Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models

Abstract:Modern generative models produce high-quality individual outputs but struggle to sample diversely, even when explicitly instructed to do so. We introduce MINE-SAMPLE-RENDER (MSR), a test-time approach that externalizes the diversity into an explicit, corpus-derived sampling procedure. Our pipeline (i) mines an interpretable categorical schema from a corpus of real artifacts and supplies statistics for sampling; (ii) samples attribute configurations from the corpus-derived statistics; (iii) uses a generative model purely as a conditional renderer of a sampled configuration. Across weather report generation, user simulation, webpage design, text-to-image generation, and agent-generated 3D assets, we evaluate the diversity and output quality of MSR-guided generation and show that MSR improves alignment with corpus distributions and diversity of generated outputs.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2610.21185 [cs.AI]

(or arXiv:2610.21185v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.21185 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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