@inproceedings{hui-etal-2025-okralong,
    title = "{O}kra{L}ong: A Flexible Retrieval-Augmented Framework for Long-Text Question Answering",
    author = "Hui, Yulong  and
      Liu, Yihao  and
      Lu, Yao  and
      Zhang, Huanchen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.890/",
    doi = "10.18653/v1/2025.findings-emnlp.890",
    pages = "16395--16409",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) encounter challenges in efficiently answering long-text questions, as seen in applications like enterprise document analysis and financial report comprehension. While conventional solutions employ long-context processing or Retrieval-Augmented Generation (RAG), they suffer from prohibitive input expenses or incomplete information. Recent advancements adopt context compression and dynamic retrieval loops, but still sacrifice critical details or incur iterative costs. To address these limitations, we propose OkraLong, a novel framework that flexibly optimizes the entire processing workflow. Unlike prior static or coarse-grained adaptive strategies, OkraLong adopts fine-grained orchestration through three synergistic components: analyzer, organizer and executor. The analyzer characterizes the task states, which guide the organizer in dynamically scheduling the workflow. The executor carries out the execution and generates the final answer. Experimental results demonstrate that OkraLong not only enhances answer accuracy by 5.7{\%}-41.2{\%}, but also achieves cost savings of 1.3x-4.7x."
}