For the WMT26 General Machine Translation Task (Unconstrained Track), we propose TRIVE (Tri-Fusion Verified Translation Agent), a model-agnostic multi-agent translation framework. TRIVE organizes translation as a closed loop of Generation–Fusion–Verification–Correction: it first generates candidates in parallel under three complementary strategies—Faithful, Fluent, and Free—then synthesizes a draft that balances semantic fidelity and natural expression, and finally iteratively refines the output via rule-based validation and large language model evaluation. Because each module is implemented primarily via prompting, TRIVE can be directly overlaid onto diverse backbone models without task-specific fine-tuning. Experiments show that overlaying TRIVE on multiple backbones consistently improves instruction-following translation quality. Finally, we submitted Gemini-3.5-Flash + TRIVE as our primary system to WMT26.

We present Qingqiu-MT-9B, an instruction-following multilingual translation model developed for the constrained track of the WMT26 General Machine Translation shared task. Our model supports translation among 20 languages and covers 22 of the 23 official language pairs. It uses a source-grounded synthesis pipeline with separate LLM-based steps for generating instance-specific translation instructions and their corresponding translations. The resulting corpus covers diverse translation requirements, task complexity, instruction formulations, domains, document lengths, and glossary constraints, reducing reliance on fixed prompt templates and supporting generalization to unseen translation instructions. Our model is based on Qwen3.5-9B and trained through two-stage full-parameter supervised fine-tuning (SFT) followed by reinforcement learning (RL). The first SFT stage performs broad adaptation on a large-scale, medium-quality corpus, while the second refines the model on a smaller, higher-quality set. The RL stage further optimizes the model with Group Relative Policy Optimization (GRPO). During development, we use an LLM-based, rubric-guided contrastive method that scores candidates by dimension and aggregates an overall score to assess translation quality. Experiments show that Qingqiu-MT-9B demonstrates strong generalization and competitiveness among similarly sized models.We release the model at https://huggingface.co/WPS-Qingqiu/Qingqiu-MT-9B.