The LLM agent's query processing procedure follows these steps: (1) The agent receives a complex natural language query regarding warehouse performance or planning. (2) The agent autonomously generates a sequence of sub-questions, formulated one at a time and conditioned on evidence from previous sub-question answers. (3) For each sub-question, the agent generates a precise NL-to-Graph Cypher query for Knowledge Graph interaction, as referenced in Hornsteiner et al. (2024) and Mandilara et al. (2025). (4) The agent retrieves relevant information. (5) The agent performs self-reflection, as referenced in Huang et al. (2022) and Madaan et al. (2023), to validate findings and correct errors in the analytical pathway.

Authors

• Person: Not available Organization: arXiv
  Leveraging Knowledge Graphs and LLM Reasoning to Identify ...

Sources

• Leveraging Knowledge Graphs and LLM Reasoning to Identify ... arxiv.org arXiv via serper

Referenced by nodes (3)

• Knowledge Graph concept
• LLM-based agent concept
• Cypher concept