README.md

---
license: mit
task_categories:
- video-text-to-text
- audio-text-to-text
tags:
- egocentric
- multimodal
- long-term-memory
- question-answering
- ai-assistant
---

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

  <!-- \u9879\u76ee\u5fbd\u7ae0 -->
  <p>
    <a href="https://huggingface.co/papers/2510.23981">
      <img alt="Hugging Face Paper" src="https://img.shields.io/badge/HuggingFace-Paper-blue">
    </a>
    <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://teleai-uagi.github.io/TeleEgo/">
      <img alt="Project 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-Code-black">
    </a>
  </p>

  <img src="https://github.com/TeleAI-UAGI/TeleEgo/blob/main/assets/teaser.png" alt="Teaser" style="width:80%; max-width:700px;">
  
  \ud83d\udce2 **Note**\uff1aThis project is still under active development, and the benchmark will be continuously updated.  
</div>



## \ud83d\udccc 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:**

- \ud83e\udde0 **Omni-scale, diverse egocentric data** from 5 roles across 4 daily scenarios.
- \ud83c\udfa4 **Multi-modal annotations**: video, narration, and speech transcripts.
- \u2753 **Fine-grained QA benchmark**: 3 cognitive dimensions, 12 subcategories.


---

## \ud83d\udcca 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

---

## \ud83e\uddea 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

---

## \ud83d\uddc2\ufe0f Repository Structure

```
TeleEgo/
\u2502
\u251c\u2500\u2500 teleego_data/                # Dataset samples / metadata (link provided separately)
\u251c\u2500\u2500 weights/                     # Pre-trained weights (MiniCPM-o, Qwen2.5-Omni, ...)
\u251c\u2500\u2500 TeleEgo_gemini25_pro_eval.py # Evaluation scripts
\u251c\u2500\u2500 TeleEgo_gpt4o_eval.py        # Evaluation scripts
\u251c\u2500\u2500 TeleEgo_minicpm_eval.py      # Evaluation scripts
\u251c\u2500\u2500 TeleEgo_qwen25_eval.py       # Evaluation scripts
\u251c\u2500\u2500 TeleEgo_qweno25_eval.py      # Evaluation scripts
\u251c\u2500\u2500 TeleEgo_videochat_eval.py    # Evaluation scripts
\u2514\u2500\u2500 README.md                    # This file
```

## \ud83d\ude80 Usage

### \ud83d\udce5 Dataset Access

Due to privacy and licensing constraints, please request access here:
\ud83d\udcdd [**Dataset Access Form**](https://huggingface.co/datasets/David0219/TeleEgo).

### \ud83e\uddea Running Evaluations

```bash
python TeleEgo_gpt4o_eval.py
```

Submit your results to our \ud83c\udfc6 [**Online Leaderboard**](https://programmergg.github.io/jrliu.github.io/#leaderboard).

---

## \ud83d\udcdc Citation

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

```bibtex
@misc{yan2025teleegobenchmarkingegocentricai,
      title={TeleEgo: Benchmarking Egocentric AI Assistants in the Wild}, 
      author={Jiaqi Yan and Ruilong Ren and Jingren Liu and Shuning Xu and Ling Wang and Yiheng Wang and Yun Wang and Long Zhang and Xiangyu Chen and Changzhi Sun and Jixiang Luo and Dell Zhang and Hao Sun and Chi Zhang and Xuelong Li},
      year={2025},
      eprint={2510.23981},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.23981}, 
}
```

## \ud83e\udeaa License

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

---

## \ud83d\udcec Contact

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

---

<div align="center">

<strong>\u2728 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. \u2728</strong>

</div>

<div align="center" style="margin-top: 10px;">
  <img src="https://github.com/TeleAI-UAGI/TeleEgo/blob/main/assets/TeleAI.jpg" alt="TeleAI Logo" width="120px" />
  &nbsp;&nbsp;&nbsp;
  <img src="https://github.com/TeleAI-UAGI/TeleEgo/blob/main/assets/TeleEgo.png" alt="TeleEgo Logo" width="120px" />
</div>