449 citations · 1.7k across the 108 of their papers we have counts for
12 papers · 1 filter
agent-risk: Risk Assessment for Changes to AI Agents
Yue Zhao, Zixiang Xu, Angela Huyue Zhang +2
Before changing a deployed AI agent (a stricter safety gate, a new model), organizations must approve the change, starting from existing logs. Extending classical risk analysis to…
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science
Jinyi Ye, Yuangang Li, Chenxiao Yu +7
Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their va…
Beyond Top- Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Wang Wei, Tiankai Yang, Samyadeep Basu +7
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redu…
WeClawArena: An Auditable Sandbox and Benchmark for Cross-User Agents Collaboration and Security in Human-Centered Agent Networks
Prince Zizhuang Wang, Aojie Yuan, Haiyue Zhang +3
Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on t…
MemoHarness: Agent Harnesses That Learn from Experience
Yue Huang, Wenjie Wang, Han Bao +7
An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. Whil…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Tianming Sha, Yue Zhao, Lichao Sun +1
Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but corre…
Agent Safety Is Action Alignment
Shawn Li, Yue Zhao
Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the c…
FORTIS: Benchmarking Over-Privilege in Agent Skills
Shawn Li, Chenxiao Yu, Han Wang +8
Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…
SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
Wenjie Wang, Yue Huang, Zhengqing Yuan +6
As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but ins…
Geometry over Density: Few-Shot Cross-Domain OOD Detection
Shawn Li, You Qin, Jiate Li +4
Out-of-distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment in high-stakes applications…
Auditable Agents
Yi Nian, Aojie Yuan, Haiyue Zhang +7
LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in the world, the question is no longer only whether harmful…
When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing
Yi Nian, Haosen Cao, Shenzhe Zhu +4
When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is oft…