Title: CycleIE: Robust Document Information Extraction through Iterative Verification and Refinement
Primary area: interpretability and explainable AI
Keywords: Information Extraction, Document Question Answering, Iterative Reasoning

Abstract: Structured information extraction (IE) from complex documents is critical for high-stakes analytics in domains like finance, law, and healthcare. Although large language models (LLMs) have significantly advanced IE, existing one-pass extraction methods often produce incomplete, inconsistent, or structurally insufficient results due to their inability to perform verification or iterative refinement. To overcome these limitations, we propose CycleIE, a novel iterative IE framework explicitly designed for robust verification and adaptive refinement of extracted data. CycleIE uniquely integrates Reactive Reasoning (ReAct) with Monte Carlo Tree Search (MCTS), creating systematic iterative feedback loops that dynamically identify and correct extraction errors, such as missing or inconsistent data. Extensive evaluations on challenging document-based QA benchmarks confirm that CycleIE substantially surpasses conventional one-pass methods, delivering over 10% relative improvements in extraction quality, especially in scenarios involving lengthy or multi-document contexts.