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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…
"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…