Author

Xiao-Yu Ma

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Jul 2026

One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization

Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text. Prompt optimization repairs such failures by rewriting the user prompt, requiring no generator retraining, and has yielded promising results. However, existi...

Hao-Yue Liu, Xiao-Yu Ma, Ye-Heng Chen et al. · arXiv.org · 1 citation

Jul 2026

Are Prompt Optimizers Blind? Cross-Modal Visual Feedback for Automatic Prompt Optimization

Cross-Modal Visual Feedback (CMVF) incorporates a failure-conditioned visual diagnosis stage, in which a stronger optimizer VLM inspects each failed image without access to predictions or labels, and an error-aware aggregation stage that compresses these observations into reusable, task-level visual blind-spot patterns...

Haoyue Liu, Xiao-Yu Ma, Yeheng Chen et al. · arXiv.org · 1 citation

#artificial intelligence Preprint Aug 2026

SEPO: Evidence-Grounded Prompt Optimization via Structural Editing

This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback that makes prompt optimisation addressable, attributable, and actionable.

Xiao-Yu Ma, Hao-Yue Liu, Yiwen Li et al. · 0 citations