Reading a base Qwen3-Omni with a logit lens at the audio-token positions, it is found that the answer to a spoken question becomes legible - in words - in the model's middle layers, before it emits any token.
Abstract
An audio language model is a black box in a specific way: we see what it says, never what it works out on the way there, and chain-of-thought monitoring helps only if the model writes its reasoning down. Reading a base Qwen3-Omni with a logit lens at the audio-token positions, we find that the answer to a spoken question becomes legible - in words - in the model's middle layers, before it emits any token. Five findings follow. (1) The readout carries concepts in neither the question, the options, nor the model's own transcription: on a clip whose verbatim transcription is empty garbling, it reconstructs Watergate and scandal, passes through the role president, and resolves to Nixon - a hidden multi-hop chain, read with no chain-of-thought. (2) The content is language-agnostic: one audio-inferred concept surfaces in several scripts at once, and 38% of top-1 readouts are Chinese on English inputs. (3) It is paralinguistic: given the same clip as audio and as the model's own emotion-free caption, the audio mind forms the sound source, speaker role, or affect that the caption discards, and answers correctly more often. (4) The audio-driven signal is absent at the input, turns on about a tenth of the way into the network, separates most cleanly from the text prior in the middle band (35-80% of depth), and activation patching shows it is causally used and committed before the last fifth of the layers. (5) Deleting single layers maps the pipeline: reading the sound in is localized to the entry layers and answer delivery to the output layer, while retrieval is distributed across the interior. Throughout, a waveform-swap control - identical text, only the sound changed - isolates the audio-driven signal from a prior over the printed options. This is a qualitative account of what an audio model works out before it speaks: the quantities are controls, not benchmark scores.
Audio language models are designed to understand speech, yet it remains unclear whether they capture how something is said beyond what is said. We present a mechanistic analysis of paralinguistic information in four open source models, Whisper-large-v2, Qwen2-Audio-7B Instruct, Qwen2.5-Omni-7B, and Chroma-4B, using the Expresso dataset with controlled speaking styles. We combine centered kernel alignment, linear probing with leave one speaker out evaluation, open ended tone prediction, and a content prosody leakage metric to trace how style information moves from the audio encoder to the final output. All models strongly encode speaking style in the late encoder, that is, the top third of the audio encoder's layers, but this information is consistently degraded before reaching the output. The projector reshapes representation geometry without removing information, while decoders differ in how much style they preserve depending on architecture and training objective. At the output level, models fall into two behaviors. Some are content driven, where predictions depend mainly on text. Others are acoustic driven, where predictions vary with speaking style. The leakage metric quantifies this difference, and qualitative results confirm it. Overall, we identify a gap between what models encode and what they use, highlighting a key limitation in current audio language models.
Bhuvan Koduru, Dareen Alharthi, Rita Singh et al.· 0 citations
Q-Guide is built, a small agent that reads a question, works out what evidence it is still missing, and calls targeted tool(s) to recover it---reading text where text is needed, zooming in where detail is needed, or grounding a region where position matters.
Modeling language use as a joint distribution over meanings, contexts, and utterances, upper bounds are derived on the probability that a decoder recovers a speaker's intended meaning from a representation of the utterance.
Results show that first-token clues can guide multi-token concept recovery, while subsequent hidden states provide vectors for readout and intervention.
Long-video question answering (QA) forces multimodal large language models (MLLMs) to work within a tight frame budget, so the choice of frames largely decides whether a question can be answered at all. The standard recipe scores every frame against the question with a pretrained image–text matching (ITM) model and keeps the top scorers. A fundamental mismatch underlies this recipe: ITM models are trained on short, concrete visual descriptions, while QA questions are interrogative and often involve abstract terms. Scored against the question alone, the ITM yields a near-random signal whenever the question is not a direct image–text match, such as one asking for the temporal order of scenes. In our LongVideoBench diagnostic analysis, the score collapses even on benchmark-provided answer-relevant frames, with more than half falling into a near-zero region —not because the encoder is faulty, but because it behaves exactly as it was trained to. We argue that this format mismatch should be absorbed at the two ends of the pipeline while the encoder itself stays frozen. On the input side, a type-conditioned routed pipeline reformulates each question into a single ITM-aligned description by selectively applying grounding, decomposition, and constrained synthesis. The ITM therefore receives exactly one description per frame, preserving the per-frame matching cost of a standard single-query baseline. On the output side, because the score distribution remains polarized and answer frames are scattered in time, we replace top- $K$ selection with a parameter-free Rosin threshold followed by a temporal maximal-marginal-relevance (MMR) step that uses frame positions alone. Across three benchmarks (LongVideoBench, Video-MME, MLVU) and and four backbones (Qwen2-VL, Qwen2.5-VL, LLaVA-OneVision, LLaVA-Video), the resulting training-free pipeline, RECAST, consistently outperforms recent frame-selection baselines without modifying the ITM encoder.
S. Han, Thang Vu, Junyeong Kim· IEEE Access· 0 citations
Can a model look at a river delta and a lightning bolt and see that they share a structure? We introduce GEB-Bench, a benchmark whose unit is an abstract structural motif--self-reference, a strange loop, a Mobius twist--in the spirit of Godel, Escher, Bach. Each motif is told in several voices: a natural scene whose composition is the structure, a folk story whose telling enacts it through a mechanically checkable form device, a mathematical theorem, and a programmatic skeleton; surface parameters are declared nuisance variables and never scored. Motifs, voices, and the structural changes between them form a small cross-modal category, and GEB-Bench's tasks are its questions. Evaluating twelve open and proprietary models, we find that abstraction failure is lawful. The central finding is a gap between recognition and cross-voice mapping: models identify a structure within one voice far better than they carry it across voices; every model pays this tax, and mapping strong enough to narrow it appears only at the frontier tier. Two patterns support it. Errors align more strongly with the designed formal geometry than with measured perceptual geometries, and frontier models from different vendors converge on the same wrong answers; and surface complexity taxes every model that reads structure, with capacity buying headroom rather than immunity. GEB-Bench is fully generative and released with its pipeline.
Tong Zhang, Zhiyuan Shi, Yun Peng et al.· 0 citations
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