Do Audio-Visual Large Language Models Really See and Hear?

📰 ArXiv cs.AI

Audio-Visual Large Language Models encode rich audio semantics but often fail to utilize them in final text generation

advanced Published 6 Apr 2026

Action Steps

Analyze the evolution of audio and visual features through different layers of an AVLLM
Examine how these features fuse to produce final text outputs
Investigate why audio semantics may not surface in final text generation
Develop techniques to improve the utilization of audio semantics in AVLLMs

Who Needs to Know This

Machine learning researchers and engineers working on multimodal models can benefit from understanding how audio and visual features are processed and fused in AVLLMs, to improve model performance and interpretability

Key Insight

💡 AVLLMs have the capability to encode rich audio semantics, but this capability is not always utilized in final text generation

Key Takeaways

Audio-Visual Large Language Models encode rich audio semantics but often fail to utilize them in final text generation

Full Article

Title: Do Audio-Visual Large Language Models Really See and Hear?

Abstract:
arXiv:2604.02605v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzing how audio and visual features evolve and fuse through different layers of an AVLLM to produce the final text outputs. We find that although AVLLMs encode rich audio semantics at intermediate layers, these capabilities largely fail to surface in the final text generation

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