Agentic AI Translate: An Agentic Translator Prototype for Translation as Communication Design
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
We present Agentic AI Translate, an agentic translator prototype that operationalises the thesis of Yamada (forthcoming) -- that the metalanguage of Translation Studies has become an instruction code for generative AI. The system replaces the dominant text-in / text-out paradigm of machine translation with a four-stage agentic cycle (Identify -> Prompt -> Generate -> Verify), preceded by an interactive specification phase in which the user composes -- through model-assisted dialogue -- a structured translation brief grounded in skopos theory, register, audience, and genre conventions. The verification stage adopts the GEMBA-MQM error-span protocol (Kocmi & Federmann, 2023) for evidence-grounded scoring, and document-level coherence is preserved through a DelTA-lite memory of proper nouns and a running bilingual summary, after Wang et al. (2025). We describe the philosophical motivation, the architectural commitments, the four reference-material categories the system consumes, and the principal design tensions the architecture makes explicit. Empirical validation is left for future work; the contribution here is conceptual and architectural -- an executable embodiment of the position that translation in the GenAI era is communication design, not text conversion.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 20 of its 32 references that are in the map. First posted on arXiv 16 May 2026.
Builds on
20 of 32 references.
Closest ancestors · 2
Same direction, newest first
- 2024DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory10 Oct
- 2024(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts20 May
Other references · 18
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
- 2023Large Language Models are not Fair Evaluatorscited by 1,085
- 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
- 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 420
- 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 338
- 2023xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detectioncited by 265
- 2022In-context Examples Selection for Machine Translationcited by 172
- 2023GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4cited by 146
- 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 145
- 2024On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Taskscited by 142
- 2023The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluationcited by 95
6 more
- 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
- 2022Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translationcited by 61
- 2024TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinementcited by 39
- 2024Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Textscited by 31
- 2024Translating Across Cultures: LLMs for Intralingual Cultural Adaptationcited by 16
- 2025Specification-Aware Machine Translation and Evaluation for Purpose Alignmentcited by 4
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- 2025MAATS: A Multi-Agent Automated Translation System Based on MQM Evaluation
- 2026OmnilingualGAIA2: Evaluating the Multilingual Gap in Frontier AI Agents
- 2026Translating the Translator: Decomposing the Cost of English-Forced Inter-Agent Communication
- 2025AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domains
- 2026Agentic Artificial Intelligence (AI): Architectures, Taxonomies, and Evaluation of Large Language Model Agents
- 2025TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
- 2026The Path Ahead for Agentic AI: Challenges and Opportunities
- 2025GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation