Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study

2025-05-12

AI Summary

This work investigates how collaborative architecture design affects collective reasoning in multi-agent large language model (LLM) systems, with a focus on expertise allocation as a critical bottleneck. Methodologically, we conduct systematic ablation studies examining the interplay among three dimensions: domain-aligned expert specialization, collaboration paradigms (structured workflows vs. diversity-driven knowledge fusion), and system scale. Our results show that domain-dependent expert alignment substantially improves reasoning accuracy; diversity-aware knowledge integration outperforms rigid task decomposition; and communication overhead constitutes the primary scalability bottleneck. Based on these findings, we propose a configurable multi-agent design framework that quantifies the compute–performance trade-off under scale expansion and empirically validates the significant gains from expert alignment on context-intensive reasoning tasks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically investigate how collaborative reasoning performance is affected by three key design dimensions: (1) Expertise-Domain Alignment, (2) Collaboration Paradigm (structured workflow vs. diversity-driven integration), and (3) System Scale. Our findings reveal that expertise alignment benefits are highly domain-contingent, proving most effective for contextual reasoning tasks. Furthermore, collaboration focused on integrating diverse knowledge consistently outperforms rigid task decomposition. Finally, we empirically explore the impact of scaling the multi-agent system with expertise specialization and study the computational trade off, highlighting the need for more efficient communication protocol design. This work provides concrete guidelines for configuring specialized multi-agent system and identifies critical architectural trade-offs and bottlenecks for scalable multi-agent reasoning. The code will be made available upon acceptance.

Problem

• Designing collaboration structure for multi-agent LLM systems
• Investigating impact of expertise alignment, collaboration paradigm, system scale
• Exploring trade-offs in scaling multi-agent systems with specialization

Innovation

• Expertise-Domain Alignment for contextual reasoning tasks
• Diversity-driven integration outperforms rigid task decomposition
• Scaling multi-agent systems with expertise specialization trade-offs