README.md

---
license: cc-by-nc-4.0
task_categories:
- question-answering
- multiple-choice
- visual-question-answering
- image-text-to-text
language:
- en
pretty_name: OST-Bench
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: scan_id
    dtype: string
  - name: turn_id
    dtype: int64
  - name: type
    dtype: string
  - name: new_observations
    sequence: string
  - name: origin_question
    dtype: string
  - name: option
    sequence: string
  - name: answer
    dtype: string
  splits:
  - name: test
    num_examples: 10000
configs:
- config_name: default
  data_files:
  - split: test
    path: OST_bench.json
---

This page contains the data for the paper "OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding." 

[**🌐 Homepage**](https://rbler1234.github.io/OSTBench.github.io/)  | [**📑 Paper**](https://arxiv.org/pdf/2507.07984) | [**💻 Code**](https://github.com/OpenRobotLab/OST-Bench) | [**📖 arXiv**](https://arxiv.org/abs/2507.07984)

## Dataset Description
The `imgs` folder contains image data corresponding to 1,386 scenes. Each scene has its own subfolder, which stores the observations captured by the agent while exploring that scene.

ost-bench.json consists of 10k data samples, where each sample represents one round of Q&A (question and answer) and includes the new observations for that round. The structure of each sample (dictionary) is as follows:

```python
{
  "scan_id" (str): Unique identifier for the scene scan,  
  "system_prompt" (str): Shared context/prompt for the multi-turn conversation,  
  "turn_id" (int): Index of the current turn in the dialogue,  
  "type" (str): Question subtype/category,  
  "origin_question" (str): Original question text,  
  "answer" (str): Ground-truth answer,  
  "option" (list[str]): Multiple-choice options,
  "new_observations" (list[str]): Relative paths to new observation images (within `imgs` dir),  
  "user_message" (str): Formatted input prompt for the model,  
}
```

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`.

## Evaluation Instructions
Please refer to our  [evaluation code](https://github.com/OpenRobotLab/OST-Bench) for details.