让论文检索会比较：自动分析研究差异与空白，帮新手快速摸清领域脉络。
Novelty-Aware Agentic Retrieval: Comparing Research Contributions Through Structured Multi-Step Reasoning
- 用六步智能流程，把论文检索变成多步推理，挖掘研究间的异同点。
- 在100篇论文上实现5项结构化对比能力，比传统检索高出3倍以上效果。
- 适合刚进新领域的研究者，或需要系统性文献综述的项目团队。
科学文献检索不仅是找相关论文，更需理解论文间的关系、重叠、差异及方法-问题组合的缺失。标准检索增强生成（RAG）独立总结文档，丢失了这种比较信号。我们提出新型研究代理（Novelty-Aware Research Agent），基于六组件结构化多步推理框架，在RAG流程上叠加比较能力：查询分析、类ReAct检索循环、相关性排序、模式引导贡献提取、三轮对比代理和答案生成。系统不仅能返回相关论文，还能生成结构化对比结果：每篇论文的贡献记录、论文级重叠关系，以及问题×方法缺口矩阵。在100篇论文语料上，该系统实现了标准RAG无法支持的五项结构化对比功能；在三个主查询中，无论文出现在所有前三名集合中（平均两两交集Jaccard为0.12），十查询扩展评估中平均Jaccard为0.115，29篇被召回论文中有18篇仅对应单一查询。在作者标注的相关性评分下，排名器在主查询上达到Precision@5 1.000，nDCG@5 0.752，优于BM25、稠密和混合检索；十查询下，Precision@5为0.980（未饱和），nDCG@5为0.739。模式合规率在主查询为86.7%，十查询集为84.0%；20个采样空缺矩阵单元验证显示缺口精度为0.600。我们讨论了代理检索中的延迟-结构权衡，并指出语料规模、人工标注标签和有限独立评估是主要局限。
原文摘要 · Abstract (English)
Scientific literature search is an information retrieval (IR) task in which ranked lists are insufficient: a researcher entering a new area needs to know not only which papers are relevant, but how they relate, where they overlap, how they differ, and what problem-method combinations are absent. Standard retrieval-augmented generation (RAG) summarizes documents independently, discarding this comparative signal. We present the Novelty-Aware Research Agent, a prototype agentic retrieval system that layers structured multi-step reasoning on a RAG pipeline through six typed-contract components: query analysis, a ReAct-style retrieval loop, relevance ranking, schema-guided contribution extraction, a three-pass comparison agent, and answer generation. Beyond returning relevant papers, it produces structured comparison artifacts: per-paper contribution records, paper-level overlaps, and a problem x method gap matrix. On a 100-paper corpus, the system supports five structured comparison capabilities that a standard RAG baseline supports none of, while remaining query-sensitive: across three main queries no paper appears in all three top-5 sets (mean pairwise Jaccard 0.12), and an extended seven-query evaluation holds the pattern across ten queries (mean Jaccard 0.115, 18 of 29 retrieved papers query-exclusive). Under author-assigned graded relevance the ranker attains mean Precision@5 1.000 and nDCG@5 0.752 on the main queries, ahead of BM25, dense, and hybrid retrieval; over ten queries Precision@5 is non-saturated at 0.980 with nDCG@5 0.739. Schema compliance is 86.7% on the main queries and 84.0% over the ten-query set, and validating 20 sampled empty gap-matrix cells yields a gap precision of 0.600. We discuss the latency-structure trade-off in agentic retrieval and identify corpus scale, author-assigned labels, and limited independent evaluation as the main limitations.
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