Author

Xingyu Zhu

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Preprint Aug 2026

PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment

Perception-Enhanced Alignment DPO (PEA-DPO), a framework for multimodal LLMs alignment, which explicitly leverages visual preference signals to overcome visual insensitivity is proposed, which demonstrates that PEA-DPO enhances sensitivity to visual context while preserving the language modeling capacity of the base model.

Jiawei Feng, Jiancan Wu, Xingyu Zhu et al. · 1 citation

Preprint Jul 2026

Robustifying Vision-Language Models via Test-Time Prompt Adaptation

This work proposes RITA, a Robust test-tIme prompt-TAdaptation framework that shifts from sample-level estimates to distribution-level alignment, and employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal semantic misalignment.

Xingyu Zhu, Huanshen Wu, Shuo Wang et al. · 1 citation