Paper
Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding
While long-form audio meeting understanding (LAMU) is garnering growing attention, task-specific question answering (QA) datasets remain scarce. Existing speech QA paradigms and state-of-the-art Speech LLMs suffer from acoustic information loss and poor long-term context memory. To address these issues, we construct the LongAudioQA dataset and propose the GRGA model, which models heterogeneous audio features into a m · Authors: Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou · Published 2026-08-25 · arXiv:2608.24048
- Published
- 2026-08-25
- Authors
- 4
- arXiv
- 2608.24048
- Category
- cs.SD
Abstract
arXiv:2608.24048
Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou
While long-form audio meeting understanding (LAMU) is garnering growing attention, task-specific question answering (QA) datasets remain scarce. Existing speech QA paradigms and state-of-the-art Speech LLMs suffer from acoustic information loss and poor long-term context memory. To address these issues, we construct the LongAudioQA dataset and propose the GRGA model, which models heterogeneous audio features into a multi-dimensional graph and leverages agent planning for retrieval and answer generation.