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The potential of Generalized Category Discovery (GCD) lies in its ability to identify previously undiscovered patterns in both labeled and unlabeled data by leveraging insights from partially labeled training samples. However, interference can arise due to the model's dual focus on discovering both novel and known categories, often leading to conflicts that obscure true patterns in the dataset. This paper presents a divide-and-conquer framework, Foundation-Adaptive Integrated Refinement (FAIR), which fine-tunes pretrained foundational weights for various purposes, divided into Foundation (pretrained weights), Adaptive (weights fine-tuned with a variance-preserving loss), and Integrated (weights adjusted for both labeled and unlabeled data). The Adaptive utilizes a newly proposed adaptive contrastive loss that introduces variances within classes to preserve the individuality of representations. The Integrated addresses inherent estimation errors while dynamically estimating the number of categories, incorporating a cosine-based perturbation mechanism as a relaxed margin to accommodate potential ground-truth deviations, rather than relying on biased estimates. Extensive experiments on six benchmark datasets demonstrate our method's effectiveness, outperforming state-of-the-art algorithms, especially on fine-grained datasets.