Scientific reading is an active process that frequently requires consulting external resources, but manual keyword searching interrupts the reading flow and imposes a high cognitive load. Existing proactive information retrieval systems often suffer from context ambiguity, as they rely solely on on-screen text and ignore the reader's specific background and intent. In this demonstration, we present H-MAPS (Hierarchical Memory-Augmented Proactive Search Assistant), a proactive literature exploration assistant that resolves this ambiguity by leveraging a three-layered hierarchical memory. Triggered by implicit reading behaviors, H-MAPS articulates the user's latent information needs into explicit natural language questions and performs neural retrieval entirely on the local device to ensure privacy. We demonstrate H-MAPS using a scenario where two researchers, specializing in NLP and HCI, read the same paper. In response, the system generates profile-specific questions and retrieves distinct literature tailored to each user.

翻译：科学阅读是一个主动过程，常需查阅外部资源，但手动关键字搜索会打断阅读流程并造成高认知负荷。现有主动式信息检索系统往往存在上下文歧义问题，因其仅依赖屏幕文本，忽视了读者的特定背景与意图。在本演示中，我们提出H-MAPS（分层记忆增强式主动搜索助手），一种通过三层分层记忆消除歧义的主动式文献探索助手。受隐性阅读行为触发，H-MAPS将用户潜在的信息需求转化为显式自然语言问题，并完全在本地设备上执行神经检索以确保隐私。我们通过自然语言处理（NLP）与人机交互（HCI）两位专业研究者阅读同一篇论文的场景进行演示。系统会据此生成面向个人档案的个性化问题，并为每位用户检索出符合其背景的差异化文献。