We describe Ṣaqr, ranked first on both scored tracks of the AISA-ArabicFC shared task on Arabic function calling, scoring 0.9279 on Track A and 0.9400 on Track B over a 1,125- instance blind test set. The system is deliber- ately plain: one Qwen3-4B model with LoRA, two epochs, provided data only. Our contri- bution is not the system but an accounting of where score comes from. We find that scale helped only up to a point in our single-run, cross-family comparison, and that the remain- ing gains came instead from inference-time de- cisions worth +0.0063 Overall A and +0.0223 Overall B (Appendix A) with no retraining. prefilling the reasoning trace so the model can- not skip it, and parsing arguments against the declared schema . We also report two proper- ties of the data that bound any system’s score: abstention is decidable from prompt structure without reading the query, and the gold anno- tation is self-inconsistent on precisely the slots that separate strong systems.

This paper presents the methods we developed for the Nuanced Arabic Dialect Identification (NADI) 2023 shared task, specifically targeting the two subtasks focussed on sentence-level machine translation (MT) of text written in any of four Arabic dialects (Egyptian, Emirati, Jordanian and Palestinian) to Modern Standard Arabic (MSA). Our team, UniManc, employed models based on T5: multilingual T5 (mT5), multi-task fine-tuned mT5 (mT0) and AraT5. These models were trained based on two configurations: joint model training for all regional dialects (J-R) and independent model training for every regional dialect (I-R). Based on the results of the official NADI 2023 evaluation, our I-R AraT5 model obtained an overall BLEU score of 14.76, ranking first in the Closed Dialect-to-MSA MT subtask. Moreover, in the Open Dialect-to-MSA MT subtask, our J-R AraT5 model also ranked first, obtaining an overall BLEU score of 21.10.