The field of audio dialogue understanding is moving towards the development of more sophisticated and multimodal models that can effectively recognize speaker intent, classify audio, and generate coherent responses in complex and noisy environments. Researchers are exploring the use of graph-informed models, multi-task fusion networks, and audio-visual input to improve the accuracy and robustness of these models. Notable papers include DialogGraph-LLM, which proposes a novel framework for end-to-end audio dialogue intent recognition, and AV-Dialog, which presents a multimodal dialog framework that uses both audio and visual cues to track the target speaker and generate coherent responses. Emotion recognition in multi-speaker conversations is also being addressed through innovative approaches such as speaker identification, knowledge distillation, and hierarchical fusion.