The field of multi-agent systems and large language models is rapidly evolving, with a focus on improving benchmarking, task-oriented dialogue systems, and data collection. Researchers are working to create more standardized and reproducible benchmarks, such as Meta-World+, to evaluate the performance of multi-task and meta-reinforcement learning agents. Additionally, there is a growing interest in developing domain-independent frameworks for task-oriented dialogue systems, which can simplify the learning complexity and enhance generalization ability. Novel multi-agent systems, such as AutoData, are being proposed to efficiently collect high-quality web-sourced datasets with minimal human intervention. Furthermore, unified codebases like MASLab are being developed to consolidate existing methods and provide a comprehensive environment for fair comparisons. Noteworthy papers include: MASLab, which integrates over 20 established methods and provides a unified environment for fair comparisons, X-MAS, which explores the paradigm of heterogeneous LLM-driven MAS and demonstrates significant performance enhancements, AutoData, which proposes a novel multi-agent system for automated web data collection with a robust architecture and a novel hypergraph cache system.