Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration

2026-05-08

AI Summary

This study addresses the challenges of rigid role assignment and complex task structures in heterogeneous multi-agent collaboration without prior coordination, which often lead to system bottlenecks, inefficiency, and uneven contribution distribution. Focusing on a kitchen scenario, the authors develop a self-organizing collaboration model that integrates mixed serial-parallel task dependencies and diverse agent roles. By combining agent-based modeling, task graph analysis, and network-theoretic methods, they uncover an “expertise dilemma”: excessive specialization results in workload imbalance and interaction homogenization. The work further elucidates the interplay among task structure, team size, and communication overhead, revealing conditions under which collaborative returns diminish. Based on these insights, the study proposes design principles for efficient multi-agent systems.

Abstract

Computational models of collaboration without prior coordination often overlook how heterogeneous agent traits and complex task structures jointly produce systemic bottlenecks, inefficiencies, and contribution inequalities. We address this by using an agent-based model of ad-hoc teamwork in a kitchen environment. Our model integrates diverse agent personas with tasks that combine serial and parallel dependencies. We identify a specialist's dilemma, where rigid role assertion generates system-level bottlenecks, amplifies workload inequality, and fosters fragmented, homophilous networks. We also find that team size and communication overhead interact with problem structure to generate diminishing returns and redundant collaboration. Linking micro-level behavior to macro-level outcomes provides insights into emergent collaboration and design principles for effective multi-agent teamwork.

Problem

• multi-agent collaboration
• ad-hoc teamwork
• systemic bottlenecks
• workload inequality
• emergent inefficiencies

Innovation

• multi-agent collaboration
• agent-based modeling
• ad-hoc teamwork
• specialist's dilemma
• emergent inefficiency