The field of dialogue systems is moving towards more dynamic, multi-turn interactions, with a focus on combining large language models (LLMs) with other techniques such as imitation learning, offline reinforcement learning, and runtime personalization. This allows for more effective tool use, improved workflow adherence, and enhanced response generation. The use of data-centric mechanisms, such as tool-augmented demonstration construction and reward-conditioned data modeling, is also becoming increasingly popular. These advancements have the potential to build domain-specialized, context-aware dialogue systems that can outperform traditional intent-based systems. Noteworthy papers include: MindFlow+, which introduces a self-evolving dialogue agent that learns domain-specific behavior, and Agent WARPP, which presents a training-free, modular framework for improving workflow adherence in LLM-based systems. TweakLLM is also notable for its novel routing architecture that dynamically adapts cached responses to incoming prompts, improving cache effectiveness without compromising user experience.