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Author

hao fang

Observed names: Fang, Hao, Hao Fang
ORCID: 0009-0004-0271-6579
Observed ORCID: 0009-0004-0271-6579
OpenAlex ID: A5022419720
When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models
2026 · Association for Computational Linguistics 2026, Chen, Bin, et al. · Underline Science Inc.
Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models
2026 · Association for Computational Linguistics 2026, Chen, Bin, et al. · Underline Science Inc.
Diffusion-Based Natural Adversarial Perturbations Towards Segment Anything Model
2026 · Haodong Xiao, Wenbo Yu, et al. · IEEE International Conference on Acoustics Speech and Signal Processing
A temporal-aware generative network for cross-modal video universal adversarial perturbation generation
2026 · Kai-Wen Zhang, Shuo-Yang Sun, et al. · Knowledge-Based Systems
Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-image Hallucination Mitigation
2026 · Xiaochen Yang, Hao Fang, et al. · Lecture notes in computer science
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Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization
2026 · Jiawei Kong, Hao Fang, et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP)
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When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models
2026 · Xiaoxiao Ma, Kuofeng Gao, et al. · Proceedings of the conference - Association for Computational Linguistics. Meeting
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Infinite Babble: Inflating 3D Vision-Language Model Inference Overhead via Adversarial Geometric Perturbation
2026 · Shuoyang Sun, Jiaxin Hong, et al. · Findings of the Association for Computational Linguistics: ACL (Findings of ACL)
Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling
2026 · Hongyao Yu, Tianqu Zhuang, et al. · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)
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Works count: 9
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