Abstract: GLOM, an innovative departure from standard deep learning architectures, has been proposed and gained special concern recently due to its good interpretability in representing part-whole relationships in computer vision. However, GLOM faces challenges in achieving agreement and is usually computationally demanding. First, current implementations struggle to produce identical vectors that reliably converge to represent nodes in a parse tree. Second, GLOM is computationally intensive due to the need to maintain equal resolution across all levels. To address these issues, inspired by contrastive learning, we proposed a contrastive agreement enhancer (CAE), which effectively promotes agreement between positive embedding pairs while pushing apart negative pairs, thereby facilitating forming distinct ``islands." Furthermore, we introduce a dissimilarity-focused head ($ H_d $) to reduce redundancy in the top-level embeddings, where embedding weights for downsampling are negatively correlated with similarity within a sliding window. The results of comparison experiments indicate that the proposed approach delicately retains informative content and significantly reduces the number of parameters. Additionally, the ablation experiments and visualization results demonstrate that CAE successfully promotes islands of agreement.