Represented but Ignored: A Causal Account of Prosodic Underuse in Audio-Language Models

音频-LLM 韵律理解错误究竟源于编码器表示不足，还是解码器推理阶段忽略？行为评测无法区分

因果框架：分离编码器表示与解码器利用两个因素，做干预实验定位失败阶段

韵律被正确编码（表示存在），但在 LLM 推理阶段被系统性忽略（利用断裂）——指明了改进方向

为实时语音 agent 的韵律感知能力提升提供了精确的技术诊断

Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content. A capable large audio-language model (audio-LLM) should therefore support expressive speech understanding, not only transcribing what was said but also interpreting how it was said. Yet behavioral evaluations alone cannot reveal why a model fails on prosodic input. An error may reflect loss of acoustic information, incorrect internal interpretation, or failure to use a repres