Summary of Xagents: a Framework For Interpretable Rule-based Multi-agents Cooperation, by Hailong Yang et al.
XAgents: A Framework for Interpretable Rule-Based Multi-Agents Cooperation
by Hailong Yang, Mingxian Gu, Renhuo Zhao, Fuping Hu, Zhaohong Deng, Yitang Chen
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed XAgents framework is an interpretable multi-agent cooperative system that leverages large language models (LLMs) for logical reasoning and domain membership calculation. Building upon the structure of multi-polar neurons, XAgents utilizes IF-THEN rules to generate responses, eliminating hallucinations and erroneous knowledge through semantic adversarial generation. The framework’s rule-based interpretability enhances user confidence. Comparative analysis with AutoAgents demonstrates superior performance across three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for large language models to learn from experts by creating a multi-agent system that can understand rules. This system, called XAgents, uses special rules to help the model make better decisions and reduce mistakes. The researchers tested XAgents with different types of data and found it outperformed other similar systems. |
