Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems

2026-04-20

AI Summary

This study addresses the limited generalization of existing adaptive multi-agent systems in cross-domain tasks, which often exhibit a “superficially effective but intrinsically flawed” coordination illusion. Through large-scale empirical analysis, it identifies two core issues—topological overfitting and illusory coordination—for the first time. Employing cross-domain task evaluation, interaction behavior analysis, and system performance comparison, the work systematically assesses generalization capabilities and collaborative mechanisms. Findings reveal that current systems struggle to transfer effectively to novel domains and display internal interactions that significantly deviate from ideal multi-agent coordination patterns. These results challenge the reliability of deploying such systems in real-world settings and advocate for a paradigm shift in evaluation—from mere outcome correctness toward intrinsic generalization capacity.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Adaptive multi-agent systems (MAS) are increasingly adopted to tackle complex problems.However, the narrow task coverage of their optimization raises the question of whether they can function as general-purpose systems.To address this gap, we conduct an extensive empirical study of adaptive MAS, revealing two key findings: (1) topological overfitting -- they fail to generalize across different domains; and (2) illusory coordination -- they achieve reasonable surface-level accuracy while the underlying agent interactions diverge from ideal MAS behavior, raising concerns about their practical utility.These findings highlight the pressing need to prioritize generalization in MAS development and motivate evaluation protocols that extend beyond simple final-answer correctness.

Problem

• generalization
• adaptive multi-agent systems
• topological overfitting
• illusory coordination
• empirical study

Innovation

• topological overfitting
• illusory coordination
• generalization
• adaptive multi-agent systems
• empirical study