Dimensions overall score 7.0

• VISTA is a model-agnostic token importance visualization technique that works without incurring additional computational cost.method 95%
• Existing attention visualization techniques often require backpropagation, resulting in nearly double the GPU memory usage and increased computational cost.limitation 90%
• VISTA uses a three-matrix analytical framework comprising Angular Deviation Matrix, Magnitude Deviation Matrix, and Dimensional Importance Matrix to generate relevance maps.method 95%
• VISTA systematically removes each token and measures the resulting impact across three complementary dimensions to derive a composite importance score.method 90%
• Many existing attention visualization techniques are tailored to specific model architectures, particularly within the Transformer family.limitation 90%
• The paper provides open-source implementations of all proposed and utilized explainability techniques to support reproducibility and wider adoption.technical 95%
• A lightweight, model-agnostic approach for attention visualization remains lacking in existing literature.limitation 85%
• VISTA generates relevance maps that illustrate token-level contributions to model predictions.method 90%

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