title: PaSa: An LLM Agent for Comprehensive Academic Paper Search
summary: 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 scholarly 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-4 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. It also 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.
paper_authors._id: 678dc283d4a7a158a8e5cf08
paper_authors.name: Yichen He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:19:13.591Z
paper_authors._id: 60ea81771cc8dc259c58e905
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60ea81771cc8dc259c58e905/kmGlaNvdS4EEHc_5qongT.jpeg
paper_authors.fullname: yichen he
paper_authors.type: user
paper_authors.user: hyc2026
paper_authors._id: 678dc283d4a7a158a8e5cf09
paper_authors.name: Guanhua Huang
paper_authors._id: 678dc283d4a7a158a8e5cf0a
paper_authors.name: Peiyuan Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:19:41.033Z
paper_authors._id: 662608797670fa2bc0d0fa0a
paper_authors.avatarUrl: /avatars/eef336e6ba43fae56c90e17f60606f4d.svg
paper_authors.fullname: fengpeiyuan
paper_authors.type: user
paper_authors.user: fpybytedance
paper_authors._id: 678dc283d4a7a158a8e5cf0b
paper_authors.name: Yuan Lin
paper_authors._id: 678dc283d4a7a158a8e5cf0c
paper_authors.name: Yuchen Zhang
paper_authors._id: 678dc283d4a7a158a8e5cf0d
paper_authors.name: Hang Li
paper_authors._id: 678dc283d4a7a158a8e5cf0e
paper_authors.name: Weinan E
paper_title: PaSa: An LLM Agent for Comprehensive Academic Paper Search
paper_summary: 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 scholarly 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-4 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. It also 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.
paper_ai_summary: A quality Paper Search agent, PaSa, uses reinforcement learning on synthetic and real academic queries, outperforming Google and GPT-4-based systems.
paper_ai_keywords: large language models reinforcement learning synthetic dataset AutoScholarQuery RealScholarQuery recall@20 recall@50 precision