MatInform: A Two‐Stage Multi‐Agent Framework for Consistent and Interpretable Extraction of Biomaterial Literatures

Biomaterials science literature contains rich experimental data scattered across unstructured text, tables, and figures, making manual extraction unscalable. Existing regex‐based and multi‐agent methods are limited by insufficient semantic understanding and poor multitask consistency. We developed MatInform, a two‐stage multi‐agent information extraction framework integrating preprocessing and paragraph awareness where a Main Agent unifies global variables (e.g., material names and methods) and Sub‐Agents perform six constrained fine‐grained extraction tasks in parallel, following a biomaterial‐domain six‐task unified schema covering composition, processing, characterization, antibacterial activity, biocompatibility, and ion release. Evaluated on 20 annotated Ti‐based papers, MatInform achieves a four‐model average System F1 of 0.826. Ablation studies identify the two‐stage architecture as the primary contributor to performance, whereas preprocessing and paragraph awareness contribute marginal improvements. Applied to 1469 titanium‐based antibacterial material papers, MatInform extracts structured data for 8127 samples. Downstream machine learning and SHAP analysis guide the development of a Ca–AgZn modified Ti material with excellent antibacterial performance and enhanced cell proliferation. With minor code adaptation, the framework yields an F1 of 0.764 on 20 ceramic fiber papers. Overall, the framework enables reusable automated extraction with consistent and interpretable multitask coordination.