MUSES: Retrieving the Literature That Shapes a Researcher's Next Paper

Much of the work that shapes a researcher's next paper is not something they went looking for: it often arrives before the project has a question anyone could search for. How much of it can be anticipated from a researcher's past publications alone? We introduce MUSES, a benchmark of 1.04 million cases built from the Semantic Scholar Open Research Corpus (S2ORC), of which 140,278 form the held-out test split. Given a researcher's papers before a cutoff, a system ranks 2.33 million earlier candidates for their next paper and is scored primarily on works the researcher had not cited before. Because a citation is weak evidence of influence, we also asked authors directly which works shaped their papers, yielding 580 author-collected evaluable cases, a distinct construct from the citation-based targets. A classifier grounded in human coding adds citation contexts where the next paper builds on a work. Across retrievers from popularity to dense encoders and a supervised model, we find that a researcher's past reveals where their next paper will look, but least well what will be new to them. History-based retrievers place a newly cited work in their top 100 for 29–34% of papers, against 5% for popularity; among author-collected cases, one retriever finds a work the authors themselves named in 56% of cases (36% when that work was new to the researcher). Yet the signal fades for works new to the researcher, most of all for those that shaped the paper: for 46% of the 6,707 test papers with such labels, no retriever we evaluate finds one in its top 1,000. Much of the remaining gap is the project itself: adding only the next paper's title raises one retriever's reach on these papers from 29% to 48%, more than switching retrievers does. MUSES shows where a researcher's record runs out and the project itself must take over.