Certain document automation scenarios, such as intelligent form infilling and privacy-strict parsing, require models to comprehend unpopulated templates. Yet existing benchmarks mainly use content-filled tables, leaving the template side of document understanding under-evaluated. To bridge this gap, we introduce NEST, a benchmark of 828 Native Empty Structural Templates from real-world, born-digital forms. NEST removes filled content while preserving complex hierarchical layouts and guided selection options in authentic administrative workflows. Through Structural Schema Parsing, Cell-Header Alignment, and Generative Infilling, NEST directly evaluates structural reasoning without content-based shortcuts. Experiments reveal a clear template–instance gap: state-of-the-art LLMs drop by 9.5 percentage points on average when evaluated on unpopulated templates rather than populated instances. We further show that Generative Infilling is a data-efficient tuning signal for template completion, raising infilling accuracy by 16.37 percentage points and the three-task average by 8.19 percentage points with only 662 (80%) training templates. NEST thus provides a benchmark for measuring template-level understanding and highlights a data-efficient route for improving template completion. The code, data, and results for this paper are available at https://github.com/cpeng-research/NEST-Benchmark.