发表机构
University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究个性化分层论文路由问题，提出无需训练基于文件夹成员决策的PaperRouter-Agent智能体，在真实个人图书馆和公共基准测试中，该智能体有效提升论文路由召回率和准确率，且成本低。
AI 中文摘要
研究人员在参考管理器中将收集的论文组织成个人文件夹层次结构，并将每篇新论文路由到其所属的文件夹中。此任务不同于标准的分层文本分类。用户的文件夹层次结构不是固定的共享分类法，而是一个私人且不断演变的民俗分类法，其文件夹含义可能是主题性、速记、基于会议地点或面向流程的，并且通常由其中存储的论文定义。我们将此设置形式化为个性化分层论文路由（PHPR）：在无需针对每个用户进行训练的情况下，将传入的论文分配到特定用户层次结构中的文件夹。我们提出了PaperRouter-Agent，这是一种无需训练的语言模型智能体，它将路由决策基于文件夹成员而非仅基于文件夹名称。该智能体首先缩小候选层次结构，检索特定文件夹的证据，通过检查成员论文来验证匹配度，并纳入过去用户拒绝的相似性门控反馈。对真实个人图书馆的形成性研究表明，PaperRouter-Agent将总体召回率@1从0.39提高到0.61，召回率@3从0.57提高到0.83，在由诸如会议地点或年份等元数据定义的组织文件夹上收益最大，单样本方法在此处效果不佳（召回率@1从0.09提高到0.50）。在公共LaMP-2基准测试中，相同方法将准确率从44.5%提高到51.5%（宏F1提高9.0），同时实际使用成本较低。
英文摘要
Researchers organize the papers they collect into personal folder hierarchies in reference managers, and route each new paper into the folder where it belongs. This task differs from standard hierarchical text classification. A user's folder hierarchy is not a fixed, shared taxonomy but a private and evolving folksonomy whose folder meanings may be topical, shorthand, venue-based, or process-oriented, and are often defined by the papers already stored inside them. We formalize this setting as personalized hierarchical paper routing (PHPR): assigning an incoming paper to folders in a user-specific hierarchy without per-user training. We propose PaperRouter-Agent, a training-free LLM agent that grounds routing decisions in folder members rather than folder names alone. The agent first narrows the candidate hierarchy, retrieves folder-specific evidence, verifies fit by inspecting member papers, and incorporates similarity-gated feedback from past user rejections. A formative study on real personal libraries shows that PaperRouter-Agent raises overall Recall@1 from 0.39 to 0.61 and Recall@3 from 0.57 to 0.83, with the largest gains on organizational folders defined by metadata such as venue or year, where single-shot methods collapses (Recall@1 0.09 to 0.50). On the public LaMP-2 benchmark, the same approach improves accuracy from 44.5% to 51.5% (+9.0 macro-F1) over a single-shot baseline, while remaining low-cost for practical use.
