---
license: mit
---
<div align="center">
  <h1>
    TeleEgo: <br> 
    Benchmarking Egocentric AI Assistants in the Wild
  </h1>

  <!-- 项目徽章 -->
  <p>
    <a href="https://arxiv.org/abs/2510.23981">
      <img alt="arXiv" src="https://img.shields.io/badge/ArXiv-2510.23981-b31b1b.svg">
    </a>
    <a href="https://programmergg.github.io/jrliu.github.io/">
      <img alt="Page" src="https://img.shields.io/badge/Project Page-Link-green">
    </a>
    <a href="https://github.com/TeleAI-UAGI/TeleEgo/">
      <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github">
    </a>
  </p>

  <!-- <img src="assets/teaser.png" alt="Teaser" style="width:80%; max-width:700px;"> -->
  
  📢 **Note**：This project is still under active development, and the benchmark will be continuously updated.  
</div>



## 📌 Introduction

**TeleEgo** is a comprehensive **omni benchmark** designed for **multi-person, multi-scene, multi-task, and multimodal long-term memory reasoning** in egocentric video streams.
It reflects realistic personal assistant scenarios where continuous egocentric video data is collected across hours or even days, requiring models to maintain and reason over **memory, understanding, and cross-memory reasoning**. **Omni** here means that TeleEgo covers the full spectrum of **roles, scenes, tasks, modalities, and memory horizons**, offering all-round evaluation for egocentric AI assistants.

**TeleEgo provides:**

- 🧠 **Omni-scale, diverse egocentric data** from 5 roles across 4 daily scenarios.
- 🎤 **Multi-modal annotations**: video, narration, and speech transcripts.
- ❓ **Fine-grained QA benchmark**: 3 cognitive dimensions, 12 subcategories.


---

## 📊 Dataset Overview

- **Participants**: 5 (balanced gender)
- **Scenarios**:
  - Work & Study
  - Lifestyle & Routines
  - Social Activities
  - Outings & Culture
- **Recording**: 3 days/participant (~14.4 hours each)
- **Modalities**:
  - Egocentric video streams
  - Speech & conversations
  - Narration and event descriptions

---

## Download

```bash
# Extract (only need to specify the first file)
7z x archive.7z.001

# Or extract to a specific directory
7z x archive.7z.001 -o./extracted_data
```

## Dataset Structure
After extraction, the dataset structure is:

```
TeleEgo/
├── merged_P1_A.json          # QA annotations for Participant 1
├── merged_P2_A.json          # QA annotations for Participant 2
├── merged_P3_A.json          # QA annotations for Participant 3
├── merged_P4_A.json          # QA annotations for Participant 4
├── merged_P5_A.json          # QA annotations for Participant 5
├── merged_P1.mp4             # Video stream for Participant 1 (~46GB)
├── merged_P2.mp4             # Video stream for Participant 2 (~35GB)
├── merged_P3.mp4             # Video stream for Participant 3 (~58GB)
├── merged_P4.mp4             # Video stream for Participant 4 (~57GB)
├── merged_P5.mp4             # Video stream for Participant 5 (~38GB)
├── timeline_P1.json          # Temporal annotations for Participant 1
├── timeline_P2.json          # Temporal annotations for Participant 2
├── timeline_P3.json          # Temporal annotations for Participant 3
├── timeline_P4.json          # Temporal annotations for Participant 4
└── timeline_P5.json          # Temporal annotations for Participant 5
```

## Alternative Download Methods

If you have difficulty accessing Hugging Face, you can also download the dataset from:

**Baidu Netdisk (百度网盘)**
```
Link: https://pan.baidu.com/s/1TSqfjqeaXdP2TWEpiy_3KA?pwd=7wmh
```

The Baidu Netdisk version contains the **uncompressed data files** (MP4 videos and JSON annotations) directly


## 🧪 Benchmark Tasks

TeleEgo-QA evaluates models along **three main dimensions**:

1. **Memory**
   - Short-term / Long-term / Ultra-long Memory
   - Entity Tracking
   - Temporal Comparison & Interval

2. **Understanding**
   - Causal Understanding
   - Intent Inference
   - Multi-step Reasoning
   - Cross-modal Understanding

3. **Cross-Memory Reasoning**
   - Cross-temporal Causality
   - Cross-entity Relation
   - Temporal Chain Understanding

Each QA instance includes:

- Question type: Single-choice, Multi-choice, Binary, Open-ended

<!-- ---

---
 -->
<!-- ## Baselines
![Baseline 1](assets/res1.png)
![Baseline 2](assets/res2.png)
---

## 🤝 Collaborators

Thanks to these amazing people for contributing to the project:

<a href="https://github.com/rebeccaeexu">
  <img src="https://avatars.githubusercontent.com/rebeccaeexu" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/DavisWANG0">
  <img src="https://avatars.githubusercontent.com/DavisWANG0" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/H-oliday">
  <img src="https://avatars.githubusercontent.com/H-oliday" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/Xiaolong-RRL">
  <img src="https://avatars.githubusercontent.com/Xiaolong-RRL" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/Programmergg">
  <img src="https://avatars.githubusercontent.com/Programmergg" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/yiheng-wang-duke">
  <img src="https://avatars.githubusercontent.com/yiheng-wang-duke" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/cocowy1">
  <img src="https://avatars.githubusercontent.com/cocowy1" width="60px" style="border-radius:50%" />
</a>
<a href="https://github.com/chxy95">
  <img src="https://avatars.githubusercontent.com/chxy95" width="60px" style="border-radius:50%" />
</a> -->


## 📜 Citation

If you find our **TeleEgo** in your research, please cite:

```bib
@article{yan2025teleego,
      title={TeleEgo: Benchmarking Egocentric AI Assistants in the Wild}, 
      author={Yan, Jiaqi and Ren, Ruilong and Liu, Jingren and Xu, Shuning and Wang, Ling and Wang, Yiheng and Wang, Yun and Zhang, Long and Chen, Xiangyu and Sun, Changzhi and others},
      journal={arXiv preprint arXiv:2510.23981},
      year={2025}
}
```

## 🪪 License

This project is licensed under the **MIT License**.
Dataset usage is restricted under a **research-only license**.

---

<!-- ## References

* EgoLife: Towards Egocentric Life Assistant [\[arXiv:2503.03803\]](https://arxiv.org/abs/2503.03803)
* M3-Agent: Seeing, Listening, Remembering, and Reasoning [\[arXiv:2508.09736\]](https://arxiv.org/abs/2508.09736)
* HourVideo: 1-Hour Video-Language Understanding [\[arXiv:2411.04998\]](https://arxiv.org/abs/2411.04998) -->


## 📬 Contact

If you have any questions, please feel free to reach out: chxy95@gmail.com.

---

<div align="center">

<strong>✨ TeleEgo is an Omni benchmark, a step toward building personalized AI assistants with true long-term memory, reasoning and decision-making in real-world wearable scenarios. ✨</strong>

</div>

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