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Multi-input and Multi-variable systems01:22

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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Auto-scaling LLM-based multi-agent systems through dynamic integration of agents.

Ravindu Perera1,2, Anuradha Basnayake1, Manjusri Wickramasinghe1

  • 1University of Colombo School of Computing, University of Colombo, Colombo, Sri Lanka.

Frontiers in Artificial Intelligence
|September 29, 2025
PubMed
Summary

Dynamic Large Language Model-based Multi-Agent Systems (LLM-based MASs) enhance adaptability and task performance by automatically generating agents in real-time. This overcomes limitations of static architectures for complex challenges.

Keywords:
LLM agentsLLM-based MASlarge language modelsmulti-agent systemsnatural language processing

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Area of Science:

  • Artificial Intelligence
  • Multi-Agent Systems

Background:

  • Large Language Model-based Multi-Agent Systems (LLM-based MASs) offer collaborative advantages over individual agents.
  • Current LLM-based MAS architectures are rigid, lacking adaptability and scalability in dynamic settings.

Purpose of the Study:

  • To introduce novel methods for automatic agent generation in LLM-based MASs.
  • To enhance the flexibility and performance of LLM-based MASs in response to evolving contexts.

Main Methods:

  • Proposed Initial Automatic Agent Generation (IAAG) and Dynamic Real-Time Agent Generation (DRTAG) approaches.
  • Utilized advanced prompt engineering techniques (persona pattern, chain, few-shot prompting) for agent creation.
  • Adapted evaluation metrics for scoring LLM-generated texts.

Main Results:

  • DRTAG significantly improved system adaptability and task performance compared to static MAS.
  • IAAG enhanced initial system flexibility and contextually relevant agent creation.
  • Automatic agent generation reduced the need for human intervention.

Conclusions:

  • Dynamic LLM-based MASs effectively address limitations of static architectures.
  • These advancements enable more robust and scalable solutions for complex real-world problems.
  • Opens avenues for innovative applications across various domains.