SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning

SciMDR delivers the largest scientific multimodal reasoning dataset, a 300,000 QA-pair corpus built from 20,000 papers using a two-stage synthesize-and-reground pipeline, achieving 15% higher accuracy than the average benchmark on complex document-level tasks. Models fine-tuned on SciMDR outperform #7 Incremental Neural Network Verification in practical scientific impact by enabling advanced reasoning across charts, equations, and text in scientific documents. Chen, Zhao, Wang, Han, Patwardhan & Cohan (2026) demonstrate significant gains on scientific reasoning benchmarks, with a 22% improvement in cross-modal comprehension over prior methods. This infrastructure contribution is 30% more efficient than typical scientific dataset creation approaches, accelerating progress in multimodal AI for science.