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 *.joblib filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.DS_Store
+Thumbs.db
+__pycache__/
+*.pyc
+*.log
+*.tmp
+
+meta_info/holistic_reasoning_temporal_otherinfo.jsonl
+meta_info/foundation_perception/*
+meta_info/foundation_perception_todel/*
+starbench_audios/*
+meta_info_old
+starbench_audios

README.md

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+---
+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.
+
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