SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced
  Academic Search
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
688042e900f4b5a05f2fbdd0
Xiaofeng Shi
admin_assigned
2025-07-23T13:36:31.609Z
642f6c64f945a8a5c9ee5b5d
https://cdn-avatars.huggingface.co/v1/production/uploads/642f6c64f945a8a5c9ee5b5d/V_4S_39gZc3ttiO4rXccj.png
XiaofengShi
user
MonteXiaofeng
688042e900f4b5a05f2fbdd1
Yuduo Li
688042e900f4b5a05f2fbdd2
Qian Kou
688042e900f4b5a05f2fbdd3
Longbin Yu
admin_assigned
2025-07-23T13:36:46.386Z
660a7b928695a785edd940e0
/avatars/8b901d77013ab52b926fdfeed68d2129.svg
Longbin Yu
user
yulongbin0516
688042e900f4b5a05f2fbdd4
Jinxin Xie
688042e900f4b5a05f2fbdd5
Hua Zhou
SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced
  Academic Search
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
SPAR, a multi-agent framework with RefChain-based query decomposition and evolution, outperforms existing systems in academic literature retrieval with a scalable and interpretable approach.
large language models LLMs multi-agent framework RefChain query decomposition query evolution SPARBench expert-annotated relevance labels F1 score AutoScholar