LLMs’ performance on machine translation (MT) tasks is often dependent on the data availability in the specific domains and language pairs that they are trained upon. To examine if multiword expressions (MWEs) still present a bottleneck for LLMs regarding language understanding and translation, we report the performance of systems from the WMT 2026 Test Suites shared task, using portions of the publicly available multilingual parallel corpus AlphaMWE as test suites. We received 31 MT systems’ outputs covering English to Chinese (zh), Polish (pl), German (de), and Arabic (ar) including Modern Standard Arabic (MSA) and two dialectal ones (Egyptian and Tunisian Arabic). We carried out automatic evaluations using BLEU, ChrF, and BERT-score to select the top 3 systems per language pair, followed up with human evaluations on the selected systems. Our findings show that figurative and MWE-related phenomena remain challenging for contemporary MT systems, automatic metrics sometimes disagree in system ranking, and human evaluation uncovers language-specific errors that remain hidden by aggregate scores. Inter-annotator analysis further reveals challenges in consistently identifying and calibrating linguistically subtle translation errors, highlighting the need for explicit evaluation guidelines and careful annotator calibration.

We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our system addresses three tasks: psychological state modeling, temporal change detection, and sequence-level summarization.For Task 1, we combine LLM-based data augmentation, DeBERTa classification, and Random Forest regression for structured state prediction. For Task 2, we use few-shot prompting with a locally deployed Llama 3.1 model to detect Switch and Escalation events using short-term temporal context. For Task 3.1, we explore both a deterministic rule-based summarization pipeline and a few-shot LLM-based approach, ranking \textbf{2nd} officially.Our RAG-based method achieves strong performance in Task 3.2, ranking \textbf{1st} for Improvement and \textbf{3rd} for Deterioration, demonstrating its ability to capture recurrent psychological change patterns across timelines. Our analysis reveals key challenges, including the mismatch between classification and regression performance, the difficulty of modeling temporal transitions, and the disagreement between semantic and similarity-based evaluation metrics.These findings highlight the complexity of modeling mental health dynamics and motivate future work on unified evaluation frameworks.We share our code and prompts at \url{https://github.com/4dpicture/CLPsych2026}