Memes have emerged to be an essential medium of online expression, where the sentiment is determined by the interaction of text and image. Sentiment analysis of memes is particularly challenging when the language is low-resource, such as Nepali, due to the lack of resources and the complex relationships between text and image modalities. In this paper, we report our submission to Subtask B of CHiPSAL 2026, where the task was sentiment analysis of Nepali text-embedded memes for three sentiment classes: Negative, Neutral, and Positive. Through this submission, we present a late fusion multimodal framework that encompasses lexical, semantic, and visual models through a cross-validated stacking approach. Our submission to the shared task competition received a Macro F1 of 0.5045 on the official test set, achieving 6th place in the leaderboard. This demonstrates the strength of well-structured late fusion approaches to multimodal sentiment analysis of text-embedded memes.

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.

Artificial Intelligence (AI) is opening new doors of learning and interaction. However, it has its share of problems. One major issue is the ability of AI to generate text that resembles human-written text. So, how can we tell apart human-written text from AI-generated text?With this in mind, we have worked on detecting AI-generated product reviews in Dravidian languages, mainly in Malayalam and Tamil. The “Shared Task on Detecting AI-Generated Product Reviews in Dravidian Languages,” held as part of the DravidianLangTech Workshop at NAACL 2025 has provided a dataset categorized into two categories, human-written review and AI-generated review. We have implemented four machine learning models (Random Forest, Support Vector Machine, Decision Tree, and XGBoost), four deep learning models (Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, and Recurrent Neural Network), and three transformer-based models (AI-Human-Detector, Detect-AI-Text, and E5-Small-Lora-AI-Generated-Detector). We have conducted a comparative study among all the models by training and evaluating each model on the dataset. We have discovered that the transformer, E5-Small-Lora-AI-Generated-Detector, has provided the best result with an F1 score of 0.8994 on the test set ranking 7th position in the Malayalam language. Tamil has a higher token overlap and richer morphology than Malayalam. Thus, we obtained a worse F1 score of 0.5877 ranking 28th position in the Tamil language among all participants in the shared task.