SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search

• URL: http://arxiv.org/abs/2507.15245v1
• Date: Mon, 21 Jul 2025 05:06:53 GMT
• Title: SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search
• Authors: Xiaofeng Shi, Yuduo Li, Qian Kou, Longbin Yu, Jinxin Xie, Hua Zhou,
• Abstract summary: We introduce SPAR, a multi-agent framework that incorporates RefChain-based query decomposition and query evolution.<n>We also construct SPARBench, a challenging benchmark with expert-annotated relevance labels.<n> Experimental results demonstrate that SPAR substantially outperforms strong baselines.
• Score: 2.8085728901059848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) have opened new opportunities for academic literature retrieval. However, existing systems often rely on rigid pipelines and exhibit limited reasoning capabilities. We introduce SPAR, a multi-agent framework that incorporates RefChain-based query decomposition and query evolution to enable more flexible and effective search. To facilitate systematic evaluation, we also construct SPARBench, a challenging benchmark with expert-annotated relevance labels. Experimental results demonstrate that SPAR substantially outperforms strong baselines, achieving up to +56% F1 on AutoScholar and +23% F1 on SPARBench over the best-performing baseline. Together, SPAR and SPARBench provide a scalable, interpretable, and high-performing foundation for advancing research in scholarly retrieval. Code and data will be available at: https://github.com/xiaofengShi/SPAR

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