Large language models increasingly generate high-quality source code, making reliable detection of machine-generated code essential for maintaining authorship integrity and software accountability. However, detection systems often degrade under distribution shift, particularly across programming languages and application domains. SemEval-2026 Task 13 Subtask A addresses this challenge through a structured OOD evaluation framework that assesses binary machine-generated code detection across unseen languages and application domains. To mitigate this limitation,we propose a robustness-oriented framework that enhances feature-fused UniXcoder representations with supervised contrastive learning, adversarial language-invariant training and uncertainty-aware filtering to promote stable and shift-resilient representations. Our proposed system achieves a macro-F1 of 0.5411 on the official test set and maintains stable performance under severe language–domain shift. Our results demonstrate that domain-level semantic variation is the primary source of degradation under distribution shift, reinforcing the importance of invariance-oriented representations for stable OOD performance

Determining whether large language models (LLMs) perform genuine formal reasoning or rely on semantic heuristics is a key challenge in NLP. Syllogistic reasoning constitutes a theoretically principled evaluation paradigm where validity is fully determined by quantifier structure, allowing systematic analysis of structural inference disentangled from semantic plausibility.SemEval-2026 Task-11, Subtask-1: Disentangling Content and Formal Reasoning in Language Models, establishes a multilingual benchmark designed to rigorously isolate formal logical validity from semantic plausibility effects. The subtask evaluates English syllogistic reasoning under a binary classification setting using Overall Accuracy (ACC) and Total Content Effect (TCE), where lower TCE indicates stronger resistance to content-induced bias.Our proposed approach combines cross-validation, structured aggregation and bias-aware evaluation to optimize the robustness–performance trade-off. It achieves 93.19\% accuracy with a TCE of 3.13, yielding a strong combined score of 38.56 under the official evaluation metric. Condition-wise and multi-run analysis confirms that robustness-focused optimization curbs content-driven errors, reinforcing the necessity of bias-aware training for formal inference

In Dravidian languages, political memes progressively shape public opinion and political discourse, influencing digital conversations andpublic narratives. Our paper proposes a multilevel multimodal framework for political meme classification in Tamil and Malayalam as part of the Multi Level Political Meme ClassificationDravidianLangTech@ACL 2026 shared task. The task has involved two levels: Level 1 has identified whether a meme expresses Troll/Oppose or Support/Praise, while Level 2 has determined the specific target category (Individual, Party, or Intersection). We have evaluated unimodal and multimodal architectures to analyze the impact of textual and visual representation. Experimental results have highlighted the importance of a multimodal approach over unimodal approaches. This workconfirms the effectiveness of combining image and text features in meme understanding. Among the evaluated models, the mBERT+ViTarchitecture has achieved the best overall performance across both languages and classification levels. According to the evaluation of shared task we achieved average F1 score of 0.72 securing the 2nd rank in Malayalam task and F1 score of 0.76 in Tamil task securing the 6th rank. However after our experimental evaluation we got best average F1 score of 0.62 for Tamil and 0.49 for Malayalam. Despite moderate results, challenges have remained mainly due to the dataset size, class imbalance, and noisy text extraction from images.

Memes, originally crafted for humor or cultural commentary, have evolved into powerful tools for spreading harmful content, particularly misogynistic ideologies. These memes sustain damaging gender stereotypes, further entrenching social inequality and encouraging toxic behavior across online platforms. While progress has been made in detecting harmful memes in English, identifying misogynistic content in Chinese remains challenging due to the language’s complexities and cultural subtleties. The multimodal nature of memes, combining text and images, adds to the detection difficulty. In the LT-EDI@LDK 2025 Shared Task on Misogyny Meme Detection, we have focused on analyzing both text and image elements to identify misogynistic content in Chinese memes. For text-based models, we have experimented with Chinese BERT, XLM-RoBERTa and DistilBERT, with Chinese BERT yielding the highest performance, achieving an F1 score of 0.86. In terms of image models, VGG16 outperformed ResNet and ViT, also achieving an F1 score of 0.85. Among all model combinations, the integration of Chinese BERT with VGG16 emerged as the most impactful, delivering superior performance, highlighting the benefit of a multimodal approach. By exploiting these two modalities, our model has effectively captured the subtle details present in memes, improving its ability to accurately detect misogynistic content. This approach has resulted in a macro F1 score of 0.90355, securing 3rd rank in the task.

Ensuring a safe and inclusive online environment requires effective hate speech detection on social media. While detection systems have significantly advanced for English, many regional languages, including Malayalam, Tamil and Telugu, remain underrepresented, creating challenges in identifying harmful content accurately. These languages present unique challenges due to their complex grammar, diverse dialects, and frequent code-mixing with English. The rise of multimodal content, including text and audio, adds further complexity to detection tasks. The shared task “Multimodal Hate Speech Detection in Dravidian Languages: DravidianLangTech@NAACL 2025” has aimed to address these challenges. A Youtube-sourced dataset has been provided, labeled into five categories: Gender (G), Political (P), Religious (R), Personal Defamation (C) and Non-Hate (NH). In our approach, we have used mBERT, T5 for text and Wav2Vec2 and Whisper for audio. T5 has performed poorly compared to mBERT, which has achieved the highest F1 scores on the test dataset. For audio, Wav2Vec2 has been chosen over Whisper because it processes raw audio effectively using self-supervised learning. In the hate speech detection task, we have achieved a macro F1 score of 0.2005 for Malayalam, ranking 15th in this task, 0.1356 for Tamil and 0.1465 for Telugu, with both ranking 16th in this task.