Large Language Models (LLMs) have been increasingly employed for query expansion. However, their generative nature often undermines performance on complex multi-hop retrieval tasks by introducing irrelevant or noisy information. To address this challenge, we propose DeCoR (Decompose and Compress for Retrieval), a framework grounded in structured information refinement. Rather than generating additional content, DeCoR strategically restructures the query's underlying reasoning process and distills supporting evidence from retrieved documents. It consists of two core components tailored to the challenges of multi-hop retrieval: (1) Query Decomposition, which decomposes a complex query into explicit reasoning steps, and (2) Query-aware Document Compression, which synthesizes dispersed evidence from candidate documents into a concise summary relevant to the query. This structured design ensures that the final query representation remains both robust and comprehensive. Experimental results demonstrate that, despite utilizing a relatively small LLM, DeCoR outperforms strong baselines that rely on larger models. This finding underscores that, in complex retrieval scenarios, sophisticatedly leveraging the reasoning and summarization capabilities of LLMs offers a more efficient and effective solution than relying solely on their generative capability.