From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
arXiv:2606.10147v1 Announce Type: cross Abstract: Multimodal Large Language Models (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 configura
The rapid advancement and deployment of multimodal AI necessitate a deeper understanding of their internal workings to optimize performance and ensure responsible development.
Understanding how MLLMs process and integrate sensory information is critical for unlocking their full potential and addressing current limitations in robustness and interpretability.
The ability to trace information flow within MLLMs could lead to more efficient architectures and better debugging, moving beyond black-box approaches to multimodal intelligence.
- · AI researchers
- · Multimodal LLM developers
- · AI infrastructure providers
- · Developers relying solely on black-box MLLM deployment
Improved performance and interpretability of multimodal AI systems.
Accelerated development of more sophisticated and reliable AI agents capable of understanding complex real-world inputs.
Potential for new AI architectures that more closely mimic biological sensory processing.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL