TeleEgo: Benchmarking Egocentric AI Assistants in the Wild

• URL: http://arxiv.org/abs/2510.23981v2
• Date: Thu, 30 Oct 2025 07:09:32 GMT
• Title: TeleEgo: Benchmarking Egocentric AI Assistants in the Wild
• Authors: Jiaqi Yan, Ruilong Ren, Jingren Liu, Shuning Xu, Ling Wang, Yiheng Wang, Yun Wang, Long Zhang, Xiangyu Chen, Changzhi Sun, Jixiang Luo, Dell Zhang, Hao Sun, Chi Zhang, Xuelong Li,
• Abstract summary: Egocentric AI assistants in real-world settings must process multi-modal inputs (video, audio, text)<n>We introduce textbfTeleEgo, a long-duration, streaming, omni-modal benchmark for evaluating egocentric AI assistants.<n>The dataset features over 14 hours per participant of synchronized egocentric video, audio, and text across four domains.
• Score: 55.53194302888826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Egocentric AI assistants in real-world settings must process multi-modal inputs (video, audio, text), respond in real time, and retain evolving long-term memory. However, existing benchmarks typically evaluate these abilities in isolation, lack realistic streaming scenarios, or support only short-term tasks. We introduce \textbf{TeleEgo}, a long-duration, streaming, omni-modal benchmark for evaluating egocentric AI assistants in realistic daily contexts. The dataset features over 14 hours per participant of synchronized egocentric video, audio, and text across four domains: work \& study, lifestyle \& routines, social activities, and outings \& culture. All data is aligned on a unified global timeline and includes high-quality visual narrations and speech transcripts, curated through human refinement.TeleEgo defines 12 diagnostic subtasks across three core capabilities: Memory (recalling past events), Understanding (interpreting the current moment), and Cross-Memory Reasoning (linking distant events). It contains 3,291 human-verified QA items spanning multiple question formats (single-choice, binary, multi-choice, and open-ended), evaluated strictly in a streaming setting. We propose two key metrics -- Real-Time Accuracy and Memory Persistence Time -- to jointly assess correctness, temporal responsiveness, and long-term retention. TeleEgo provides a realistic and comprehensive evaluation to advance the development of practical AI assistants.

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