Simple Test Time Scaling for Machine Translation: Kaze- MT at the WMT 25 General Translation Task (2025)

Abstract: This paper describes the Kaze-MT submission to the WMT25 General Machine Translation task (Japanese–Chinese). Our system deliberately adopts a minimalist Test-Time Scaling (TTS) pipeline with three stages—Sampling, Scoring, and Selection—while avoiding any task-specific fine-tuning, in-context exemplars, or bespoke decoding heuristics. In the sampling stage, we use the zero-shot Qwen2.5-72B-Instruct model to generate 512 candidate translations under a fixed temperature schedule designed to encourage lexical and syntactic diversity without sacrificing fluency. In the scoring stage, each candidate is evaluated by multiple reference-free quality estimation (QE) models—KIWI-22, MetricX-24 Hybrid-XXL, and Remedy-24-9B. The selection stage aggregates metric-specific rankings and chooses the candidate with the lowest mean rank, which we found more stable than averaging raw scores across heterogeneous ranges. We submit to both constrained and unconstrained tracks with minimal configuration changes. According to official preliminary results, our submissions are competitive on automatic metrics; in human evaluation, Kaze-MT falls within the 8–13 cluster, delivering performance comparable to CommandA-WMT and DeepSeek-V3 and outperforming other large LLM baselines such as Mistral-Medium and other extensively tuned MT systems.

## introduction allocating additional computation at inference time-commonly referred to as test-time scaling (tts) or best-of-n (bon)-can improve quality without the overhead of scaling training to ever larger models @xcite @xcite . in machine translation, tts has a long history via candidate reranking using quality estimation (qe) metrics @xcite @xcite . rather than optimizing a particular reranking recipe, @xcite study scaling laws for tts-mt and find that scaling n for bestof-n brings performance improvements for highresource languages. we adopt this minimalist perspective. our submission, kaze-mt, relies on a strong, off-the-shelf llm for diverse candidate generation and on robust, reference-free qe models for selection. our submission targets the wmt25 japanese-chinese track and intentionally avoids any task-specific parameter updates or domain adaptation. the pipeline is deliberately simple: (i) sampling, (ii) scoring, and (iii) selection-yet competitive against substantially engineered systems. beyond reporting official results, we discuss metric-human preference gaps and practical considerations for scaling tts under realistic compute constraints. on the official wmt25 @xmath0, kaze-mt attains a strong position under automatic metrics and competitive human judgments despite using no fine-tuning or in-context exemplars. in autorank (an ensemble of kiwi-xl, gemba-esa-cmda, gemba-esa-gpt-4.1, metricx-24 hybrid-xl, and xcomet-xl), our primary system ranks 4/41 submissions ( in the final official human evaluation, kaze-mt falls in the 8-13 cluster ( ## task overview the wmt25 japanese-chinese track evaluates systems with both automatic metrics and human judgments. the constrained track limits models and resources (e.g., parameter @xmath1, whereas the unconstrained track permits any publicly available model or data. our pipeline fits both settings with minor differences in the scoring configuration (e.g., which qe variants are permitted). we submitted a primary system built on qwen2.5-72b-instruct and a contrastive system built on qwen2.5-14b-instruct @xcite . the contrastive run was not included in autorank or human evaluation by the organizers; thus, we only report the 72b primary system in this paper. ## data because kaze-mt is purely zero-shot, no pretraining or fine-tuning data are used beyond the official test set. the wmt25 materials contain multiple domains and are provided at the document level. very long contexts may degrade generation performance and stability; therefore, we segment documents into paragraph units simply using a double-newline delimiter (\n\n). we retain original sentence order within each paragraph and do not apply additional filtering or normalization beyond standard unicode cleanup. ## conclusion we presented kaze-mt, a simple yet competitive tts system for japanese-chinese mt. by pairing diverse zero-shot sampling from a strong llm with robust qe-based selection, we achieve strong results without any fine-tuning or in-domain resources. the modest gap between autorank and human ranking of our submission indicates that evaluation-on-the-features bias may exist, and tts approach largely depends on the quality and robustness of quality estimation metrics. reduce metric coupling and improving alignment of quality estimation methods with human preferences remains an important future work.