@inproceedings{he-etal-2026-trace,
    title = "{TRACE}: Traversal Retrieval-Augmented Chain of Evidence for Document Understanding",
    author = "He, Liqi  and
      Li, Zuchao  and
      Huang, Hao  and
      Wang, Ping",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.445/",
    pages = "9806--9825",
    ISBN = "979-8-89176-390-6",
    abstract = "Early Long-context Document Visual Question Answering (DocVQA) methods struggle with preserving visual semantics or handling finite context windows. Conversely, recent RAG-based approaches suffer from ``semantic gaps'' and ``structural disconnections'' due to passive retrieval mechanisms that ignore logical dependencies. To address these challenges, we introduce TRACE (Traversal Retrieval-Augmented Chain of Evidence). By navigating a Bi-Layered Graph that encodes both physical adjacency and semantic relevance, TRACE transforms retrieval from static matching into adaptive evidence chain construction. Furthermore, we propose M5BookVQA, a benchmark designed to assess deep, multi-hop reasoning in books, addressing the limitations of existing datasets. Extensive experiments show that TRACE achieves an average accuracy improvement of 14.07{\%} on M5BookVQA and exhibits robust generalization with a 13.38{\%} gain across four established benchmarks. Our source code is available at https://github.com/shimurenhlq/TRACE."
}