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  # Spatial-Temporal Intelligence Benchmark (STI-Bench)
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- [![arXiv](https://img.shields.io/badge/arXiv-2503.23765-b31b1b.svg)](https://arxiv.org/abs/2503.23765) [![Hugging Face Datasets](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/MIRA-SJTU/STI-Bench) [![GitHub Repo](https://img.shields.io/badge/GitHub-Code-lightgrey)](https://github.com/MIRA-SJTU/STI-Bench)[![Homepage](https://img.shields.io/badge/Homepage-STI--Bench-brightgreen)](https://mira-sjtu.github.io/STI-Bench.io/)
 
 
 
 
 
 
 
 
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  This repository contains the Spatial-Temporal Intelligence Benchmark (STI-Bench), introduced in the paper [“STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?”](https://arxiv.org/abs/2503.23765), which evaluates the ability of Multimodal Large Language Models (MLLMs) to understand spatial-temporal concepts through real-world video data.
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  # Spatial-Temporal Intelligence Benchmark (STI-Bench)
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+ <div style="text-align: center">
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+ <a href="https://arxiv.org/abs/2503.23765"><img src="https://img.shields.io/badge/arXiv-2503.23765-b31b1b.svg" alt="arXiv"></a>
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+ <a href="https://huggingface.co/datasets/MIRA-SJTU/STI-Bench"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue" alt="Hugging Face Datasets"></a>
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+ <a href="https://github.com/MIRA-SJTU/STI-Bench"><img src="https://img.shields.io/badge/GitHub-Code-lightgrey" alt="GitHub Repo"></a>
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+ <a href="https://mira-sjtu.github.io/STI-Bench.io/"><img src="https://img.shields.io/badge/Homepage-STI--Bench-brightgreen" alt="Homepage"></a>
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+ </div>
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+ <div style="text-align: center">
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+ <a href="https://mp.weixin.qq.com/s/yIRoyI1HbChLZv4GuvI7BQ"><img src="https://img.shields.io/badge/量子位-red" alt="量子位"></a>
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+ </div>
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  This repository contains the Spatial-Temporal Intelligence Benchmark (STI-Bench), introduced in the paper [“STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?”](https://arxiv.org/abs/2503.23765), which evaluates the ability of Multimodal Large Language Models (MLLMs) to understand spatial-temporal concepts through real-world video data.
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