BindRAG: Binding-Aware Retrieval For Multi-Hop Question Answering

Large language models (LLMs) often struggle with knowledge-intensive questions that require retrieving and combining evidence across multiple reasoning steps, often from different documents. Retrieval-augmented generation (RAG) mitigates this limitation by providing external evidence, but conventional retrieval is poorly suited to multi-hop questions where evidence needed at a later step depends on an intermediate answer discovered earlier. Decomposition and iterative retrieval methods address this by generating follow-up queries step by step, but they typically leave the dependency on intermediate answers implicit or commit to a single intermediate answer, allowing early errors to propagate to later retrieval. We present BindRAG, a multi-hop retrieval framework that explicitly binds intermediate answers to downstream retrieval queries, making sequential dependencies directly executable. BindRAG further maintains a query-aware memory that preserves alternative bindings and hop-structured evidence while retrieval proceeds. The highest-ranked binding advances the primary retrieval path, while alternative candidates and their supporting evidence are retained for final resolution. This allows uncertain intermediate decisions to be reconsidered without repeatedly changing the retrieval trajectory. Across HotpotQA, MuSiQue, and 2WikiMultiHopQA, BindRAG improves answer accuracy by an average of 10.3 percentage points over the strongest baseline.