We describe our system for Subtask A1 (binary hateful meme detection) of the ArGuard 2026 shared task (Alam et al., 2026). Our final system fine-tunes MARBERTv2 with class-weighted loss and macro-F1-based checkpoint selection, then applies a post-hoc decision-threshold calibration to correct for a class-distribution shift we found between the training/development splits (37.8% Hateful) and the held-out test split (29.6% Hateful). This calibration step requires no retraining and adds 3.3 macro-F1 points (0.7286 → 0.7616) on the official test split. On the same test split, this calibrated system also outperforms two alternatives we evaluated there – multi-seed ensembling and naive multimodal late-fusion. They match or exceed backbone-substitution and multi-model-ensemble variants that we were only able to evaluate on local development data. We report our full experimental log, including the negative results, with each result explicitly labeled by evaluation split, and compare our score to the AHA-Memes benchmark, where a much larger vision-language model reaches a similar range. Code and configuration files are released for reproducibility.

We describe the QuadCore submission to Sub task B2 of the ArGuard 2026 shared task, which asks systems to assign an Arabic prompt addressed to a large language model to one of seven harm domains. Our system is a soft-voting ensemble of three Arabic en coders with complementary pre-training cor pora- MARBERTv2, CAMeLBERT-DA and AraBERTv02-Twitter- each fine-tuned under two random seeds, with the annotated dialect prepended to the prompt as a textual condition ing prefix; it uses no external data, no augmen tation and no generative models. It reached 0.7665 macro-F1 on the official test set, rank ing 4th of the 16 teams that submitted to the subtask. Using the gold labels released after the evaluation period, we then show that this sin gle number conflates two regimes: on the first 4,809 test items, whose label prior, dialect mix and lexical profile match the training data, the same predictions reach 0.9044 macro-F1, while on a contiguous final block of 1,170 items they collapse to 0.2073. That block is entirely Mod ern Standard Arabic, contains no adult-content instance, and its nearest-neighbour label agree ment with the training set is 0.1829 against 0.7777 elsewhere- close to the 1/7 chance rate. A substantial portion of our system’s errors on the official test set is therefore attributable to this shift rather than to the difficulty of harm domain classification itself.