AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation

AdaGATE is a training-free evidence controller for multi-hop Retrieval-Augmented Generation (RAG) designed to address noisy or redundant retrieved evidence in limited contexts. It frames evidence selection as a token-constrained repair problem, combining entity-centric gap tracking and targeted micro-query generation to balance coverage, corroboration, and novelty.