Enterprise AI Analysis: Planning Before Retrieval

Optimizing KG-Augmented Reasoning with Explicit Planning

Large language models (LLMs) excel at open-domain reasoning but often generate inconsistent or unverifiable answers. Retrieval-augmented generation (RAG) improves factual grounding, yet current KG-RAG systems rely on heuristic retrieval and lack interpretability. This dissertation proposes a planner-executor frame-work that formalizes retrieval as a structured planning problem. The planner analyzes a question, identifies relational constraints, and infers the underlying KG topology to generate an optimized retrieval plan. The executor follows this plan on Wikidata with bounded exploration, early stopping, and re-planning when con-straints fail. This design enables controlled, auditable reasoning that balances completeness and efficiency. Evaluation will focus on retrieval faithfulness, reasoning accuracy, and computational cost using Wikidata-based QA benchmarks. Additional studies will examine constraint prioritization, query topology, and cross-model plan transfer between large and small LLMs. By integrating explicit planning into KG-RAG, we aim to develop scalable and interpretable reasoning systems that combine the structure of symbolic search with the adaptability of neural generation.