层次的多代理强化学习用于检索增强的工业文档问题答案
Yihong Qian1, Baoli Han2, Yufeng Yuan2
1State Grid Zhejiang Electric Power Co., Ltd., Shaoxing Power Supply Company, Shaoxing, Zhejiang, China. qianyihong9760@163.com.
Scientific reports
|March 15, 2026
概括
本研究介绍了MARL-RAGDoc,这是一个用于多式联络文档分析的新框架. 它通过动态适应各种查询来增强信息检索和推理,优于现有方法.
科学领域:
- 人工智能的人工智能
- 信息检索 信息检索
- 文件分析 文件分析
背景情况:
- 多模式工业文档集成文本,图像和布局,用于域名知识.
- 现有的检索增强生成 (RAG) 框架使用静态策略,限制了对复杂查询和交叉模式依赖性的适应性.
- 这导致不完整的证据检索和长文档场景中低于最佳的推理.
研究的目的:
- 开发一个适应性框架,用于多式联运检索-增强推理.
- 在RAG框架中解决静态检索和融合政策的局限性.
- 提高复杂,长长的多式联络文档上的推理性能.
主要方法:
- 提出了MARL-RAGDoc,一个分层的多代理强化学习框架.
- 实施了用于动态模式权重和检索深度分配的协调代理.
- 利用专门的代理来选择文本,图像和表格证据,并使用协作推理模块来优化政策.
主要成果:
- 在多式联络文档基准上,MARL-RAGDoc在基准方法上表现优越.
- 在检索准确度和推理性能方面取得了持续的改进.
- 该框架被证明是计算效率高的.
结论:
- 马尔-RAGDoc有效地解决了多式联运文档理解和推理的挑战.
- 适应性,多代理方法提高了信息检索和推理准确度.
- 该框架为复杂的长文档场景提供了计算效率高的解决方案.
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