The field of large language models (LLMs) is moving towards the development of more complex and collaborative systems, with a focus on multi-agent architectures. This shift is driven by the potential of these systems to tackle complex tasks that are difficult or impossible for single agents to solve. Researchers are exploring new ways to enable multiple agents to work together effectively, including the development of new benchmarks and evaluation metrics. One of the key challenges in this area is designing systems that can scale to large numbers of agents, and researchers are investigating various approaches to address this challenge. Another important area of research is the development of more efficient and effective communication protocols for multi-agent systems. Overall, the field is advancing rapidly, with new architectures and techniques being proposed to improve the performance and collaboration of multi-agent LLMs. Notable papers include: AgentsNet, which proposes a new benchmark for multi-agent reasoning, and Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents, which introduces a framework for concurrent execution of multiple multi-agent teams. Additionally, How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs presents a novel approach to training a single leader LLM to coordinate a team of untrained peer agents, and GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems introduces a graph-based evaluation framework that analyzes the internal collaboration process of multi-agent systems.