Multi-Hop QA

QA: Question Answering

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16 papers in the last four weeks, up 45% on the four weeks before. 0.2% of all new papers.

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Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
Oct 6, 2026Yang Hong, Yajun Yang, Xin Wang +2Multi-Hop QA LLM Reasoning with Graphs
Tianjin University, Tianjin, China · Beijing Jiaotong University, Beijing, China
T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search
Oct 5, 2026Olga Tsymboi, Ramil Latypov, Aleksandr Medvedev +5Multi-Hop QA Agentic Search
T-Tech
Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents
Oct 5, 2026Mohamed Chenene, Carlos Rosas-Hinostroza, Anastasia Stasenko +2Multi-Hop QA Knowledge Graph Question Answering
PleIAs · Lattice, ENS-PSL, CNRS, Sorbonne Nouvelle · Université Paris Dauphine-PSL +4
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Oct 1, 2026Gianluca Bonifazi, Christopher Buratti, Michele Marchetti +5Multi-Hop QA Retrieval-Augmented Generation
Polytechnic University of Marche, Ancona, Italy · University of Modena and Reggio Emilia, Modena, Italy
PhantomEnvironments: Training LLM Agents in Fictional Worlds
Sep 30, 2026Anmol Kabra, Swathi Saravana Selvam, Albert Gong +5Multi-Hop QA Reinforcement Learning
Cornell University · Stanford University
Corpus-Guided Dual-Path Propagation for Graph Retrieval-Augmented Generation
Sep 29, 2026Baoxian Liu, Tong WeiMulti-Hop QA Graph Retrieval-Augmented Generation
College of Software Engineering, Southeast University, Nanjing 210096, China · Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China · School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
Follow the Entities: A Corpus Map for Agentic Search
Sep 29, 2026Soyeong Jeong, Sujay Kumar Jauhar, Sung Ju Hwang +1Multi-Hop QA Agentic Search
KAIST · Microsoft
REALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing
Sep 29, 2026Jiawen Tao, Xiaokun Yuan, Yaoming Li +4Multi-Hop QA Reasoning Evaluation
Hunyuan Team, Tencent · Peking University
AgentHop: A Diagnostic Benchmark for Agentic Multi-Hop Scientific Question Answering
Sep 28, 2026Chanhee Park, Jeongho Yoon, Sungbin Han +2Multi-Hop QA Tool-Augmented Language Model Agents
Korea University · Sookmyung Women’s University
STITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generation
Sep 28, 2026Haodong Yang, Mengzhu Chen, Jia CaiMulti-Hop QA Retrieval-Augmented Generation
School of Statistics And Data Science, Guangdong University of Finance & Economics Guangzhou, China
Evidence-Inference Reconstruction: When The Evidence Is Recalled But The Reasoning Goes Wrong
Sep 27, 2026Megan Diehl, Ser-Nam LimMulti-Hop QA Evidence-Grounded Reasoning
University of Central Florida
Dr. Free: You Don't Need Difficulty Rewards for Self-Evolving Search Agents
Sep 27, 2026Zhipeng Qian, Zihan Liang, Yufei Ma +10Multi-Hop QA Agentic Search
VCIP, School of Computer Science, Nankai University · Kuaishou Technology · Xiamen University
Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices
Sep 23, 2026Yuze Ren, Shaoheng Fan, Tao Wang +2Multi-Hop QA Question Answering
ZenSmart Technology (Beijing) Co., Ltd., China
Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering
Sep 15, 2026Kevin Mo, Nathan Mo, Richard ZhuMulti-Hop QA Multi-Hop Reasoning
Independent · Northwestern University
Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering
Sep 14, 2026Tharaka D. Fonseka, Niraj K. JhaMulti-Hop QA Knowledge Augmentation for Language Models
Dept. of Electrical and Computer Engineering, Princeton University
Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy
Sep 14, 2026Gengxian Zhou, Jian Xu, Zichen Tang +3Multi-Hop QA Retrieval-Augmented Generation
Beijing University of Posts and Telecommunications · Zhongguancun Academy, Beijing · MAIS, Institute of Automation, Chinese Academy of Sciences +1
MOSAIC: Query-Aware Exploration Policy Adaptation for GraphRAG
