@inproceedings{ji-etal-2026-retrieval,
title = "Retrieval{--}Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends",
author = "Ji, Yuelyu and
Li, Zhuochun and
Meng, Rui and
He, Daqing",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.7/",
pages = "84--103",
ISBN = "979-8-89176-478-1",
abstract = "Multi-hop question answering (QA) requires systems to iteratively retrieve evidence and reason across multiple hops. While recent RAG and agentic methods report strong results, the underlying retrieval{--}reasoning process is often left implicit, making procedural choices hard to compare across model families. This survey takes the execution procedure as the unit of analysis and introduces a four-axis framework covering overall execution plan, index structure, next-step control strategies and triggers, and stop/continue criteria. Using this schema, we map representative multi-hop QA systems and synthesize reported ablations and empirical tendencies on standard benchmarks, including HotpotQA, 2WikiMultiHopQA, and MuSiQue. We highlight recurring trade-offs among effectiveness, efficiency, and evidence faithfulness, and conclude with open challenges for retrieval{--}reasoning agents, including structure-aware planning, transferable control policies, and robust stopping under distribution shift."
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<abstract>Multi-hop question answering (QA) requires systems to iteratively retrieve evidence and reason across multiple hops. While recent RAG and agentic methods report strong results, the underlying retrieval–reasoning process is often left implicit, making procedural choices hard to compare across model families. This survey takes the execution procedure as the unit of analysis and introduces a four-axis framework covering overall execution plan, index structure, next-step control strategies and triggers, and stop/continue criteria. Using this schema, we map representative multi-hop QA systems and synthesize reported ablations and empirical tendencies on standard benchmarks, including HotpotQA, 2WikiMultiHopQA, and MuSiQue. We highlight recurring trade-offs among effectiveness, efficiency, and evidence faithfulness, and conclude with open challenges for retrieval–reasoning agents, including structure-aware planning, transferable control policies, and robust stopping under distribution shift.</abstract>
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%0 Conference Proceedings
%T Retrieval–Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends
%A Ji, Yuelyu
%A Li, Zhuochun
%A Meng, Rui
%A He, Daqing
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F ji-etal-2026-retrieval
%X Multi-hop question answering (QA) requires systems to iteratively retrieve evidence and reason across multiple hops. While recent RAG and agentic methods report strong results, the underlying retrieval–reasoning process is often left implicit, making procedural choices hard to compare across model families. This survey takes the execution procedure as the unit of analysis and introduces a four-axis framework covering overall execution plan, index structure, next-step control strategies and triggers, and stop/continue criteria. Using this schema, we map representative multi-hop QA systems and synthesize reported ablations and empirical tendencies on standard benchmarks, including HotpotQA, 2WikiMultiHopQA, and MuSiQue. We highlight recurring trade-offs among effectiveness, efficiency, and evidence faithfulness, and conclude with open challenges for retrieval–reasoning agents, including structure-aware planning, transferable control policies, and robust stopping under distribution shift.
%U https://aclanthology.org/2026.groundlm-1.7/
%P 84-103
Markdown (Informal)
[Retrieval–Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trends](https://aclanthology.org/2026.groundlm-1.7/) (Ji et al., GroundLM 2026)
ACL