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

Kostas Triaridis, Morris E. Sharp, Nebojsa Jojic

Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models: 8 upvotes on Hugging Face Daily Papers, #56 of 84 papers on 2026-10-16. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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