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arXiv · 2610.41390

Hierarchical Dual-Graph Retrieval for Long-form Audio Reasoning

Eunsang Lee·Taein Ju·Jiyoung Lee
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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 https://github.com/ewha-mmai/audiohelix.

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Eunsang Lee, Taein Ju, Jiyoung Lee. 2026-10-30. Hierarchical Dual-Graph Retrieval for Long-form Audio Reasoning. https://arxiv.org/abs/2610.41390

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