Multi-Agent Collaboration via Cross-Team Orchestration (2406.08979v2)

Abstract: 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

• The paper presents a novel framework that orchestrates multiple agent teams to overcome single-team limitations by exploring diverse decision paths.
• It employs parallel processing, greedy pruning, and hierarchy partitioning to optimize communication and enhance content quality in software and story generation.
• Empirical evaluations on GPT-3.5-Turbo demonstrate that the framework outperforms baselines in completeness, executability, and overall output quality.

Multi-Agent Software Development through Cross-Team Collaboration

The paper "Multi-Agent Software Development through Cross-Team Collaboration" introduces a novel framework named Cross-Team Collaboration (CTC) aimed at enhancing the efficacy of LLMs in software and story generation tasks. The central premise of this work is to strategically orchestrate multiple agent teams to collaborate on complex tasks, leveraging diverse perspectives to improve content quality. This summary provides a detailed analysis of the methodology, empirical results, and implications of this research.

Methodology

The proposed methodology centers around addressing the limitations inherent in conventional single-team frameworks where each phase in a development process yields a solitary possible outcome. This conventional approach restricts the exploration of multiple potential decision paths, often resulting in suboptimal solutions. To mitigate this, the authors propose the Cross-Team Collaboration framework, which enables multiple agent teams to collaborate and share insights at critical phases, thereby exploring a broader solution space.

Single-Team Execution

The Single-Team Execution mechanism forms the basis of the collaboration framework. Here, each team is composed of agent roles such as instructor and assistant, sequentially completing subtasks through multi-turn dialogue. This design aims to refine the content iteratively, ensuring a thorough exploration of the requirements and solutions.

Cross-Team Collaboration

Building on the single-team paradigm, Cross-Team Collaboration involves multiple teams working in parallel on the same task. These teams generate diverse solutions independently and then interact at key phases to exchange insights, which are filtered through a greedy pruning mechanism to discard low-quality content. Additionally, a Hierarchy Partitioning mechanism is employed to manage communication load, where teams are divided into groups for collaborative aggregation of content. This process continues iteratively until a superior, consolidated solution is achieved.

Experimental Results

Empirical evaluation of the CTC framework was conducted on software and story generation tasks using GPT-3.5-Turbo. The experiments compared the performance of CTC against state-of-the-art baselines including GPT-Engineer, ChatDev, MetaGPT, and AgentVerse. Key metrics included Completeness, Executability, Consistency, and overall Quality.

Software Generation

In the domain of software development, CTC demonstrated substantial improvements across all evaluation metrics. The Completeness and Executability of the generated software showed marked enhancement, reflecting the framework's ability to produce more robust and operational code. Specifically, the CTC framework achieved a Quality score of 0.840, significantly outperforming other baselines such as ChatDev, which scored 0.779.

Story Generation

To assess the generalizability of the CTC framework, the authors extended their experiments to story generation tasks. Metrics for evaluation included Grammar and Fluency, Context Relevance, Logic Consistency, and overall Quality. Here too, CTC exhibited superior performance, underscoring its versatility in handling both software and natural language generation tasks.

Implications and Future Directions

The findings from this research underscore the potential of multi-agent, multi-team collaboration frameworks in enhancing the quality of complex content generation tasks. The ability to efficiently integrate insights from multiple teams allows for a more comprehensive exploration of the solution space, leading to higher quality outcomes.

Practical Implications

1. Software Development: The CTC framework can be a highly valuable tool for software engineering teams, enhancing the automation and quality assurance processes in software development.
2. Story Generation: In creative industries, such as content creation and narrative design, the framework's ability to produce coherent and high-quality stories can significantly reduce the time and effort required for content generation.

Theoretical Implications

The research introduces novel mechanisms such as Hierarchy Partitioning and Greedy Pruning, which contribute to the broader understanding of multi-agent system design. These mechanisms can be further explored and refined to enhance their efficiency and applicability across diverse domains.

Future Developments

Future research could investigate more sophisticated partitioning and aggregation strategies, as well as alternative communication paradigms such as debate mechanisms to further optimize the multi-team collaboration process. Additionally, exploring broader real-world applications of this framework can demonstrate its efficacy and adaptability across various content generation tasks.

In conclusion, the paper provides substantial evidence that Cross-Team Collaboration significantly improves the performance of autonomous agents in complex tasks. By fostering a collaborative environment among multiple agent teams, the framework opens new avenues for research and application in both software and natural language generation domains.