Heterogeneous LLM agents in supply chain simulations exhibit myopic self-interested behaviors that worsen inefficiencies, but information sharing mitigates these effects.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
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RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.
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Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Heterogeneous LLM agents in supply chain simulations exhibit myopic self-interested behaviors that worsen inefficiencies, but information sharing mitigates these effects.
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RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
RADAR is a redundancy-aware, query-adaptive framework that uses conditional discrete graph diffusion to generate efficient communication topologies for multi-agent LLM systems, outperforming baselines on six benchmarks with higher accuracy and lower token use.