LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer

• URL: http://arxiv.org/abs/2505.19567v1
• Date: Mon, 26 May 2025 06:30:13 GMT
• Title: LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer
• Authors: Rasoul Zahedifar, Sayyed Ali Mirghasemi, Mahdieh Soleymani Baghshah, Alireza Taheri,
• Abstract summary: The LLM-Agent-Controller is a multi-agent large language model (LLM) system developed to address a wide range of problems in control engineering (Control Theory)<n>A supervisor oversees high-level decision-making and workflow coordination, enhancing the system's reliability and efficiency.<n>It is designed to function without requiring users to have prior knowledge of Control Theory, enabling them to input problems in plain language and receive complete, real-time solutions.
• Score: 2.4392539322920763
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study presents the LLM-Agent-Controller, a multi-agent large language model (LLM) system developed to address a wide range of problems in control engineering (Control Theory). The system integrates a central controller agent with multiple specialized auxiliary agents, responsible for tasks such as controller design, model representation, control analysis, time-domain response, and simulation. A supervisor oversees high-level decision-making and workflow coordination, enhancing the system's reliability and efficiency. The LLM-Agent-Controller incorporates advanced capabilities, including Retrieval-Augmented Generation (RAG), Chain-of-Thought reasoning, self-criticism and correction, efficient memory handling, and user-friendly natural language communication. It is designed to function without requiring users to have prior knowledge of Control Theory, enabling them to input problems in plain language and receive complete, real-time solutions. To evaluate the system, we propose new performance metrics assessing both individual agents and the system as a whole. We test five categories of Control Theory problems and benchmark performance across three advanced LLMs. Additionally, we conduct a comprehensive qualitative conversational analysis covering all key services. Results show that the LLM-Agent-Controller successfully solved 83% of general tasks, with individual agents achieving an average success rate of 87%. Performance improved with more advanced LLMs. This research demonstrates the potential of multi-agent LLM architectures to solve complex, domain-specific problems. By integrating specialized agents, supervisory control, and advanced reasoning, the LLM-Agent-Controller offers a scalable, robust, and accessible solution framework that can be extended to various technical domains.

Related papers

• Heterogeneous Group-Based Reinforcement Learning for LLM-based Multi-Agent Systems [25.882461853973897]
  We propose Multi-Agent Heterogeneous Group Policy Optimization (MHGPO), which guides policy updates by estimating relative reward advantages.<n>MHGPO eliminates the need for Critic networks, enhancing stability and reducing computational overhead.<n>We also introduce three group rollout sampling strategies that trade off between efficiency and effectiveness.
  arXiv  Detail & Related papers  (2025-06-03T10:17:19Z)
• Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems [4.683612295430957]
  This paper presents a novel approach for unified retrieval-augmented generation (RAG) systems using the recent emerging large language model (LLM) agent concept.<n>We propose a trainable agent framework called Agent-UniRAG for unified retrieval-augmented LLM systems.<n>The main idea is to design an LLM agent framework to solve RAG tasks step-by-step based on the complexity of the inputs.
  arXiv  Detail & Related papers  (2025-05-28T16:46:31Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents [4.57755315319748]
  Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making.<n>These frameworks predominantly serve developers with extensive technical expertise.<n>Only 0.03 % of the global population possesses the necessary programming skills.
  arXiv  Detail & Related papers  (2025-02-09T16:53:56Z)
• MALT: Improving Reasoning with Multi-Agent LLM Training [66.9481561915524]
  MALT (Multi-Agent LLM Training) is a novel post-training strategy that divides the reasoning process into generation, verification, and refinement steps.<n>On MATH, GSM8K, and CSQA, MALT surpasses the same baseline LLM with a relative improvement of 15.66%, 7.42%, and 9.40% respectively.
  arXiv  Detail & Related papers  (2024-12-02T19:30:36Z)
• MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming [10.461509044478278]
  MaCTG (MultiAgent Collaborative Thought Graph) is a novel multi-agent framework that employs a dynamic graph structure.<n>It autonomously assigns agent roles based on programming requirements, dynamically refines task distribution, and systematically verifies and integrates project-level code.<n>MaCTG significantly reduced operational costs by 89.09% compared to existing multi-agent frameworks.
  arXiv  Detail & Related papers  (2024-10-25T01:52:15Z)
• ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise [14.14268499543524]
  Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. We introduce ControlAgent, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise.
  arXiv  Detail & Related papers  (2024-10-17T17:42:48Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms [55.77492625524141]
  EvoAgent is a generic method to automatically extend specialized agents to multi-agent systems.<n>We show that EvoAgent can significantly enhance the task-solving capability of LLM-based agents.
  arXiv  Detail & Related papers  (2024-06-20T11:49:23Z)
• Adaptive In-conversation Team Building for Language Model Agents [33.03550687362213]
  Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks.<n>Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent.<n>A comprehensive evaluation across six real-world scenarios demonstrates that Captain Agent significantly outperforms existing multi-agent methods.
  arXiv  Detail & Related papers  (2024-05-29T18:08:37Z)
• Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning [56.82041895921434]
  Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities. When used as agents for dealing with complex problems in the real world, their performance is far inferior to large commercial models such as ChatGPT and GPT-4.
  arXiv  Detail & Related papers  (2024-03-29T03:48:12Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)
• AgentBench: Evaluating LLMs as Agents [88.45506148281379]
  Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.