Rilo: Query Planning with Latent Relational Operators for Multi-Hop RAG

Multi-hop retrieval requires connecting evidence across documents and deciding where to search next. The scale and sparsity of the surface-relation space complicate direct planning of multi-hop retrieval. We introduce RILO, a retrieval planner that learns a compact vocabulary of composable latent relational operators. These operators capture transformations shared across surface relations and are trained on corpus-derived paths to predict evidence endpoints. Given a question and retrieved evidence, RILO composes the operators into candidate multi-step plans. Each plan and its predicted endpoint guide a single corpus query without online graph traversal. A budget-aware controller selects the next query or stops based on expected evidence gain. RILO then updates its evidence state and replans as new passages arrive. In controlled evaluations on 2WikiMultiHopQA, HotpotQA, and MuSiQue, RILO achieves 73.10% macro complete-support coverage within the top ten passages (SC@10). With at most three corpus calls, it exceeds the strongest evaluated baseline by 3.86 percentage points.