Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs

Researchers demonstrate that vector-based retrieval systems fail on queries requiring structural reasoning over knowledge graphs, proposing instead an LLM Query Planner with typed traversal primitives that outperforms traditional approaches. The study reveals that LLM capability gaps in graph reasoning stem not from model intelligence but from insufficient computational operators, with implications for enterprise knowledge systems.

This research addresses a fundamental limitation in how large language models interact with structured data systems. Traditional Retrieval-Augmented Generation relies on vector similarity to find relevant information, but this approach systematically fails when queries demand multi-step reasoning across interconnected entities—a common requirement in supply chain, logistics, and scientific domains. The aerospace supply chain evaluation demonstrates that five of 23 query categories were structurally unreachable using vector retrieval alone, creating a critical gap between what users expect and what systems deliver.

The operator vocabulary thesis represents a significant conceptual shift in understanding LLM limitations. Rather than attributing failures to insufficient model reasoning capabilities, the researchers identify that providing appropriate computational tools—typed graph traversal primitives and graph computation operations—enables LLMs to solve previously intractable queries. The LLM Query Planner with nine traversal primitives achieved F1 scores of 0.632 compared to 0.472 for bespoke handlers, suggesting that general-purpose tool-augmented reasoning outperforms specialized approaches.

For organizations managing complex knowledge graphs, this research validates that strategic tool design matters more than model selection. Enterprise systems handling supply chains, scientific repositories, or interconnected datasets stand to benefit from restructuring their retrieval infrastructure around typed operations rather than embedding-based similarity. The identified measurement gap—where entity-level F1 scores underestimate structural query accuracy—also indicates that existing evaluation metrics may misrepresent system performance on practical applications.

Future work should focus on scaling these typed operations across larger graphs and determining which query categories require hierarchical reasoning strategies versus flat traversal patterns.

• →Vector similarity retrieval systematically fails on structural queries requiring multi-step reasoning across interconnected entities.
• →LLM graph reasoning limitations stem from insufficient computational operators, not insufficient model intelligence.
• →An LLM Query Planner with typed traversal primitives outperforms bespoke handlers by 34% (F1: 0.632 vs 0.472).
• →LLMs selectively adopt graph computation tools for query categories where traversal-only approaches fail.
• →Current entity-level evaluation metrics underestimate performance on structural queries with comprehensive correct answers.