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

• URL: http://arxiv.org/abs/2507.07532v2
• Date: Fri, 11 Jul 2025 10:29:39 GMT
• Title: Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings
• Authors: Berkant Turan, Suhrab Asadulla, David Steinmann, Wolfgang Stammer, Sebastian Pokutta,
• Abstract summary: We introduce the Neural Concept Verifier (NCV), a unified framework combining PVGs with concept encodings for interpretable, nonlinear classification in high-dimensional settings.<n>NCV achieves this by utilizing recent minimally supervised concept discovery models to extract structured concept encodings from raw inputs.
• Score: 20.59727124775316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to complex inputs such as high-dimensional images. Conversely, Concept Bottleneck Models (CBMs) effectively translate such data into interpretable concepts but are limited by their reliance on low-capacity linear predictors. In this work, we introduce the Neural Concept Verifier (NCV), a unified framework combining PVGs with concept encodings for interpretable, nonlinear classification in high-dimensional settings. NCV achieves this by utilizing recent minimally supervised concept discovery models to extract structured concept encodings from raw inputs. A prover then selects a subset of these encodings, which a verifier -- implemented as a nonlinear predictor -- uses exclusively for decision-making. Our evaluations show that NCV outperforms CBM and pixel-based PVG classifier baselines on high-dimensional, logically complex datasets and also helps mitigate shortcut behavior. Overall, we demonstrate NCV as a promising step toward performative, verifiable AI.

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