Papers by Chien-yu Huang
PlanRAG-Audio: Planning and Retrieval Augmented Generation for Long-form Audio Understanding (2026.findings-acl)
Copied to clipboard
Masao Someki, Chien-yu Huang, Siddhant Arora, Samuele Cornell, Markus Müller, Nathan Susanj, Rupak Vignesh Swaminathan, Grant Strimel, Jing Liu, Shinji Watanabe
| Challenge: | Long-form audio understanding poses significant challenges due to the extreme length of audio sequences and the need to reason over heterogeneous acoustic cues distributed over time. |
| Approach: | They propose a retrieval-augmented generation framework for scalable long-form audio understanding . planRAG-Audio explicitly plans which modalities and temporal spans are required for a given query . |
| Outcome: | Experiments show that planRAG-Audio reduces the length of inputs for long-form audio models . the proposed framework can efficiently reason over long-term speech data . |