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home / papers / 2026 / IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review

IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review

Paper · 2026 · Fengbo Ma, Zixin Rao, Xiaoting Li et al.

Summary

Researchers often need to pull precise details, such as experimental settings or metadata, out of a single scientific paper. Standard RAG pipelines rank chunks by surface similarity and ignore document structure, while literature agents are designed to search external sources rather than stay within one given text. The authors define a new task, IntraView, which asks a system to answer research-driven queries using only a supplied paper and to say explicitly when the answer is absent. Their agent, IntrAgent, imitates how people read. It first ranks sections by relevance, using an LLM that reasons over the preserved section hierarchy. It then reads sections one by one, gathering details until a sufficiency check judges that enough evidence exists to answer. To evaluate this, they release IntraBench, built from expert-written questions about papers in five STEM fields. Free-form answers are mapped to multiple-choice options by an LLM for scoring. Across seven backbone LLMs, IntrAgent beats RAG and research-agent baselines by 13.2% in average cross-domain accuracy. This makes it a practical, hallucination-aware tool for extracting facts from papers.

Key points

  • IntraBench: 315 test instances drawn from five domains: material science, engineering, public health, earth science, and physics. It is available on Hugging Face as IntrAgent/IntraBench, and the code is on GitHub.
  • Baselines: vanilla RAG (the strongest baseline) plus adapted literature agents, namely PaperQA, PaperQA2, QASA, and SciMaster.
  • Ablation of the sufficiency check: removing it on the physics subset (GPT-4o backbone) cuts accuracy from 75.4% to 32.2%. The main failure modes are incomplete retrieval and hallucination.
  • Mapping-model validation: GPT-4.1 matched expert correctness labels on 63 of 65 physics instances, so it serves as the default mapper.
  • Robustness and limitations: when section headings were rewritten in beginner, noisy, or Shakespearean styles, accuracy fell only from 89.2% to 84.6%. The system currently ignores figures and tables, and review papers are not covered.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of GeorgiaAthens · United States17 works · 26,331 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksInformation Systems · 1,437 worksGeneral Social Sciences · 227 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen and Zhen Xiang. 2026. IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.29

@inproceedings{ma2026intragent,
  title = {{IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review}},
  author = {Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen and Zhen Xiang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.29},
  url = {https://doi.org/10.18653/v1/2026.acl-long.29}
}

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