arXiv:2610.28146v1 Announce Type: new Abstract: Large Audio-Language Models (LALMs) have shown increasing potential in audio understanding and reasoning by generating intermediate reasoning steps before answering. However, directly eliciting long-form reasoning from audio does not always lead to more reliable predictions. Even reasoning-oriented LALMs may gradually shift from acoustic evidence to language-continuation patterns during generation. A natural solution is to use contrastive decoding with an audio-masked branch, but directly applying this strategy to plain chain-of-thought reasoning is often ineffective, since the masked branch lacks sufficient audio-related context and provides an unstable reference. To address this issue, we propose \textbf{SKAD} (\textbf{Sk}etch-Guided \textbf{A}udio-Contrastive \textbf{D}ecoding), a training-free inference-time method for audio reasoning. SKAD first constructs an acoustic evidence sketch that summarizes question-relevant auditory information. It then performs contrastive decoding between an audio-conditioned reasoning branch and a sketch-only branch. By using the sketch as a shared semantic scaffold, SKAD makes the masked branch more informative and encourages decoding decisions that are more dependent on the original audio. We evaluate SKAD on Qwen2.5-Omni-7B and Step-Audio-R1 across three audio understanding and reasoning benchmarks: MMAR, MMAU, and MMSU. Experimental results show that SKAD consistently improves the average accuracy of both models across all benchmarks. Further ablations show that direct contrastive decoding without the acoustic evidence sketch provides limited or negative gains, confirming the importance of constructing a reliable contrastive reference before audio-contrastive decoding.