@inproceedings{xu-etal-2026-stage,
title = "{STAGE}-{RAG}: Continuous Entity State Evolution over Dual-Space Subgraphs for Multi-Hop Reasoning",
author = "Xu, Xinran and
Chen, Shaowen and
Ma, Yu and
Lin, Fulin and
Wu, Zhaofen and
Wang, Hongwei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.228/",
pages = "4792--4812",
ISBN = "979-8-89176-462-0",
abstract = "Maintaining logical coherence in multi-hop Retrieval-Augmented Generation (RAG) remains a challenging problem, as systems must integrate fragmented evidence without suffering reasoning drift. Iterative frameworks enhance information coupling through query refinement, yet often depend on lossy compressed representations that obscure subtle semantics. Conversely, global graph-based methods ensure structural connectivity but carry topological noise and high computational costs. To overcome these issues, we propose STAGE-RAG, a STAte-aware Graph Evolution framework that conceptualizes multi-hop reasoning as continuous Bayesian entity-state evolution on a bi-relational chunk subgraph. By tracking entity states composed of identities and dynamic belief scores, our method implicitly guides reasoning via state-weighted propagation. A dual-path retrieval strategy constructs local subgraphs to prune irrelevant nodes while retaining fine-grained logical dependencies. Evaluations on HotpotQA, MuSiQue, 2WikiMultiHopQA and MoreHopQA indicate that STAGE-RAG consistently surpasses state-of-the-art baselines in both accuracy and efficiency."
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<abstract>Maintaining logical coherence in multi-hop Retrieval-Augmented Generation (RAG) remains a challenging problem, as systems must integrate fragmented evidence without suffering reasoning drift. Iterative frameworks enhance information coupling through query refinement, yet often depend on lossy compressed representations that obscure subtle semantics. Conversely, global graph-based methods ensure structural connectivity but carry topological noise and high computational costs. To overcome these issues, we propose STAGE-RAG, a STAte-aware Graph Evolution framework that conceptualizes multi-hop reasoning as continuous Bayesian entity-state evolution on a bi-relational chunk subgraph. By tracking entity states composed of identities and dynamic belief scores, our method implicitly guides reasoning via state-weighted propagation. A dual-path retrieval strategy constructs local subgraphs to prune irrelevant nodes while retaining fine-grained logical dependencies. Evaluations on HotpotQA, MuSiQue, 2WikiMultiHopQA and MoreHopQA indicate that STAGE-RAG consistently surpasses state-of-the-art baselines in both accuracy and efficiency.</abstract>
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%0 Conference Proceedings
%T STAGE-RAG: Continuous Entity State Evolution over Dual-Space Subgraphs for Multi-Hop Reasoning
%A Xu, Xinran
%A Chen, Shaowen
%A Ma, Yu
%A Lin, Fulin
%A Wu, Zhaofen
%A Wang, Hongwei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-stage
%X Maintaining logical coherence in multi-hop Retrieval-Augmented Generation (RAG) remains a challenging problem, as systems must integrate fragmented evidence without suffering reasoning drift. Iterative frameworks enhance information coupling through query refinement, yet often depend on lossy compressed representations that obscure subtle semantics. Conversely, global graph-based methods ensure structural connectivity but carry topological noise and high computational costs. To overcome these issues, we propose STAGE-RAG, a STAte-aware Graph Evolution framework that conceptualizes multi-hop reasoning as continuous Bayesian entity-state evolution on a bi-relational chunk subgraph. By tracking entity states composed of identities and dynamic belief scores, our method implicitly guides reasoning via state-weighted propagation. A dual-path retrieval strategy constructs local subgraphs to prune irrelevant nodes while retaining fine-grained logical dependencies. Evaluations on HotpotQA, MuSiQue, 2WikiMultiHopQA and MoreHopQA indicate that STAGE-RAG consistently surpasses state-of-the-art baselines in both accuracy and efficiency.
%U https://aclanthology.org/2026.findings-emnlp.228/
%P 4792-4812
Markdown (Informal)
[STAGE-RAG: Continuous Entity State Evolution over Dual-Space Subgraphs for Multi-Hop Reasoning](https://aclanthology.org/2026.findings-emnlp.228/) (Xu et al., Findings 2026)
ACL