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.

翻译：真实世界中的第一人称人工智能助手必须处理多模态输入（视频、音频、文本），实时响应，并保持不断演化的长期记忆。然而，现有基准测试通常孤立地评估这些能力，缺乏真实的流式场景，或仅支持短期任务。我们提出\\textbf{TeleEgo}，一个用于在真实日常情境中评估第一人称人工智能助手的长时、流式、全模态基准。该数据集包含每位参与者超过14小时的同步第一人称视频、音频和文本数据，覆盖四个领域：工作与学习、生活方式与日常、社交活动以及外出与文化。所有数据均在统一的全局时间线上对齐，并包含通过人工精修的高质量视觉叙述和语音转录。TeleEgo定义了涵盖三个核心能力的12项诊断子任务：记忆（回忆过去事件）、理解（解释当前时刻）和跨记忆推理（关联远距离事件）。它包含3,291个人工验证的问答项，涵盖多种问题形式（单选、二元、多选和开放式），并在严格的流式设置下进行评估。我们提出两个关键指标——实时准确率和记忆保持时间——以共同评估正确性、时间响应性和长期保留能力。TeleEgo提供了一个真实且全面的评估框架，以推动实用人工智能助手的发展。