StanceEval-2026 tests Arabic stance classification under target shift, with evaluation targets absent from the labelled task data. We test two ways to supply target information at inference time. For Track 1, we retrieve eight labelled Women Driving demonstrations from the external ArabicStanceX corpus and score the three label tokens with Gemma-4-31B-IT. For the unseen-target track, we prepend cached target context prompted to be factual and non-evaluative, then fuse label predictions and label-token scores from multiple LLMs, with final assignments constrained by published class totals. The systems obtain 85.22 and 91.78 F_avg2 on Tracks 1 and 2, ranking sixth and fourth in the final standings. On held-out ArabicStanceX tweets, direct prompting scores 79.45 F_avg2; eight random Women Driving demonstrations raise it to 84.78, and similarity retrieval reaches 89.04. Across three target-held-out Mawqif evaluations, a fixed target card raises Gemma-4-31B-IT from 86.54 to 86.93, whereas QLoRA lowers Gemma-4-12B-IT from 84.43 to 83.77. In these separate public evaluations, same-target demonstrations and retrieval improve performance, factual target context provides small, target-dependent gains, and the tested source-only adapter does not improve.

We report the first-place system in AraGenre 2026 (0.7352 Hierarchical Macro F1, 18 teams). The task assigns each of 27,972 hidden-test Arabic segments one of 6 broad and one of 74 specific genres, given only as English prose definitions, with the specific-label inventory disjoint from training. Our pipeline updates no model parameters but is not free of task-specific effort: the judge prompt carries 74 hand-written cues, and components were selected on the public leaderboard. A family-restricted, definition-guided LLM judge scores candidate genres setwise under a hierarchically constrained decoding scheme, whose five-step pipeline reaches 0.7139. That base led on Specific Macro F1 but trailed on Broad Macro F1, so we add a correction stage: two instruction models re-predict the specific genre, each shown the entire 74-definition taxonomy, and a label is edited only where both agree against the base. That system scores 0.892 Broad Macro F1, alongside fewer topic-trap calls, where the subject a text discusses displaces the genre it instantiates. In a post-evaluation audit of instances that the base labelled as book descriptions, Religious calls fall 394 → 233 → 119 as the prompt adds the type-versus-topic convention and then the full taxonomy. Refinements and an attractor drain reach 0.7352.

Argument exact-match (ArgEM) dominates the AISA-ArabicFC score. Under one prompt, a base gemma-4-12B-it reaches 0.9853 FnAcc but only 0.6940 ArgEM on dev, suggesting that argument convention rather than tool selection is the binding constraint. Fine-tuning on the provided train split raises ArgEM to 0.8880 while FnAcc increases only from 0.9853 to 0.9945, yet the model still fails 8.4% of the 10,500 positive training rows. Across rules, retrieval, prompting, selection, reranking, and fusion, dev-selected gains did not consistently transfer beyond development; smaller gains from other tested interventions remain unresolved. Explicit convention cards add 7.5 ArgEM points on a dev-selected 228-row subset for an unconfigured model, but give no measurable gain to the already configured pipeline. A scorer-equivalent gold answer occurs in a 20-sample pool for 34/56 (60.7%) rows missed by the raw fine-tune, but our selectors cannot exploit it. Our official prompt-only gemma-4-31B-it submission placed 6th of 17 on Track A, 5th of 17 on Track B, and 4th of 21 on Track C. The 12B fine-tune used for analysis was developed first but inadvertently omitted from the blind-test submission, so it has no official test score.

We describe Thakaa’s systems for NADI 2026 subtasks 1.1 and 1.3 and Task 2, covering noisy country-level Arabic automatic speech recognition (ASR), Tunisian code-switched ASR, and spoken dialect identification. The ASR systems combine heterogeneous connectionist temporal classification (CTC), encoder–decoder, and audio-LLM recognizers through ROVER, with country-aware routing for subtask 1.1 and a frame-rate-corrected CTC anchor for subtask 1.3. The dialect identifier fuses Ara-BEST-RQ acoustic scores with lexical evidence extracted from Cohere and Whisper transcripts by a partially fine-tuned MARBERTv2 model. Our official test scores were 50.41% WER on subtask 1.1, 14.53% WER on subtask 1.3, and 56.15% accuracy on Task 2, ranking third, third, and first, respectively. Across the three systems, the largest gains came from explicit control of alignment, temporal resolution, and evidence diversity rather than from model scale alone.

We report first-place systems for all four evaluated AlexandriaX-2026 settings: both English-to-dialect translation tracks, cross-dialect translation, and translation-error detection. In unconstrained translation, our minimum Bayes risk (MBR) selection restricts output authority: four local systems can supply the translation, while seven additional systems contribute only reference evidence. Candidate-constrained MBR beats the strongest complete candidate in all 13 dialects and gains 1.2554 macro spBLEU overall, reaching 33.4891. It also surpasses a retrospective oracle selecting the best whole candidate system per dialect. In the constrained track, country-routed LoRA adapters over a shared NileChat-3B-Base backbone incorporate the released Libyan and Sudanese supervision absent from the training and development splits, raising macro spBLEU from 29.5969 to 30.4233. Post-evaluation, a single adapter trained on all released Alexandria data reaches 30.4733 macro spBLEU; averaging model weights across three training seeds raises this to 30.9726 while retaining one model for inference. For cross-dialect translation, nonparallel target-dialect retrieval guides deliberation, raising Gemma-4-31B-IT without fine-tuning from 24.6898 to 28.4011 spBLEU. For translation-error detection, GPT-5.5 proposes semantic error regions, while MARBERTv2 controls their character boundaries and corroborates additional spans, raising Overall from 0.4691 to 0.4982.