From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
Quick Answer
This study investigates the information flow in Audio-Visual Large Language Models (AVLLMs) like Qwen2.5-Omni and Video-SALMONN2 Plus, revealing that audio-visual signals are integrated through sequential and parallel pathways.
Quick Take
The findings suggest that discarding certain token types post-integration can enhance model efficiency without compromising predictions, paving the way for advancements in applications.
Key Points
- AVLLMs utilize sequential pathways for audio-visual video integration, similar to .
- In interleaved audio-visual settings, information routing shifts to parallel streams.
- Discarding certain token types post-integration shows minimal impact on predictions.
- Findings are consistent across models like Qwen2.5-Omni and Video-SALMONN2 Plus.
- Study lays groundwork for future interpretability and efficiency in MLLMs.
Paper Resources
Source Excerpt
arXiv:2606. 10147v1 Announce Type: new Abstract: Multimodal (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer? Despite their growing role in research and real-world applications, the internal pathways through which audio and visual tokens influence the final prediction remain poorly understood.
In this study, we examine audio-visual information flow inside Audio-Visual Large Language Models (AVLLMs), tracing how AVLLMs route, utilize, and integrate audio and visual information across two input configurations, audio-visual video and multiple interleaved audio-visual items. …
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