4 citations · 5 across the 13 of their papers we have counts for
14 papers
AISPA: User-Centric System Prompt Auditing for Large Language Model Applications
Xiangning Lin, Shenzhe Zhu, Shu Yang +23
System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are r…
ProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
Shu Yang, Difei Xu, Jiaxin Pei +1
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than…
SelfMem: Self-Optimizing Memory for AI Agents
Shu Yang, Junchao Wu, Derek F. Wong +1
While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory systems to effectively leverage h…
Multi-User Large Language Model Agents
Shu Yang, Shenzhe Zhu, Hao Zhu +5
Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a…
STARS: Skill-Triggered Audit for Request-Conditioned Invocation Safety in Agent Systems
Guijia Zhang, Shu Yang, Xilin Gong +1
Autonomous language-model agents increasingly rely on installable skills and tools to complete user tasks. Static skill auditing can expose capability surface before deployment, bu…
Hierarchical Alignment: Enforcing Hierarchical Instruction-Following in LLMs through Logical Consistency
Shu Yang, Zihao Zhou, Di Wang +1
Large language models increasingly operate under multiple instructions from heterogeneous sources with different authority levels, including system policies, user requests, tool ou…
RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang +7
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well,…
Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models
Jiayi Zhang, Shu Yang, Junchao Wu +2
Fine-tuning Large Language Models on a political topic will significantly manipulate their political stance on various issues and unintentionally affect their stance on unrelated t…
Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs
Shu Yang, Junchao Wu, Xuansheng Wu +3
Large Reasoning Models (LRMs) have achieved remarkable performance on complex tasks by engaging in extended reasoning before producing final answers, yet this strength introduces t…
Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning
Junchi Yao, Jianhua Xu, Tianyu Xin +4
The rise of Large Language Model-based Multi-Agent Planning has leveraged advanced frameworks to enable autonomous and collaborative task execution. Some systems rely on platforms…
Understanding Aha Moments: from External Observations to Internal Mechanisms
Shu Yang, Junchao Wu, Xin Chen +4
Large Reasoning Models (LRMs), capable of reasoning through complex problems, have become crucial for tasks like programming, mathematics, and commonsense reasoning. However, a key…
Understanding the Repeat Curse in Large Language Models from a Feature Perspective
Junchi Yao, Shu Yang, Jianhua Xu +3
Large language models (LLMs) have made remarkable progress in various domains, yet they often suffer from repetitive text generation, a phenomenon we refer to as the "Repeat Curse"…
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation
Tong Li, Shu Yang, Junchao Wu +6
We present a comprehensive evaluation framework for assessing Large Language Models' (LLMs) capabilities in suicide prevention, focusing on two critical aspects: the Identification…
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements
Shu Yang, Shenzhe Zhu, Zeyu Wu +7
We introduce Fraud-R1, a benchmark designed to evaluate LLMs' ability to defend against internet fraud and phishing in dynamic, real-world scenarios. Fraud-R1 comprises 8,564 fraud…