CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has significantly enhanced large language models (LLMs) in knowledge-intensive tasks by incorporating external knowledge retrieval. However, existing RAG frameworks primarily rely on semantic similarity and correlation-driven retrieval, limiting their ability to distinguish true causal relationships from spurious associations. This results in responses that may be factually grounded but fail to establish cause-and-effect mechanisms, leading to incomplete or misleading insights. To address this issue, we introduce Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation (CDF-RAG), a framework designed to improve causal consistency, factual accuracy, and explainability in generative reasoning. CDF-RAG iteratively refines queries, retrieves structured causal graphs, and enables multi-hop causal reasoning across interconnected knowledge sources. Additionally, it validates responses against causal pathways, ensuring logically coherent and factually grounded outputs. We evaluate CDF-RAG on four diverse datasets, demonstrating its ability to improve response accuracy and causal correctness over existing RAG-based methods. Our code is publicly available atthis https URLelakhatibi/CDF-RAG.

@article{khatibi2025_2504.12560,
  title={ CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation },
  author={ Elahe Khatibi and Ziyu Wang and Amir M. Rahmani },
  journal={arXiv preprint arXiv:2504.12560},
  year={ 2025 }
}