Vision Language Models (VLMs) are increasingly used in place of traditional OCR pipelines for document understanding. We show that VLMs do not always transcribe text literally. When the input contains imperfect text, they often rewrite it into a more plausible form, a behavior that clean-text OCR benchmarks do not detect. We introduce FaithC4, a multilingual perturbation benchmark of 1,455 single-page documents (English, Chinese, Korean) with three perturbation families: scramble, random substitution, and visually similar substitution. We use it to evaluate 15 systems spanning general-purpose VLMs, OCR-specialized VLMs, and traditional OCR pipelines. Traditional OCR is near-perfect on perturbed text, OCR-specialized VLMs degrade by 0.2–2.3 points of Word Error Rate (WER), and general-purpose VLMs degrade by up to 4.5 points in English language. To explain this gap, we probe Qwen3-VL-4B layer-by-layer and find that rewriting occurs when a perturbed word’s final-layer FFN representation stays close to the original word’s encoding, while faithful transcription occurs when the representation is sufficiently disrupted. Shorter words give the model less evidence to disrupt the representation and are therefore rewritten far more often than longer ones, with 4-character words rewritten more than ten times as often as words of 8 or more characters. We will release FaithC4 and our evaluation code upon acceptance to support future research on transcription faithfulness.

Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts. Deploying them means choosing how to feed the document to the model, which retriever to use when only a subset of pages is sent, and whether to run the model agentically or as a static pipeline. We study these choices on two long-document QA benchmarks with both frontier API and open-weight VLMs. First, on MMLongBench-Doc our six-tool agent with page, table, figure, and search calls pays off only once the answering VLM is large enough: with Qwen3.5-4B and 9B it trails static page input, with Qwen3.5-27B it draws level, and with Sonnet 4.5 it leads. On LongDocURL it is level with or ahead of static input at every reader. Its lead over the strongest static pipeline is clearest with the frontier reader on MMLongBench-Doc and narrows to within noise on LongDocURL. Second, retrieval modality matters more than the specific retriever: the strongest image retriever leads the strongest text pipeline, and on the text side a single off-the-shelf cross-encoder rerank essentially matches a much heavier multi-stage LLM pipeline. Top-k image retrieval is also the most token-efficient input at every reader we paired it with, at roughly a seventh to a quarter of the tokens of sending every page. Third, cutting across all three choices, three of our strongest pipelines succeed on different questions, and an oracle that picks the best pipeline per question gains roughly thirteen points over the best single pipeline, though evidence-type routing recovers almost none of it.