Language Model Teams as Distributed Systems

2026-03-12

AI Summary

This work addresses the absence of a systematic framework for evaluating the effectiveness, structural design, and team size selection of large language model (LLM) ensembles. It pioneers the integration of distributed systems theory into LLM team research by conceptualizing such teams as distributed intelligent systems. Combining multi-agent coordination mechanisms with system architecture analysis, the study establishes a cross-disciplinary analytical framework. This framework uncovers fundamental parallels between LLM teams and traditional distributed systems in terms of performance, scalability, and fault tolerance, thereby offering both theoretical foundations and practical guidance for the design, evaluation, and optimization of LLM teams.

Technology Category

• Multiagent Systems: Teamwork
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) are growing increasingly capable, prompting recent interest in LLM teams. Yet, despite increased deployment of LLM teams at scale, we lack a principled framework for addressing key questions such as when a team is helpful, how many agents to use, how structure impacts performance -- and whether a team is better than a single agent. Rather than designing and testing these possibilities through trial-and-error, we propose using distributed systems as a principled foundation for creating and evaluating LLM teams. We find that many of the fundamental advantages and challenges studied in distributed computing also arise in LLM teams, highlighting the rich practical insights that can come from the cross-talk of these two fields of study.

Problem

• language model teams
• distributed systems
• multi-agent systems
• team performance
• LLM coordination

Innovation

• Language Model Teams
• Distributed Systems
• Multi-Agent LLMs
• Systematic Framework
• Cross-disciplinary Insights