Papers by Huanqian Yan

2 papers
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models (2026.findings-acl)

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Challenge: Existing defense methods rely on fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks.
Approach: They propose a decoding-level defense mechanism that employs a lightweight discriminator to iteratively steer the decoding process toward safety.
Outcome: The proposed method improves safety performance by up to 33.40% without fine-tuning on multiple MLLMs.
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction (2026.findings-acl)

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Challenge: Existing Large Language Model (LLM)-based mobile agents follow explicit user instructions without personalized needs.
Approach: They propose a user preference learning strategy enhanced with a Personal Reward Model to improve personalization performance.
Outcome: The proposed agent achieves state-of-the-art performance while maintaining competitive instruction execution performance.

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