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Add DyBench dataset card and link to paper (#2)

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- Add DyBench dataset card and link to paper (d0dd589bb494288404e8171c65866ec96897d7ec)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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  1. README.md +27 -3
README.md CHANGED
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  ---
 
 
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  license: mit
 
 
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  extra_gated_fields:
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  Name: text
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  Company/Organization: text
@@ -15,6 +19,26 @@ configs:
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  data_files: json/moving_direction.json
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  - config_name: reversible_dynamics
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  data_files: json/reversible_dynamics.json
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- language:
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- - en
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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  license: mit
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+ task_categories:
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+ - video-text-to-text
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  extra_gated_fields:
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  Name: text
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  Company/Organization: text
 
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  data_files: json/moving_direction.json
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  - config_name: reversible_dynamics
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  data_files: json/reversible_dynamics.json
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+ ---
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+
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+ # DyBench
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+
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+ [**Project Page**](https://ddz16.github.io/crpo.github.io/) | [**Paper**](https://huggingface.co/papers/2605.21988) | [**GitHub**](https://github.com/ddz16/CRPO)
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+ DyBench is a paired counterfactual video benchmark introduced in the paper "[Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning](https://huggingface.co/papers/2605.21988)".
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+ The benchmark is designed to evaluate the **spatiotemporal sensitivity** of Video Large Language Models (Video LLMs). It addresses the issue of models relying on "shortcuts" (such as single-frame cues or language priors) rather than tracking actual video dynamics. DyBench utilizes a strict pair-accuracy metric that requires a model to correctly answer questions for both original and counterfactual versions of a video.
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+ ### Dataset Details
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+ DyBench consists of **3,014 videos** covering three primary categories of spatiotemporal dynamics:
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+ - **Reversible Dynamics**: Evaluating if models understand physical processes that can be temporally reversed.
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+ - **Moving Direction**: Tracking the spatial trajectory and direction of motion.
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+ - **Event Sequence**: Understanding the temporal order in which events occur.
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+
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+ ### Data Structure
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+ The dataset is organized into three configurations corresponding to the tasks above:
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+ - `event_sequence`
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+ - `moving_direction`
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+ - `reversible_dynamics`
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+
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+ Each configuration contains JSON files mapping videos to their respective questions and ground-truth answers.