Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents evolve through amendments. Existing time-sensitive retrieval methods address only the *disjoint*-evolving environment, where each update is an independent snapshot. However, laws, policies, and regulations often operate in *overlapping*-evolving environments, where amendments override earlier clauses while preserving most content, creating strong semantic overlap across versions. We propose ***TimelyRAG***, a retriever-agnostic framework that incorporates temporal distance into ranking to align queries with version-appropriate documents. We also introduce ***TimelyQABench***, the first benchmark for regulation-heavy domains with overlapping-evolving challenges. Experiments show consistent gains, up to +28.6% in nDCG@10, highlighting the importance of temporal reasoning for reliable QA over evolving documents. All resources are available at https://github.com/kaist-dmlab/TimelyRAG.