Stateful Iterative Parsing for Global‑Coherent Hierarchical Structure Extraction from Unbounded Documents

Parsing long, unstructured documents into hierarchical machine-readable formats faces a fundamental tension between document length and limited LLM context. Prevailing one-shot or stateless chunk-based methods suffer from the “lost-in-the-middle” phenomenon and fragmented structures. We propose an iterative, stateful editing process. This paper introduces the Iterative Structured Content Filling (ISCF) framework, which incrementally builds a global tree through LLM-driven steps under partial observability. ISCF integrates three pillars: (1) Dynamic Boundary Re-alignment (DBR) for unsupervised chunk optimization via semantic coherence and uncertainty; (2) Adaptive Structural Masking & Hierarchical-Aware Path Embedding (HAPE) for efficient global awareness within finite context; (3) Bijective Path-Content Mapping ensuring near-lossless reconstruction. Efficiency analysis shows ISCF's latency is lower than sliding-window baselines. Extensive experiments on financial, legal, scientific, and news documents demonstrate superior accuracy and robustness.