Papers by Hongran An
MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery (2026.acl-demo)
Copied to clipboard
| Challenge: | Existing approaches to scientific hypothesis discovery operate autonomously with little to no human guidance. |
| Approach: | They propose a framework that empowers scientists to steer the generative process via explicit signals. |
| Outcome: | The proposed framework outperforms existing models in terms of quality and accuracy of the input signals. |
Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)
Copied to clipboard
Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun
| Challenge: | Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores. |
| Approach: | They propose a benchmark for score-level musical understanding across textual and visual modalities. |
| Outcome: | The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others. |