Papers by Dengpan Ye

2 papers
SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking (2026.acl-long)

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Challenge: Existing methods for jailbreak ignore the semantic differences between categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness.
Approach: They propose a category-aware jailbreak framework that incorporates the semantic category of harmful questions into prompt generation.
Outcome: The proposed framework improves attack success rates and category alignment and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines.
Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)

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Challenge: a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code.
Approach: They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code.
Outcome: The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples .

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