“Traditional machine translation (NMT) often lacks macro-level contextual awareness, leading to inconsistent use of technical terminology and frequent disruption of document structure. In contrast, LLMs represent a breakthrough at the cognitive level.”

Root Cause Analysis

From 'sentence mapping' to 'holistic comprehension'

Traditional tools are primarily based on NMT models and tend to process content sentence by sentence. O.Translator, built on large language models such as GPT, Claude, and Gemini, features full-document 'long window memory' that enables it to comprehend cross-paragraph logic, puns, and industry-specific subtext like a human expert.

Layout is semantics.

Standard translation tools treat documents as plain text streams. O.Translator employs Document Object Model (DOM) reconstruction technology, treating layout as part of the semantic layer, and flawlessly preserves font, size, color, and the positioning of text and images during language conversion.

Final Solution Summary

This is not simply a language conversion, but a high-fidelity reconstruction of both the content and structure of the document.