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

Abstract: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.

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2608.24048 [cs.SD]

(or arXiv:2608.24048v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2608.24048 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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