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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Olas de cambio: Un marco conceptual para explorar cómo los sistemas multiagente basados en modelos de lenguaje

Seyed Hossein Hosseini1, Babak Zolghadr-Asli2, Henrikki Tenkanen1

  • 1Department of Built Environment, School of Engineering, Aalto University, Espoo, Finland.

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|December 25, 2025
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Resumen
Este resumen es generado por máquina.

Los Multiagentes basados en Modelos de Lenguaje Grandes (LLM-MA) ofrecen nuevas soluciones para tareas complejas de ingeniería del agua. Estos sistemas inteligentes mejoran el análisis de datos, la modelización y la toma de decisiones para una gestión del agua adaptativa y rastreable.

Palabras clave:
Sistemas de IA adaptativaSistemas de apoyo a la decisiónIA generativaMultiagente basado en modelos de lenguaje grandes (LLM-MA)Ingeniería del agua

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Área de la Ciencia:

  • Ingeniería Ambiental
  • Inteligencia Artificial
  • Gestión de Recursos Hídricos

Sus antecedentes:

  • La ingeniería del agua enfrenta desafíos en la integración de datos, análisis, modelización y toma de decisiones.
  • La colaboración interdisciplinaria es crucial pero a menudo difícil en los proyectos de ingeniería del agua.

Objetivo del estudio:

  • Explorar la integración de Multiagentes basados en Modelos de Lenguaje Grandes (LLM-MA) en las prácticas de ingeniería del agua.
  • Identificar cómo los LLM-MA pueden apoyar y facilitar operaciones avanzadas en el campo.
  • Desarrollar un marco fundamental para comprender el papel futuro de los LLM-MA en la ingeniería del agua.

Principales métodos:

  • Investigación de las capacidades lingüísticas de los Modelos de Lenguaje Grandes (LLM).
  • Análisis de la arquitectura modular, escalable y colaborativa de los sistemas LLM-MA.
  • Identificación de aplicaciones prácticas y casos de uso potenciales dentro de la ingeniería del agua.

Principales resultados:

  • Los LLM-MA pueden permitir soluciones oportunas, adaptativas y rastreables para los desafíos de la ingeniería del agua.
  • Las aplicaciones identificadas incluyen la detección de caídas de presión, la gestión de inundaciones y la negociación basada en agentes para soluciones equilibradas.
  • Se destacaron tanto las capacidades como las limitaciones de los LLM-MA en este dominio.

Conclusiones:

  • Los LLM-MA presentan una vía prometedora para mejorar la eficiencia y la eficacia en la ingeniería del agua.
  • Se proponen recomendaciones prácticas para la implementación exitosa de LLM-MA en el campo.
  • Este estudio sienta las bases para la investigación y el desarrollo futuros en ingeniería del agua impulsada por IA.