@inproceedings{lee-etal-2026-agentic,
    title = "Agentic Verification for Ambiguous Query Disambiguation",
    author = "Lee, Youngwon  and
      Hwang, Seung-won  and
      Wu, Ruofan  and
      Yan, Feng  and
      Xu, Danmei  and
      Akkad, Moutasem  and
      Yao, Zhewei  and
      He, Yuxiong",
    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.1932/",
    pages = "38781--38796",
    ISBN = "979-8-89176-395-1",
    abstract = "We study ambiguous-query disambiguation in retrieval-augmented generation (RAG). Prior Diversify-then-Verify (DtV) pipelines first generate interpretations and then retrieve evidence, often introducing ungrounded queries that cannot be answered from the corpus and requiring costly post-hoc pruning and verification. We propose VerDICT, a novel approach that unifies diversification with verification by integrating retriever relevance and generator answerability feedback early. This not only reduces cascading errors but also enables parallelism. On ASQA, VerDICT improves grounding-aware F1 by an average of 23{\%} over the strongest baselines across multiple LLM backbones."
}