TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking
为解决文本转音乐系统性能评估难题，TTM-Bench框架通过统一协议衡量音乐内容一致性及计算效率，提供可解释的评估指标。
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Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks
本文提出了一种基于神经元激活的符号框架，使用SMT求解器等逻辑引擎有效计算深度神经网络行为的解释，解决了现有技术无法处理深层架构的问题。
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Partial Identification under Causal Orders by Linear Programming
该研究通过线性编程方法，利用因果顺序对反事实查询进行部分识别，解决了在领域知识不完整情况下的非参数识别问题。
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Test-Time Collaborative Classification over Multi-Agent Networks
研究提出一种多智能体网络分布式二分类协作框架，通过独立训练模型在测试时交换本地决策统计来协同预测，探讨了通信预算和学习规则下的性能。
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"If It Looks Like a User": Measuring Real-Time Moderation Effects via Social Media Simulation
This study addresses the overestimation of real-time efficacy in static social media moderation assessments by constructing an empirically calibrated agent-based simulation framework. Utilizing CMA-ES evolutionary strategies to optimize parameters and replicate authentic statistical characteristics, this work quantitatively reveals for the first time how compensatory user reposting significantly undermines governance effectiveness in dynamic moderation environments. The findings confirm that actual moderation performance falls below static estimates due to these adaptive user behaviors. Consequently, this research proposes a high-fidelity simulation methodology that overcomes traditional evaluation biases, providing a reliable dynamic benchmark and theoretical foundation for optimizing content moderation strategies.
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TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking
为解决文本转音乐系统性能评估难题，TTM-Bench框架通过统一协议衡量音乐内容一致性及计算效率，提供可解释的评估指标。
0 citationsRead paper
Neuron Activation-based Computation of Logical Explanations for Deep Neural Networks
本文提出了一种基于神经元激活的符号框架，使用SMT求解器等逻辑引擎有效计算深度神经网络行为的解释，解决了现有技术无法处理深层架构的问题。
0 citationsRead paper
Partial Identification under Causal Orders by Linear Programming
该研究通过线性编程方法，利用因果顺序对反事实查询进行部分识别，解决了在领域知识不完整情况下的非参数识别问题。
0 citationsRead paper
Test-Time Collaborative Classification over Multi-Agent Networks
研究提出一种多智能体网络分布式二分类协作框架，通过独立训练模型在测试时交换本地决策统计来协同预测，探讨了通信预算和学习规则下的性能。
0 citationsRead paper
"If It Looks Like a User": Measuring Real-Time Moderation Effects via Social Media Simulation
This study addresses the overestimation of real-time efficacy in static social media moderation assessments by constructing an empirically calibrated agent-based simulation framework. Utilizing CMA-ES evolutionary strategies to optimize parameters and replicate authentic statistical characteristics, this work quantitatively reveals for the first time how compensatory user reposting significantly undermines governance effectiveness in dynamic moderation environments. The findings confirm that actual moderation performance falls below static estimates due to these adaptive user behaviors. Consequently, this research proposes a high-fidelity simulation methodology that overcomes traditional evaluation biases, providing a reliable dynamic benchmark and theoretical foundation for optimizing content moderation strategies.
0 citationsRead paper