<ul data-eligibleForWebStory="false"><li>Prover-Verifier Games (PVGs) are effective for verifiability in nonlinear classification models, but have not been applied to high-dimensional images.</li><li>Concept Bottleneck Models (CBMs) can interpret complex data but rely on low-capacity linear predictors.</li><li>Neural Concept Verifier (NCV) combines PVGs with concept encodings for interpretable, nonlinear classification in high-dimensional settings.</li><li>NCV utilizes concept encodings extracted from raw inputs, outperforming CBMs and pixel-based PVG classifier baselines, showing promise for verifiable AI.</li></ul>

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

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