  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.
