license: mit
task_categories:
- video-text-to-text
- audio-text-to-text
tags:
- egocentric
- multimodal
- long-term-memory
- question-answering
- ai-assistant
TeleEgo:
Benchmarking Egocentric AI Assistants in the Wild
\ud83d\udce2 Note\uff1aThis project is still under active development, and the benchmark will be continuously updated.
\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:
Memory
- Short-term / Long-term / Ultra-long Memory
- Entity Tracking
- Temporal Comparison & Interval
Understanding
- Causal Understanding
- Intent Inference
- Multi-step Reasoning
- Cross-modal Understanding
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.
\ud83e\uddea Running Evaluations
python TeleEgo_gpt4o_eval.py
Submit your results to our \ud83c\udfc6 Online Leaderboard.
\ud83d\udcdc Citation
If you find our TeleEgo in your research, please cite:
@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.
\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
