Audio-conditioned language models often underuse acoustic cues such as prosody, emotion, and non-speech sounds, raising the question of whether ASR-supervised frontends discard this information before it reaches the LM. We test whether the frontend is responsible by comparing Whisper-Tiny and Whisper-Small with EnCodec, DAC-VAE, and WavTokenizer in a shared Qwen3.5-4B audio-LM pipeline on ASR, emotion recognition, and sound captioning. Encoder replacement alone does not resolve this underuse: Whisper variants remain strongest overall, including on emotion and environmental sound captioning. To localize the failure, we trace task-relevant information through the encoder, projector, LM layers, and LM head. Linear probes and geometric analyses show that discriminative acoustic structure remains recoverable at the final LM layer, even when MCQA accuracy trails probe accuracy by up to 83 points. Because the answer format and decoding procedure are controlled, this task-dependent gap points to content-specific readout failure rather than generic format bias. LogitLens analyses and a targeted LM head intervention support the conclusion that acoustic underuse is not explained solely by encoder-side information loss and that readout alignment can be a dominant bottleneck.

Large language models (LLMs) provide excellent performance, but their practical deployment is limited by the substantial compute and memory demands of large models and the latency of auto-regressive decoding. To mitigate these inefficiencies, block pruning reduces the number of executed transformer blocks, effectively lowering latency while preserving architectural coherence. However, existing methods typically rely on representation similarity or computationally expensive sensitivity analyses to estimate block importance, thereby neglecting task-aware model behavior. To address this limitation, we introduce Task-aware Block Pruning (TaBP), a novel approach that directly captures task-specific inference dynamics by quantifying block-level uncertainty from the statistics of each block’s early-exited output distribution on a calibration dataset. Since output distributions reflect the model’s confidence and decision uncertainty conditioned on downstream tasks, these statistics provide a principled signal for identifying blocks that are less critical for task performance. Extensive experiments demonstrate that TaBP preserves downstream task performance while substantially reducing inference latency and computational cost, without relying on cost-heavy sensitivity analyses. To facilitate reproducibility and further research, we release our implementation of TaBP on GitHub.