File size: 4,457 Bytes
d77820e 8f33470 d77820e 71367a6 d77820e f8a7816 d77820e 71367a6 d77820e f8a7816 d77820e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | ---
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.jsonl
---
# 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://egomemreason.github.io/>
- 🎬 **EgoLife video frames (separate license):** <https://egolife-ai.github.io/>
- 📄 **Paper:** <https://arxiv.org/abs/2605.09874>
## 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_q001",
"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
@misc{wang2026egomemreasonmemorydrivenreasoningbenchmark,
title={EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding},
author={Ziyang Wang and Yue Zhang and Shoubin Yu and Ce Zhang and Zengqi Zhao and Jaehong Yoon and Hyunji Lee and Gedas Bertasius and Mohit Bansal},
year={2026},
eprint={2605.09874},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.09874},
}
```
|