11 papers
Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
Jiawen Zhang, Kejia Chen, Jiachen Ma +7
Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic si…
Mitigating Many-shot Jailbreak Attacks with One Single Demonstration
Kejia Chen, Jiawen Zhang, Boheng Li +6
Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demonstrations. We study why this a…
Confidence-Aware Alignment Makes Reasoning LLMs More Reliable
Kejia Chen, Jiawen Zhang, Yihong Wu +5
Large reasoning models often reach correct answers through flawed intermediate steps, creating a gap between final accuracy and reasoning reliability. Existing alignment strategies…
Adaptive Forensic Feature Refinement via Intrinsic Importance Perception
Jiazhen Yang, Junjun Zheng, Kejia Chen +5
With the rapid development of generative models and multimodal content editing technologies, the key challenge faced by synthetic image detection (SID) lies in cross-distribution g…
Understanding and Preserving Safety in Fine-Tuned LLMs
Jiawen Zhang, Yangfan Hu, Kejia Chen +7
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential to substantially degrade safety…
Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
Jiawen Zhang, Lipeng He, Kejia Chen +4
Fine-tuning safety-aligned large language models (LLMs) can substantially compromise their safety. Previous approaches require many safety samples or calibration sets, which not on…