Work

Don’t Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding

Year: 2026

Type: conference-paper

Abstract: While long-form audio meeting understanding (LAMU) is garnering growing attention, taskspecific 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.GitHub for data and code.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1038

Authors: Quanwei Tang, Dong Zhang, Shoushan Li, Guodong Zhou

Institution: Soochow University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Government of Jiangsu Province

OpenAlex ID: W7166854572