[2601.11556] CSyMR: Benchmarking Compositional Music Information Retrieval in Symbolic Music Reasoning

Computer Science > Machine Learning arXiv:2601.11556 (cs) [Submitted on 16 Dec 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:CSyMR: Benchmarking Compositional Music Information Retrieval in Symbolic Music Reasoning Authors:Boyang Wang, Yash Vishe, Xin Xu, Zachary Novack, Xunyi Jiang, Julian McAuley, Junda Wu View a PDF of the paper titled CSyMR: Benchmarking Compositional Music Information Retrieval in Symbolic Music Reasoning, by Boyang Wang and 6 other authors View PDF HTML (experimental) Abstract:Natural language information needs over symbolic music scores rarely reduce to a single step lookup. Many queries require compositional Music Information Retrieval (MIR) that extracts multiple pieces of evidence from structured notation and aggregates them to answer the question. This setting remains challenging for Large Language Models due to the mismatch between natural language intents and symbolic representations, as well as the difficulty of reliably handling long structured contexts. Existing benchmarks only partially capture these retrieval demands, often emphasizing isolated theoretical knowledge or simplified settings. We introduce CSyMR-Bench, a benchmark for compositional MIR in symbolic music reasoning grounded in authentic user scenarios. It contains 126 multiple choice questions curated from community discussions and professional examinations, where each item requires chaining multiple atomic analyses over a score to derive implicit musical eviden...