We describe our system for SemEval-2026 Task 13, Subtask B, which focuses on multi-class authorship attribution for code: given a code snippet, the goal is to predict whether it is human-written or generated by one of ten LLM families. The task presents two central challenges: severe class imbalance and long input sequences that frequently exceed the context length of encoder-based Transformers. To address these issues, we adopt a window-based fine-tuning and inference framework. During training, we randomly sample 512-token windows from each snippet and optimize a class-weighted cross-entropy objective with label smoothing. At inference time, we apply a sliding-window strategy and aggregate window-level logits to obtain a snippet-level prediction. We fine-tune three pretrained code encoders (CodeBERT, UniXcoder, and StarEncoder) under this framework and combine their outputs via majority voting. On the official validation split, our best single model (StarEncoder) achieves 0.60 macro F1. On the final test set, the three-model ensemble reaches 0.41 macro F1, ranking 10th on the leaderboard. Our results demonstrate that window-based modeling combined with imbalance-aware optimization provides a robust and reproducible baseline for multi-class LLM attribution under distribution shift.

We describe the UniBuc-SB submission to the ArchEHR-QA shared task, which involved generating grounded answers to patient questions based on electronic health records. Our system exceeded the performance of the provided baseline, achieving higher performance in generating contextually relevant responses. Notably, we developed our approach under constrained computational resources, utilizing only a single NVIDIA RTX 4090 GPU. We refrained from incorporating any external datasets, relying solely on the limited training data supplied by the organizers. To address the challenges posed by the low-resource setting, we leveraged off-the-shelf pre-trained language models and fine-tuned them minimally, aiming to maximize performance while minimizing overfitting.