The field of Large Language Model (LLM) multi-agent systems is rapidly evolving, with a focus on developing more scalable, adaptable, and robust architectures. Recent research has emphasized the importance of self-evolution, recursive self-generation, and dynamic communication structures in achieving these goals. Notably, innovative frameworks have been proposed to address the limitations of traditional multi-agent systems, including the use of pyramid-like DAG-based structures, dual-audit mechanisms, and agent self-evolution mechanisms. These advancements have led to significant performance gains in various benchmarks and applications, such as deep research, code generation, and natural language conversation. Noteworthy papers include: InfiAgent, which proposes a self-evolving pyramid agent framework for infinite scenarios, achieving 9.9% higher performance compared to similar frameworks. MAS$^2$, which introduces a self-generative, self-configuring, and self-rectifying multi-agent system, achieving performance gains of up to 19.6% over state-of-the-art systems.