Bangla social media contains several types of hate speech and slurs, but automatic detection is tough due to linguistic complexity, data imbalance and limited resources. We address this challenge in the BLP-2025 shared task by combining Token-Aware Adversarial Contrastive Training (TACT) with Layer-wise Learning Rate Decay (LLRD) to fine-tune transformer models like BanglaBERT, MuRIL, mE5-base and Twitter XLM-R. To capture the complementary strengths of each model, we aggregate the model outputs through logits ensembling and get a robust system for multiclass classification. On the official test set, our model achieved F1 scores of 0.7362 for hate type, 0.7335 for severity, and 0.7361 for target ranking, placing it 1st, 2nd, and 3rd, respectively. The findings indicate that adversarial fine-tuning with logits ensemble learning is a robust way to detect hate speech in resource-limited languages and provides valuable insights for multilingual and low-resource NLP research.

Large Language Models (LLMs) have shown great code-generation capabilities, but their performance in low-resource languages like Bangla is largely unexplored. We participated in BLP-2025 Task 2: Code Generation in Bangla, where we built a pipeline to interpret and execute Bangla instructions using GPT-5. Extensive experiments were conducted with proprietary (GPT-4o Mini, GPT-5 Mini, GPT-5) and open-source (LLaMA 3-8B, TigerLLM-1B-it) models under translation and assertion settings. Results show that GPT-5, with translation and assertion, scored 83.8%, outperformed all baselines, while open-source models lagged due to limited Bangla adaptation. Assertion-based prompting always improved syntactic correctness, and fine-tuning reduced hallucinations across open-source models. We ranked 7th on the official leaderboard with an approach which is competitive and generalizable. Overall, our results show that translation quality, data normalization, and prompt design are key components of low-resource code generation. Furthermore, the proposed BanglaCode benchmark and preprocessing architecture provide a basis for further multilingual code-generation research.