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arXiv · 2605.14306

Towards Recursive Self-Evolving Agentic Literature Retrieval

Yuwen Du·Tian Jin·Jing Kang·Xianghe Pang·Jingyi Chai·Tingjia Miao·Fenyi Liu·WenHao Wang·Sikai Yao·Yuzhi Zhang·Siheng Chen
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Abstract

Scientific literature retrieval must understand complex search intents while preserving source authenticity. Traditional keyword and embedding-based systems return authentic sources but miss nuanced intents, whereas large language models capture richer intents but may fabricate citations. We introduce PaSaMaster, a Recursive Self-Evolving agentic literature retrieval system that iteratively analyzes intent, retrieves verified papers and ranks them with evidence-grounded relevance scores. PaSaMaster combines self-evolving retrieval that refines search intent from ranked evidence over time, hallucination-free ranking over verified papers rather than generated citations, and cost-efficient planning--retrieval separation that reserves frontier LLMs for intent understanding while delegating retrieval and scoring to lightweight models and customized corpora. Across 38 disciplines in PaSaMaster-Bench, PaSaMaster achieves a 16.5$\times$ higher F1-score than Google Scholar and a 37.8\% higher F1-score than GPT-5.2 at about 1\% of the cost, while reducing source hallucination from 32.66\% in generative LLMs to zero: https://github.com/sjtu-sai-agents/PaSaMaster

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Yuwen Du, Tian Jin, Jing Kang, Xianghe Pang, Jingyi Chai, Tingjia Miao, Fenyi Liu, WenHao Wang, Sikai Yao, Yuzhi Zhang, Siheng Chen. 2026-05-14. Towards Recursive Self-Evolving Agentic Literature Retrieval. https://arxiv.org/abs/2605.14306

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