Sep 12, 2026EunKyeong Lee, Kyeong-Jin Oh, Jinwon Kim +4Multi-Hop QA Graph Retrieval-Augmented Generation
KT Corporation
LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
Sep 9, 2026Daniel Alejandro Coll Tejeda, Pedro García López, Daniel Barcelona-PonsMulti-Hop QA Retrieval-Augmented Generation
Universitat Rovira i Virgili
The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs
Sep 9, 2026Arquimedes CanedoMulti-Hop QA LLM Grounding
Siemens Digital Industries Software Princeton, NJ, USA
Iris: Climbing to the Search Frontier
Sep 3, 2026Ziyuan Liu, Hengqi Liu, Zichuan Wang +8Multi-Hop QA Web Search Agents
AllSpark Team
Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
Sep 1, 2026Haruto Sato, Yuki Tanaka, Ren Nakamura +2Multi-Hop QA Evidence-Grounded Reasoning
Independent Researcher
ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation
Sep 1, 2026Siyuan Zhang, Hanchen Wang, Dong Wen +2Multi-Hop QA Graph Retrieval-Augmented Generation
University of Technology Sydney · University of New South Wales
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Aug 31, 2026Jun Hyeong Kim, Dongki Kim, Yinhua Piao +1Multi-Hop QA Biomedical QA
KAIST
Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering
Aug 31, 2026Jueun Kim, Sungho Park, Wook-Shin HanMulti-Hop QA Question Decomposition
Department of Computer Science and Engineering, POSTECH · Graduate School of Artificial Intelligence, POSTECH
PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation
Aug 30, 2026Haokun Deng, Xunkai Li, Hongchao Qin +1Multi-Hop QA Retrieval-Augmented Generation
Department of Computer Science, Beijing Institute of Technology
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Aug 23, 2026Jun Chen, Yongchao Liu, Pengyu Qiu +6Multi-Hop QA Agentic RAG
1Shenzhen University, Shenzhen, China · 2Great Bay University, Dongguan, China · 3Ant Group, Hangzhou, China +1
XHotpotQA: A Benchmark for Cross-Lingual Knowledge Composition in Multi-Hop Question Answering
Aug 23, 2026Iman Barati, Arash Ghafouri, Behrouz Minaei-BidgoliMulti-Hop QA Cross-Lingual Reasoning in Language Models
School of Computer Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
Better Decomposition, Free Aggregation: A Synthesizer-Folding Framework for Multilingual Multi-Hop Question Answering
Aug 13, 2026Yilin Wang, Yuchun Fan, Weidong Bao +5Multi-Hop QA Retrieval-Augmented Generation
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China
EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrieval
Aug 13, 2026Xinlong Xu, Yoshua Y. LiMulti-Hop QA Graph-Based Retrieval
Nanjing University of Information Science and Technology · Meituan
SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges
Aug 12, 2026Yuchao Wu, Junqin Li, XingCheng Liang +4Multi-Hop QA Retrieval-Augmented Generation
Zleap AI

Oct 6, 2026, cs.CLJ/K move · Enter open · S save

Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering

Yang Hong, Yajun Yang, Xin Wang, Liping Jing+1

Tianjin University, Tianjin, China · Beijing Jiaotong University, Beijing, China

Multi-Hop QA LLM Reasoning with Graphs Graph-Based Retrieval Knowledge Graph Question Answering

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Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge source for reliable reasoning. However, existing LLM-guided graph reasoning methods typically rely on hop-wise greedy or beam-style pruning during evidence retrieval. Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA). FoG iteratively constructs a question-relevant evidence subgraph and uses far-to-near feedback to guide path exploration, and maintains a compact memory subgraph to support continued exploration. Extensive experiments on widely used KBQA benchmarks demonstrate that FoG achieves state-of-the-art performance, with a particularly large improvement of 16.58% in Hit on CWQ, while also reducing LLM calls and token usage. Our code is available at https://github.com/yhong7/FoG .

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