Rather than producing visibly corrupted text, language-model-assisted systems can return fluent, well-formatted output containing silent substitutions, omitted table rows, or repetition loops.
The article distinguishes OCR-plus-LLM post-correction, VLM-native transcription, and agentic orchestration. It argues that character- and word-error metrics can obscure concentrated errors in consequential fields such as totals, identifiers, codes, and account numbers. Its proposed production direction is evidence-linked, field-level validation rather than relying on output fluency or token confidence.