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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AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
Feb 22, 2026Qijie You, Wenkai Yu, Wentao ZhangMulti-Hop QA LLM Agent Evaluation
University of Science and Technology Beijing · 2Peking University · 3Zhongguancun Academy +1
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Jan 28, 2026Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo +3Multi-Hop QA LLM Reasoning with Graphs
Graphcore Research
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
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Nov 6, 2025Yuanning Cui, Zequn Sun, Wei Hu +1Multi-Hop QA LLM Grounding
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China +2
DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections
Aug 20, 2025Jiwon Park, Seohyun Pyeon, Jinwoo Kim +4Multi-Hop QA Scientific QA
Pohang University of Science and Technology, Pohang-si, South Korea · The University of Melbourne, Melbourne, Australia · The University of Western Australia, Perth, Australia
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
May 20, 2025Ruiyi Yang, Hao Xue, Imran Razzak +3Multi-Hop QA Multi-Agent LLM Systems
University of New South Wales Sydney, NSW, Australia · Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi, UAE · Griffith University Queensland, Australia +1
Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions
Nov 15, 2024Yutao Hou, Yajing Luo, Zhiwen Ruan +4Multi-Hop QA LLM Evaluation
Shanghai University of Finance and Economics · Southern University of Science and Technology · University of Edinburgh +1
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
Oct 22, 2024Xiaochen Wang, Liang Chen, Reza Haf Zhe Yang +5Multi-Hop QA LLM Self-Correction
School of Software & Microelectronics, Peking University · International Digital Economy Academy · National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University +1

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AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

Qijie You, Wenkai Yu, Wentao Zhang

University of Science and Technology Beijing · 2Peking University · 3Zhongguancun Academy+1

Multi-Hop QA LLM Agent Evaluation AI Agent Benchmarks Agentic RAG Agentic Reasoning RAG Evaluation

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With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step interaction, serves as a critical testbed for assessing such capabilities. However, existing benchmarks typically provide only final questions and answers, while lacking the intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query. This limitation prevents researchers from analyzing at which step an agent fails and restricts more fine-grained evaluation of model capabilities. Moreover, most current benchmarks are manually constructed, which is both time-consuming and labor-intensive, while also limiting scalability and generalization. To address these challenges, we introduce AgenticRAGTracer, the first Agentic RAG benchmark that is primarily constructed automatically by large language models and designed to support step-by-step validation. Our benchmark spans multiple domains, contains 1,305 data points, and has no overlap with existing mainstream benchmarks. Extensive experiments demonstrate that even the best large language models perform poorly on our dataset. For instance, GPT-5 attains merely 22.6% EM accuracy on the hardest portion of our dataset. Hop-aware diagnosis reveals that failures are primarily driven by distorted reasoning chains -- either collapsing prematurely or wandering into over-extension. This highlights a critical inability to allocate steps consistent with the task's logical structure, providing a diagnostic dimension missing in traditional evaluations. We believe our work will facilitate research in Agentic RAG and inspire further meaningful progress in this area. Our code and data are available at https://github.com/YqjMartin/AgenticRAGTracer.

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