MatCurvs: Article Real-Coordinate Curve Extraction for Agent-Ready Materials Reasoning

In materials-science papers, quantitative evidence—XRD peak shifts, Raman ID/IG ratios, high-frequency Z′ intercepts in EIS—lives in line plots. Hosted vision–language models describe these plots fluently but cannot recover the (x, y) values an agent needs to query, compare, or integrate. We release three coupled artifacts. MatCurvs is a real-coordinate chart benchmark for materials-science spectra, organized in three tiers spanning XRD, Raman, XPS, XAFS, EIS, GCD, and CV: 1,375,165 classified subfigures from 55,763 articles (L0); 204 panels with manual per-curve pixel polylines (L1); and 8,026 verified real-coordinate curves over 3,817 panels (L2). MatDeplot is a local extractor that recovers 45.8% of curves within a 5-pixel IoU tolerance against L1 ground truth, versus ≤1.2% for the strongest hosted VLM—a 38× pixel-anchoring gap, closed in roughly 1.5 seconds per image. MatCurvs-Reasoning is a 14,740-question deterministic numerical evaluation: feeding MatDeplot’s extracted (x, y) data to an LLM cuts median relative error from 54% for an image-only VLM to about 4%, and a text LLM with the data alone matches the same VLM prompted with both image and data. By turning each published chart into a numerically queryable curve, the released artifacts let a deployer compute derived materials parameters—Raman ID/IG disorder ratios, XRD Scherrer crystallite sizes, and phase-purity audits—directly from a literature-scale chart corpus, where most papers under-report these numbers and the chart is the only available record.