@InProceedings{guttmann-nowakowski:2026:wmt,
  author    = {Guttmann, Kamil  and  Nowakowski, Artur},
  title     = {Laniqo at WMT26 Video Subtitle Translation Shared Task: Multi-Objective Fusion of Pareto-Optimal Candidates},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2607--2614},
  abstract  = {This paper describes Laniqo's submission to the WMT26 Video Subtitle Translation Shared Task, translating subtitles from Simplified Chinese into English, Thai, Indonesian, Malay, and Traditional Chinese under a 20B-parameter, open-license model constraint. Our system optimizes translation quality and subtitle compliance entirely at inference time, without fine-tuning, by combining hedged multi-prompt candidate generation, language-identification and compliance pruning, multi-objective Pareto reranking, reasoning-based candidate fusion, and a fallback/compression step. We screened four open-weight models and compared the two strongest, Qwen3.5-9B and Gemma-4-12B-it, across the full pipeline on the complete test corpus. Gemma-4-12B-it reached significantly higher compliance rates in every target language, thus it was chosen as our final system.},
  url       = {https://aclanthology.org/2026.wmt-1.203}
}