CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks
As LLMs demonstrate remarkable reasoning capabilities, LLM-powered agents are seen as key to achieving AGI (Artificial General Intelligence) and are widely applied in various complex real-world scenarios. Nevertheless, existing studies still suffer from missing steps, deviated task execution and inc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5663 |
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| author | Zhonghe Han Jiaxin Wang Xiaolu Yan Zhiying Jiang Yuanben Zhang Siye Liu Qihang Gong Chenwei Song |
| author_facet | Zhonghe Han Jiaxin Wang Xiaolu Yan Zhiying Jiang Yuanben Zhang Siye Liu Qihang Gong Chenwei Song |
| author_sort | Zhonghe Han |
| collection | DOAJ |
| description | As LLMs demonstrate remarkable reasoning capabilities, LLM-powered agents are seen as key to achieving AGI (Artificial General Intelligence) and are widely applied in various complex real-world scenarios. Nevertheless, existing studies still suffer from missing steps, deviated task execution and incorrect tool selection. This paper proposes CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents, comprising the Plan Agent (as a precise task planner), the Tool Agent (as a proficient tool user) and the Reflect Agent (as an objective task evaluator). These agents simulate the social division of labor and synergistic cooperation to enable each agent to perform different specialized capabilities in order to solve complex tasks together. Through the above mechanism, the CoReaAgents framework has the skills of prospective thinking and flexible execution. To verify the capability of the CoReaAgents framework, this paper conducts extensive experiments on different complex tasks such as tool learning, math reasoning and multi-hop QA. The results show that the CoReaAgents framework outperforms various comparative methods in both quantity and quality. |
| format | Article |
| id | doaj-art-11e6d863e9e24b82a9e0f269ee9cc048 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-11e6d863e9e24b82a9e0f269ee9cc0482025-08-20T03:14:42ZengMDPI AGApplied Sciences2076-34172025-05-011510566310.3390/app15105663CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning TasksZhonghe Han0Jiaxin Wang1Xiaolu Yan2Zhiying Jiang3Yuanben Zhang4Siye Liu5Qihang Gong6Chenwei Song7Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaInstitute of Tracking and Telecommunications Technology, Beijing 100094, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, ChinaAs LLMs demonstrate remarkable reasoning capabilities, LLM-powered agents are seen as key to achieving AGI (Artificial General Intelligence) and are widely applied in various complex real-world scenarios. Nevertheless, existing studies still suffer from missing steps, deviated task execution and incorrect tool selection. This paper proposes CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents, comprising the Plan Agent (as a precise task planner), the Tool Agent (as a proficient tool user) and the Reflect Agent (as an objective task evaluator). These agents simulate the social division of labor and synergistic cooperation to enable each agent to perform different specialized capabilities in order to solve complex tasks together. Through the above mechanism, the CoReaAgents framework has the skills of prospective thinking and flexible execution. To verify the capability of the CoReaAgents framework, this paper conducts extensive experiments on different complex tasks such as tool learning, math reasoning and multi-hop QA. The results show that the CoReaAgents framework outperforms various comparative methods in both quantity and quality.https://www.mdpi.com/2076-3417/15/10/5663LLMLLM-powered agentartificial intelligencemulti-agent systemsmachine learningsmart tools and applications |
| spellingShingle | Zhonghe Han Jiaxin Wang Xiaolu Yan Zhiying Jiang Yuanben Zhang Siye Liu Qihang Gong Chenwei Song CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks Applied Sciences LLM LLM-powered agent artificial intelligence multi-agent systems machine learning smart tools and applications |
| title | CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks |
| title_full | CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks |
| title_fullStr | CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks |
| title_full_unstemmed | CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks |
| title_short | CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks |
| title_sort | coreaagents a collaboration and reasoning framework based on llm powered agents for complex reasoning tasks |
| topic | LLM LLM-powered agent artificial intelligence multi-agent systems machine learning smart tools and applications |
| url | https://www.mdpi.com/2076-3417/15/10/5663 |
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