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title: "Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting — Stuff That Spins"
description: "arXiv:2607.02637v1 Announce Type: new Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data fo…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:01.794437+00:00"
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# Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02637  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02637v1 Announce Type: new Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potential typically involve 1) training or fine-tuning generators, or 2) using lightweight post-hoc adaptation like prompt engineering or inference-time guidance, making them generator-specific and expertise-intensive. We study a complementary question: given a fixed pool of gene

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