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

TL;DR

AdaGATE is a novel evidence controller for multi-hop retrieval-augmented generation that explicitly repairs missing facts and balances relevance, redundancy, and gap coverage, improving robustness and efficiency.

Contribution

It introduces AdaGATE, a training-free, gap-aware evidence selection method that enhances multi-hop RAG performance under noisy and limited retrieval conditions.

Findings

Achieves highest evidence F1 scores across all tested conditions.

Uses 2.6x fewer tokens than Adaptive-k in evidence selection.

Improves robustness of multi-hop RAG with explicit gap-aware repair.

Abstract

Retrieval-augmented generation (RAG) remains brittle on multi-hop questions in realistic deployment settings, where retrieved evidence may be noisy or redundant and only limited context can be passed to the generator. Existing controllers address parts of this problem, but typically either expand context additively, select from a fixed top-k set, or optimize relevance without explicitly repairing missing bridge facts. We propose AdaGATE, a training-free evidence controller for multi-hop RAG that frames evidence selection as a token-constrained repair problem. AdaGATE combines entity centric gap tracking, targeted micro-query generation, and a utility based selection mechanism that balances gap coverage, corroboration, novelty, redundancy, and direct question relevance. We evaluate AdaGATE on HotpotQA under clean, redundancy, and noise injected retrieval conditions. Across all three settings, AdaGATE achieves the best evidence F1 among the compared controllers, reaching 62.3% on clean data and 71.2% under redundancy injection, while using 2.6x fewer input tokens than Adaptive-k. These results suggest that explicit gap-aware repair, combined with token-efficient evidence selection, improves robustness in multi-hop RAG under imperfect retrieval. Our code and evaluation pipeline are available at https://github.com/eliguo/AdaGATE.