---
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"
---
## 🌈Overview
We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce a STAR-Bench to measure it. STAR-Bench combines a Foundational Acoustic Perceptionsetting (six attributes under absolute and relative regimes) with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories.
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 linguistically hard-to-describe cues.
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.
## 📊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.
Error distribution across temporal and spatial Tasks:
## 💡 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
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.
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.
## ✒️Citation
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TBD
```
## 📄 License
  **Usage and License Notices**: The data and code are intended and licensed for research use only.
