Simultaneous Machine Translation (SiMT) generates target translations while reading the source sentence. It relies on a policy to determine the optimal timing for reading sentences and generating translations. Existing SiMT methods generally adopt the traditional Transformer architecture, which concurrently determines the policy and generates translations. While they excel at determining policies, their translation performance is suboptimal. Conversely, Large Language Models (LLMs), trained on extensive corpora, possess superior generation capabilities, but it is difficult for them to acquire translation policy through the training methods of SiMT. Therefore, we introduce Agent-SiMT, a framework combining the strengths of LLMs and traditional SiMT methods. Agent-SiMT contains the policy-decision agent and the translation agent. The policy-decision agent is managed by a SiMT model, which determines the translation policy using partial source sentence and translation. The translation agent, leveraging an LLM, generates translation based on the partial source sentence. The two agents collaborate to accomplish SiMT. Experiments demonstrate that Agent-SiMT attains state-of-the-art performance.

翻译：摘要：同步机器翻译（SiMT）在读取源句的同时生成目标译文，其核心依赖于确定最佳阅读与生成时机的策略。现有SiMT方法通常采用传统Transformer架构，同步执行策略决策与翻译生成。尽管这类方法在策略判定方面表现优异，但其翻译性能存在不足。相比之下，经过大规模语料训练的大语言模型（LLMs）具备卓越的生成能力，却难以通过SiMT训练方法习得翻译策略。为此，我们提出Agent-SiMT框架，融合LLMs与传统SiMT方法的优势。该框架包含策略决策智能体与翻译智能体：前者由SiMT模型管控，基于部分源句及译文制定翻译策略；后者依托LLM，根据部分源句生成译文。两个智能体协同完成SiMT任务。实验表明，Agent-SiMT取得了当前最优性能。