Led by my student Tsofia Cohen. Annotated dataset, trained extraction and problem-solving models (e.g., Qwen-32B), and the MUSE knowledge base are openly available (see links in paper). arxiv.org/abs/2608.10974
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Tom Hope
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Researcher with public evidence across AI research, NLP & language.
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Assistant professor, research scientist | boosting scientific discovery with AI, NLP, IR, KG, HCI | @ai2.bsky.social
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MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales arxiv.org
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MUSE, a knowledge base for AI scientists (and humans, too) to discover fine-grained technical research problems, solutions and their *rationales* , extracted from full-text papers across all domains on arXiv. 36,960 Problem–Solution–Rationale triplets across scientific domains.
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