ViVerse-A1: Ascending Generating Vietnamese Poetry with Semantic and Cultural Fidelity

Despite their increasing sophistication in content generation, contemporary Large Language Models (LLMs) consistently fail to master the nuanced artistry of Vietnamese poetry, particularly its intricate semantic layers, figurative language, phonaesthetics, and deep-seated cultural resonance. This research confronts this challenge with the introduction of ViVerse-A1, a bespoke neural architecture optimized for Vietnamese poetic expression. ViVerse-A1's novelty lies in its hybrid training methodology, which synergizes targeted fine-tuning with advanced auxiliary frameworks, including a Masked Language Model and our proposed Masked-Token Reasoning Language Model. This design empowers the model to develop a granular understanding of the structural, prosodic, lexical, and diverse conventions of Vietnamese literature. Rigorous comparative analyses confirm that ViVerse-A1's performance surpasses that of leading state-of-the-art models, demonstrating a unique ability to generate poetry that is both technically proficient and expressively profound. The success of ViVerse-A1 signals a promising trajectory for AI-driven systems designed for deep semantic and cultural intelligence. To facilitate further research, the source code and all related materials are provided publicly at https://github.com/tph-kds/ViVerse_A1