We describe the Team Yalla submission to the AISA-ArabicFC shared task on Arabic function calling, which ranked second in both tracks (0.9273 Track A, 0.9395 Track B), within 0.0006 of the winning system. The headline metric, exact argument match (ArgEM), is unforgiving: a single wrong surface form loses the whole example. The provided targets bury the scored object inside a rationale: the tool name and argument dictionary are only 30.3% of the target characters. We trained on the 30.3%, which is our largest single gain (+0.020 ArgEM in a matched comparison). A matched follow-up shows that reasoning need not be removed entirely: emitting it after the arguments trails the JSON-only target by only 0.008 ArgEM. We add a reinforcement-learning stage that uses the official scorer as the reward, and combine three deliberately diverse fine-tunes with a per-argument majority vote, which repairs individual argument slots rather than choosing whole predictions. Every member is a LoRA fine-tune of a public 4B or 7B Qwen model trained on the official split alone, with no distillation and no external data. We note that two of the three voting members emit no rationale, so our Track B think field is attached after prediction from a separate reasoning-trained model whose predicted call for that row is identical: it is a post-hoc explanation, not the computation that produced the arguments. We report the experiments that failed alongside those that worked.

Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.