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Yu-Bo Zhu

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Preprint Oct 2026

Towards Unified Evaluation of Prompt Enhancers for Video Generation

Modern video generators can realize increasingly complex visual narratives, positioning the prompt enhancer (PE) as a critical bridge from concise user instructions and multimodal references to structured cinematic plans. However, existing PE evaluation relies on rendered videos, imposing substantial computational and...

Ya-Wen Shao, Yu-Bo Zhu, Zi-Yun Dai et al. · 0 citations
Preprint Sep 2026

WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation

WanPE formulates shot-level cinematic plans via video-grounded reverse construction and employs Semantic-Consistency GRPO (SC-GRPO) to faithfully preserve user requirements across shots and over time, leading all evaluated commercial offerings at 5-15 seconds and remains competitive with Seedance 2.5 at 30 seconds.

Yu-Bo Zhu, Ya-Wen Shao, Zi-Yun Dai et al. · 0 citations
Preprint Aug 2026

VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

VIVAS is proposed, a framework built upon the unified token space paradigm, which introduces a dense-structural-semantic vision tokenizer, which expands the textual vocabulary into a unified vision-language vocabulary by incorporating a visual vocabulary.

Zhe-Han Kan, Yu-Bo Zhu, Xing-Hua Jiang et al. · 0 citations
Preprint Aug 2026

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

UVU effectively synergizes pixel-level visual perception with semantic-level visual understanding, internalizing visual reconstruction capabilities and unlocking the facilitative role of visual supervision in enhancing understanding in the pre-training stage.

Zhe-Han Kan, Xing-Hua Jiang, Yu-Bo Zhu et al. · 1 citation
Preprint Aug 2026

When Does Visual Generation Help Visual Understanding in Unified Multimodal Models?

VGAU-Diag is introduced, a fine-grained evaluation framework for vision generation-assisted understanding that stratifies samples by difficulty, enables unified evaluation of multiple reasoning paradigms, and uses Oracle-Ass Reference Protocols.

Yu-Bo Zhu, Zhe-Han Kan, Jing-Yi Yang et al. · 2 citations
Preprint Aug 2026

PURPOSE: Poisoning Conflict Resolution in RAG via Proxy-Fact-Grounded Updates

PURPOSE is proposed, a strict black-box poisoning attack that reframes the injection as an update that minimizes conflict, rather than as a counter-claim, and identifies non-contradicting injection as a practical mode to enhance poisoning attack.

Zijian Wang, Yubo Zhu, M. Dong et al. · 0 citations

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