REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2511.09966v1
• Date: Fri, 14 Nov 2025 01:22:32 GMT
• Title: REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering
• Authors: Yijie Zhu, Haojie Zhou, Wanting Hong, Tailin Liu, Ning Wang,
• Abstract summary: Sub-task Planner (SP) and Fact Extractor (FE) are developed.<n>SP maintains a global perspective, guiding the overall reasoning direction and evaluating the task state.<n>FE performs fine-grained analysis over retrieved content to extract reliable answers and clues.
• Score: 3.547564482333453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Recursive Evaluation and Adaptive Planning (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's performance on complex, data-scarce tasks. We conduct extensive experiments on multiple public multi-hop datasets, and the results demonstrate that our method significantly outperforms existing RAG methods in both in-domain and out-of-domain settings, validating its effectiveness in complex multi-hop reasoning tasks.

Related papers

• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuning [82.925106913459]
  Reinforcement finetuning (RFT) is a key technique for aligning Large Language Models (LLMs) with human preferences and enhancing reasoning.<n>We introduce BOTS, a unified framework for Bayesian Online Task Selection in RFT reinforcement finetuning.
  arXiv  Detail & Related papers  (2025-10-30T11:15:23Z)
• HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search [85.12447821237045]
  HiRA is a hierarchical framework that separates strategic planning from specialized execution.<n>Our approach decomposes complex search tasks into focused subtasks, assigns each subtask to domain-specific agents equipped with external tools and reasoning capabilities.<n> Experiments on four complex, cross-modal deep search benchmarks demonstrate that HiRA significantly outperforms state-of-the-art RAG and agent-based systems.
  arXiv  Detail & Related papers  (2025-07-03T14:18:08Z)
• RISE: Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answering [25.84802288928995]
  We propose RISE:Reasoning Enhancement via Iterative Self-Exploration, a novel framework designed to enhance models' reasoning capability.<n>It involves three key steps in addressing MHQA tasks: question decomposition, retrieve-then-read, and self-critique.<n>Experiments on multiple MHQA benchmarks demonstrate that RISE significantly improves reasoning accuracy and task performance.
  arXiv  Detail & Related papers  (2025-05-28T03:48:28Z)
• TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs [50.820065021136024]
  DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs)<n>Recent methods have attempted to replicate R1's reasoning capabilities in multimodal settings.<n>We propose TACO, a novel reinforcement learning algorithm for visual reasoning.
  arXiv  Detail & Related papers  (2025-05-27T06:30:48Z)
• DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering [57.875992666888855]
  Multi-Hop Question Answering (MHQA) tasks pose challenges in orchestrating multi-step reasoning across diverse knowledge domains.<n>We propose DualRAG, a synergistic dual-process framework that seamlessly integrates reasoning and retrieval.
  arXiv  Detail & Related papers  (2025-04-25T10:43:53Z)
• Credible Plan-Driven RAG Method for Multi-Hop Question Answering [2.5772544412212985]
  We propose PAR-RAG (Plan-then-Act-and-Review RAG), a novel framework inspired by the PDCA (Plan-Do-Check-Act) cycle.<n>Par-RAG selects exemplars matched by the semantic complexity of the current question to guide complexity-aware top-down planning.<n>A dual-verification mechanism evaluates and corrects intermediate errors, ensuring that the reasoning process remains factually grounded.
  arXiv  Detail & Related papers  (2025-04-23T15:03:17Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework.<n>It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge.<n>The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds [25.30163649182171]
  In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences.<n>We propose the procedurally generated Markov Decision Processes, named AnyMDP.<n>Through a carefully designed randomization process, AnyMDP is capable of generating high-quality tasks on a large scale while maintaining relatively low structural biases.<n>Our results demonstrate that, with a sufficiently large scale of AnyMDP tasks, the proposed model can generalize tasks that were not considered in the training set through versatile in-context learning paradigms.
  arXiv  Detail & Related papers  (2025-02-05T03:59:13Z)
• Understanding the Design Decisions of Retrieval-Augmented Generation Systems [7.10184268156888]
  Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities.<n>We present the first comprehensive study of three universal RAG deployment decisions.
  arXiv  Detail & Related papers  (2024-11-29T04:25:31Z)
• Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks [68.49251303172674]
  State-of-the-art large language models (LLMs) exhibit impressive problem-solving capabilities but may struggle with complex reasoning and factual correctness. Existing methods harness the strengths of chain-of-thought and retrieval-augmented generation (RAG) to decompose a complex problem into simpler steps and apply retrieval to improve factual correctness. We introduce Critic-guided planning with Retrieval-augmentation, CR-Planner, a novel framework that leverages fine-tuned critic models to guide both reasoning and retrieval processes through planning.
  arXiv  Detail & Related papers  (2024-10-02T11:26:02Z)
• Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning [94.76546523689113]
  We introduce CodePlan, a framework that generates and follows textcode-form plans -- pseudocode that outlines high-level, structured reasoning processes. CodePlan effectively captures the rich semantics and control flows inherent to sophisticated reasoning tasks. It achieves a 25.1% relative improvement compared with directly generating responses.
  arXiv  Detail & Related papers  (2024-09-19T04:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.