Improve dataset card: Add task categories, language, tags, paper links, sample usage, and citation

README.md

@@ -1,24 +1,31 @@
 ---
 license: cc-by-nc-4.0
 modalities:
-  - audio
-  - text
+- audio
+- text
+language:
+- en
+task_categories:
+- audio-text-to-text
+tags:
+- 4d-intelligence
+- spatio-temporal-reasoning
+- audio-reasoning
+- audio-benchmark
 configs:
 - config_name: temporal_reasoning
   data_files:
   - split: test
-    path: "meta_info/holistic_reasoning_temporal.json"
+    path: meta_info/holistic_reasoning_temporal.json
   default: true
-
 - config_name: spatial_reasoning
   data_files:
   - split: test
-    path: "meta_info/holistic_reasoning_spatial.json"
-
+    path: meta_info/holistic_reasoning_spatial.json
 - config_name: perception
   data_files:
   - split: test
-    path: "meta_info/foundation_perception.json"
+    path: meta_info/foundation_perception.json
 ---
 
 <div align="center">
@@ -57,7 +64,7 @@ configs:
   </p>
   <p align="center" style="font-size: 1em; margin-top: -1em"> <sup>*</sup>  Equal Contribution. <sup>&dagger;</sup>Corresponding authors. </p>
   <p align="center" style="font-size: 1.2em; margin-top: 0.5em">
-    📖<a href="">arXiv</a> 
+    📖<a href="https://huggingface.co/papers/2510.24693">Paper</a> | 📖<a href="https://arxiv.org/abs/2510.24693">arXiv</a>
     |🏠<a href="https://github.com/InternLM/StarBench">Code</a>
     |🌐<a href="https://internlm.github.io/StarBench/">Homepage</a>
     | 🤗<a href="https://huggingface.co/datasets/internlm/STAR-Bench">Dataset</a>
@@ -72,7 +79,7 @@ We formalize <strong>audio 4D intelligence</strong> that is defined as reasoning
   <img src="assets/teaser.png" alt="teaser" width="100%"> 
 </p>
 Unlike prior benchmarks where caption-only answering reduces accuracy slightly, STAR-Bench induces far larger drops (-31.5\% temporal, -35.2\% spatial), evidencing its focus on <strong>linguistically hard-to-describe cues</strong>. 
-Evaluating 19 models reveals substantial gaps to humans and a capability hierarchy. Our STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world. 
+Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy. Our STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world. 
 
 Benchmark examples are illustrated below. You can also visit the [homepage](https://internlm.github.io/StarBench/) for a more intuitive overview.
 </p>
@@ -117,19 +124,122 @@ For the holistic spatio-temporal reasoning task, the curation process comprises
   <img src="assets/pipeline.png" alt="pipeline" width="90%"> 
 </p>
 
+## 🛠️ Sample Usage
+The `ALMEval_code/` is partially adapted from [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) and [Kimi-Audio-Evalkit](https://github.com/MoonshotAI/Kimi-Audio-Evalkit).  
+It provides a unified evaluation pipeline for multimodal large models on **STAR-Bench**.
 
-## ✒️Citation
+
+**Step 1: Prepare Environment**
+
+```bash
+git clone https://github.com/InternLM/StarBench.git
+cd StarBench
+conda activate starbench python==3.10.0
+pip install -r requirements.txt
+cd ALMEval_code
 ```
-TBD
+
+**Step 2: Get STAR-Bench v1.0 Dataset**
+
+Download STAR-Bench v1.0 dataset from 🤗[HuggingFace](https://huggingface.co/datasets/internlm/STAR-Bench)
+```bash
+huggingface-cli download --repo-type dataset --resume-download internlm/STAR-Bench --local-dir your_local_data_dir 
 ```
 
-## 📄 License
-![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) **Usage and License Notices**: The data and code are intended and licensed for research use only.
+**Step 3: Set Up Your Model for Evaluation**
+
+Currently supported models include: `Qwen2.5-Omni`, `Qwen2-Audio-Instruct`, `DeSTA2.5-Audio`, `Phi4-MM`, `Kimi-Audio`, `MiDashengLM`, `Step-Audio-2-mini`, `Gemma-3n-E4B-it`, `Gemini` and `GPT-4o Audio`.
+<!-- `Ming-Lite-Omni-1.5`,`Xiaomi-MiMo-Audio`,`MiniCPM-O-v2.6`,`Audio Flamingo 3`, -->
+
+To integrate a new model, create a new file `yourmodel.py` under the `models/` directory and implement the function generate_inner().
+
+✅ Example: generate_inner()
+```
+def generate_inner(self, msg):
+    """
+    Args:
+        msg: dict, input format as below
+    """
+    msg = {
+        "meta": {
+            "id": ...,
+            "task": ...,
+            "category": ...,
+            "sub-category": ...,
+            "options": ...,
+            "answer": ...,
+            "answer_letter": ...,
+            "rotate_id": ...,
+        },
+        "prompts": [
+            {"type": "text", "value": "xxxx"},
+            {"type": "audio", "value": "audio1.wav"},
+            {"type": "text", "value": "xxxx"},
+            {"type": "audio", "value": "audio2.wav"},
+            ...
+        ]
+    }
+    # Return the model's textual response
+    return "your model output here"
+```
+  
+**Step 4: Configure Model Settings**
+
+Modify the configuration file: `/models/model.yaml`.
+
+For existing models, you may need to update parameters such as `model_path` to match your local model weight path.
+
+To add a new model variant, follow these steps:
+  1. Create a new top-level key for your alias (e.g., 'my_model_variant:').
+  2. Set 'base_model' to the `NAME` attribute of the corresponding Python class.
+  3. Add any necessary arguments for the class's `__init__` method under `init_args`.
+
+Example:
+```
+qwen25-omni:
+  base_model: qwen25-omni
+  init_args:
+    model_path: your_model_weight_path_here
+```
 
+**Step 5: Run Evaluation**
 
+Run the following command:
+```
+python ./run.py \
+  --model qwen25-omni \
+  --data starbench_default \
+  --dataset_root your_local_data_dir  \
+  --work-dir ./eval_results
+```
 
+Evaluation results will be automatically saved to the ./eval_results directory.
 
+You can also evaluate specific subtasks or their combinations by modifying the `--data` argument.
+The full list of available task names can be found in
+`ALMEval_code/datasets/__init__.py.`
 
+Example: Evaluate only the temporal reasoning and spatial reasoning tasks:
+```bash
+python ./run.py \
+  --model qwen25-omni \
+  --data tr sr \
+  --dataset_root your_local_data_dir  \
+  --work-dir ./eval_results
+```
 
+## ✒️Citation
+```bibtex
+@article{liu2025starbench,
+  title={STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence}, 
+  author={Liu, Zihan and Niu, Zhikang and Xiao, Qiuyang and Zheng, Zhisheng and Yuan, Ruoqi and Zang, Yuhang and Cao, Yuhang and Dong, Xiaoyi and Liang, Jianze and Chen, Xie and Sun, Leilei and Lin, Dahua and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2510.24693},
+  year={2025}
+}
+```
 
+## 📄 License
+![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) **Usage and License Notices**: The data and code are intended and licensed for research use only.
 
+## Acknowledgement
+We sincerely thank <a href="2077ai.com" target="_blank">2077AI</a> for providing the platform that supported our data annotation, verification, and review processes.