This paper presents the Fine-Tashkeel system for Task 2 of the KSAA-2026 Shared Task on Automatic Diacritization of Speech Dictation. Diacritization of speech-derived Arabic text poses challenges due to dialectal variation, morphological ambiguity, and the absence of acoustic cues in text-only pipelines. Our approach treats diacritization as a character-level sequence-to-sequence task, mapping undiacritized text directly to its fully diacritized form. We evaluate 18 models spanning text-only, ASR-augmented, and fine-tuned configurations, finding that text-only Seq2Seq approaches outperform off-the-shelf multimodal models—a gap we attribute to task mismatch in generic ASR systems rather than an inherent audio limitation. Our best submission, using zero-shot inference without task-specific training, achieved a Diacritic Error Rate (DER) of 10.56%, Word Error Rate (WER) of 34.47%, and Sentence Error Rate (SER) of 79.88%, ranking 5th out of 7 teams. Per-nationality error analysis reveals significant dialectal variation (Egyptian 3.70% vs. Algerian 13.73% DER), and diagnostic analysis confirms that case endings and vowel ambiguity are the primary bottlenecks. Code and evaluation scripts are publicly available.

Function calling connects language models to external tools, but Arabic structured tool use remains insufficiently evaluated. We present , a shared task at ArabicNLP 2026 built around 12,220 Arabic requests spanning five dialect groups and eight service domains. Each request has four candidate schemas drawn from a registry of 27 tools, including seven tools that never occur as gold calls; 223 requests require abstention. Systems decide whether to call, select a function, and extract its arguments. Two ranked tracks evaluate core calling and reasoning-trace compliance, with a third diagnostic examining dialect robustness. Twenty-two teams submitted to the blind phase, and 21 produced valid predictions on 1,125 test requests. We analyze 73 submissions and 13 system descriptions. A LoRA-adapted 4B model ranks first on both competition tracks, with scores of 0.9279 and 0.9400, while argument exact match peaks at 0.8816. However, a deterministic rule combining candidate order and released metadata matches the best observed function-name accuracy of 0.9973, limiting what this metric establishes about language understanding. Exact argument extraction provides most of the competitive separation. Maghrebi has the lowest score for every system, although its small test subset and differences in example composition limit interpretation. We provide public task data, a baseline checkpoint, the scorer, and final rankings, and document evaluation shortcuts and remaining reproducibility limitations.[<https://huggingface.co/spaces/TuwaiqAcademy/AISA-ArabicFC-Shared-Task>]