大型音频语言模型中音频-文本融合的因果追踪

Computer Science > Sound arXiv:2603.13768 (cs) [Submitted on 14 Mar 2026 (v1), last revised 23 Sep 2026 (this version, v2)] Title:Causal Tracing of Audio-Text Fusion in Large Audio Language Models Authors:Wei-Chih Chen, Chien-yu Huang, Hung-yi Lee View a PDF of the paper titled Causal Tracing of Audio-Text Fusion in Large Audio Language Models, by Wei-Chih Chen and 2 other authors View PDF HTML (experimental) Abstract:Despite the strong performance of large audio language models (LALMs) in various tasks, exactly how and where they integrate acoustic features with textual context remains unclear. We adapt causal tracing to investigate the internal information flow of LALMs during audio comprehension. By conducting layer-wise and token-wise analyses across DeSTA, Qwen, and Voxtral, we evaluate the causal effects of individual hidden states. Layer-wise analysis identifies different fusion strategies, from progressive integration in DeSTA to abrupt late-stage fusion in Qwen. Token-wise analysis shows that the final sequence token acts as an informational bottleneck where the network decisively retrieves relevant information from the audio. We also observe an attention-like query mechanism at intermediate token positions that triggers the model to pull task-relevant audio context. These findings provide a clear characterization of when and where multi-modal integration occurs within LALMs. Comments: Subjects: Sound (cs.SD); Computation and Language (cs.CL) Cite as: arXiv:2603.13768 [cs.SD] (or arXiv:2603.13768v2 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2603.13768 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wei-Chih Chen [view email] [v1] Sat, 14 Mar 2026 05:40:59 UTC (1,374 KB) [v2] Wed, 23 Sep 2026 16:48:46 UTC (1,373 KB) Full-text links: Access Paper: View a PDF of the paper titled Causal Tracing of Audio-Text Fusion in Large Audio Language Models, by Wei-Chih Chen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SD new | recent | 2026-03 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)