Recent advances in Referring Expression Comprehension (REC) have been largely driven by supervised learning on curated datasets, where each expression is assumed to refer to exactly one known object. However, such assumptions rarely hold in real-world scenarios, where expressions can refer to multiple objects, fail to refer to any, or involve novel categories and complex semantics. These challenges define the task of open-world REC, which demands robust generalization and structured reasoning beyond the scope of traditional REC methods. In this work, we introduce a novel, training-free framework that decouples visual perception from linguistic reasoning to address open-world REC in a zero-shot setting. Our method first transforms the visual scene into a rich textual representation using an open-vocabulary multimodal perception module. It then employs a reasoning language model to interpret the referring expression and perform explicit logical inference over the perceived scene, enabling transparent decision-making and strong generalization in open-world scenarios. Experiments on three standard REC benchmarks as well as two more challenging ones, gRefCOCO and D$^3$, demonstrate that our framework achieves highly competitive zero-shot performance, often surpassing supervised baselines.