Update README.md

# MECoBench

MECoBench is a benchmark for systematically evaluating multimodal multi-agent collaboration in embodied environments.

It contains 192 task cases constructed in VirtualHome, covering two collaboration structures:

* **Parallel collaboration:** 96 task cases in which agents operate in a shared environment and can complete independent subtasks concurrently.
* **Sequential collaboration:** 96 task cases in which agents operate in disjoint spatial regions and must coordinate through object handovers.

## Dataset Files

| File | Description | Number of cases |
| ----------------- | ------------------------------ | --------------: |
| `parallel.json` | Parallel collaboration tasks | 96 |
| `sequential.json` | Sequential collaboration tasks | 96 |
## Download

Download the complete dataset with:

```python
from huggingface_hub import snapshot_download

snapshot_download(
repo_id="q-i-n-g/MECoBench",
repo_type="dataset"
)
```

Alternatively, using the Hugging Face CLI:

```bash
hf download q-i-n-g/MECoBench \
--repo-type dataset
```

## Evaluation

The dataset is designed for use with the MECoBench simulator, which is based on VirtualHome v2.3.0 and includes additional multi-agent actions, agent constraints, rendering capabilities, character assets, and stability fixes.

The simulator, benchmark code, and evaluation scripts are available in the [MECoBench GitHub repository](https://github.com/q-i-n-g/MECoBench).

README.md

@@ -1,3 +1,15 @@
 ---
 license: mit
----
+language:
+- en
+tags:
+- multi-agent
+- multimodal
+- embodied-ai
+- collaboraion
+- benchmark
+- virtualhome
+pretty_name: MECoBench
+size_categories:
+- n<1K
+---