We participated in the Fifth UNLP shared task on multi-domain document understanding, where systems must answer Ukrainian multiple-choice questions from PDF collections and localize the supporting document and page. We propose a retrieval-augmented pipeline built around three ideas: contextual chunking of PDFs, question-aware dense retrieval and reranking conditioned on both the question and answer options, and constrained answer generation from a small set of reranked passages. Our final system uses Qwen3-Embedding-8B for retrieval, a fine-tuned Qwen3-Reranker-8B for passage ranking, and Qwen3-32B for answer selection. On a held-out split, reranking improves Recall@1 from 0.6957 to 0.7935, while using the top-2 reranked passages raises answer accuracy from 0.9348 to 0.9674. Our best leaderboard run reached 0.9452 on the public leaderboard and 0.9598 on the private leaderboard. The main lesson of this shared task is that, under strict code-competition constraints, preserving document structure and making relevance estimation aware of the answer space are more important than adding complex downstream heuristics.

We participated in the Fourth UNLP shared task on detecting social media manipulation in Ukrainian Telegram posts, addressing both multilabel technique classification and token-level span identification. We propose two complementary solutions: for classification, we fine-tune the decoder-only model with class-balanced grid-search thresholding and ensembling. For span detection, we convert causal LLM into a bidirectional encoder via masked language modeling pretraining on large Ukrainian and Russian news corpora before fine-tuning. Our solutions achieve SOTA metric results on both shared task track. Our work demonstrates the efficacy of bidirectional pretraining for decoder-only LLMs and robust threshold optimization, contributing new methods for disinformation detection in low-resource languages.