EviPort: From Passage Relevance to Evidence Sufficiency in Budgeted Multi-Hop Retrieval

Retrieval-augmented generation (RAG) grounds Large Language Models (LLMs) in external evidence, but multi-hop questions require a complementary set of passages rather than several individually relevant ones. Under a tight context budget, one-shot dense retrieval can repeatedly retrieve evidence for the same readily retrievable subproblem while missing semantically distant bridge or terminal facts. Existing multi-hop approaches improve evidence discovery through iterative retrieval, query decomposition, or structured corpus reasoning, but generally do not explicitly allocate limited context slots across dependent evidence needs. We introduce EviPort, a framework that represents multi-hop questions as dependency-aware evidence ports without task-specific training or corpus graph construction. Passage-derived answers ground downstream ports, enabling each evidence need to trigger an independent full-corpus search before evidence assembly under a fixed reader budget. Across HotpotQA, 2WikiMultiHopQA, and MuSiQue, EviPort consistently improves both evidence retrieval and downstream question answering over strong dense, iterative, and graph-based baselines. These results support framing multi-hop retrieval as budgeted evidence-set construction rather than independent passage ranking.