# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 2 Related Work
### Research Status of LLMs and Multi-Agent System  
In recent years, LLMs have rapidly emerged as a focal point of research in the field of natural language processing (NLP). These models demonstrate exceptional capabilities in natural language understanding, reasoning, decision-making, and text generation. As research has progressed, significant advancements have been made in the study of LLMs, with numerous innovative techniques being developed to enhance their performance and broaden their application scope. The introduction of the Transformer architecture [22] has laid the foundation for the development of LLMs, significantly enhancing the model's ability to handle long-range dependencies through the self-attention mechanism. The application of LangChain [23] technology provides tools and framework support for LLMs, enabling the models to integrate external data and functions more efficiently when handling complex tasks, thereby improving the accuracy and diversity of problem-solving. The combination of retrieval-augmented generation (RAG) technology allows LLMs to leverage external knowledge bases during text generation, enhancing the precision and richness of the generated text. Additionally, research on pre-training and fine-tuning strategies [24] is also a crucial direction in the field of LLMs. Through pre-training on large-scale corpora, LLMs can learn rich language representations, while fine-tuning specific tasks further improves the model's adaptability to particular domains. The proposal of multi-agent systems (MAS) [25] offers new insights for the further optimization of LLMs. A multi-agent system is a distributed system composed of multiple autonomous agents that can be used to solve complex problems through collaboration and competition among the agents. Currently, research on the application of multi-agent structures in the LLMs domain is still in its infancy, but some preliminary achievements have been made. Researchers are exploring ways to effectively combine multi-agent systems into LLMs to address collaboration and decision-making challenges in complex tasks. For instance, the extended coevolution theory proposed by [PERSON] et al. [26] focuses on studying the behaviors and interactions of different entities in dynamic environments, highlighting the potential of LLMs in facilitating collaboration in multi-agent systems. The AutoGen framework developed by [PERSON] et al. [27] allows multiple agents to collaborate via LLMs, supporting flexible dialogue patterns, with empirical studies demonstrating its effectiveness across various domains. This lays the foundation for complex LLMs-based applications, showcasing how multi-agent systems can leverage LLMs capabilities to enhance task execution. [PERSON] et al. [28] conducted a comprehensive survey on LLM-based multi-agent systems, proposing a unified workflow framework encompassing key components and revealing their wide applications in problem-solving and world modeling. They also discussed the current challenges and future directions in this field. As technology continues to mature, LLM-based multi-agent systems have been widely applied across various industries, with their influence continually expanding. In the healthcare sector, their applications are primarily focused on areas such as diagnostic assistance [29], telemedicine [30], and clinical trial screening [31]. They have also demonstrated significant capabilities in bioinformatics. For example, the BRAD digital assistant developed by [PERSON] et al. [32] can automate bioinformatics workflows. In the field of cybersecurity, their applications cover various aspects, including automated penetration testing, security analysis, and threat intelligence [33; 34; 35]. In chemical research, their potential is evident in advancing autonomous chemical research [36]. In the field of coal mine safety management, researchers have attempted to apply LLM-based multi-agent systems to safety information retrieval and intelligent mining. [PERSON] et al.