Multi-Agent Collaboration via Cross-Team Orchestration (2025)

Abstract: Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software development, where agents can collaborate in a team like humans, following predefined phases to complete sub-tasks sequentially. However, for an agent team, each phase yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently leading to suboptimal results or extensive trial and error. To address this, we introduce Cross-Team Orchestration (Croto), a scalable multi-team framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. Experiments reveal a notable increase in software quality compared to state-of-the-art baselines. We further tested our framework on story generation tasks, which demonstrated a promising generalization ability of our framework in other domains. The code and data is available at https://github.com/OpenBMB/ChatDev/tree/macnet

## introduction the rapid advancement of large language models (llms) has yielded remarkable achievements across various domains like natural language processing @xcite , and software development @xcite @xcite . however, limitations like hallucinations inherent in their standalone capabilities @xcite , impede llm's ability to generate usable content for task solving when † equal contribution. corresponding author. however, a single team can only execute all phases sequentially according to its predefined configuration (e.g., the number of agents, agent profiles, and llm hyperparameters), and its decision path is fixed @xcite . this design may lead to repetitive errors with a specific configuration when encountering a particular type of problem, hindering self-correction. furthermore, it limits the agents' ability to explore more diverse and effective decision paths. while graph-like paradigms self-organize agents through dynamic optimization of nodes and edges @xcite , they require extensive task-specific customization for all nodes and edges. this complexity complicates their usage and hinders seamless generalization to heterogeneous downstream tasks, making them impractical in many scenarios. additionally, organizing agents into a graph structure may reduce the task-solving independence of agents, which is crucial for fostering diverse insights into solutions. therefore, it is beneficial to introduce multiple agent teams that are aware of each other, enabling them to collaborate effectively to explore more potential paths without needing specific adjustments while also maintain their independence as a team process self-sufficient. then the challenge becomes: how can multi-agent systems obtain and utilize insights from others to achieve a superior outcome? in this paper, we propose cross-team orchestration (croto), a framework that carefully orchestrates different single teams into a multi-team collaborative structure, each team has the same task assignment to communicate in a collaborative environment. specifically, our framework enables different teams to independently propose various task-oriented solutions as insights (single-team proposal) and then communicate for insights interchange in some important phases (multi-team aggregation) that boost subsequent task resolution. different solutions from various teams are divided into groups by a hierarchy partitioning mechanism and then synthesized by a greedy aggregation mechanism that aggregates various solutions and insights into a superior one collaboratively. through our experiments with 15 tasks from different categories and styles selected from the srdd dataset @xcite for software generation (programming-language-oriented reasoning), we demonstrate a significant improvement in software quality using the proposed framework. we highlight the importance of diversity across teams and emphasize the importance of fostering a cross-team collaboration environment to bolster teams' performance through our pruning mechanism. furthermore, to further demonstrate the generalizability of our framework, we extended its application to the domain of story generation (natural-language-oriented reasoning), incorporating 10 tasks from the rocstories dataset @xcite . the results revealed a notable improvement in story quality. our findings underscore the efficacy and promising generalization of our framework in complex tasks. in summary, our contributions are threefold: ## related work trained on vast datasets with extensive parameters, llms have revolutionized the landscape of natural language processing @xcite @xcite . a notable breakthrough lies in the llm-based autonomous agents @xcite @xcite , where these agents exhibit proficiency in planning @xcite , memory @xcite , and tool use @xcite @xcite , thus enabling independent operation within intricate real-world contexts @xcite @xcite , thereby transforming fundamental llms into versatile autonomous agents @xcite @xcite . along this line, multiagent collaboration has proven beneficial in uniting the expertise of diverse agents for autonomous tasksolving @xcite @xcite , which has widely propelled progress across various domains such as software development @xcite , medical treatment @xcite , educational teaching @xcite and embodied control @xcite . in contrast to simple majority voting, where agents act independently @xcite , the concept of collective emergence @xcite @xcite suggests that effective collaboration should form an integrated system that fosters interdependent interactions and thoughtful decision-making @xcite . recent research has focused on differentiating agents into distinct roles and encouraging interactions for diverse tasks solving or complex simulation @xcite @xcite @xcite . studies on exploring organizing agents in hierarchical tree structures for information propagation @xcite or in graph-based structures with predefined nodes and edges @xcite demonstrating that increasing the number and diversity of agents can enhance performance in multi-agent systems. mac-net @xcite revealed that the quality of solutions follows a logistic growth pattern as the number of agents scales. unlike these approaches, our work envisions multi-agent teams as collaborative units, enabling both inter-and intra-team collaborations for optimized solutions generation. ## preliminaries definition 1 (chain as a team) a single-team (c) is conceptualized as a chain-like structure @xcite composed of a series of task-oriented phases (p i ) that sequentially address the resolution of tasks which can be formulated as: we refer to such a chain-like structure as a team that could participate in our croto framework. ## methodology 4.1 cross-team orchestration facing diverse tasks, a chain-as-a-team often tackles tasks in isolation. while this process is streamlined, it lacks the diversity of insights beneficial to explore a broader range of decision paths for superior task solutions. a straightforward attempt to break this isolation is to run n teams simultaneously on the same task and ensemble their results. however, this straightforward ensembling approach fails to capitalize on the potential for mutual awareness and collaboration among teams during intermediate phases. this is analogous to exploring n paths in parallel without leveraging the intersections of their intermediate nodes, which could enable the exploration of additional paths. nevertheless, this approach introduces new challenges, including a significant increase in communication overhead and the risk of incorporating noise from underperforming teams. moreover, the lack of independence among teams can diminish the diversity of solutions, as teams may converge on similar features. to alleviate these issues, we propose croto, a novel cross-team collaborative framework that orchestrates the parallel executions of multiple singleteam with configurable temperature and length of chains 1 each team is assigned the same task objective, and they collectively propose various taskoriented solutions at each phase based on their unique perspectives. at predefined key phases such as design or writing, where critical decisions or significant solution modifications occur, the framework identifies corresponding key phases in other teams. if such phases exist, these teams pause their workflows and extract solutions for cross-team interactions (e). during this interaction process, solutions generated by teams are first grouped and then iteratively aggregated into more refined solutions. this interactive dynamic can be modeled as: ## conclusion recognizing the inherent limitations of a single team in obtaining and leveraging external insights when completing complex tasks such as software development, we introduce a novel multi-team framework called croto. this framework carefully orchestrates multiple teams with the same task objective, enabling them to jointly propose diverse task-oriented decisions, interact at key phases, and collaboratively aggregate various solutions into a final superior outcome. without requiring taskspecific customization, our quantitative analysis demonstrates significant improvements in outcome quality in software development and story generation, highlighting the framework's scalability and potential generalizability. we anticipate that our insights will initiate a paradigm shift in the design of llm agents, advancing them toward multi-team collaboration and enhancing solution generation quality across a broader range of complex tasks.