TeleEgo:
Benchmarking Egocentric AI Assistants in the Wild

📢 Note：This project is still under active development, and the benchmark will be continuously updated.

📌 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.

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📊 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

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Download

# 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

📜 Citation

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

@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.

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📬 Contact

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

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✨ 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. ✨