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@@ -1,41 +1,42 @@
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  ---
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- license: cc
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  task_categories:
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  - question-answering
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  - multiple-choice
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  - visual-question-answering
 
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  language:
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  - en
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  pretty_name: OST-Bench
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  size_categories:
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- - 1K<n<
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  dataset_info:
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  features:
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- - name: scan_id
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- dtype: string
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- - name: turn_id
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- dtype: int64
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- - name: type
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- dtype: string
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- - name: new_observations
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- sequence: string
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- - name: origin_question
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- dtype: string
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- - name: option
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- sequence: string
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- - name: answer
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- dtype: string
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  splits:
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- - name: test
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- num_examples: 10000
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  configs:
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- - config_name: default
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- data_files:
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- - split: test
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- path: OST_bench.json
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  ---
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- This page contains the data for the paper "OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Unstanding."
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  [**🌐 Homepage**](https://rbler1234.github.io/OSTBench.github.io/) | [**πŸ“‘ Paper**](https://arxiv.org/pdf/2507.07984) | [**πŸ’» Code**](https://github.com/OpenRobotLab/OST-Bench) | [**πŸ“– arXiv**](https://arxiv.org/abs/2507.07984)
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@@ -61,4 +62,4 @@ ost-bench.json consists of 10k data samples, where each sample represents one ro
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  Samples with the same `scan_id` belong to the same multi-turn conversation group. During model evaluation, each multi-turn conversation group is processed as a unit: the shared `system_prompt` is provided, and new observations along with questions are fed in sequentially according to `turn_id`.
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  ## Evaluation Instructions
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- Please refer to our [evaluation code](https://github.com/rbler1234/OST-Bench) for details.
 
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  ---
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+ license: cc-by-nc-4.0
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  task_categories:
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  - question-answering
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  - multiple-choice
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  - visual-question-answering
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+ - image-text-to-text
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  language:
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  - en
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  pretty_name: OST-Bench
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  size_categories:
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+ - 10K<n<100K
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  dataset_info:
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  features:
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+ - name: scan_id
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+ dtype: string
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+ - name: turn_id
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+ dtype: int64
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+ - name: type
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+ dtype: string
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+ - name: new_observations
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+ sequence: string
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+ - name: origin_question
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+ dtype: string
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+ - name: option
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+ sequence: string
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+ - name: answer
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+ dtype: string
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  splits:
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+ - name: test
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+ num_examples: 10000
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  configs:
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+ - config_name: default
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+ data_files:
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+ - split: test
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+ path: OST_bench.json
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  ---
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+ This page contains the data for the paper "OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding."
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  [**🌐 Homepage**](https://rbler1234.github.io/OSTBench.github.io/) | [**πŸ“‘ Paper**](https://arxiv.org/pdf/2507.07984) | [**πŸ’» Code**](https://github.com/OpenRobotLab/OST-Bench) | [**πŸ“– arXiv**](https://arxiv.org/abs/2507.07984)
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  Samples with the same `scan_id` belong to the same multi-turn conversation group. During model evaluation, each multi-turn conversation group is processed as a unit: the shared `system_prompt` is provided, and new observations along with questions are fed in sequentially according to `turn_id`.
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  ## Evaluation Instructions
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+ Please refer to our [evaluation code](https://github.com/OpenRobotLab/OST-Bench) for details.