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README.md

@@ -1,3 +1,119 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-nc-4.0
+language:
+  - en
+task_categories:
+  - question-answering
+  - visual-question-answering
+task_ids:
+  - multiple-choice-qa
+pretty_name: EgoMemReason
+size_categories:
+  - n<1K
+tags:
+  - egocentric-video
+  - long-video-understanding
+  - memory
+  - multimodal
+  - benchmark
+  - video-qa
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: annotations_public.json
+---
+
+# EgoMemReason
+
+**A Memory-driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding.**
+
+500 multiple-choice questions over **week-long egocentric video** (built on [EgoLife](https://egolife-ai.github.io/)) that evaluate three complementary kinds of memory:
+
+- **Entity memory** — track how object states evolve across days
+- **Event memory** — recall and order activities separated by hours or days
+- **Behavior memory** — abstract recurring patterns from sparse, repeated observations
+
+Average **5.1 evidence segments per question** and **25.9 hours of memory backtracking** — 2× both metrics over the strongest prior week-long benchmark.
+
+## Links
+
+- 🧠 **Leaderboard (HF Space):** <https://huggingface.co/spaces/Ted412/EgoMemReason>
+- 💻 **Code & reference eval scripts:** <https://github.com/Ziyang412/EgoMemReason>
+- 🌐 **Project page:** <https://Ziyang412.github.io/EgoMemReason>
+- 🎬 **EgoLife video frames (separate license):** <https://egolife-ai.github.io/>
+- 📄 **Paper:** *coming soon*
+
+## Composition
+
+| Memory type | Capability (`query_type`) | # Qs |
+|---|---|---:|
+| Entity   | Cumulative State Tracking | 100 |
+| Entity   | Temporal Counting         | 100 |
+| Event    | Event Ordering            | 100 |
+| Event    | Event Linking             | 100 |
+| Behavior | Spatial Preference        |  50 |
+| Behavior | Activity Pattern          |  50 |
+| **Total** | | **500** |
+
+## Schema
+
+This dataset releases the **public** version — questions and options only, no answer keys (the held-out answer key lives in a private dataset, and submissions are scored against it by the leaderboard Space).
+
+```json
+{
+  "example_id": 1,
+  "p_id": "A1_JAKE_DAY7_19_00_00",
+  "identity": "A1_JAKE",
+  "query_time": "DAY7, 19:00:00",
+  "question": "What do I most often eat for breakfast?",
+  "options": {
+    "A": "Pancake",
+    "B": "Rice",
+    "C": "Burger",
+    "D": "Dumplings"
+  },
+  "query_type": "Activity Pattern"
+}
+```
+
+Note that **questions have 4-10 options** (letters A-J). The valid answer set for any given question is the keys of its `options` dict; Event Ordering questions tend to have the most options.
+
+## How to evaluate
+
+1. Get this dataset:
+   ```python
+   from datasets import load_dataset
+   ds = load_dataset("Ted412/EgoMemReason")["test"]
+   ```
+2. Get the underlying EgoLife video frames (separate license, see <https://egolife-ai.github.io/>) — we don't redistribute video here.
+3. For each item, sample frames from `(identity, query_time)` backwards in time and run your model to pick one letter from `options.keys()`.
+4. Format the predictions as a JSON list:
+   ```json
+   [
+     {"example_id": 1, "predicted_answer": "A"},
+     ...
+   ]
+   ```
+5. Submit it on the leaderboard Space: <https://huggingface.co/spaces/Ted412/EgoMemReason>. Per-split + overall accuracy are computed automatically.
+
+The reference inference scripts for 12 MLLMs and 5 agentic frameworks (Gemini, GPT-5, Qwen3-VL, InternVL3.5, Molmo2, VideoLLaMA3, InternVideo2.5, LongVA, AVP, Ego-R1, SiLVR, WorldMM, …) live in the [GitHub repo](https://github.com/Ziyang412/EgoMemReason).
+
+## License
+
+- **EgoMemReason annotations** (this dataset): [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — academic research and benchmarking are permitted; commercial use requires written permission.
+- **EgoLife video frames** (not redistributed here): governed by the [EgoLife data license](https://egolife-ai.github.io/) — you must accept their terms separately.
+
+## Citation
+
+```bibtex
+@article{wang2026egomemreason,
+  title   = {EgoMemReason: A Memory-driven Reasoning Benchmark for
+             Long-Horizon Egocentric Video Understanding},
+  author  = {Wang, Ziyang and Zhang, Yue and Yu, Shoubin and Zhang, Ce and
+             Zhao, Zengqi and Yoon, Jaehong and Lee, Hyunji and
+             Bertasius, Gedas and Bansal, Mohit},
+  year    = {2026},
+  journal = {arXiv preprint}
+}
+```