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By clarifying when and why gap modification changes model behavior, this account provides a principled basis for selecting gap interventions in similarity-based vision-language systems across evaluated downstream tasks.
This work proposes ARGTCA, which represents (class, attribute) pairs as nodes in a Symbolic Attribute Graph and trains a Graph Attention Network (GAT) using contrastive objectives to produce structurally informed embeddings that capture inter-attribute dependencies.
G-Loss is presented, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold to build a document-similarity graph that captures global semantic relationships.
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