@inproceedings{uniyal-etal-2026-agentic,
    title = "Agentic Context Strategies for Multi-Format Document Understanding: When Should Language Models Use Tools?",
    author = "Uniyal, Mansi  and
      Singh, Mukul  and
      Nadel, Ryan",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.133/",
    pages = "1956--1966",
    ISBN = "979-8-89176-394-4",
    abstract = "Large language models face fundamental trade-offs when processing long documents: full context is expensive and may exceed limits, while RAG risks missing relevant information. We evaluate four context strategies across six frontier models on three document formats (Word, Excel, and PowerPoint). Our key finding: agentic tool-augmented approaches dramatically outperform passive strategies, with RAG+Tools achieving 46{\%} accuracy vs 6{\%} for RAG-only. Tool benefits are consistent across formats (+28-40 points) and models. We further show that (1) intelligent routing matters more than iteration count, (2) tools provide unique capability beyond reasoning loops, and (3) forcing active exploration matches providing context proactively. These results suggest tool augmentation is crucial for complex document QA."
}