Multi-Hop Reasoning

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9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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AlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs
Apr 3, 2026Zahra Makki Nayeri, Mohsen RezvaniKG Completion Network Intrusion Detection
Faculty of Computer Engineering at Shahrood University of Technology, Shahrood, Iran.
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
Apr 2, 2026Dat Tran, Douwe KielaMulti-Agent LLM Systems Multi-Agent Systems
Stanford University
GraphWalk: Enabling Reasoning in Large Language Models through Tool-Based Graph Navigation
Apr 2, 2026Taraneh Ghandi, Hamidreza Mahyar, Shachar KlaimanLLM Reasoning with Graphs LLM Tool Use
Faculty of Science, McMaster University · BASF Digital Solutions
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Mar 22, 2026Shuai Wang, Yinan YuLLM Reasoning with Graphs Knowledge Graph Question Answering
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, SE-41296 Gothenburg, Sweden
Reasoning Topology Matters: Network-of-Thought for Complex Reasoning Tasks
Mar 21, 2026Fan HuangLLM Reasoning LLM Reasoning with Graphs
Indiana University Bloomington
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Mar 17, 2026Xiaojie Gu, Sherry T. Tong, Aosong Feng +8LLM Evaluation Multi-Hop Reasoning
The University of Tokyo · Yale University · Stanford University +3
Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs
Mar 14, 2026Hang Gao, Dimitris N. MetaxasGraph Retrieval-Augmented Generation Graph-Based Retrieval
Department of Computer Science, Rutgers University, Piscataway, New Jersey, USA.
TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
Mar 12, 2026Qianggang Ding, Haochen Shi, Luis Castejón Lozano +7Finance LLM Reasoning with Graphs
DIRO & Institut Courtois, Université de Montréal, Montreal, QC, Canada · Mila – Québec AI Institute, Montreal, QC, Canada · Aily Labs, Madrid, Spain +2
Learning from Synthetic Data Improves Multi-hop Reasoning
Mar 2, 2026Anmol Kabra, Yilun Yin, Albert Gong +6RL for Language Model Reasoning Multi-Hop Reasoning
Cornell University · University of Cambridge · Stanford University
Neural-Symbolic Logic Query Answering in Non-Euclidean Space
Feb 25, 2026Lihui LiuNeuro-Symbolic Reasoning Knowledge Graph Question Answering
Wayne State University, Detroit, Michigan, USA
Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
Feb 23, 2026Wuzhenghong Wen, Bowen Zhou, Jinwen Huang +5Knowledge Graph Question Answering Temporal QA
School of Internet of Things, Nanjing University of Posts and Telecommunications · Faculty of Medicine & Health, University of New South Wales, Sydney, Australia · Beijing Information Science and Technology University +2
To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning
Jan 13, 2026Rubing Chen, Jian Wang, Wenjie Li +2Multi-Hop QA Retrieval-Augmented Generation
Department of Computing, The Hong Kong Polytechnic University
Kinship Data Benchmark for Multi-hop Reasoning
Jan 12, 2026Tianda Sun, Dimitar KazakovLLM Evaluation Synthetic Benchmark Generation
Department of Computer Science University of York, UK
CQD-SHAP: Explainable Complex Query Answering via Shapley Values
Oct 17, 2025Parsa Abbasi, Stefan HeindorfNeuro-Symbolic Reasoning Knowledge Graph Question Answering
Department of Computer Science, Paderborn University, Germany
Unveiling the Mechanisms of Multi-Hop Reasoning in Transformers via Identity Bridge
Sep 29, 2025Pengxiao Lin, Zheng-An Chen, Zhi-Qin John XuTransformer Interpretability OOD Generalization
School of Mathematical Sciences, Shanghai Jiao Tong University · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University · Shanghai Seres Information Technology Co., Ltd, Shanghai 200040, China.
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Feb 17, 2025Runxuan Liu, Bei Luo, Jiaqi Li +5LLM Reasoning with Graphs Graph-Based Retrieval
Harbin Institute of Technology, Harbin, China · Beijing University of Posts and Telecommunications, Beijing, China · Joint Laboratory of HIT and iFLYTEK, Beijing, China +2
Is Complex Query Answering Really Complex?
Oct 16, 2024Cosimo Gregucci, Bo Xiong, Daniel Hernandez +4Knowledge Graph Question Answering Knowledge Graphs
Institute for Artificial Intelligence, University of Stuttgart, Germany · Stanford University · School of Informatics University of Edinburgh Edinburgh, UK +1
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Apr 1, 2024Paritosh Parmar, Eric Peh, Ruirui Chen +4Video QA Causal Reasoning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Nanyang Technological University · National University of Singapore +1

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AlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs

Zahra Makki Nayeri, Mohsen Rezvani

Faculty of Computer Engineering at Shahrood University of Technology, Shahrood, Iran.

KG Completion Network Intrusion Detection Multi-Hop Reasoning Knowledge Graph Question Answering

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Cyber-attacks continue to grow in scale and sophistication, yet existing network intrusion detection approaches lack the semantic depth required for path reasoning over attacker-victim interactions. We address this by first modelling network alerts as a knowledge graph, then formulating hyper-relational alert prediction as a hyper-relational knowledge graph completion (HR-KGC) problem, representing each network alert as a qualified statement (h, r, t, Q), where h and t are source and destination IPs, r denotes the attack type, and Q encodes flow-level metadata such as timestamps, ports, protocols, and attack intensity, going beyond standard KGC binary triples (h, r, t) that would discard this contextual richness. We introduce five models across three contributions: first, Hyper-relational Neural Bellman-Ford (HR-NBFNet) extends Neural Bellman-Ford Networks to the hyper-relational setting with qualifier-aware multi-hop path reasoning, while its multi-task variant MT-HR-NBFNet jointly predicts tail, relation, and qualifier-value within a single traversal pass; second, AlertStar fuses qualifier context and structural path information entirely in embedding space via cross-attention and learned path composition, and its multi-task extension MT-AlertStar eliminates the overhead of full knowledge graph propagation; third, HR-NBFNet-CQ extends qualifier-aware representations to answer complex first-order logic queries, including one-hop, two-hop chain, two-anchor intersection, and union, enabling multi-condition threat reasoning over the alert knowledge graph. Evaluated inductively on the Warden and UNSW-NB15 benchmarks across three qualifier-density regimes, AlertStar and MT-AlertStar achieve superior MR, MRR, and Hits@k, demonstrating that local qualifier fusion is both sufficient and more efficient than global path propagation for hyper-relational alert prediction.