Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories

Authors:Joseph Chee Chang, Michael D'Arcy, Amy X. Zhang, Pao Siangliulue, Sangho Suh, Aakanksha Naik, Jena D. Hwang, Javier Ramos Benitez, Stella Wroblewski, Matt Latzke, Michael Cuoco, Ruben Lozano-Aguilera, Kris Ganjam, Joel Chan, Doug Downey, Peter Jansen, Kyle J. Travaglini, Daniel S. Weld

Abstract:A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observations scattered across many papers, each describing related concepts but often in different terms. Which concepts matter most also depends on their preferences and research questions. Recent approaches scale theory synthesis with LLMs, but automate away choices and intuitions from researchers. We present Asterism, which extracts observations from hundreds of papers as concept-relation triples, with concepts unified in a hierarchical ontology. Researchers curate an evidence graph using the ontology and aggregate observations at different levels of granularity to focus theory formation on specific phenomena of interest. In a field deployment (n=10), researchers worked from observations to theories, and kept concepts and hypotheses fitting their preferences. In two case studies, teams of immunology and agriculture researchers discovered mechanisms outside their standard analyses and constructed hypotheses worth follow-up experiments.

Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)

arXiv:2610.02673 [cs.HC]

(or arXiv:2610.02673v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2610.02673 arXiv-issued DOI via DataCite (pending registration)