@inproceedings{he-etal-2025-pasa,
    title = "{P}a{S}a: An {LLM} Agent for Comprehensive Academic Paper Search",
    author = "He, Yichen  and
      Huang, Guanhua  and
      Feng, Peiyuan  and
      Lin, Yuan  and
      Zhang, Yuchen  and
      Li, Hang  and
      E, Weinan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.572/",
    pages = "11663--11679",
    ISBN = "979-8-89176-251-0",
    abstract = "We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholar queries. We optimize PaSa using reinforcement learning with a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained academic queries and corresponding papers sourced from top-tier AI conference publications. Additionally, we develop RealScholarQuery, a benchmark collecting real-world academic queries to assess PaSa performance in more realistic scenarios. Despite being trained on synthetic data, PaSa significantly outperforms existing baselines on RealScholarQuery, including Google, Google Scholar, Google with GPT-4o for paraphrased queries, ChatGPT (search-enabled GPT-4o), GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably, PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78{\%} in recall@20 and 39.90{\%} in recall@50, and exceeds PaSa-GPT-4o by 30.36{\%} in recall and 4.25{\%} in precision. Model, datasets, and code are available at https://github.com/bytedance/pasa.Demo: https://pasa-agent.ai"
}