A gent I nit: Initializing LLM -based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025)

Abstract: Proper initialization is crucial for any system, particularly in multi-agent systems (MAS), where it plays a pivotal role in determining both the system’s efficiency and effectiveness. However, existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages. Inspired by the principles of effective team composition, we propose , which aims to optimize the structure of agent teams. Specifically, in addition to multi-round interactions and reflections between agents during agent generation, AgentInit incorporates a Natural Language to Format mechanism to ensure consistency and standardization. Balanced team selection strategies using Pareto principles are subsequently applied to jointly consider agent team diversity and task relevance to promote effective and efficient collaboration and enhance overall system performance. Experiments show that AgentInit consistently outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks, achieving an overall performance improvement of up to 1.2 and 1.7, respectively, while also significantly reducing token consumption. Further analysis confirms its strong transferability to similar tasks and verifies the effectiveness of its key components, demonstrating its capability and adaptability as a reliable MAS initialization method. Source code and models are available at https://github.com/1737423697/AgentInit.

## introduction recent advances in large language models (llms) @xcite @xcite have enabled autonomous agents @xcite capable of complex task solving @xcite . extending beyond single-agent * corresponding author settings, multi-agent systems (mas) provide a promising framework for coordinating diverse agents toward human-like collaboration and improved performance @xcite @xcite @xcite . initialization plays a critical role in mas, as it defines the roles and responsibilities of each agent, thereby ensuring efficient task execution and enhancing overall system performance @xcite . while many existing mas frameworks still rely on manual design @xcite @xcite , recent approaches such as agent-verse @xcite , autoagents @xcite , and evoagent @xcite aim to automate agent generation. this automation represents a key step in the initialization process and contributes to improved scalability and adaptability of mas. while automated mas initialization has made progress, existing methods still struggle to adequately consider effective future collaboration between agents. this leads to teams with irrelevant and overlapping agents, which can cause task @xcite , negatively impacting overall system performance. exist-ing initialization methods attempt to optimize team configuration using llms, but these approaches are often constrained by inherent biases @xcite . for example, llms tend to avoid criticizing their own outputs due to self-preference bias @xcite , which limits their ability to identify and eliminate redundant or low-quality agents. as a result, these methods struggle to provide an efficient and well-coordinated team structure at the initialization stage, highlighting the need for a more robust initialization mechanism. the challenges of initializing mas suggest the need for structural optimization. in real-world organizations, streamlining personnel and refining team structures improve efficiency @xcite . similarly, recent mas research shows that removing redundant agents enhances system performance @xcite . in light of these findings, we propose agentinit, a framework that optimizes team structure by balancing diversity and task relevance. as illustrated in figure 1 , agentinit consists of: (1) standardized agent generation: it decomposes the query into sub-tasks and generates a set of candidate agents over multiple iterations, with feedback used to refine them. to enhance agent quality and ensure a fairer selection process, we apply the nl-to-format approach @xcite , which standardizes agent representations for more consistent evaluation. (2) balanced team selection: this module treats team optimization as a multi-objective problem, considering two main objectives: task relevance and agent diversity. by constructing a pareto optimal set @xcite , the llm-powered selector identifies the most effective team compositions. a series of experiments is conducted across interactive scientific simulations and diverse nlp tasks, such as reasoning, mathematics, code generation, and writing, demonstrating that agentinit consistently outperforms existing initialization methods in terms of performance. additionally, experiments across various mas frameworks further validate its superior adaptability. in summary, this paper makes the following contributions: ## analysis in this section, we conduct experiments using the qwen2.5 model, following the same settings outlined in section 4.1, with the framework instantiated under the complete graph configuration. ## conclusion drawing inspiration from real-world strategies for optimizing team structures, we introduce agentinit, a novel approach for mas initialization with strong transferability across similar tasks. by jointly optimizing at both the individual and team levels, agentinit ensures that agents are highly aligned with task objectives and that the resulting teams exhibit clear division of labor and effective collaboration. experimental results across a diverse set of tasks and mas frameworks show that agentinit consistently enhances system performance and token consumption. these findings provide practical insights for future work on mas initialization.