This paper describes our multilingual coreference system developed for the CRAC 2026 unconstrained track. We introduce a unified, single-model architecture based on Conditional Random Fields (CRFs) that supports 20 languages. Notably, our approach achieves multilingual resolution without the use of large language models (LLMs) or pretrained weights. In contrast to resource-intensive neural methods, the proposed model is efficient, and suitable for deployment on standard hardware (CPUs). It uses linguistic and contextual features to capture coreference relations across languages with diverse syntactic and morphological properties. Model training was conducted using the official data distributions released for the CRAC 2026 shared task. This methodology provides a robust, scalable solution for multilingual NLP, demonstrating high utility within resource-constrained environments. The results highlight that feature-driven structured models remain effective for complex cross-lingual tasks. The performance on test data is similar to the results obtained for the development data.