@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://preview.aclanthology.org/ingest-acl/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."
}