CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
CIPL框架通过模拟敏感信息处理流程，评估大型语言模型代理中的隐私泄露问题，揭示了存储标签不决定泄露风险，并提供了跨不同组件的统一评估方法。
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A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters
本文研究了基于Transformer的时间序列预测中外部变量的准入问题，提出了一种轻量级预编码器门控机制，并通过多个数据集验证了其有效性。
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ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
本文提出ASLEval框架，通过预注册隐藏目标集、测量所有可见出口并保留内部痕迹来解决多步骤会话中隐私暴露错位的问题。
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Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs
This study addresses the problem of finding the optimal teaching sequence that minimizes learning cost in scenarios with prerequisite dependencies. The problem is modeled as a stochastic shortest path problem, and we propose an exact reduction based on lattice theory that transforms it into a deterministic shortest path problem, revealing that the computational difficulty stems not from stochasticity but from the combinatorial complexity inherent in the dependency structure. We theoretically prove the problem to be NP-hard; however, by leveraging dynamic programming, A* search, and feedback arc set reductions, we identify a “doubly simple” regime in real-world course data where A* efficiently solves instances with state-space size linear in the number of concepts. A computable diagnostic metric, \( m\Delta \), further enables practical assessment of instance hardness.
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Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots
Service robots face significant challenges in segmenting glass surfaces using RGB-D cameras in real-world scenarios due to glass transparency, strong reflections, and occlusions. To address these issues, this paper proposes a Weighted Feature Fusion (WFF) module that enables dynamic, adaptive fusion of RGB and depth features; the module is plug-and-play and compatible with multiple mainstream segmentation backbones. Furthermore, we introduce MJU-Glass—the first real-world glass segmentation dataset collected *in situ* by service robots—filling a critical gap in publicly available glass segmentation data. Integrating WFF into architectures such as PSPNet yields substantial improvements in segmentation robustness without compromising computational efficiency: boundary IoU increases by 7.49%, and mean IoU also improves significantly, thereby effectively reducing robot collision risk during navigation and interaction.
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CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
CIPL框架通过模拟敏感信息处理流程，评估大型语言模型代理中的隐私泄露问题，揭示了存储标签不决定泄露风险，并提供了跨不同组件的统一评估方法。
0 citationsRead paper
A Lightweight Plug-in Gate for Transformer-Based Time-Series Forecasters
本文研究了基于Transformer的时间序列预测中外部变量的准入问题，提出了一种轻量级预编码器门控机制，并通过多个数据集验证了其有效性。
0 citationsRead paper
ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
本文提出ASLEval框架，通过预注册隐藏目标集、测量所有可见出口并保留内部痕迹来解决多步骤会话中隐私暴露错位的问题。
0 citationsRead paper
Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs
This study addresses the problem of finding the optimal teaching sequence that minimizes learning cost in scenarios with prerequisite dependencies. The problem is modeled as a stochastic shortest path problem, and we propose an exact reduction based on lattice theory that transforms it into a deterministic shortest path problem, revealing that the computational difficulty stems not from stochasticity but from the combinatorial complexity inherent in the dependency structure. We theoretically prove the problem to be NP-hard; however, by leveraging dynamic programming, A* search, and feedback arc set reductions, we identify a “doubly simple” regime in real-world course data where A* efficiently solves instances with state-space size linear in the number of concepts. A computable diagnostic metric, \( m\Delta \), further enables practical assessment of instance hardness.
0 citationsRead paper
Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots
Service robots face significant challenges in segmenting glass surfaces using RGB-D cameras in real-world scenarios due to glass transparency, strong reflections, and occlusions. To address these issues, this paper proposes a Weighted Feature Fusion (WFF) module that enables dynamic, adaptive fusion of RGB and depth features; the module is plug-and-play and compatible with multiple mainstream segmentation backbones. Furthermore, we introduce MJU-Glass—the first real-world glass segmentation dataset collected *in situ* by service robots—filling a critical gap in publicly available glass segmentation data. Integrating WFF into architectures such as PSPNet yields substantial improvements in segmentation robustness without compromising computational efficiency: boundary IoU increases by 7.49%, and mean IoU also improves significantly, thereby effectively reducing robot collision risk during navigation and interaction.
0 citationsRead paper