SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding

Scientific papers require models to integrate evidence across text, equations, figures, tables, code, and datasets while preserving its provenance. Existing benchmarks typically assess these capabilities separately, making it difficult to determine how well models support realistic scientific-reading workflows. We introduce SCIDOCBENCH, a workflow-centered benchmark containing 124 expert-authored and difficulty-screened questions across seven research-assistant capability groups, 19 subtasks, and five scientific domains. Each question is instantiated in four matched settings formed by pairing its bilingual variants with the All Images First and Markdown Interleaved document representations, yielding 496 evaluation instances. The strongest evaluated model, Claude-Opus-5, scores 62.6 out of 100, with remaining gaps in evidence localization, structured information extraction, cross-document synthesis, and robustness to document representation. To convert these diagnostics into scalable training signals, we introduce SCIDOCIR, a structured representation of scientific document objects, layout and cross-reference relations, and provenance. Using SCIDOCIR, we construct SCIDOCDATASET, which contains 4K supervised fine-tuning instances and 10K reinforcement-learning instances, built on 14 verifiable training subtasks. Posttraining Qwen3.6-27B on task-aligned data improves its SCIDOCBENCH score from 40.03 to 45.33 with supervised fine-tuning and to 45.74 with subsequent reinforcement learning. Both adapted models preserve DocVQA and InfoVQA performance and improve ChartQA accuracy over the original model by 0.80 and 3.40 points, respectively. Together, SCIDOCBENCH, SCIDOCIR, and SCIDOCDATASET form an evaluation-to-training framework that connects capability diagnosis with verifiable training-data construction for scientific-document assistants.