Abstract
Large audio language models (LALMs) struggle with long-form audio reasoning, as processing entire recordings often limits to capture query-relevant content distributed across distant segments while incurring substantial computational cost. We introduce AudioHelix, a hierarchical DualGraph framework tailored for LALMs' retrieval-augmented generation (RAG) systems. Unlike existing text-only or non-graph RAG approaches, AudioHelix connects related textual and acoustic evidence across temporally distant segments for multi-hop reasoning. Extensive experiments on TraceAV-Bench, MMAU-Pro, and AudioMarathon demonstrate that AudioHelix consistently outperforms full-audio processing and existing RAG methods. The project site is this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.41390
