Steering Where to Listen: Instruction-Based Activation Steering Redirects Temporal Attention in Large Audio-Language Models

Researchers developed instruction-based vector steering to redirect temporal attention in Large Audio-Language Models (LALMs), enabling these systems to concentrate on acoustically relevant regions without retraining. The technique achieves 60-68% accuracy in locating queried sound events, substantially outperforming standard prompting methods, revealing how LALMs encode temporal structure in audio understanding.

This research addresses a fundamental interpretability challenge in Large Audio-Language Models by introducing a mechanistic intervention that exposes and redirects how these systems allocate attention across temporal audio signals. The instruction-based vector steering approach differs from conventional methods by contrasting activations from differently instructed prompts while holding audio constant, creating steering vectors that meaningfully reshape model behavior without fine-tuning.

The work builds on growing interest in mechanistic interpretability of large language and multimodal models. While most attention research focuses on text-based transformers, audio understanding requires temporal localization capabilities that remain poorly understood. This research demonstrates that LALMs develop implicit representations of temporal structure, and that steering vectors can expose and manipulate these representations in interpretable ways.

For the AI development community, these findings carry significant implications. A training-free probing method enables researchers to investigate model internals without computational overhead, accelerating interpretability research. The technique's success on multiple model architectures (Qwen2-Audio and Audio Flamingo 3) suggests generalizability. Understanding temporal attention mechanisms could improve audio model design and enable better control over model outputs for audio-to-text tasks.

Looking forward, researchers should explore whether steering vectors transfer across model scales and architectures, and whether similar techniques apply to other modalities. The work opens pathways for developing more transparent audio-language systems, which matters as these models increasingly power real-world applications in content analysis, accessibility tools, and information retrieval systems.

• →Instruction-based vector steering redirects LALM temporal attention without retraining, achieving 60-68% accuracy in sound event localization versus 31-46% for standard prompting
• →The method reveals LALMs encode interpretable temporal structure internally, enabling training-free probing of model mechanics
• →Steering vectors constructed from contrasted activations outperform both direct prompting and audio-based steering approaches
• →The technique generalizes across model architectures including Qwen2-Audio and Audio Flamingo 3, suggesting broad applicability
• →Results demonstrate mechanistic properties of instruction steering in multimodal models, with implications for AI interpretability and transparency