Beyond the Prompt: Recovering Hidden Concepts in Text-to-Image Models

Zhengfeng Zheng, Minghong Fang · 2026 · arXiv

DOI: 10.48550/arXiv.2610.27473 · open-access PDF

Abstract

Text-to-image models can generate visual concepts that their prompts never mention, and recent work shows that such associations can be implanted so that ordinary prompts trigger them. Auditing this behavior requires recovering the description that a prompt omits. Existing prompt optimizers are not well suited to this task. Gradient-based methods require backward access to the scoring encoder. Discrete evolutionary search treats semantically unrelated tokens as equally close. Language-model-guided search requires one inference call per candidate, which limits exploration. This paper presents E

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