The field of audio-language models is moving towards more robust and multimodal reasoning capabilities. Recent developments have focused on enhancing the ability of models to reason with audio signals, incorporating tools such as noise suppression and source separation to improve performance in complex acoustic scenarios. Additionally, there is a growing emphasis on spatial reasoning and geometry-aware audio encoding, which enables models to better understand and interpret auditory perception. Another key area of research is investigating the faithfulness of chain-of-thought representations in large audio-language models, which is critical for safety-sensitive applications. Noteworthy papers include: Thinking with Sound, which introduces a framework for equipping large audio-language models with audio chain-of-thought capabilities, resulting in substantial improvements in robustness. OWL, which presents a geometry-aware audio encoder and a spatially grounded chain-of-thought to rationalize over direction-of-arrivals and distance estimates, achieving state-of-the-art performance on spatial reasoning benchmarks.