works on

From the 1 of 14 linked papers with an AI index.

collaborators

14 papers

cs.AI2026

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

Xiangning Lin, Shenzhe Zhu, Shu Yang +23

The paper presents AISPA, a user‑centric framework for auditing the system prompts that guide large language model behavior in commercial AI products, and reports findings from ana…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.AI2026

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…

cs.CL2026

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…