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<dim:field authority="0000-0002-8334-4719" element="contributor" qualifier="author" confidence="ACCEPTED" language="es-ES" mdschema="dc">de Curtò i Díaz, Joaquim</dim:field>
<dim:field authority="e7ba7b46-6476-45d8-a54f-a0e4b396d3ff" element="contributor" qualifier="author" confidence="ACCEPTED" language="es-ES" mdschema="dc">de Zarzà i Cubero, Irene</dim:field>
<dim:field element="date" qualifier="accessioned" mdschema="dc">2026-06-26T05:53:10Z</dim:field>
<dim:field element="date" qualifier="available" mdschema="dc">2026-06-26T05:53:10Z</dim:field>
<dim:field element="date" qualifier="issued" language="es_ES" mdschema="dc">2026-06-23</dim:field>
<dim:field element="identifier" qualifier="issn" language="es_ES" mdschema="dc">2169-3536</dim:field>
<dim:field element="identifier" qualifier="uri" language="es_ES" mdschema="dc">https://doi.org/10.1109/ACCESS.2026.3706338</dim:field>
<dim:field element="identifier" qualifier="uri" mdschema="dc">http://hdl.handle.net/11531/110916</dim:field>
<dim:field element="description" language="es_ES" mdschema="dc">Artículos en revistas</dim:field>
<dim:field element="description" qualifier="abstract" language="es-ES" mdschema="dc">El artículo presenta Coevolutionary Generative Societies, una arquitectura para sociedades de agentes basadas en modelos de lenguaje que combina deliberación mediante LLM con teoría evolutiva de la cooperación. El sistema integra aprendizaje social, refuerzo individual y señales asesoras ponderadas por confianza para generar coordinación emergente en redes de interacción. Los autores evalúan el modelo en distintas topologías de red y juegos sociales, mostrando que la fundamentación en teoría de juegos evolutiva mejora la cooperación, la resiliencia ante perturbaciones y la estabilidad frente a enfoques basados solo en razonamiento narrativo. Los resultados indican que la estructura de la red influye de forma significativa en el nivel de cooperación y en la desigualdad de resultados.</dim:field>
<dim:field element="description" qualifier="abstract" language="en-GB" mdschema="dc">Recent generative-agent frameworks demonstrate that large language models can produce socially plausible behavior in multi-agent simulations, yet their strategy dynamics remain ungrounded. This article introduces Coevolutionary Generative Societies, an architecture that combines LLM deliberation with evolutionary cooperation theory to produce stable and resilient cooperative equilibria on interaction networks. Agents update their cooperation propensity using social learning, individual reinforcement, and an advisory signal from a Solver–Critic–Aggregator pipeline. Experiments on small-world, scale-free, and random networks show that theory-grounded LLM societies achieve higher cooperation, recover from adversarial shocks, and outperform narrative-only and structural baselines. The findings show that grounding LLM-agent societies in evolutionary game-theoretic update rules is essential for emergent coordination.</dim:field>
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<dim:field element="rights" language="es_ES" mdschema="dc">Creative Commons Reconocimiento-NoComercial-SinObraDerivada España</dim:field>
<dim:field element="rights" qualifier="uri" language="es_ES" mdschema="dc">http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dim:field>
<dim:field element="source" language="es_ES" mdschema="dc">Revista: IEEE Access, Periodo: 1, Volumen: , Número: , Página inicial: 1, Página final: 29</dim:field>
<dim:field element="title" language="es_ES" mdschema="dc">Theory-Grounded LLM Societies for Emergent Coordination</dim:field>
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<dim:field element="keywords" language="es-ES" mdschema="dc">Sistemas multiagente, Modelos de lenguaje, Teoría de juegos evolutiva, Coordinación emergente, Cooperación</dim:field>
<dim:field element="keywords" language="en-GB" mdschema="dc">Multi-agent systems, Large language models, Evolutionary game theory, Emergent coordination, Cooperation</dim:field>
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