We describe our submission to the WMT26 Low-Resource Arabic–Asian Language Translation shared task, covering ten translation directions between Arabic and five Asian languages. Our central question is whether a post-hoc controller can improve translation quality beyond a fine-tuned backbone without modifying any model weights. For the five into-Arabic directions, our primary system jointly fine-tunes facebook/nllb-200-3.3B on all source languages and augments it at inference time with per-direction NTK-Mirror controllers, each containing fewer than 6K parameters, that rescale frozen transformer output channels. For the five from-Arabic directions, we use Qwen2.5-7B-Instruct as the backbone, adapted via QLoRA and likewise augmented with NTK-Mirror. In the official evaluation, our system ranked first on Bengali-to-Arabic and Indonesian-to-Arabic, second on Hindi-to-Arabic and Urdu-to-Arabic, and third on English-to-Arabic, placing in the top three across all five into-Arabic directions. Post-submission analysis indicates that the weaker results in the from-Arabic directions stem primarily from a model-selection decision made at submission time.

Propaganda, a pervasive tool for influenc- ing public opinion, demands robust auto- mated detection systems, particularly for under- resourced languages. Current efforts largely focus on well-resourced languages like English, leaving significant gaps in languages such as Arabic. This research addresses these gaps by introducing MultiProp Framework, a cross- lingual meta-learning framework designed to enhance propaganda detection across multiple languages, including Arabic, German, Italian, French and English. We constructed a mul- tilingual dataset using data translation tech- niques, beginning with Arabic data from PTC and WANLP shared tasks, and expanded it with translations into German Italian and French, further enriched by the SemEval23 dataset. Our proposed framework encompasses three distinct models: MultiProp-Baseline, which combines ensembles of pre-trained models such as GPT-2, mBART, and XLM-RoBERTa; MultiProp-ML, designed to handle languages with minimal or no training data by utiliz- ing advanced meta-learning techniques; and MultiProp-Chunk, which overcomes the chal- lenges of processing longer texts that exceed the token limits of pre-trained models. To- gether, they deliver superior performance com- pared to state-of-the-art methods, representing a significant advancement in the field of cross- lingual propaganda detection.