Papers with Multi-hop reasoning
AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks provide only final questions and answers, while lacking intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query. |
| Approach: | They propose to build a multi-hop reasoning model that is primarily constructed automatically by large language models and designed to support step-by-step validation. |
| Outcome: | The proposed benchmark spans multiple domains, contains 1,305 data points, and has no overlap with existing mainstream benchmarks. |
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. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
Copied to clipboard
| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)
Copied to clipboard
| Challenge: | Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation. |
| Approach: | They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs. |
| Outcome: | The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance. |
SCE: Semantic Consistency Enhanced Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multihop reasoning fail to address the problem of spurious paths . existing approaches neglect the internal semantic consistency of the reward function . |
| Approach: | They propose a framework that incorporates semantic consistency into the reward function to guide multi-hop reasoning. |
| Outcome: | The proposed framework outperforms baseline methods and facilitates more interpretable reasoning paths. |
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context (2025.findings-naacl)
Copied to clipboard
| Challenge: | Multi-hop reasoning requires multi-step reasoning based on supporting documents within a given context. |
| Approach: | They propose a method that prompts the model by repeatedly presenting the context. |
| Outcome: | The proposed method improves the F1 score by 30%p on multi-hop QA tasks and increases accuracy by 70%p on a synthetic task. |
Multi-Hop Knowledge Graph Reasoning with Reward Shaping (D18-1)
Copied to clipboard
| Challenge: | Multi-hop reasoning is an effective approach for query answering over incomplete knowledge graphs (KGs). |
| Approach: | They propose to adopt a pretrained one-hop embedding model to estimate reward of unobserved facts and to force agents to explore diverse set of paths using randomly generated edge masks. |
| Outcome: | The proposed model reduces false negative supervision and counters spurious search trajectories by forcing the agent to explore a diverse set of paths using randomly generated edge masks. |
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering (2021.naacl-main)
Copied to clipboard
| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
| Approach: | They propose a method that retrieves and reranks evidence facts jointly . they propose to account for links between sentences and coverage with the given query . |
| Outcome: | The proposed approach achieves state-of-the-art evidence retrieval performance on two multi-hop question answering datasets. |
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature. |
| Approach: | They propose a multi-hop reasoning method that injects high quality symbolic rules into the model's reasoning process and employs partially random beam search. |
| Outcome: | The proposed method outperforms existing multi-hop reasoning methods in terms of Hit@1 and MRR. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable . |
| Approach: | They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark . |
| Outcome: | The proposed framework outperforms existing models in terms of performance and interpretability. |
Reasoning with Memory: Adaptive Information Management for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Multi-hop reasoning remains a fundamental challenge for Retrieval-Augmented Generation systems. |
| Approach: | They propose a framework that provides a dynamic cognitive workspace for multi-hop reasoning . it uses an explicit working memory that persists across retrieval cycles and is continuously updated . |
| Outcome: | The proposed framework achieves state-of-the-art performance over existing systems on eight QA benchmarks. |