Prompt Tuning (PT) is a widely used strategy for adapting pre-trained Vision-Language Models (VLMs) to various downstream tasks. Conventional PT methods evaluate performance separately on known (base) and unknown (new) classes. However, in real-world scenarios, models often encounter inputs without prior knowledge of their class domain. This challenge has motivated the development of Open-world Prompt Tuning (OPT), which requires models to first determine whether a sample belongs to base or new classes and then classify it accordingly. In this work, we carefully review existing OPT methods and identify three key limitations: (L1) incomplete evaluation metrics, (L2) time-consuming and memory-intensive OOD detection methods, and (L3) insufficiently comprehensive optimization strategies. To address these issues, we first tackle L1 by proposing two novel metrics to explicitly evaluate adaptability and generalization under the OPT setting, forming a more comprehensive evaluation framework. For L2, we propose a training-free OOD detection method called Entropy-weighted Rank-normalized Fusion (ERF), which first applies rank normalization to both the maximum and the sum of base-class probabilities, followed by an entropy-weighted fusion of the normalized values. For L3, we propose a plug-and-play Gated Dual-Merging (GDM) strategy to strengthen the classifier’s capability. GDM performs selective merging at the weight level based on an adaptive criterion and combines fine-tuned and LLM-boosted logits at the output level. Extensive experiments on three PT baselines across 11 datasets demonstrate the effectiveness of our proposed ERF and GDM.