Papers by Yingkai Hua

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Challenge:	Existing approaches to deception detection and defenses are inadequate . Existing methods do not integrate with agent decision-making .
Approach:	They propose a framework that integrates hybrid-reward learning with asymmetric penalties and experience summarization to distill failure patterns into transferable guidance.
Outcome:	The proposed framework reduces deception susceptibility by 53.8% while maintaining task performance, establishing an effective foundation for robust web agent deployment.