Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute–object compositions by leveraging shared knowledge from seen primitives. While recent works have begun to incorporate test-time adaptation to mitigate performance degradation caused by label-space distribution shifts, they suffer from primitive-level knowledge underutilization and cross-modal semantic asymmetry, hindering robust understanding of novel compositions. To address these issues, we introduce MLDA, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment. Specifically, we construct and progressively update composition- and primitive-level prototype sets to accumulate structured semantic knowledge. By augmenting test inputs with diverse visual perspectives and enriched compositional descriptions, MLDA leverages optimal transport to achieve consistent matching of semantically salient information across modalities. Furthermore, composition activation scores are dynamically updated over the test stream to emphasize informative compositions and suppress irrelevant ones. Extensive experiments are conducted to verify the effectiveness of the proposed method. The code is available at https://github.com/keepgoingjkg/MLDA.