1 citations · 1 across the 7 of their papers we have counts for
11 papers
Purpose IR: Compiling User Purpose for Privacy-Preserving Agent Workflow State
Dongsheng Chen, Xiangyu Zhao, Lei Ma +2
LLM-agent workflows routinely transform rich observations into plans, summaries, memories, handoffs, and other reusable state. This creates a privacy boundary that task relevance a…
Raising the Bar for Chinese Adolescent LLM Safety: A Culturally-Grounded, Fine-Grained Benchmark
Jinxiang Wang, Yifan Liu, Jing Tan +3
Safety risks in conversations with adolescents are not always explicit. A request may appear harmless unless a model considers the user's age, circumstances, and earlier turns. Exi…
SaplingGuard: A Multidimensional-Profile-Aware Multi-Agent Guardrail for Developmentally Safe Adolescent-LLM Interaction
Jing Tan, Yifan Liu, Yi Lin +6
As adolescents increasingly use LLMs in everyday life, ensuring safe and developmentally appropriate responses has become essential. However, existing LLM guardrails primarily targ…
Says Block, Still Acts: Why LLM Safety Judgments Fail to Govern Action in LLM Agents
Dongsheng Chen, Xiangyu Zhao, Xin Yao +1
Large language model agents can correctly judge that an action should be blocked while still preferring to take it. We ask why this judgment-action disconnect arises, and whether e…
OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets
Dongsheng Chen, Xiangyu Zhao, Xin Yao +1
AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, tools, and execution backends operat…
"Allow" to Achieve, Over-Privileged Inadvertently: The Unintended Cost of Task-Completion-Driven Pop-up Decisions in Mobile GUI Agents
Dongsheng Chen, Yuxuan Li, Guanhua Chen +5
Mobile GUI agents routinely encounter system permission dialogs during task execution, yet their ability to grant only permissions that are necessary for the delegated task remains…
InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
Yuanshao Zhu, Yuxuan Liang, Xiangyu Zhao +5
The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and privacy-preserving data sharing. Ho…
ARGOS: Automated Functional Safety Requirement Synthesis for Embodied AI via Attribute-Guided Combinatorial Reasoning
Dongsheng Chen, Yuxuan Li, Yi Lin +6
Ensuring functional safety is essential for the deployment of Embodied AI in complex open-world environments. However, traditional Hazard Analysis and Risk Assessment (HARA) method…
GPRec: Bi-level User Modeling for Deep Recommenders
Yejing Wang, Dong Xu, Xiangyu Zhao +7
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a divers…
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning
Jiashi Gao, Ziwei Wang, Xiangyu Zhao +3
Federated learning (FL), integrating group fairness mechanisms, allows multiple clients to collaboratively train a global model that makes unbiased decisions for different populati…
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching
Xiao Han, Zijian Zhang, Xiangyu Zhao +6
As urban residents demand higher travel quality, vehicle dispatch has become a critical component of online ride-hailing services. However, current vehicle dispatch systems struggl…