S$^2$COPE: Self-Supervised Concept Discovery via Preference Learning

• URL: http://arxiv.org/abs/2606.14586v1
• Date: Fri, 12 Jun 2026 16:02:44 GMT
• Title: S$^2$COPE: Self-Supervised Concept Discovery via Preference Learning
• Abstract summary: We introduce Self-Supervised Concept discOvery via Preference lEarning (model), a label-free framework that resolves this dilemma.<n>Our work suggest that interpretability can emerge through a model's autonomous interaction with incidental visual structures, without any human supervision.
• Score: 15.951097715837799
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current representation learning paradigms force a fundamental compromise: self-supervised methods scale to massive datasets but yield opaque features, whereas interpretable models remain bottlenecked by the need for dense human annotation. We introduce Self-Supervised Concept discOvery via Preference lEarning (\model), a label-free framework that resolves this dilemma. Instead of treating Vision-Large-Language Models (VLLMs) as static feature extractors, \model leverages them as active participants in a self-supervised preference optimization loop. By autonomously hypothesizing, validating, and reinforcing candidate visual attributes directly from raw imagery, our framework discovers novel, structured concepts without a single label. Extensive experiments across natural, medical, and physics domains demonstrate that \model successfully extracts domain-specific concepts where standard VLLMs often fail to generate. By amortizing concept discovery directly into the VLLM backbone through our self-supervised preference objective -- rather than relying on static generation and disjoint filtering -- we achieve up to a 24-point absolute improvement in downstream top-1 classification accuracy on unseen data. Our work suggest that interpretability can emerge through a model's autonomous interaction with incidental visual structures, without any human supervision.

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