Logic-Based Verification of Task Allocation for LLM-Enabled Multi-Agent Manufacturing Systems

2026-04-18

AI Summary

This work addresses the safety risks in highly flexible multi-agent manufacturing systems driven by large language models (LLMs), where frequent system reconfigurations can lead to hazardous behaviors due to the absence of formal safety guarantees. To mitigate this issue, the paper proposes a novel framework that integrates LLMs with formal verification by combining temporal logic and discrete event systems to validate LLM-generated task allocation plans prior to execution. This approach uniquely synergizes the adaptive planning capabilities of LLMs with logic-based safety assurance mechanisms. The framework has been validated in a multi-robot assembly scenario, demonstrating its ability to proactively identify and rectify unsafe behaviors, thereby ensuring the safety and reliability of the manufacturing process.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Planning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Manufacturing industries are facing increasing product variability due to the growing demand for personalized products. Under these conditions, ensuring safety becomes challenging as frequent reconfigurations can lead to unintended hazardous behaviors. Multi-agent control architectures have been proposed to improve flexibility through decentralized decision-making and coordination. However, these architectures are based on predefined task models, which limit their ability to adapt task planning to new product requirements while preserving safety. Recently, large language models have been introduced into manufacturing systems to enhance adaptability, but reliability remains a key challenge. To address this issue, we propose a control architecture that leverages the flexibility of large language models while preserving safety on the manufacturing shop floor. Specifically, the proposed framework verifies large language model-enabled task allocations by using temporal logic and discrete event systems. The effectiveness of the proposed framework is demonstrated through a case study that involves a multi-robot assembly scenario, showing that unsafe tasks can be allocated safely before task execution.

Problem

• task allocation
• safety verification
• large language models
• multi-agent manufacturing systems
• logic-based verification

Innovation

• large language models
• task allocation
• temporal logic
• discrete event systems
• safety verification