Papers by Changjiang Zhou
VideoEraser: Concept Erasure in Text-to-Video Diffusion Models (2025.emnlp-main)
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| Challenge: | Experimental results show that VideoEraser outperforms prior methods regarding efficacy, integrity, fidelity, robustness, and generalizability. |
| Approach: | They propose a training-free framework that prevents T2V diffusion models from generating videos with undesirable concepts even when explicitly prompted with those concepts. |
| Outcome: | The proposed framework outperforms existing methods in erasure, celebrity erasion, and explicit content erasing tasks. |
“I See What You Did There”: Can Large Vision-Language Models Understand Multimodal Puns? (2026.acl-long)
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Naen Xu, Jiayi Sheng, Changjiang Li, Chunyi Zhou, Yuyuan Li, Tianyu Du, Jun Wang, Zhihui Fu, Jinbao Li, Shouling Ji
| Challenge: | Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor. |
| Approach: | They propose a multimodal pun generation pipeline and a model to evaluate their understanding of puns. |
| Outcome: | The proposed benchmark improves the understanding of multimodal puns by 16.5% in the F1 test. |
ACIArena: Toward Unified Evaluation for Agent Cascading Injection (2026.acl-long)
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Hengyu An, Minxi Li, Jinghuai Zhang, Naen Xu, Chunyi Zhou, Changjiang Li, Xiaogang Xu, Tianyu Du, Shouling Ji
| Challenge: | Existing studies consider only limited attack strategies and simplified MAS settings, limiting their generalizability and comprehensive evaluation. |
| Approach: | They propose a framework to evaluate the robustness of Multi-Agent Systems (MAS) they propose unified evaluation suites spanning attack surfaces and attack objectives . |
| Outcome: | ACIArena provides a benchmark of 1,356 test cases for evaluating MAS robustness . it covers six widely used MAS implementations and provides measurable results . |
A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)
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| Challenge: | Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. |
| Approach: | They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries. |
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)
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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji
| Challenge: | Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output. |
| Approach: | They propose a method that directly modifies model weights to map a trigger to an attacker-specified response. |
| Outcome: | The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility. |