The TMEX Benchmark and a Fixed-Topology Multi-Agent Extraction Framework for Building an AI-Ready Topological Magnetism Database

Traditional scientific databases have largely relied on experimental data collected and curated by researchers. As data-driven and AI-assisted research has expanded, large language models and multi-agent systems have made it feasible to construct AI-ready databases by extracting structured information from scientific literature at scale. However, topological-magnetism papers span diverse material systems, experimental methods, and physical descriptions, so deciding which scientific dimensions to preserve and how to organize the evidence still requires domain expertise. Together with physics experts, we develop TMEX (Topological Magnetism EXtraction), an expert benchmark that integrates a domain schema, expert-annotated gold records, and a semantic judge. Guided by TMEX, we iteratively refine a fixed-topology multi-agent workflow that routes single-material and multi-material papers, indexes figure evidence to schema fields, and assigns seven scientific dimensions to parallel specialists. The judge's field-level scores localize extraction errors and guide targeted revisions to the agent topology and prompts. Instantiated on GPT-5.5, the fixed topology reaches 0.924 F1 on TMEX (0.942 precision, 0.910 recall). On Eval 53, with the backbone held fixed, DeepSeek-V4-Pro improves from 0.773 to 0.927 F1 (+15.4) at $0.19 per paper. We release TMEX, the pipeline code, and the TMEX database of 8,082 papers.