纳米医学研究者常面临文献碎片化、方向选择困难的痛点，pArticleMap通过证据驱动的假设生成帮你发现被忽视的研究交叉点，做纳米药物设计或跨学科转化的团队值得一试。
纳米医学研究分散在大量文献中，现有AI主要聚焦于性质预测和配方优化，缺乏对研究方向选择的证据支持。研究者提出pArticleMap系统，结合文章嵌入、相似图分析、稀疏前沿提取和结构化证据包检索，利用大语言模型在低密度桥接区域和聚类界面生成引文支持的假设。在回顾性基准测试中，系统在任务级保留假设上实现了10.8%的黄金回收率和15.9%的召回@10，61.0%的未来邻域率表明系统能准确预测研究前沿。人机一致性中等，表明系统作为辅助工具而非替代专家判断。
Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine
Nanomedicine research spans delivery chemistry, immunology, imaging, biomaterials, and disease-specific translational science, yet its conceptual design space remains fragmented across a large and heterogeneous literature. To date, artificial intelligence in nanomedicine has focused primarily on property prediction and formulation optimization, with much less attention to evidence-grounded discovery support at the level of research direction selection. We introduce pArticleMap, a literature-mapping and research-hypothesis-generation system that combines article embeddings, similarity-graph analysis, sparse frontier extraction, structured evidence-pack retrieval, and an audited large-language-model (LLM) workflow for grounded ideation. Rather than forecasting future concept co-occurrence, pArticleMap targets low-density article-level bridge regions and cluster interfaces, then generates and scores citation-grounded hypotheses with large language models in an agentic setup. We evaluate the system with a retrospective realization benchmark (generate later literature under a historical cutoff) and a blinded human reader assessment layer across cue-conditioned nanomedicine tasks. Across 4 selected retrospective bundles, pArticleMap generated ideas and selected task-retained hypotheses (winner ideas) under the benchmark protocol. For task-level retained hypotheses, a pooled gold recovery rate of 10.8% was obtained, with a recall@10 of 15.9% and a future-neighborhood rate of 61.0%, indicating that the system often reached the correct forward-looking neighborhood (paper ideas) even without exact paper-level recovery. Human-agent agreement is modest overall, indicating that internal scoring is useful as a support signal but does not replace expert judgment. These results position pArticleMap as a conservative, evidence-grounded research assistant for nanomedicine.