🟦 Unified Translation OS v1.0

A Language-Agnostic Operating System for High-Precision Translation & LQA

Author: Hideyuki Okabe

Unified Translation OS v1.0 is a language-agnostic translation operating system that treats translation not as word substitution, but as structural mapping across meaning, pragmatics, and cultural context.

It is designed for:

• Professional translators

• LQA / TQE evaluators

• NLP / morphology researchers

• LLM-based translation agents

• Localization engineers

• Anyone needing reproducible, high-precision translation workflows

This OS is a human-usable and machine-usable distillation of years of translation, morphology analysis, and multimodal evaluation work.

---

🧠 1. Core Principle

🔷 Translation is not “replacing words.”

🔷 Translation is “semantic–structural–pragmatic mapping.”

translation_is_not: "Blind substitution of words"
translation_is: "Semantic + Pragmatic + Cultural Structure Mapping"

forbidden_operations:
- blind_literal_translation
- source_order_imitation
- intuition_without_evidence
- dictionary_only_decision

---

🏗 2. OS Architecture (Layer Model)

Unified Translation OS is structured as a 7-layer cognitive OS.

---

LAYER 0 — Philosophical Core

Meaning > Structure > Syntax > Words
Meaning layout has priority over surface word order.

---

LAYER 1 — Structural Engine

• Semantic Layout Engine

• Syntactic Flow Engine

• Pragmatic Context Engine

• Register & Tone Controller

---

LAYER 2 — Translation Pipeline (6 Steps)

1. Semantic Core Extraction

2. Structural Mapping

3. Syntactic Optimization

4. META-Style Evaluation (YES/NO/TRUE/FALSE)

5. Recursive Refinement (ΔS reduction)

6. Cultural Naturalness Verification

Both humans and LLMs can follow this pipeline identically.

---

LAYER 3 — Unified Scoring Matrix

Quality is rated across:

• semantic_fidelity

• structural_flow

• lexical_precision

• pragmatic_fit

• cultural_adaptation

• reader_resonance

Each dimension is explicit, explainable, and fully reproducible.

---

LAYER 4 — Recursive Improvement Engine

Refinement rules:

• Lower ΔS (entropy of structure)

• Normalize semantic layout

• Increase clarity & structural coherence

• Optimize tone and register

---

LAYER 5 — FPE & Counter-Evidence System

A translation is only accepted after counter-evidence review:

• Why is alternative A incorrect?

• Why is the final version correct?

• Which structural element failed?

• What cultural nuance was violated?

• What caused the misinterpretation?

---

LAYER 6 — Command Interface

Conceptual commands:

/translate "text" --mode=uiux
/analyze "translation" --semantic --structural
/refine "translation" --recursive --delta-s
/evaluate "translation" --meta --evidence

---

LAYER 7 — Testing / Templates

See /examples/ for:

• UI/UX translation samples

• Marketing text

• High-difficulty Estonian morphology tests

• Full LQA checklist

---

📚 3. Repository Contents

/spec
unified_translation_os_core.md

/examples
en-ja_uiux_example.md
en-ja_marketing_example.md
en-et_high_difficulty_example.md
qa_checklist_template.md

/prompts
translator_mode_prompt.md
qa_evaluator_prompt.md
llm_integration_prompt.md

/meta
design_principles.md
roadmap_v1_v2.md

---

🎛 4. How to Use

For Translators

• Follow the 6-phase pipeline

• Prioritize structure → syntax → surface wording

• Apply ΔS reduction

• Use evidence-based evaluation

For LQA Evaluators

• Apply Unified Scoring Matrix

• Use counter-evidence method

• Produce reproducible YES/NO/TRUE/FALSE verdicts

For LLMs

Minimal system prompt:

You are running Unified Translation OS v1.0.
Follow:
1) Semantic extraction
2) Structure mapping
3) Draft translation
4) META evaluation
5) Recursive refinement
6) Final output with evidence

---

🛣 5. Roadmap

v1.1

• More examples

• JA→EN module

• Estonian presets

v1.2

• JSON/YAML schema

• Compressed OS for LLM embedding

v2.0

• Integration with Unified Cognitive OS
  (Agent0 × Estonian v5 × ModelRefiner v4 × META v3)

---

⚖ License

MIT License (recommended)

---

💬 Contact

Created by Hideyuki Okabe
LinkedIn: https://www.linkedin.com/in/hideyuki-okabe-1a2287253/

GitHub: https://github.com/hideyuki001

Pull requests and research collaborations are welcome.

Quick Start

Unified Translation OS is a modular translation pipeline:

1. Extract semantic core
2. Map structures across languages
3. Apply morphology-aware reasoning
4. Generate draft translation
5. Evaluate with META Core
6. Refine using ΔS reduction

examples/
  en-ja_uiux_example.md
  en-ja_marketing_example.md
  en-et_high_difficulty_example.md

Use these templates to integrate with your localization workflow.