_name: cognitive_mapping
  data_files:
  - split: test
    path: cognitive_mapping/test-00000.parquet
---


<h1 align="center">Communicating about Space: Language-Mediated Spatial Integration Across Partial Views</h1>

<p align="center">
  <a href="https://arxiv.org/abs/2603.27183"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-Comm--About--Space-red?logo=arxiv" height="20"/> </a>
  <a href="https://github.com/ankursikarwar/Cosmic"><img src="https://img.shields.io/badge/github-Comm--About--Space-blue?logo=github" alt="Github"/> </a>
</p>

<div align="justify">

Humans routinely transform local, viewpoint-dependent observations into shared spatial models through language. COSMIC asks whether MLLMs can do the same. The benchmark places two static agents in the same indoor scene from different egocentric viewpoints. The agents must communicate exclusively through natural language to jointly solve a spatial QA task.

</div>

## Usage

```python
from datasets import load_dataset

ds_anchor_recognition = load_dataset("mair-lab/Cosmic", name="anchor_recognition", split="test")
ds_global_counting = load_dataset("mair-lab/Cosmic", name="global_counting", split="test")
ds_relative_distance = load_dataset("mair-lab/Cosmic", name="relative_distance", split="test")
ds_relative_direction = load_dataset("mair-lab/Cosmic", name="relative_direction", split="test")
ds_cognitive_mapping = load_dataset("mair-lab/Cosmic", name="cognitive_mapping", split="test")

```

## Citation

```bibtex
@misc{sikarwar2026communicatingspacelanguagemediatedspatial,
      title={Communicating about Space: Language-Mediated Spatial Integration Across Partial Views},
      author={Ankur Sikarwar and Debangan Mishra and Sudarshan Nikhil and Ponnurangam Kumaraguru and Aishwarya Agrawal},
      year={2026},
      eprint={2603.27183},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.27183},
}
```