将翻译视为沟通设计，用智能体循环实现更精准的跨文化传达。
Agentic AI Translate: An Agentic Translator Prototype for Translation as Communication Design
- 采用四阶段智能体循环：识别、提示、生成、验证，取代传统文本输入输出模式。
- 通过结构化翻译简报与注册理论，确保译文符合受众和语体要求。
- 适合关注生成式AI时代翻译哲学与系统设计的研究者与实践者。
我们提出Agentic AI Translate，一个智能体翻译原型，践行山田（即将出版）的观点——翻译研究的元语言已演变为生成式AI的指令代码。该系统将主流机器翻译的文本输入/输出范式，替换为包含四个阶段的智能体循环（识别→提示→生成→验证），并在前序阶段引入交互式说明环节，用户通过模型辅助对话构建结构化翻译简报，依据目的论、语域、目标受众和体裁惯例。验证阶段采用GEMBA-MQM错误段协议（Kocmi & Federmann, 2023）进行证据驱动评分，文档级连贯性则通过DelTA-lite记忆机制（保留专有名词）及持续更新的双语摘要（Wang et al., 2025）得以维持。本文阐述其哲学动机、架构选择、所依赖的四类参考材料，以及架构中凸显的核心设计张力。实证验证留待未来工作；本研究贡献在于概念与架构层面——为生成式AI时代翻译即沟通设计这一立场提供可执行的实现范例。
原文摘要 · Abstract (English)
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.
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