Improve dataset card: Add task categories, tags, HF paper link, and sample usage

README.md

@@ -1,9 +1,13 @@
 ---
-pretty_name: ENACT
 language:
 - en
+license: mit
+size_categories:
+- 1K<n<10K
 task_categories:
 - visual-question-answering
+- image-text-to-text
+pretty_name: ENACT
 configs:
 - config_name: default
   data_files:
@@ -26,32 +30,32 @@ dataset_info:
     sequence: string
   - name: gt_answer
     sequence: int32
-license: mit
 tags:
 - agent
-size_categories:
-- 1K<n<10K
+- robotics
+- embodied-cognition
 ---
 
 # ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction
 
 ENACT is a benchmark dataset for evaluating **embodied cognition** in vision–language models via **egocentric world modeling**. It probes whether models can reason about how the world changes under sequences of actions, using long-horizon household activities in a mobile manipulation setting.
 
-- **Project page:** https://enact-embodied-cognition.github.io/  
-- **Code & evaluation:** https://github.com/mll-lab-nu/ENACT  
+-   **Paper:** [https://huggingface.co/papers/2511.20937](https://huggingface.co/papers/2511.20937)
+-   **Project page:** https://enact-embodied-cognition.github.io/  
+-   **Code & evaluation:** https://github.com/mll-lab-nu/ENACT  
 
 
 ## Dataset Summary
 
 Each ENACT example is a **multi-image, multi-step reasoning problem** built from robot trajectories:
 
-- **Forward world modeling**  
-  - Input: one **current state image**, several **future state images** (shuffled), and a list of **actions in correct order**.  
-  - Task: output a Python list of integers giving the **correct chronological order of future images** (e.g., `[1, 3, 2]`).
+-   **Forward world modeling**  
+    -   Input: one **current state image**, several **future state images** (shuffled), and a list of **actions in correct order**.  
+    -   Task: output a Python list of integers giving the **correct chronological order of future images** (e.g., `[1, 3, 2]`).
 
-- **Inverse world modeling**  
-  - Input: an **ordered sequence of images** showing state changes, plus **actions in shuffled order**.  
-  - Task: output a Python list of integers giving the **correct chronological order of actions** (e.g., `[2, 3, 1]`).
+-   **Inverse world modeling**  
+    -   Input: an **ordered sequence of images** showing state changes, plus **actions in shuffled order**.  
+    -   Task: output a Python list of integers giving the **correct chronological order of actions** (e.g., `[2, 3, 1]`).
 
 All images are egocentric RGB observations rendered from long-horizon household tasks (e.g., assembling gift baskets, bringing water, preparing lunch boxes, cleaning up a desk).
 
@@ -106,30 +110,57 @@ Each line in `enact_ordering.jsonl` is a JSON object:
 }
 ```
 
-* **`id`** – unique identifier for this QA instance.
-* **`type`** – question type and horizon, e.g. `forward_world_modeling_3_steps` or `inverse_world_modeling_4_steps`.
-* **`task_name`** – underlying household task instance.
-* **`key_frame_ids`** – frame indices of selected key frames in the trajectory.
-* **`images`** – relative paths to PNG images:
+*   **`id`** – unique identifier for this QA instance.
+*   **`type`** – question type and horizon, e.g. `forward_world_modeling_3_steps` or `inverse_world_modeling_4_steps`.
+*   **`task_name`** – underlying household task instance.
+*   **`key_frame_ids`** – frame indices of selected key frames in the trajectory.
+*   **`images`** – relative paths to PNG images:
 
-  * index 0 is the **current state**;
-  * subsequent entries are **future states** (forward) or later states (inverse).
-* **`question`** – natural language prompt specifying the task setup, actions, and the required output as a Python list of integers.
-* **`gt_answer`** – ground-truth ordering of image or action labels (list of integers, e.g. `[1, 3, 2]`).
+    *   index 0 is the **current state**;
+    *   subsequent entries are **future states** (forward) or later states (inverse).
+*   **`question`** – natural language prompt specifying the task setup, actions, and the required output as a Python list of integers.
+*   **`gt_answer`** – ground-truth ordering of image or action labels (list of integers, e.g. `[1, 3, 2]`).
 
 
-## Usage
-To evaluate, follow the scripts in the code repository: [https://github.com/mll-lab-nu/ENACT](https://github.com/mll-lab-nu/ENACT)
+## Sample Usage
+To evaluate your model on the ENACT dataset, follow these steps:
+
+1.  **Download the ENACT QA dataset:**
+    ```bash
+    python scripts/helpers/download_dataset.py
+    ```
+
+2.  **Run your model** on `data/QA/enact_ordering.jsonl` to generate predictions. Your model should output a JSONL file (e.g., `enact_ordering_mymodel.jsonl`) where each line contains the original fields plus an `answer` field as a stringified list (e.g., `"[2, 1]"`).
+
+3.  **Evaluate your predictions:**
+    ```bash
+    enact eval enact_ordering_mymodel.jsonl --analyze-wrong-cases
+    ```
+
+4.  **Check results:**
+    ```bash
+    cat data/evaluation/meta_performance/enact_ordering_mymodel.json
+    ```
+
+5.  **For batch evaluation of multiple models:**
+    ```bash
+    enact eval model_outputs_directory/ --analyze-wrong-cases
+    cat data/evaluation/batch_evaluation_summary.json
+    ```
+
+For more details on data generation and advanced evaluation options, please refer to the [code repository](https://github.com/mll-lab-nu/ENACT).
 
 
 ## Citation
 
-If you use ENACT, please cite the paper:
-```
+If you use ENACT in your research, please cite the paper:
+```bibtex
 @article{wang2025enact,
   title={ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction},
   author={Wang, Qineng and Huang, Wenlong and Zhou, Yu and Yin, Hang
           and Bao, Tianwei and Lyu, Jianwen and Liu, Weiyu and Zhang, Ruohan
-          and Wu, Jiajun and Li, Fei-Fei and Li, Manling}
+          and Wu, Jiajun and Li, Fei-Fei and Li, Manling},
+  journal={arXiv preprint arXiv:2511.20937},
+  year={2025}
 }
 ```