Prompt recovery in large language models (LLMs) is the task of inferring the communicative intent and stylistic framing of the original instruction from model-generated output. This task is especially challenging for low-resource Dravidian languages such as Telugu, where agglutinative morphology, register variation, and scarce annotated data complicate stylistic modelling. In this paper, we present our system for the Shared Task on Prompt Recovery for LLM in Telugu at DravidianLangTech @ ACL 2026, which aims to classify Telugu transcript excerpts into nine communicative style categories: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive.We have implemented a transformer-based approach using ai4bharat/IndicBERTv2-MLM-only, MuRIL-base and Telugu-BERT for Telugu communicative style classification. Our system fine-tunes the pretrained Indic language training samples to capture stylistic patterns in Telugu transcripts. Our approach achieved a macro F1 score of 0.2993 on the evaluation set, demonstrating the potential of Indic-focused pretrained models for stylistic analysis in low-resource language settings.Controlled ablations reveal that label smoothing benefits stronger Indic backbones but degrades weaker ones, and that surface linguistic feature augmentation does not complement rich contextual representations on small datasets.

Memes serve as a method to express feelings such as humor, sarcasm, and diverse viewpoints. The task of identifying sentiment in memes is becoming increasingly complex, particularly in low-resource languages like Nepali where memes often combine images, texts, and code-mixed language. However, multimodal methods for sentiment analysis in Nepali memes seem to be insufficient. In this paper, we present our system for the Subtask B(Sentiment Analysis) for Shared Task on Multimodal Hate and Sentiment Understanding in Low-Resource Memes@CHiPSAL LREC 2026. We implement various unimodal models, such as XLM-RoBERTa-large,MuRIL-base, Twitter-XLM-R for text. Moreover, we incorporate BLIP-2 captions to enhance visual-text understanding and adopted a multimodal approach that fuses textual embeddings, image embeddings, caption embeddings, and similarity scores. The fused features process through cross-attention and a dense neural network for classification, with focal loss and class weighting used to improve performance. Our approach achieved a macro F1 score of 0.50 securing 7th place and highlighting the importance of cross-modal interaction and large-scale pretrained vision-language models for robust meme understanding in sentiment analysis.