{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:21:43Z","timestamp":1780622503492,"version":"3.54.1"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for AI Research and Education at Cedars-Sinai Medical Center"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health USA","doi-asserted-by":"crossref","award":["U01 AG066833"],"award-info":[{"award-number":["U01 AG066833"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health USA","doi-asserted-by":"crossref","award":["R01 LM014572"],"award-info":[{"award-number":["R01 LM014572"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health USA","doi-asserted-by":"crossref","award":["R01 LM010098"],"award-info":[{"award-number":["R01 LM010098"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>LLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprecise vector similarity search. ESCARGOT aims to overcome these issues by combining LLMs with a dynamic Graph of Thoughts and biomedical knowledge graphs, improving output reliability, and reducing hallucinations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Result<\/jats:title>\n                  <jats:p>ESCARGOT significantly outperforms industry-standard RAG methods, particularly in open-ended questions that demand high precision. ESCARGOT also offers greater transparency in its reasoning process, allowing for the vetting of both code and knowledge requests, in contrast to the black-box nature of LLM-only or RAG-based approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>ESCARGOT is available as a pip package and on GitHub at: https:\/\/github.com\/EpistasisLab\/ESCARGOT.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf031","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T01:35:20Z","timestamp":1737596120000},"source":"Crossref","is-referenced-by-count":17,"title":["ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning"],"prefix":"10.1093","volume":"41","author":[{"given":"Nicholas","family":"Matsumoto","sequence":"first","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunjun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Moran","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center , West Hollywood, CA 90069,","place":["United 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