Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
ISBN 0897912276
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A multi-agent extension of empowerment produces emergent group organizations in tendon-coupled agent pairs and controllable Vicsek flocks.
Genetic algorithm optimizes parameters of multi-agent flocking models to match user-defined objectives, with alignment emerging from spacing maintenance.
Neural cellular automata with heterogeneous communication protocols show slower consensus and partial divergence on density classification, with robustness from diverse training.
citing papers explorer
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Do Matching Mechanisms Work with LLM Agents?
Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
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Multi-Agent Empowerment and Emergence of Complex Behavior in Groups
A multi-agent extension of empowerment produces emergent group organizations in tendon-coupled agent pairs and controllable Vicsek flocks.
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EvoFlock: evolved inverse design of multi-agent motion
Genetic algorithm optimizes parameters of multi-agent flocking models to match user-defined objectives, with alignment emerging from spacing maintenance.
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Communication Heterogeneity and Collective Consensus in Neural Cellular Automata
Neural cellular automata with heterogeneous communication protocols show slower consensus and partial divergence on density classification, with robustness from diverse training.