The field of audio processing is witnessing significant advancements with the development of general-purpose audio understanding frameworks. Researchers are exploring novel approaches to pre-training and fine-tuning models for improved performance on various tasks, including audio reasoning, sound event detection, and audio source separation. A key trend is the integration of multimodal information, combining audio with text, vision, or other modalities to enhance semantic understanding and achieve state-of-the-art results. Notable papers in this area include OpenBEATs, which achieves impressive performance on six bioacoustics datasets and five reasoning datasets, and Detect Any Sound, which proposes a query-based framework for open-vocabulary sound event detection. Other noteworthy works, such as TTMBA and DFR, demonstrate the potential of text-to-audio generation and multi-modal few-shot segmentation, respectively. These innovative approaches are pushing the boundaries of what is possible in audio processing and paving the way for future breakthroughs.