@inproceedings{lee-etal-2026-eair,
    title = "{EAIR}: Entity-aware Inference-Time Knowledge Routing for Multi-Hop Knowledge Editing",
    author = "Lee, Jungyu  and
      Lee, Kunhui  and
      Lee, Gyun  and
      Na, Seung-Hoon",
    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.873/",
    pages = "17622--17645",
    ISBN = "979-8-89176-395-1",
    abstract = "Existing in-context editing (ICE) methods for multi-hop knowledge editing commonly suffer from paraphrase sensitivity, which refers to the phenomenon where these methods are not sufficiently robust to paraphrased multi-hop questions. To improve retrieval accuracy and knowledge routing to address paraphrase sensitivity, this paper proposes a novel entity-aware inference-time knowledge routing method, referred to as EAIR, which consists of four major steps: 1) Entity-referential query decomposition, which decomposes the original question into multiple entity-referential sub-question instructions; 2) Entity-aware retrieval, which leverages the previously reference-resolved topic entity in the retrieval step; 3) Evidence-conditioned contrastive decoding, which discourages the model from relying on its parametric knowledge and steers the model toward following retrieved edits; 4) Reflection-based knowledge routing, which additionally filters decoding results using refusal-style reflection to mitigate the risk introduced by contrastive decoding. Experimental results across the MQuAKE benchmark family and model scales show that EAIR achieves the highest strict case accuracy in 11 of 12 settings, substantially reducing paraphrase sensitivity."
}