MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales

Scientific papers contain fine-grained records of problem solving: authors mention techni- cal obstacles and methods that were used to address them, often along with reasoning on why those methods were chosen. We introduce MUSE (Mining Underlying Scientific Explana- tions), a full-text, multi-domain resource of sci- entific Problem–Solution–Rationale (P–S–R) triplets. We curate 579 expert-annotated full- text paragraphs, with a rich annotation schema covering salient problem, solution, and ratio- nale spans, solves and rationale_of links, and conceptual coreference. A modular extrac- tion pipeline scales this annotation to build a high-quality knowledge base of 37K source- grounded P–S–R triplets. We evaluate the extraction components and include a prelimi- nary experiment training a rationale-supervised LLM for scientific problem solving. Inter- estingly, we find that rationale supervision improves performance on complex, multi- constraint problems but can harm performance on simpler ones.