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arXiv · 2603.06854

Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering

Neta Glazer·Lenny Aharon·Ethan Fetaya
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Abstract

Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a small set of audio-specialist attention heads whose audio attention yields a ``listening'' signal. We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting. Leveraging this localization, we construct an audio--silence steering direction and apply an inference-time activation intervention to the final representation, amplifying the model's audio effect. To demonstrate the utility of this intervention, we show on MMAU that this improves accuracy by up to +8.0 percentage points on two Qwen-based LALMs, without any parameter updates.

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Neta Glazer, Lenny Aharon, Ethan Fetaya. 2026-03-06. Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering. https://arxiv.org/abs/2603.06854

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