The field of audio research is moving towards a more comprehensive understanding of multimodal audio, incorporating both acoustic and linguistic information. Recent studies have focused on developing large-scale datasets and models that can effectively process and generate spatial audio, leading to improved performance in tasks such as audio spatialization and sound event localization. Additionally, there is a growing interest in exploring the relationship between language and audio, with joint language-audio embedding models being evaluated for their ability to capture perceptual dimensions of timbre. Noteworthy papers include: MRSAudio, which introduces a large-scale multimodal spatial audio dataset, and Do Audio LLMs Really LISTEN, or Just Transcribe?, which presents a controlled benchmark to disentangle lexical reliance from acoustic sensitivity in emotion understanding. Other notable works include LSZone, which proposes a lightweight spatial information modeling architecture for real-time in-car multi-zone speech separation, and Beyond Discrete Categories, which introduces a continuous Valence-Arousal model for pet vocalization analysis. These innovative approaches are advancing the field and enabling more accurate and robust audio understanding and generation.