ExBind is a controlled diagnostic benchmark designed to isolate the visual-to-executable correspondence layer between semantic localization and action execution in multimodal coding and editing systems. ^[1] Researchers demonstrated that Vision-Language Models (VLMs) often rewrite imperfect text into more plausible forms instead of transcribing it faithfully. ^[2] Qwen2.5-VL-3B achieved 98.4% candidate validity but only 76.4% exact accuracy on the benchmark. ^[3] Evaluation of 15 systems spanning general-purpose VLMs, OCR-specialized VLMs, and traditional OCR pipelines showed that general-purpose VLMs degrade by up to 6.9 points in Word Error Rate under perturbation. ^[4] Probing the Qwen3-VL-4B model layer-by-layer identified that rewriting fires only when a perturbed word's final layer FFN representation stays close to the original encoding. ^[5] The authors introduced FaithC4, a multilingual perturbation benchmark consisting of 1,455 single-page documents in English, Chinese, and Korean. ^[6]