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CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions
Deep neural networks achieve high accuracy through layered numerical transformations, yet their decisions remain difficult to audit because decision evidence is encoded in hidden activations rather than explicit rules. This paper introduces CRISP, a framework that reconstructs the last-layer activat
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