This paper presents a CLIP-based transfer learning approach for classifying historical archive images in the Nakba Image Classification Shared Task at the Nakba-NLP 2026 Workshop (LREC 2026). The task involves distinguishing images depicting destroyed or damaged infrastructure from those showing intact scenes using a dataset of 2,001 images collected from Instagram posts published by Palestinian content creators and journalists in Gaza between October 2023 and December 2025. Our method employs the CLIP ViT-B/32 visual encoder with selective fine-tuning of the final transformer block and a lightweight classification head. To address class imbalance, we apply focal loss along with standard data augmentation and threshold optimization. Experimental results show that the proposed model outperforms several CNN baselines and achieves an F1-score of 0.877 on the blind test set, securing 4th place in the shared task.

Open-set spoken intent recognition for low-resource Arabic varieties must handle both automatic speech recognition (ASR) errors and intents absent from training. We describe CIS-OpenSLU, our NADI 2026 Task 5.1 system, which transcribes Tunisian Arabic speech with Whisper Turbo and classifies the resulting text using word- and character-level TF–IDF features with a LinearSVC. On ASR-transcribed validation audio, training the classifier on Whisper-generated transcripts rather than gold transcripts raises weighted F1 from 49.25% to 69.78%, showing that matching training and inference text distributions is crucial. We rank test utterances by the maximum SVM decision score and reject low-confidence examples as unknown; the final system achieves 71.54% weighted F1 and 72.50% accuracy, ranking second. Auxiliary audio and multi-modal experiments further show that strong unseen-intent detection in leave-one-intent-out simulation does not necessarily transfer to genuinely unseen test intents.

Islamic question-answering systems can produce fluent Arabic while altering Qur’anic wording, Hadith text, narrator chains, or source attribution. We present Tathabbut for two IslamicEval 2026 settings: fine-grained verification and canonical correction. For verification, Canonical-Provenance Evidence Fusion (CPEF) classifies Ayah, matn, isnad, and claimed-source segments by combining canonical retrieval with collection, narrator, response-level, contradiction, and exact-matn provenance evidence. CPEF achieves 0.9452 macro accuracy on the official Test set. For correction, Type-Aware Selective Canonical Retrieval (TASCR) reranks authoritative Qur’anic and Hadith candidates using lexical, memory-based, contextual, and source evidence, and abstains when confidence is insufficient. TASCR achieves 86.29% Test accuracy. A post-hoc Development diagnostic finds the gold correction for 319/327 (97.55%) recoverable cases in an expanded candidate pool, indicating that candidate discrimination and abstention are larger bottlenecks than retrieval recall. Neither submitted component invokes a generative LLM at inference time.