We describe the Durham-Jazan submission to AISA-ArabicFC 2026, an Arabic function-calling task in which a system selects one tool from four candidates and fills its arguments, scored on tool choice (FnAcc) and on strict exact match of the whole argument dictionary (ArgEM). Across six QLoRA fine-tunes of ALLaM-7B and Qwen2.5-7B, trained on the provided data, negatives derived from it and 68 hand-written no-tool utterances, and run on one 16 GB GPU, we vote on each argument separately, then apply two deterministic rules estimated from training statistics. The system reaches ArgEM 0.824 on development under the final evaluator and 0.826 on the blind test, placing 9th of 17 in Track A, 8th of 17 in Track B and 11th of 21 in Track C. Our main findings concern the benchmark. The reference tool is listed first on every training and development row that calls one, in every data release. Shuffling the candidates at inference costs the voted system 0.202 FnAcc and 0.190 ArgEM, and retraining one adapter per base on permuted candidates removes the dependence with no loss of ArgEM. One post-processing rule contributes 0.028 ArgEM and is the only component whose gain separates from zero, the other targets a convention the final data release removed and is inert, and the largest effect we can measure comes from the benchmark’s construction rather than from our modelling.

We describe the Durham-Jazan submissions to the Daleel 2026 shared task on Arabic argumentative discourse mining, which comprises multi-label paragraph classification (Task 1) and argumentative span detection (Task 2), each with a Closed and an Open track. Our Task 2 Closed system routes the Modern Standard Arabic editorials to CAMeLBERT and the noisier debate transcripts to MARBERTv2. The Open system blends the two families at character level and adds synthetic training data from a large language model for rare-class generalisation, generated by a segment-concatenation recipe that makes span offsets exact by construction. The Task 1 systems add back-translation, targeted augmentation of the rare classes, and a QLoRA-tuned Qwen3-32B for the editorials. Everything else trains on one 16 GB consumer GPU. Our official test scores are 0.729 and 0.736 overlap-F1 on Task 2 and 0.657 and 0.669 macro-F1 on Task 1 (Closed and Open). We placed first of two on Task 1 Open, first of one on Task 2 Open, fifth of ten on Task 1 Closed, and sixth of eight on Task 2 Closed. Our central finding concerns our own systems on this data. Recalibrating per-class thresholds on validation outputs overfits them. Under a controlled protocol the held-out effect is negative at every tuning-set size we tested, while the apparent in-sample gain shrinks as the tuning set grows. An audit of eighteen interventions from our campaign is consistent with this, though observational. A size-matched study links the editorial deficit that every team showed to the genre composition of the training data. We release our code and synthetic data.