基于分层记忆的智能文献助手，能理解用户背景并主动推荐相关论文。
H-MAPS: Hierarchical Memory-Augmented Proactive Search Assistant for Scientific Literature
- 通过三层分层记忆捕捉阅读者背景与意图，解决信息检索歧义。
- 根据用户研究方向自动生成个性化问题，本地完成检索保障隐私。
- 适合科研人员在读论文时快速获取针对性文献，提升阅读效率。
科学阅读是一个需要频繁查阅外部资源的主动过程，但手动关键词搜索会打断阅读流并带来高认知负担。现有主动信息检索系统常因仅依赖屏幕文本而存在上下文歧义，忽略读者的具体背景与意图。本文展示H-MAPS（Hierarchical Memory-Augmented Proactive Search Assistant），一种通过三层分层记忆机制解决该问题的主动文献探索助手。当检测到隐式阅读行为时，H-MAPS将用户的潜在信息需求转化为明确的自然语言问题，并在本地设备上执行神经检索，确保隐私安全。我们在一个实验中让两位分别专攻NLP和HCI的研究者阅读同一论文，系统据此生成与其个人背景匹配的问题，并返回各自相关的文献。
原文摘要 · Abstract (English)
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.
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