<p>Multilingual pretrained language models have enabled effective cross-lingual transfer for many natural language processing tasks; however, their performance remains limited for low-resource languages, even within typologically related language families. A major cause is severe resource imbalance, where high-resource languages dominate training and induce negative transfer on closely related but under-resourced languages, particularly for structure-sensitive tasks such as Named Entity Recognition (NER). This paper proposes Curriculum-Guided Contrastive Learning (CGCL), a unified training framework for low-resource cross-lingual NER that jointly addresses training dynamics and representation alignment. CGCL integrates multi-view contrastive learning with adaptive curriculum scheduling that explicitly controls both language-level exposure and instance-level difficulty during training. While the framework is language-family agnostic, it allows difficulty modeling to adapt to family-specific structural properties, including agglutinative morphology in Turkic languages and lexical variability in the Austronesian language family. We conduct a comprehensive evaluation on a Wikipedia-based NER benchmark across two typologically distinct but structurally comparable language families: Turkic languages and Austronesian languages. Using a unified experimental protocol, CGCL consistently outperforms multilingual fine-tuning, instruction-tuned baselines, and approaches that employ curriculum learning or contrastive learning in isolation. The largest improvements are observed in the lowest-resource languages and in structurally challenging instances, demonstrating CGCL’s effectiveness in mitigating negative transfer. These results indicate that how multilingual models are trained is at least as important as the data they are trained on, and highlight curriculum-guided contrastive learning as a scalable and robust solution for low-resource cross-lingual NER.</p>