Papers by Jingyuan Yan
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. |
FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy (2026.findings-acl)
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| Challenge: | Complex flight tasks require both intricate, long-horizon decision-making and precise operations. |
| Approach: | They propose a LLM-based copilot system that addresses deficiencies in adaptability and fine-grained decision support while integrating with a high-fidelity environment. |
| Outcome: | The proposed system shortens task completion time while attaining a level of performance approaching that of a human instructor. |
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction (2026.findings-acl)
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Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang, Yu Tian
| 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. |