---
title: 'PaperQA: Retrieval-Augmented Generative Agent for Scientific Research'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'This note is CC BY 4.0. It links to the paper and does not reproduce it. Paper license: arXiv non-exclusive distribution (verified on arXiv abs page 2026-11-11; http://arxiv.org/licenses/nonexclusive-distrib/1.0/).'
source: https://iinventedlight.com/library/notes/2312-07559-paperqa.md
source_license: 'arXiv default (non-exclusive distribution license)'
attribution: 'Pointer note by iinventedlight (AI-assisted). Links to: Jakub Lála, Odhran O''Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G. Rodriques, Andrew D. White, PaperQA: Retrieval-Augmented Generative Agent for Scientific Research (arXiv). This note does not reproduce the paper.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Abs page read 2026-11-11. The paper is not reproduced here.'
category: arxiv
topic_area: 'ai-research: AI-for-science'
tags: arxiv, pointer, AI-for-science, RAG
arxiv_id: 2312.07559
alias: 2312.07559-paperqa
format: pointer
type: pointer
sources:
  - id: S1
    title: 'PaperQA: Retrieval-Augmented Generative Agent for Scientific Research'
    publisher: arXiv
    authors: 'Jakub Lála, Odhran O''Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G. Rodriques, Andrew D. White'
    url: https://arxiv.org/abs/2312.07559
    version_read: 'v2 (14 Dec 2023)'
    accessed: 2026-11-11
    license: 'arXiv default (non-exclusive distribution license)'
---
# PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

## Summary
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research (2023), by Jakub Lala, Odhran O'Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G. Rodriques, and Andrew D. White, is at https://arxiv.org/abs/2312.07559. The authors describe an agent that retrieves over full-text scientific articles, assesses the relevance of sources and passages, and then answers with retrieval-augmented generation. They report that, viewed as a question answering model, it exceeds existing language models and agents on current science QA benchmarks. To get closer to how people actually read the literature, they also introduce LitQA, a more complex benchmark that requires retrieval and synthesis from full-text papers, and they report that PaperQA matches expert human researchers on LitQA. They introduce LitQA, a more complex benchmark that requires retrieval and synthesis of information from full-text scientific papers across the literature. Viewing this agent as a question answering model, they find it exceeds performance of existing LLMs and LLM agents on current science QA benchmarks. They also report that PaperQA matches expert human researchers on LitQA. Title, authors, abstract, and comments were taken from the arXiv abs page. The version is v2 (14 Dec 2023), submitted 8 Dec 2023.

## Claims
Pointer notes do not quote the paper. See Verification and Sources for the link and license.

## Verification
- Format: pointer note only. The paper is not reproduced here.
- Why it matters: It matters because it is a concrete retrieval-augmented agent for scientific literature, with an emphasis on using sources rather than answering from model memory alone.
- Reproduced: not reproduced (2026-11-11).
- Related work: Related notes already in the library include [Self-RAG](https://iinventedlight.com/library/notes/2310-11511-self-rag/) and [RAG basics](https://iinventedlight.com/library/notes/genai-rag-embeddings-vector-search/).
- Limits: This is a pointer note only. Benchmark comparisons, including the LitQA human comparison, are the authors' reported results from the v2 paper (14 Dec 2023).
- License: arXiv default (non-exclusive distribution license) on the abs page (2026-11-11; http://arxiv.org/licenses/nonexclusive-distrib/1.0/). Submitted 8 Dec 2023 (v1); last revised 14 Dec 2023 (v2). Sources: [S1] https://arxiv.org/abs/2312.07559, v2 (14 Dec 2023). checked_on: 2026-11-11. Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it. The note itself is CC BY 4.0.

## When to apply
Use when looking at citation-backed literature agents, or at LitQA as a full-text scientific QA setting.

## Facts
- id: F1
  claim: 'PaperQA is a retrieval-augmented agent for answering questions over the scientific literature, including retrieval over full-text articles and relevance assessment of sources and passages.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC-BY-4.0
- id: F2
  claim: 'The authors introduce LitQA, a benchmark that requires retrieval and synthesis from full-text scientific papers.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC-BY-4.0
- id: F3
  claim: 'They report that PaperQA matches expert human researchers on LitQA.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC-BY-4.0

## Sources
- [S1] arXiv 2312.07559, https://arxiv.org/abs/2312.07559. Paper license: arXiv default (non-exclusive distribution license). v2 (14 Dec 2023). Accessed 2026-11-11. This note links to the paper and does not reproduce it.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).