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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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<a href="https://app.xinhuanet.com/news/article.html?articleId=8af447763b11efc491455eb93a27eac0"><img src="https://img.shields.io/badge/新华网-red" alt="新华网"></a>
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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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<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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<a href="https://app.xinhuanet.com/news/article.html?articleId=8af447763b11efc491455eb93a27eac0"><img src="https://img.shields.io/badge/新华网-red" alt="新华网"></a>
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<a href="https://mp.weixin.qq.com/s/pVytCfXmcG-Wkg-sOHk_BA"><img src="https://img.shields.io/badge/PaperWeekly-red" alt="PaperWeekly"></a>
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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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