REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering
Yijie Zhu, Haojie Zhou, Wanting Hong, Tailin Liu, Ning Wang
Abstract
Retrieval-augmented generation (RAG) has been extensively employed to mitigate hallucinations in large language models (LLMs). However, existing methods for multi-hop reasoning tasks often lack global planning, increasing the risk of falling into local reasoning impasses. Insufficient exploitation of retrieved content and the neglect of latent clues fail to ensure the accuracy of reasoning outcomes. To overcome these limitations, we propose **R**ecursive **E**valuation and **A**daptive **P**lanning (REAP), whose core idea is to explicitly maintain structured sub-tasks and facts related to the current task through the Sub-task Planner (SP) and Fact Extractor (FE) modules. SP maintains a global perspective, guiding the overall reasoning direction and evaluating the task state based on the outcomes of FE, enabling dynamic optimization of the task-solving trajectory. FE performs fine-grained analysis over retrieved content to extract reliable answers and clues. These two modules incrementally enrich a logically coherent representation of global knowledge, enhancing the reliability and the traceability of the reasoning process. Furthermore, we propose a unified task paradigm design that enables effective multi-task fine-tuning, significantly enhancing SP
BibTeX
@inproceedings{aaai2026_reapenhancingrag,
title = {REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering},
author = {Yijie Zhu and Haojie Zhou and Wanting Hong and Tailin Liu and Ning Wang},
booktitle = {AAAI 2026},
year = {2026}
}