给AI喂更多数据没用，得先知道它哪里瞎
文档解析AI的常规训练思路是喂更多、更杂的数据，但研究者发现：数据覆盖再广，模型在没见过的版式和拍摄条件下照样翻车。这篇的做法分两步：先造出各种脏乱差的文档图(歪的、糊的、反光的)，再专门用一个测试集去查它到底在哪些地方出错，然后针对性地补数据。结果在4个评测里全部第一，在真实劣质文档上比单纯堆数据多涨了4分。它不是你明天能用上的东西，但它点破了一个趋势：AI的下一步不是更大，而是更知道自己不知道什么。
📄 原文摘要(英文)
Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are biased toward common document types and clean digital pages, while expanding coverage alone does not specify how to address a parser's remaining weaknesses. We present WeVisDoc, a two-stage data-centric framework for robust end-to-end document parsing. Stage I broadens semantic, structural, and appearance coverage through heterogeneous data and structure-preserving degradation synthesis. Stage II uses a held-out probe to measure the Stage I parser's residual errors within fixed visual-structural clusters. These diagnostics guide targeted data construction and reallocation of the target-token budget. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. Compared with Stage I, Stage II improves Overall scores for the 2B and 4B models on both benchmarks, with larger gains on the degraded PureDocBench tracks, including a 4.03-point gain for the 4B model on the Real Degraded track.