Users show limited grasp of GenAI smartphone data practices but heightened privacy concerns across collection, storage, and control, calling for better transparency and user controls.
Privacy issues in large language models: A survey
6 Pith papers cite this work. Polarity classification is still indexing.
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KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
PrivUn shows privacy unlearning in LLMs produces gradient-driven ripple effects and only shallow forgetting across layers, with new strategies proposed for deeper removal.
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
citing papers explorer
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Understanding User Privacy Perceptions of GenAI Smartphones
Users show limited grasp of GenAI smartphone data practices but heightened privacy concerns across collection, storage, and control, calling for better transparency and user controls.
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KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
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How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
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PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning
PrivUn shows privacy unlearning in LLMs produces gradient-driven ripple effects and only shallow forgetting across layers, with new strategies proposed for deeper removal.
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A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.