Generative Retrieval (GR) maps queries to documents by generating discrete identifiers (DocIDs).However, offline DocID assignment and constrained decoding often prevent GR from capturing query-specific intent, especially when documents express multiple or unseen intents (i.e., intent misalignment).We introduce Dynamic Docid Decoding (D3), an inference-time mechanism that adaptively refines DocIDs through delayed, query-informed identifier expansion.D3 uses (a) verification to detect intent misalignment and (b) dynamic decoding to extend DocIDs with query-aligned tokens, even those absent from the pre-indexed vocabulary, enabling plug-and-play DocID expansion beyond the static vocabulary while adding minimal overhead.Experiments on NQ320k and MS-MARCO show that D3 consistently improves retrieval accuracy, especially on unseen and multi-intent documents, across various GR models, including a +2.4%p nDCG@10 gain on the state-of-the-art model.