A Preliminary Study of AI Agent Model in Machine Translation (2025)

Abstract: We present IR_Multi-agentMT, our submission to the WMT25 General Shared Task. The system adopts an AI-agent paradigm implemented through a multi-agent workflow, Prompt Chaining, in combination with RUBRIC-MQM, an automatic MQM-based error annotation metric. Our primary configuration follows the Translate–Postedit–Proofread paradigm, where each stage progressively enhances translation quality. We conduct a preliminary study to investigate (i) the impact of initial translation quality and (ii) the effect of enforcing explicit responses from the Postedit Agent. Our findings highlight the importance of both factors in shaping the overall performance of multi-agent translation systems.

## introduction an ai agent is a computational system that operates autonomously, guided by environmental observations, and often equipped with adaptive learning capabilities @xcite . large language models (llms), which have become central to the development of ai agents, demonstrate advanced reasoning, contextual understanding, and flexible workflows across a wide range of tasks, including machine translation (mt) @xcite . recent work by briva-iglesias (2025) explored a multi-agent system composed of four agents-a translator, fluency reviewer, adequacy reviewer, and editor-highlighting its potential for driving future advancements. inspired by this line of research, we participate in this year's mt track with a multi-agent workflow. our goal is to leverage smaller models to achieve performance competitive with, or even superior to, larger models, while simultaneously reducing computational costs. ## participating task the shared task aims to evaluate translation performance across a broad range of languages, domains, genres, and modalities. we participate in the multilingual subtask, which covers 30 target languages, with czech, english, and japanese serving as the source languages. our system is categorized as unconstrained. the source data consists of 29,957 segments, corresponding to 102,060 paragraphs. our approach performs translation inference on a segment-by-segment basis. in cases where paragraph boundaries are ignored in the original segmentation, we re-split the data into paragraphs and translate them independently. ## model details we employ prompt engineering for all agents, using gpt-4o-mini-2024-07-18 as the primary baseline. the model is configured with a temperature of 1 and a maximum token length of 1024. although this setup is suboptimal in terms of reproducibility, our iterative pilot studies suggest that these parameters allow the model to explore a wider error space and generate more effective modifications, thereby improving overall translation quality. future work will focus on developing a more stable and reproducible experimental environment. ## experiment we evaluate our multi-agent pipeline through experiments on the wmt24 english-spanish dataset @xcite . we take a subset of 304 unique source segments with balanced domain distribution (literary, news, social media, and speech) and use 23 translations, summing up to 6,992 segments for analysis. our experiments are structured in two parts. first, we obtain initial translations from deepl (deepl) and marianmt @xcite , cost-efficient models, and gpt-4o-mini, our baseline model, and compare the final translation quality produced by the multi-agent workflows. we use chrf++ @xcite and comet @xcite . second, we analyze translations generated with both the original and the refined versions of rubric-mqm, with particular attention to cases where (i) no-error labels are produced and (ii) source phrases are erroneously marked as errors. ## conclusion our experiments on the multi-agent pipeline indicate that higher initial translation quality leads to better final outcomes. furthermore, enforcing the postedit agent to identify more errors is essential for ensuring meaningful revisions within the workflow. in this way, ir_multi-agentmt demonstrates the potential to achieve translation quality comparable to that of larger models, while operating at roughly half the cost. a more detailed discussion of these findings will be provided in the main system paper.