Challenge: Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning .
Approach: They propose a synergistic dual-process framework that integrates reasoning and retrieval.
Outcome: The proposed framework improves answer accuracy and coherence even in smaller-scale models.

Similar Papers

HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation (2025.findings-acl)

Copied to clipboard

Challenge: Traditional retrieval systems focus on lexical or semantic similarity rather than logical relevance.
Approach: They propose a new RAG framework that augments retrieval with logical reasoning . hopRAG uses a retrieve-reason-prune mechanism to explore multi-hop neighbors .
Outcome: The proposed framework outperforms conventional retrieval systems and state-of-the-art benchmarks on multi-hop QA tasks.
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)

Copied to clipboard

Challenge: Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions.
Approach: They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result.
Outcome: The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision.
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning (2025.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to retrievalaugmented generation (RAG) are limited when applied to heterogeneous documents . flattening tables and chunking strategies disrupt tabular structure, leads to information loss, and undermines reasoning capabilities of LLMs in multi-hop, global queries.
Approach: They propose a SQL-based framework that unifies textual understanding and complex manipulations over tabular data.
Outcome: The proposed framework outperforms baselines on public datasets and HeteQA on heterogeneous document question answering.
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation (2026.acl-long)

Copied to clipboard

Challenge: Existing KG-RAG systems collapse all reasoning hops into a single representation, flat embedding space, suppressing this implicit structure and causing noisy or drifted path exploration.
Approach: They propose a symmetric multi-view framework that decouples queries and KGs into aligned, head-specific retrieval spaces.
Outcome: The proposed framework achieves state-of-the-art retrieval and QA performance on WebQSP and CWQ, and significantly reduces hallucination.
FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)

Copied to clipboard

Challenge: Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation.
Approach: They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning.
Outcome: Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning.
LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025.coling-main)

Copied to clipboard

Challenge: Existing approaches to multi-hop question answering emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information.
Approach: They propose a Local-tO-Global optimized retrieval method to discover more beneficial information and improve tuplet objective loss.
Outcome: The proposed method outperforms state-of-the-art models and significantly improves multi-hop reasoning.
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)

Copied to clipboard

Challenge: Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results.
Approach: They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model.
Outcome: The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model .
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)

Copied to clipboard

Challenge: Empirical results show that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.
Approach: They propose a method which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation.
Outcome: The proposed method outperforms baselines on three multi-hop QA datasets.
CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026.acl-long)

Copied to clipboard

Challenge: Existing methods for iterative retrieval-augmented generation (iRAG) suffer from greedy single-path expansion and granularity–demand mismatch .
Approach: They propose a model that constructs candidate triples and history-conditionally integrates them to distill core triples to generate the next-hop query.
Outcome: The proposed model mitigates the greedy single-path expansion and granularity–demand mismatch by preserving multiple plausible evidence chains.
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering (2024.acl-long)

Copied to clipboard

Challenge: Existing approaches to solve multi-hop question are constrained by the retriever and the noise in the retrieved documents.
Approach: They propose a framework that integrates parametric knowledge of large language models with external documents to solve a multi-hop question.
Outcome: The proposed framework is based on the parametric knowledge of LLMs and external documents to solve a multi-hop question.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations