@inproceedings{lau-etal-2026-breaking,
title = "Breaking the Static Graph: Context-Aware Traversal for Graph-Based {RAG}",
author = "Lau, Kwun Hang and
Zhang, Fangyuan and
Ruan, Boyu and
Zhou, Yingli and
Guo, Qintian and
Zhang, Ruiyuan and
Zhou, Xiaofang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.290/",
pages = "5849--5863",
ISBN = "979-8-89176-395-1",
abstract = "Recent advances in Retrieval-Augmented Generation (RAG) have shifted from simple vector similarity to structure-aware approaches like HippoRAG, which leverage Knowledge Graphs (KGs) and Personalized PageRank (PPR) to capture multi-hop dependencies. However, these methods suffer from a ``Static Graph Fallacy'': fixed transition probabilities set during indexing ignore query-dependent edgerelevance, causing semantic drift where random walks are diverted into high-degree ``hub'' nodes before reaching critical evidence. Models often achieve high partial recall but fail to retrieve the complete evidence chain for multi-hop queries. To address this, we propose CatRAG, Context-Aware Traversal for robust RAG, which builds on the HippoRAG 2 and transforms the static KG into a query-adaptive navigation structure. CatRAG steers the random walk via three mechanisms: (1) Symbolic Anchoring, injecting weak entity constraints to regularize the random walk; (2) QueryAware Dynamic Edge Weighting, dynamically modulating graph structure to prune irrelevant paths and amplify query-aligned ones; and (3) Key-Fact Passage Weight Enhancement, a cost-efficient bias anchoring the walk to key evidence. Experiments across multi-hop benchmarks show that CatRAG outperforms state-of-the-art baselines. While standard Recall gains are modest, CatRAG achieves substantial improvements in reasoning completeness{---}the capacity to recover entire evidence chains without gaps. These results reveal that CatRAG effectively bridges the gap between retrieving partial context and enabling fully grounded reasoning. Resources are available at https://github.com/kwunhang/CatRAG."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lau-etal-2026-breaking">
<titleInfo>
<title>Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kwun</namePart>
<namePart type="given">Hang</namePart>
<namePart type="family">Lau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fangyuan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Boyu</namePart>
<namePart type="family">Ruan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yingli</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qintian</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruiyuan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaofang</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Recent advances in Retrieval-Augmented Generation (RAG) have shifted from simple vector similarity to structure-aware approaches like HippoRAG, which leverage Knowledge Graphs (KGs) and Personalized PageRank (PPR) to capture multi-hop dependencies. However, these methods suffer from a “Static Graph Fallacy”: fixed transition probabilities set during indexing ignore query-dependent edgerelevance, causing semantic drift where random walks are diverted into high-degree “hub” nodes before reaching critical evidence. Models often achieve high partial recall but fail to retrieve the complete evidence chain for multi-hop queries. To address this, we propose CatRAG, Context-Aware Traversal for robust RAG, which builds on the HippoRAG 2 and transforms the static KG into a query-adaptive navigation structure. CatRAG steers the random walk via three mechanisms: (1) Symbolic Anchoring, injecting weak entity constraints to regularize the random walk; (2) QueryAware Dynamic Edge Weighting, dynamically modulating graph structure to prune irrelevant paths and amplify query-aligned ones; and (3) Key-Fact Passage Weight Enhancement, a cost-efficient bias anchoring the walk to key evidence. Experiments across multi-hop benchmarks show that CatRAG outperforms state-of-the-art baselines. While standard Recall gains are modest, CatRAG achieves substantial improvements in reasoning completeness—the capacity to recover entire evidence chains without gaps. These results reveal that CatRAG effectively bridges the gap between retrieving partial context and enabling fully grounded reasoning. Resources are available at https://github.com/kwunhang/CatRAG.</abstract>
<identifier type="citekey">lau-etal-2026-breaking</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.290/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>5849</start>
<end>5863</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG
%A Lau, Kwun Hang
%A Zhang, Fangyuan
%A Ruan, Boyu
%A Zhou, Yingli
%A Guo, Qintian
%A Zhang, Ruiyuan
%A Zhou, Xiaofang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lau-etal-2026-breaking
%X Recent advances in Retrieval-Augmented Generation (RAG) have shifted from simple vector similarity to structure-aware approaches like HippoRAG, which leverage Knowledge Graphs (KGs) and Personalized PageRank (PPR) to capture multi-hop dependencies. However, these methods suffer from a “Static Graph Fallacy”: fixed transition probabilities set during indexing ignore query-dependent edgerelevance, causing semantic drift where random walks are diverted into high-degree “hub” nodes before reaching critical evidence. Models often achieve high partial recall but fail to retrieve the complete evidence chain for multi-hop queries. To address this, we propose CatRAG, Context-Aware Traversal for robust RAG, which builds on the HippoRAG 2 and transforms the static KG into a query-adaptive navigation structure. CatRAG steers the random walk via three mechanisms: (1) Symbolic Anchoring, injecting weak entity constraints to regularize the random walk; (2) QueryAware Dynamic Edge Weighting, dynamically modulating graph structure to prune irrelevant paths and amplify query-aligned ones; and (3) Key-Fact Passage Weight Enhancement, a cost-efficient bias anchoring the walk to key evidence. Experiments across multi-hop benchmarks show that CatRAG outperforms state-of-the-art baselines. While standard Recall gains are modest, CatRAG achieves substantial improvements in reasoning completeness—the capacity to recover entire evidence chains without gaps. These results reveal that CatRAG effectively bridges the gap between retrieving partial context and enabling fully grounded reasoning. Resources are available at https://github.com/kwunhang/CatRAG.
%U https://aclanthology.org/2026.findings-acl.290/
%P 5849-5863
Markdown (Informal)
[Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG](https://aclanthology.org/2026.findings-acl.290/) (Lau et al., Findings 2026)
ACL
- Kwun Hang Lau, Fangyuan Zhang, Boyu Ruan, Yingli Zhou, Qintian Guo, Ruiyuan Zhang, and Xiaofang Zhou. 2026. Breaking the Static Graph: Context-Aware Traversal for Graph-Based RAG. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5849–5863, San Diego, California, United States. Association for Computational Linguistics.