Papers by Chunyi Zhou

6 papers
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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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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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 .
IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents (2025.emnlp-main)

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Challenge: Existing methods for detecting Indirect Prompt Injection (IPI) attacks rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors.
Approach: They propose a novel task execution paradigm that models the agents’ task execution process as a traversal over a planned Tool Dependency Graph (TDG).
Outcome: The proposed model reduces unintended tool invocations triggered by injected instructions, enhancing robustness against IPI attacks.
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)

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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.
PerMemSafe: Benchmarking Implicit Personalized Safety of Long Horizon Self-Evolving Agents (2026.findings-acl)

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Challenge: Existing self-evolving agents have a low safety rate in long-horizon interactions . however, this reliance on context-independent safety evaluations is insufficient .
Approach: They propose a framework that explicitly models personalized risk inference and memory evolution.
Outcome: The proposed framework improves implicit personalized safety by 23.8% over prior frameworks while maintaining helpfulness in long-horizon interactions.

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