README.md

---
license: cc-by-nc-4.0
modalities:
  - audio
  - text
configs:
- config_name: temporal_reasoning
  data_files:
  - split: test
    path: "meta_info/holistic_reasoning_temporal.json"
  default: true

- config_name: spatial_reasoning
  data_files:
  - split: test
    path: "meta_info/holistic_reasoning_spatial.json"

- config_name: perception
  data_files:
  - split: test
    path: "meta_info/foundation_perception.json"
---

<div align="center">
  <div align="center">
    <img src="assets/4d_logo.png" width="160"/>
    <h1 align="center">
      STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
    </h1>
  </div>
  <p align="center">
    <a href="https://scholar.google.com/citations?user=iELd-Q0AAAAJ"><strong>Zihan Liu<sup>*</sup></strong></a>
    ·
    <a href="https://scholar.google.com/citations?user=mXSpi2kAAAAJ&hl=zh-CN"><strong>Zhikang Niu<sup>*</sup></strong></a>
    ·
    <a href="https://github.com/akkkkkkkkki/"><strong>Qiuyang Xiao</strong></a>
    ·
    <a href="https://scholar.google.com/citations?user=WYwBrzAAAAAJ&hl=en"><strong>Zhisheng Zheng</strong></a>
    ·
    <a href="https://github.com/yrqqqq404"><strong>Ruoqi Yuan</strong></a>
    ·
    <a href="https://yuhangzang.github.io/"><strong>Yuhang Zang<sup>&dagger;</sup></strong></a>
    </br>
    <a href="https://scholar.google.com/citations?user=sJkqsqkAAAAJ"><strong>Yuhang Cao</strong></a>
    ·
    <a href="https://lightdxy.github.io/"><strong>Xiaoyi Dong</strong></a>
    ·
    <a href="https://scholar.google.com/citations?user=P4yNnSkAAAAJ&hl=zh-TW"><strong>Jianze Liang</strong></a>
    ·
    <a href="https://scholar.google.com/citations?user=d6u01FkAAAAJ&hl=en"><strong>Xie Chen</strong></a>
    ·
     <a href="https://scholar.google.com/citations?user=QVHvhM4AAAAJ&hl=en"><strong>Leilei Sun</strong></a>
    ·
     <a href="http://dahua.site/"><strong>Dahua Lin</strong></a>
    ·
     <a href="https://myownskyw7.github.io/"><strong>Jiaqi Wang<sup>&dagger;</sup></strong></a>
  </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://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>
          
  </p> 
</div>


## 🌈Overview
We formalize <strong>audio 4D intelligence</strong> that is defined as reasoning over sound dynamics in time and 3D space, and introduce a <strong>STAR-Bench</strong> to measure it. STAR-Bench combines a <strong>Foundational Acoustic Perception</strong>setting (six attributes under absolute and relative regimes) with a <strong>Holistic Spatio-Temporal Reasoning</strong> setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories.
<p style="text-align: center;"> 
  <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. 

Benchmark examples are illustrated below. You can also visit the [homepage](https://internlm.github.io/StarBench/) for a more intuitive overview.
</p>
<p style="text-align: center;"> 
  <img src="assets/bench_examples.png" alt="STAR-Bench Examples" width="100%"> 
</p>
  
<!-- A comparative overview of our benchmark against other representative audio benchmarks is shown below.
<p style="text-align: center;"> 
<img src="assets/bench_compare.png" alt="Comparison among Benchmarks" width="100%"> 
</p>  -->



## 📊Results and Analysis
Evaluation results of various models on STAR-Bench v0.5 are shown below.
The leaderboard for v1.0 will be released soon.
<p style="text-align: center;">
  <img src="assets/results.png" alt="Results" width="100%">
</p>
Error distribution across temporal and spatial Tasks:
<p style="text-align: center;">
  <img src="assets/error_dist.png" alt="Results" width="100%">
</p>

## 💡 Key Insights
- 🔥**A clear capability hierarchy between the two groups.** Closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and reasoning. 
- 🔥 **Enhancing dense audio captioning.** Open-source models struggle to produce dense, fine-grained captions, which limits their perceptual sensitivity and ability to extract embedded knowledge. Bridging this gap is a crucial first step. 
- 🔥 **Improving multi-audio reasoning.** Open-source models lag significantly in comparing, integrating, and grounding information across multiple audio clips. 
- 🔥 **Moving beyond channel-averaged audio preprocessing.** The common practice of averaging multi-channel audio into a mono signal is a major bottleneck for spatial reasoning. Developing architectures that natively process multi-channel cues is essential for unlocking genuine spatial awareness.



## ⚙️Data Curation
<p style="text-align: center;">
<img src="assets/data_dist.png" alt="" width="90%"> 
</p>
 All audio for the foundational perception task is synthesized using precise parameterization or the Pyroomacoustics physics-based simulator, providing complete control over acoustic parameters. Domain experts rigorously validate the task difficulty
levels, which are then calibrated through human testing.</br>
For the holistic spatio-temporal reasoning task, the curation process comprises four key stages, including human annotation and final selection based on human performance, as illustrated below.
<p style="text-align: center;">
  <img src="assets/pipeline.png" alt="pipeline" width="90%"> 
</p>


## ✒️Citation
```
TBD
```

## 📄 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.