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This paper empirically investigates the generalization capabilities of adaptive multi-agent systems (MAS) across different domains. The study reveals that adaptive MAS suffer from "topological overfitting," failing to generalize to new environments, and exhibit "illusory coordination," where surface-level accuracy masks a breakdown in meaningful agent interactions. These findings challenge the notion of adaptive MAS as general-purpose systems and underscore the importance of evaluating internal agent dynamics, not just final outputs.
Adaptive multi-agent systems can ace the test while completely misunderstanding the material, achieving superficially good results through internally broken coordination.
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.
