Information workers' productivity is significantly influenced by their cognitive states and physiological responses. AI assistants such as ChatGPT, Copilot, and others have become integral components of knowledge-intensive workplaces. These AI assistants utilize pre-defined user preferences and chat interaction histories, thus confining themselves to reactive exchanges, lacking sufficient adaptability. Consequently, they fail to cater to individual user preferences and are unable to adapt to their psychophysiological states, diminishing potential productivity gains. To bridge this gap, we introduce AwareLLM, a novel multimodal framework that integrates egocentric vision, pupillometry, eye-gaze tracking, posture detection, heart activity, and the inferencing capabilities of large language models (LLMs) to create a proactive and context-aware ecosystem. AwareLLM dynamically adapts to users' psychophysiological states while analyzing temporal patterns and behavioral tendencies to provide personalized and timely interventions. We evaluated AwareLLM through a user study with 20 participants, comparing it to a standard LLM assistant across multiple tasks. Our results show statistically significant improvements in task performance, along with reductions in cognitive fatigue and mental demand. Participants described AwareLLM's personalized interventions as timely and relevant, helping them boost their confidence and deepen engagement with their work. AwareLLM opens new avenues for Human-AI collaboration where technology adapts to our needs rather than us adhering to technological constraints.

翻译：信息工作者的生产力受其认知状态与生理反应显著影响。以ChatGPT、Copilot为代表的AI助手已成为知识密集型工作场所的核心组件。这些AI助手基于预设的用户偏好与聊天交互历史运行，局限于被动式应答，缺乏充分的自适应能力。因此，它们既无法满足个体用户偏好，亦不能适应其心理生理状态，从而削弱了潜在的生产力提升空间。为弥合这一鸿沟，我们提出AwareLLM——一种融合自我中心视觉、瞳孔测量、眼动追踪、姿态检测、心脏活动监测及大语言模型推理能力的新型多模态框架，构建主动式、情境感知的生态系统。AwareLLM在分析时间模式与行为倾向的同时，动态适配用户的心理生理状态，提供个性化与适时干预。我们通过包含20名参与者的用户研究评估AwareLLM，在多任务场景中将其与标准LLM助手进行对比。结果表明，任务性能获得统计显著性提升，同时认知疲劳与心智负荷显著降低。参与者描述AwareLLM的个性化干预具有及时性与相关性，有助于增强自信并深化工作投入。AwareLLM开辟了人机协作的新路径——技术主动适配人类需求，而非人类屈从技术约束。