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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Making waves: A conceptual framework exploring how large language model-based multi-agent systems could reshape water

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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Summary
This summary is machine-generated.

Large Language Model-based Multi-Agents (LLM-MAs) offer new solutions for complex water engineering tasks. These intelligent systems enhance data analysis, modeling, and decision-making for adaptive and traceable water management.

Keywords:
Adaptive AI systemsDecision support systemsGenerative AILarge language model-based multi-agent (LLM-MA)Water engineering

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Water engineering faces challenges in data integration, analysis, modeling, and decision-making.
  • Cross-disciplinary collaboration is crucial but often difficult in water engineering projects.

Purpose of the Study:

  • To explore the integration of Large Language Model-based Multi-Agents (LLM-MAs) into water engineering practices.
  • To identify how LLM-MAs can support and facilitate advanced operations in the field.
  • To develop a foundational framework for understanding the future role of LLM-MAs in water engineering.

Main Methods:

  • Investigating the linguistic capabilities of Large Language Models (LLMs).
  • Analyzing the modular, scalable, and collaborative architecture of LLM-MA systems.
  • Identifying practical applications and potential use cases within water engineering.

Main Results:

  • LLM-MAs can enable timely, adaptive, and traceable solutions for water engineering challenges.
  • Identified applications include pressure drop detection, flood management, and agent-based negotiation for balanced solutions.
  • Highlighted both the capabilities and limitations of LLM-MAs in this domain.

Conclusions:

  • LLM-MAs present a promising avenue for enhancing efficiency and effectiveness in water engineering.
  • Practical recommendations are proposed for the successful implementation of LLM-MAs in the field.
  • This study lays the groundwork for future research and development in AI-driven water engineering.