| # R<sup>3</sup>-Bench: Read the Room Reasoning Benchmark |
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| [](https://creativecommons.org/licenses/by-nc/4.0/) |
| [](https://openreview.net/pdf?id=TJilJnZjpw) |
| [](https://huggingface.co/datasets/nlxpku/R3-Bench) |
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| This is the official repository for **R<sup>3</sup>-Bench**, introduced in the paper: |
| **"Read the Room: Video Social Reasoning with Mental-Physical Causal Chains"** (ICLR 2026). |
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| R<sup>3</sup>-Bench is an evaluation benchmark with fine-grained annotations of belief, intent, desire, emotion, and their causal chains in complex social scenarios. |
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| ## 📑 Table of Contents |
| - [Dataset Structure](#-dataset-structure) |
| - [Download Videos](#-download-videos) |
| - [Evaluation Settings](#-evaluation-settings) |
| - [License](#-license) |
| - [Citation](#-citation) |
| - [Contact](#-contact) |
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| ## 📂 Dataset Structure |
| The dataset is organized as follows: |
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| **videos.csv**: Contains video ids and their corresponding start and end times (seconds). |
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| **r3-bench-hard.json**: Contains the question-answer pairs in R<sup>3</sup>-Bench-Hard. |
| ``` |
| { |
| submission_id: { |
| "Human_Annotation_Data": { |
| "QID": str, |
| "Question": str, |
| "Answer_Index": int, |
| "Options": List[str], |
| }, |
| "YouTube_ID": str, |
| "Start_Seconds": int, |
| "End_Seconds": int, |
| } |
| } |
| ``` |
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| - **r3-bench-dx.json**: Contains the chain structure and question-answer pairs in R<sup>3</sup>-Bench-DX. "Belief-X-I/Intent-X-I/Desire-X-I/Emotion-X-I" means the mental state of person X, where 'I' stands for index. "Event-I" means the event, where 'I' stands for index. "Sub-Chain-I" means the subchain, where 'I' stands for index. |
| ``` |
| { |
| submission_id: { |
| chain_id: { |
| "Referents": { |
| Referent-X: str, the referent of person X, |
| }, |
| "Nodes": { |
| "Belief-X-I/Intent-X-I/Desire-X-I/Emotion-X-I/Event-I": { |
| "QID": str, |
| "Description": str, |
| "Question": str, |
| "Answer_Index": int, |
| "Options": List[str], |
| } |
| }, |
| "Sub-Chains": { |
| Sub-Chain-I: { |
| "Why_QA": { |
| "QID": str, |
| "Question": str, |
| "Answer_Index": int, |
| "Options": List[str], |
| },' |
| "How/What_QA": { |
| "QID": str, |
| "Question": str, |
| "Answer_Index": int, |
| "Options": List[str], |
| }, |
| "Reasons": list of node ids, the reasons in the subchain, |
| "Result": node id, the result in the subchain, |
| }, |
| } |
| } |
| }, |
| "YouTube_ID": str, |
| "Start_Seconds": int, |
| "End_Seconds": int, |
| } |
| ``` |
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| ## 📥 Download Videos |
| We provide video ids and their corresponding start and end times in **videos.csv**. You can download the videos using the YouTube API or any YouTube video downloader by specifying the video id and the time range. |
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| ## 🚀 Evaluation Settings |
| Our evaluation was conducted using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit.git). |
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| The evaluation prompt **without subtitles** is: |
| ``` |
| These are the frames of a video. Select the best answer to the following multiple-choice question based on the video. Based on your understanding, respond with only the letter (A, B, C, D, or E) of the correct option. |
| Question: {question} |
| {options (separated with '\n')} |
| Answer: |
| ``` |
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| We use Whisper large-v2 to extract subtitles from the videos. The evaluation prompt **with subtitles** is: |
| ``` |
| These are the frames of a video. This video's subtitles are listed below: |
| {subtitles} |
| Select the best answer to the following multiple-choice question based on the video. Based on your understanding, respond with only the letter (A, B, C, D, or E) of the correct option. |
| Question: {question} |
| {options (separated with '\n')} |
| Answer: |
| ``` |
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| For the video, we extract **16 frames** at a resolution of **640x360**. |
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| ## 📜 License |
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| - Our dataset is licensed under **[Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)**. |
| - The **videos** are collected from public sources (YouTube) and are subject to their respective original licenses. We only provide video IDs and corresponding start and end times for academic research purposes. |
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| ## 📖 Citation |
| If you find our dataset or paper useful in your research, please consider citing: |
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| ```bibtex |
| @inproceedings{ |
| niu2026read, |
| title={Read the Room: Video Social Reasoning with Mental-Physical Causal Chains}, |
| author={Lixing Niu and Jiapeng Li and Xingping Yu and Xinyi Dong and Shu Wang and Ruining Feng and Bo Wu and Ping Wei and Yisen Wang and Lifeng Fan}, |
| booktitle={The Fourteenth International Conference on Learning Representations}, |
| year={2026}, |
| url={https://openreview.net/forum?id=TJilJnZjpw} |
| } |
| ``` |
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| ## ✉️ Contact |
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| For any questions, feedback, or issues regarding the dataset, please open an issue in this repository or contact: |
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| **Lixing Niu**: lxniu@stu.pku.edu.cn |
