This paper presents an overview of the SIGTURK 2026 Shared Task on Terminology-Aware Machine Translation for English-Turkish Scientific Texts. We address the critical challenge of terminological accuracy in low-resource settings by constructing the first terminology-rich English-Turkish parallel corpus, comprising 3,300 sentence pairs from STEM domains with 10,157 expert-validated term pairs. The shared task consists of three subtasks: term detection, expert-guided correction, and end-to-end post-editing. We evaluate state-of-the-art baselines (including GPT-5.2 and Claude Sonnet 4.5) alongside participant systems employing diverse strategies from fine-tuning to Retrieval-Augmented Generation (RAG). Our results highlight that while massive generalist models dominate zero-shot detection, smaller, domain-adapted models using Supervised Fine-Tuning and Reinforcement Learning can significantly outperform them in end-to-end post-editing. Furthermore, we find that rigid retrieval pipelines often disrupt fluency, whereas Chain-of-Thought prompting allows models to integrate terminology more naturally. Despite these advances, a significant gap remains between automated systems and human expert performance in strict terminology correction.