{"ID":2874234,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05072","arxiv_id":"2509.05072","title":"Finding your MUSE: Mining Unexpected Solutions Engine","abstract":"Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research.","short_abstract":"Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, a...","url_abs":"https://arxiv.org/abs/2509.05072","url_pdf":"https://arxiv.org/pdf/2509.05072v1","authors":"[\"Nir Sweed\",\"Hanit Hakim\",\"Ben Wolfson\",\"Hila Lifshitz\",\"Dafna Shahaf\"]","published":"2025-09-05T13:13:19Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\"]","methods":"[\"LoRA\"]","has_code":false}