This research introduces a novel approach combining causal graphs with large language models to enhance complex reasoning capabilities in knowledge-intensive domains.

• Improves medical question-answering accuracy by up to 10%
• Enhances reasoning transparency through explicit causal relationships
• Reduces hallucinations by grounding LLM outputs in structured knowledge
• Enables better incorporation of domain-specific knowledge

For healthcare applications, this advancement means more reliable clinical decision support, improved diagnostic reasoning, and better explainability of AI-generated medical insights—critical for high-stakes medical scenarios where understanding causal relationships is essential.

Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs