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The Structure-Content Trade-off in Knowledge Graph Retrieval: A Diagnostic Study of Question Decomposition

Large Language Models are increasingly combined with knowledge graphs to support multi-hop factual reasoning. A classical strategy for handling complex questions in such settings is question decomposition, where a question is broken into simpler subquestions to guide retrieval. While decomposition can improve relevance, its impact on the structure and connectivity of the retrieved information, as well as the implications for downstream reasoning, remain unclear. In this work, we present a diagnostic study of the effects of question decomposition on knowledge graph retrieval. We use a simple parametric interpolation between retrieval guided by the original question and its subquestions, allowing us to vary retrieval focus in a controlled manner. By softly anchoring subquestion-level retrieval to the original question, we allow structural properties of the retrieved subgraph to change naturally, without post-hoc enforcement of connectivity. Across different multi-hop QA benchmarks, we observe a consistent structure-content trade-off: subquestion-focused retrieval improves content precision but fragments the retrieved graph, whereas question-focused retrieval preserves structural coherence at the cost of relevance. Downstream QA performance peaks at intermediate settings, where sufficient connectivity emerges while maintaining high relevance. These results highlight the importance of jointly considering content and structure when designing retrieval strategies for reasoning over structured knowledge.