Do Audio-Visual Large Language Models Really See and Hear?
| Authors | Ramaneswaran Selvakumar et al. |
| Year | 2026 |
| HF Upvotes | 7 |
| arXiv | 2604.02605 |
| Download | |
| HF Page | View on Hugging Face |
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 when audio conflicts with vision. Probing analyses show that useful latent audio information is present, but deeper fusion layers disproportionately privilege visual representations that tend to suppress audio cues. We further trace this imbalance to training: the AVLLM's audio behavior strongly matches its vision-language base model, indicating limited additional alignment to audio supervision. Our findings reveal a fundamental modality bias in AVLLMs and provide new mechanistic insights into how multimodal LLMs integrate audio and vision.
Engineering Breakdown
Plain English
This paper performs the first mechanistic interpretability study of Audio-Visual Large Language Models (AVLLMs) — systems designed to understand both audio and visual inputs together. The researchers discovered that while these models do encode rich audio information in their intermediate layers, this audio understanding largely disappears in the final text output when audio and visual information conflict. Instead, the fusion layers disproportionately privilege visual information, suppressing audio cues. The root cause traces back to training: AVLLMs inherit the vision-language bias from their base models, meaning they behave more like vision-language models than true multimodal fusion systems.
Core Technical Contribution
This is the first mechanistic interpretability analysis of AVLLMs, using probing techniques to trace how audio and visual features evolve across layers and identify where multimodal fusion breaks down. The key technical insight is that the failure of audio understanding in final outputs is not due to lack of latent audio information — the signal is present in intermediate representations — but rather due to architectural and training dynamics that actively suppress audio in favor of vision. The authors demonstrate this through layer-by-layer analysis showing that fusion layers specifically privilege visual tokens over audio tokens. This moves beyond simple accuracy metrics and reveals the actual information flow pathology in these systems.
How It Works
The methodology starts with an AVLLM that accepts audio and visual inputs and generates text outputs. The researchers insert probes at different layers to measure how much audio and visual semantic information exists in the intermediate representations — essentially asking 'can we decode audio concepts from this layer's activations?' They find that early and middle layers contain strong audio semantics, but these get progressively suppressed as information flows through fusion layers toward the decoder. The authors then trace causal connections by examining how the base vision-language model (before audio adaptation) behaves compared to the full AVLLM. By comparing attention patterns, token importance, and information flow across the full network, they identify exactly where and why audio information is being deprioritized relative to vision.
Production Impact
For production systems, this reveals a critical flaw in current AVLLMs: they may not actually be performing true multimodal fusion despite claiming to. If you deploy an AVLLM for tasks requiring genuine audio-visual reasoning (e.g., video understanding where sound is semantically important), you should expect degraded audio understanding whenever visual and audio cues conflict. The practical implication is that you cannot assume audio contributes equally in multimodal inference pipelines — you need explicit evaluation on audio-conditional tasks. Teams building production AVLLMs should: (1) probe intermediate representations during development to catch fusion imbalances early, (2) design explicit architectural mechanisms to preserve audio information through fusion layers, (3) create training datasets that force genuine audio-visual reasoning rather than allowing the model to ignore audio. This work provides concrete diagnostic techniques to measure multimodal fusion quality rather than relying on aggregate metrics.
Limitations and When Not to Use This
The paper identifies the problem but doesn't fully propose solutions for fixing the audio suppression in fusion layers — it's primarily diagnostic. The analysis is mechanistic and layer-wise, which assumes you can access and examine internal activations; this level of interpretability may not be feasible in black-box commercial AVLLMs. The work likely focuses on specific AVLLM architectures (the paper suggests testing on particular models), so the findings may not generalize to AVLLMs with fundamentally different fusion mechanisms. Additionally, the paper doesn't deeply explore whether certain task distributions or training objectives could mitigate this vision-bias problem, leaving open questions about whether AVLLMs can be trained to achieve true balanced multimodal understanding.
Research Context
This work builds on the broader mechanistic interpretability literature (similar to work analyzing attention patterns and information flow in language models) but applies it to the emerging area of multimodal large language models. It directly addresses a gap in multimodal ML: while there's extensive work on vision-language models and recent enthusiasm for AVLLMs as unified multimodal interfaces, little was known about how well these systems actually fuse different modalities internally. The paper relates to research on modality bias in multimodal learning and informs the design of better multimodal architectures. It opens up a research direction: applying mechanistic interpretability to understand and debug other multimodal fusion failures, potentially leading to improved training procedures or architectural designs for balanced multimodal understanding.
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