The study investigates the internal workings of an audio language model (Qwen3-Omni) by applying a logit lens to its middle layers. It finds that the model’s reasoning about spoken questions becomes legible in words before any token is emitted, revealing language‑agnostic, paralinguistic, and temporally distinct signals that are causally used in the network’s decision process. The authors demonstrate that these signals can be isolated and mapped to specific layers, providing a qualitative account of how the model processes audio input.
arXiv:2608. 19211v1 Announce Type: cross Abstract: Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content.
arXiv:2609.14174v1 Announce Type: new Abstract: Asked to describe what one of six speakers in a recording talks about, audio language models describe the right one on 6 to 16% of trials, below the 16...
arXiv:2609.05871v1 Announce Type: cross Abstract: Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-s...
The paper investigates how Audio Large Language Models (Audio LLMs) actually use audio input to determine answers, rather than relying on textual cues. It finds that replacing audio with silence or unrelated audio degrades performance more after training than before, that acoustic information shapes representations in early-to-middle layers and influences final predictions in middle-to-late layers, and that training impacts specific layer bands most strongly. These observations offer a mechanistic view of how training enhances the use of acoustic evidence in Audio LLMs.
The study applies causal tracing to large audio language models (LALMs) to uncover how they fuse acoustic and textual information. Layer‑wise analysis reveals distinct fusion strategies—progressive integration in DeSTA versus abrupt late‑stage fusion in Qwen—while token‑wise analysis identifies the final sequence token as an informational bottleneck that decisively retrieves audio content. Additionally, an attention‑like query mechanism at intermediate tokens is observed, prompting the model to pull task‑relevant audio context.
The paper introduces IMAVB, a 500‑clip benchmark that tests whether omnimodal large language models can detect when a textual premise contradicts their visual or audio input. Experiments on eight open‑source models and Gemini 3.1 Pro reveal a Representation‑Action Gap: internal states encode mismatches, yet the models rarely reject false premises, exhibiting under‑rejection or over‑rejection. A probe‑guided logit adjustment improves rejection behavior, suggesting the main bottleneck is in translating perception to action rather than in perception itself.
arXiv:2606. 10147v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer?
arXiv:2609.07474v2 Announce Type: replace Abstract: Language models compute over tokens: language is their input, their output, and increasingly their internal representation. Whether language should...
arXiv:2608. 19515v1 Announce Type: new Abstract: Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged.
Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged. Yet existing benchmarks typically evaluate prosodic perception, response appropriateness, and task-oriented dialogue in isolation, making it difficult to test whether prosodic evidence changes downstream decisions.
arXiv:2608.28609v1 Announce Type: cross Abstract: A personalized agent needs a user memory: a persistent model of who its user is. Today it is almost always text -- transcripts and captions retrieved...
The asymmetry between language production and perception has been well-documented in psycholinguistics. Whether large language models (LLMs) exhibit a functionally analogous distinction remains an open question, particularly given that LLMs rely on the same underlying mechanism (next-token prediction) for both input and output processing.