{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T20:24:19Z","timestamp":1794255859040,"version":"4.9.0"},"reference-count":91,"publisher":"National Academy of Sciences","issue":"46","license":[{"start":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T00:00:00Z","timestamp":1794182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005156","name":"Alexander von Humboldt-Stiftung","doi-asserted-by":"publisher","award":["Ref\u00a03.5 - 1229900 - CHN - HFST-P"],"award-info":[{"award-number":["Ref\u00a03.5 - 1229900 - CHN - HFST-P"]}],"id":[{"id":"10.13039\/100005156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"EC | European Research Council","doi-asserted-by":"publisher","award":["ERC-2024-STG 101165179 ArtDisQ"],"award-info":[{"award-number":["ERC-2024-STG 101165179 ArtDisQ"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2064\/1 Project 390727645"],"award-info":[{"award-number":["EXC 2064\/1 Project 390727645"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,11,17]]},"abstract":"<jats:p>\n                    The rapid growth of scientific literature makes it increasingly challenging for researchers to identify novel and impactful ideas, especially across disciplines. Modern AI systems offer new opportunities for scientific ideation, but how compelling are AI-generated ideas, and how can their quality be improved? Here, we introduce\n                    <jats:sc>SCIMUSE<\/jats:sc>\n                    , which generates personalized research ideas using a knowledge graph of 58 million papers and a large language model (LLM). A central focus of this work is to understand how interesting these ideas are. Therefore, we conducted a large-scale evaluation in which more than 100 research group leaders\u2014spanning the natural sciences to the humanities\u2014rated over 4,400 personalized ideas according to their level of interest. Overall, expert ratings were modest (mean 2.40 on a 5-point scale, most common rating 1), while 24.9% of ideas were rated 4 or 5. We find that supplying concept pairs selected using the knowledge graph does not improve expert-rated interest over a titles-only GPT baseline. High-citation-predicted pairs even showed a weak tendency (1.94\n                    <jats:italic toggle=\"yes\">\u03c3<\/jats:italic>\n                    ) toward lower interest than random pairs. Nevertheless, graph features can be used to control properties of ideas, and, using this unique evaluation dataset, we show that idea interest can be predicted with both a supervised neural network based on graph features and a zero-shot ranking approach based on an LLM. Our work provides an AI methodology for generating scientific ideas and a large-scale interdisciplinary expert evaluation, paving the way to study and improve difficult-to-measure metrics such as expert-perceived scientific interestingness.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2611349123","type":"journal-article","created":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T20:00:41Z","timestamp":1794254441000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":0,"title":["Generating interesting scientific ideas using knowledge graphs and LLMs: Evaluations with 100 research group leaders"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0734-432X","authenticated-orcid":false,"given":"Xuemei","family":"Gu","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/020as7681","id-type":"ROR","asserted-by":"publisher"}],"name":"Max Planck Institute for the Science of Light","place":["Erlangen, Germany"]},{"id":[{"id":"https:\/\/ror.org\/05qpz1x62","id-type":"ROR","asserted-by":"publisher"}],"name":"Institute of Condensed Matter Theory and Optics, Faculty of Physics and Astronomy, Friedrich Schiller University Jena","place":["Jena, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Krenn","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/020as7681","id-type":"ROR","asserted-by":"publisher"}],"name":"Max Planck Institute for the Science of Light","place":["Erlangen, Germany"]},{"id":[{"id":"https:\/\/ror.org\/03a1kwz48","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Computer Science, Faculty of Science, University of T\u00fcbingen","place":["T\u00fcbingen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2026,11,9]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aao0185"},{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781108610834"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1057\/s41599-021-00903-w"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1201765"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02198-9"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1509757112"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1914370116"},{"key":"e_1_3_4_8_2","doi-asserted-by":"crossref","unstructured":"J. Sybrandt I. Tyagin M. Shtutman I. Safro \u201cAgatha: Automatic graph mining and transformer based hypothesis generation approach\u201d in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (M. d\u2019Aquin S. Dietze C. Hauff E. Curry P. Cudr\u00e9-Mauroux Eds. (Association for Computing Machinery 2020) pp. 2757\u20132764.","DOI":"10.1145\/3340531.3412684"},{"key":"e_1_3_4_9_2","unstructured":"R. Nadkarni Scientific language models for biomedical knowledge base completion: An empirical study. arXiv [Preprint] (2021). https:\/\/arxiv.org\/abs\/2106.09700 (Accessed 25 October 2026)."},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00735-0"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36741-4"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/add6ef"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-023-01648-z"},{"key":"e_1_3_4_14_2","first-page":"279","article-title":"Scimon: Scientific inspiration machines optimized for novelty","volume":"1","author":"Wang Q.","year":"2024","unstructured":"Q. Wang, D. Downey, H. Ji, T. Hope, Scimon: Scientific inspiration machines optimized for novelty. Annu. Meet. Assoc. Comput. Linguist. 1, 279\u2013299 (2024).","journal-title":"Annu. Meet. Assoc. Comput. Linguist."},{"key":"e_1_3_4_15_2","unstructured":"J. Baek S. K. Jauhar S. Cucerzan S. J. Hwang Researchagent: Iterative research idea generation over scientific literature with large language models. arXiv [Preprint] (2024). https:\/\/arxiv.org\/abs\/2404.07738 (Accessed 25 October 2026)."},{"key":"e_1_3_4_16_2","unstructured":"Z. Yang Large language models for automated open-domain scientific hypotheses discovery. arXiv [Preprint] (2023). https:\/\/arxiv.org\/abs\/2309.02726 (Accessed 25 October 2026)."},{"key":"e_1_3_4_17_2","unstructured":"W. Wang An LLM-based scientific paper idea proposer. arXiv [Preprint] (2024). https:\/\/arxiv.org\/abs\/2410.23166 (Accessed 25 October 2026)."},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","unstructured":"K. Pu \u201cIdeaSynth: Iterative research idea development through evolving and composing idea facets with literature-grounded feedback\u201d in Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems N. Yamashita Eds. 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