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Do differentiated roles in LLM multi-agent systems yield complementary contributions?

Establish whether role differentiation within multi-agent systems composed of large language model agents entails complementary contributions across agents.

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Background

The framework advanced in the paper connects synergy with non-overlapping, jointly informative contributions across agents. Complementarity is crucial for translating differentiation into functional gains, yet prior work has not conclusively shown that role differentiation among LLM agents reliably produces complementary information contributions.

The authors provide task-specific evidence of complementarity under certain prompting conditions, but emphasize that the general question of whether differentiation implies complementarity remains unresolved in the broader domain.

References

While these principles likely apply to LLM-based collectives given the universality of the integration-segregation tradeoff, it remains unclear whether agents develop differentiated roles, whether such roles complement each other, how to steer it with prompts, and what role ToM capacity of models plays for collaboration.

— Emergent Coordination in Multi-Agent Language Models (2510.05174 - Riedl, 5 Oct 2025) in Related Work, Section: Related Work