Multi-agent MT : Deploying AI Agent in the WMT 25 Shared Task (2025)

Abstract: We present Multi-agentMT, our system for the WMT25 General Shared Task. The model adopts Prompt Chaining, a multi-agent workflow combined with Rubric-MQM, an automatic MQM-based error annotation metric. Our primary submission follows a Translate–Postedit–Proofread pipeline, in which error positions are explicitly marked and iteratively refined. Results suggest that a semi-autonomous agent scheme for machine translation is feasible with a smaller, earlier-generation model in low-resource settings, achieving comparable quality at roughly half the cost of larger systems.

## introduction an ai agent is a computational system that operates autonomously, guided by environmental observations, and often incorporates adaptive learning abilities @xcite . recent advances in large language models (llms) have greatly enhanced ai agents by enabling stronger reasoning, contextual understanding, and flexible task execution, particularly in machine translation (mt) @xcite . building on this progress, briva-iglesias (2025) proposed a multiagent mt system with four agents-translator, fluency reviewer, adequacy reviewer, and editor-which, while still preliminary, demonstrates promising potential. inspired by this approach, we participate in this year's wmt (conference on machine translation) general task with an ai multi-agent workflow. our objective is to develop a smaller model that surpasses larger counterparts, thereby showcasing the potential of ai agents in mt while substantially reducing computational cost. this year's competition focuses on translating texts across a broad spectrum of languages, domains, genres, and formats. we addressed the multilingual subtask covering 30 languages, with czech, english, and japanese as source languages. by adhering to prompt engineering without prioritizing specific languages, our system can be categorized as both a contrastive and unconstrained model. one major challenge stemmed from the dataset structure. following last year's convention, the dataset included document boundaries, with segments composed of multiple sentences or paragraphs separated by one or two newline characters. this format yielded 29,957 segments or 102,060 paragraphs. our initial submission translated at the segment level but often failed to respect paragraph boundaries-merging or omitting content, particularly within the tpp workflow (see § 5.2). to address this, we later split segments into individual paragraphs and translated them independently during inference. apart from this adjustment, most translations were performed at the segment level. in the official results, our system did not undergo human evaluation because it belonged to the unconstrained category @xcite . nevertheless, preliminary rankings based on automatic metrics @xcite suggest that the architecture is particularly effective for low-resource languages. the most notable outcome is observed for english-serbian, although the underlying factors remain unclear. considering that our baseline model is not the most up-to-date, this result could be better with other more recent light models in the multi-agentmt architecture. the remainder of this paper is organized as follows. section 2 details the multi-agent architecture. section 4 presents experimental settings based on the wmt24++ dataset @xcite , and section 5 reports results and analysis. the appendix provides additional details of the prompt designs. ## performance in this section, we present the performance of multi-agentmt under the submitted configuration. ## experiment this section evaluates the relative effectiveness of our model-a compact, earlier-generation variant-on two directions discussed above: english-serbian and english-icelandic. we use the wmt24++ dataset @xcite , which provides an english source and 55 target-language translations, together with up to two references (a human translation and a post-edited version). after filtering low-quality segments using comet, we retain 960 source segments per language pair. translations are generated with the tp and tpp workflows. we report bleu @xcite , chrf @xcite , and ter @xcite using sacrebleu @xcite ; we also report comet @xcite with both references and the reference-free cometkiwi @xcite . to quantify the magnitude of edits introduced by each workflow, we additionally compute ter between the tp and tpp outputs. for cost-efficiency, we record token counts for each setting. for efficiency, we replace the translate agent with off-the-shelf translations from gemini-1.5-flash (rather than generating outputs with gpt-4o-mini as in our submission), and set the decoding temperature to 0 for reproducibility. ## conclusion we presented the potential of an ai agent workflow based on translate-postedit-proofread with a lightweight llm, submitted as our primary system to the wmt25 general shared task. official results indicate that the model is promising in low-resource settings, outperforming systems not specifically trained for such languages. our experiments further show that the postedit agent plays a central role in introducing semantic-level revisions and mitigating hallucinations. under the hypothesis that comparable quality to large models such as gpt-4o can be achieved, the workflow reduces cost to roughly half. a definitive conclusion, however, requires validation through human evaluation. listing 1: prompt of postedit agent. the use of reference is optional. format: { "<error @xmath0: { "category": "<@xmath1, "severity": <1-4>, "suggestion": "<@xmath2, ... }