Selected work

Representative Papers

A Cognition-Emotion-Personality Framework for Modeling Human-Like Awareness and Behavior in Emergency Evacuations

Jun 28, 2026

This study addresses the limitations of existing evacuation models, which often assume fully rational agents with perfect global knowledge and thus fail to capture the heterogeneity and complexity of human behavior under emergency conditions. To overcome this, the authors propose a unified agent-based modeling framework that integrates cognition, emotion, social interaction, and personality traits. A key innovation is the explicit incorporation of neuroticism into a continuous fear dynamics model, combined with event certainty, memory-driven exit knowledge, individualized decision thresholds, and a forgetting mechanism. Simulation results demonstrate that the model effectively reproduces empirically observed phenomena such as evacuation delays, crowd confusion, injuries, and social influence effects, revealing how cognitive constraints, emotional fluctuations, and personality differences significantly impair evacuation efficiency.

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Exploring the connection between coding habits and cognitive styles in malware developers

Jun 04, 2026

This study addresses a critical gap in cybersecurity research by systematically examining the relationship between malware developers’ coding behaviors and their cognitive styles, an aspect largely overlooked in prior work that predominantly focuses on attack techniques. For the first time, code metrics are employed as behavioral proxies, integrating static application security testing (SAST) with software engineering measures—such as cyclomatic complexity, use of abstraction mechanisms, and vulnerability distributions—to comparatively analyze leaked malware samples against benign open-source projects. The findings reveal that malicious code tends to be smaller in scale, lacks documentation, exhibits higher function complexity, employs fewer abstraction mechanisms, and contains vulnerability types typically avoided by legitimate developers. These patterns reflect distinct motivational drivers, risk tolerance, and development priorities among malware authors, underscoring a strategy prioritizing efficiency and stealth over maintainability, thereby offering a novel empirical foundation for profiling cybercriminal behavior.

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Estimating the expected output of wide random MLPs more efficiently than sampling

May 06, 2026

This work addresses the challenge of efficiently estimating the expected output of wide random multilayer perceptrons (MLPs) under Gaussian inputs, a task for which conventional sampling-based methods incur prohibitive computational costs—particularly in rare-event probability estimation. To overcome this limitation, the authors propose a sampling-free algorithm that leverages the asymptotic properties of wide networks to analytically propagate activation distributions and compute expected outputs. By integrating cumulant analysis, Hermite polynomial expansions, and Gaussian integration theory, the method achieves accurate estimates without Monte Carlo sampling. Theoretical analysis and empirical experiments demonstrate that the proposed approach significantly reduces FLOPs compared to traditional sampling while attaining comparable mean squared error. Moreover, it outperforms Monte Carlo methods in both rare-event estimation and model training, effectively mitigating catastrophic tail risks.

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On the Foundations of Trustworthy Artificial Intelligence

Mar 25, 2026

This work addresses the fundamental challenge that cross-platform nondeterminism undermines the reliability of AI system verification, thereby eroding the foundation of trustworthy AI. We propose the “Determinism Thesis,” asserting that deterministic inference is both necessary and sufficient for achieving key trustworthiness properties—including fairness, robustness, privacy, security, and alignment. To realize this, we develop a pure integer inference engine that eliminates nondeterminism inherent in IEEE 754 floating-point arithmetic and introduce “trust entropy” to quantify the cost of nondeterminism. Implemented in Rust, our system integrates hash-consistency validation with on-chain blockchain proofs. Across 82 cross-architecture (ARM/x86) tests—using models up to 6.7B parameters—it achieves zero hash mismatches, perfect output consistency across four geographically distributed nodes, and successful verification via 356 on-chain transactions.

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Multi-Agent-Based Simulation of Archaeological Mobility in Uneven Landscapes

Mar 03, 2026

This study addresses the reconstruction of ancient human movement, interaction, and transport behaviors in complex terrains from static archaeological evidence. To this end, we propose a multi-agent simulation framework that integrates high-fidelity 3D terrain modeling, heterogeneous agents—including humans, pack animals, and wheeled vehicles—and a hybrid navigation mechanism combining global path planning with reinforcement learning–based local dynamic adjustments. The framework incorporates empirically derived mobility parameters such as load capacity and slope tolerance, enabling efficient and interpretable large-scale simulations. We demonstrate its utility through applications in pursuit-evasion scenarios and comparative analyses of transport modes, revealing the significant influence of terrain morphology, visibility, and agent heterogeneity on ancient mobility patterns.

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Recent publications

Latest Papers

A Cognition-Emotion-Personality Framework for Modeling Human-Like Awareness and Behavior in Emergency Evacuations

Jun 28, 2026

This study addresses the limitations of existing evacuation models, which often assume fully rational agents with perfect global knowledge and thus fail to capture the heterogeneity and complexity of human behavior under emergency conditions. To overcome this, the authors propose a unified agent-based modeling framework that integrates cognition, emotion, social interaction, and personality traits. A key innovation is the explicit incorporation of neuroticism into a continuous fear dynamics model, combined with event certainty, memory-driven exit knowledge, individualized decision thresholds, and a forgetting mechanism. Simulation results demonstrate that the model effectively reproduces empirically observed phenomena such as evacuation delays, crowd confusion, injuries, and social influence effects, revealing how cognitive constraints, emotional fluctuations, and personality differences significantly impair evacuation efficiency.

0 citationsRead paper

Exploring the connection between coding habits and cognitive styles in malware developers

Jun 04, 2026

This study addresses a critical gap in cybersecurity research by systematically examining the relationship between malware developers’ coding behaviors and their cognitive styles, an aspect largely overlooked in prior work that predominantly focuses on attack techniques. For the first time, code metrics are employed as behavioral proxies, integrating static application security testing (SAST) with software engineering measures—such as cyclomatic complexity, use of abstraction mechanisms, and vulnerability distributions—to comparatively analyze leaked malware samples against benign open-source projects. The findings reveal that malicious code tends to be smaller in scale, lacks documentation, exhibits higher function complexity, employs fewer abstraction mechanisms, and contains vulnerability types typically avoided by legitimate developers. These patterns reflect distinct motivational drivers, risk tolerance, and development priorities among malware authors, underscoring a strategy prioritizing efficiency and stealth over maintainability, thereby offering a novel empirical foundation for profiling cybercriminal behavior.

0 citationsRead paper

Estimating the expected output of wide random MLPs more efficiently than sampling

May 06, 2026

This work addresses the challenge of efficiently estimating the expected output of wide random multilayer perceptrons (MLPs) under Gaussian inputs, a task for which conventional sampling-based methods incur prohibitive computational costs—particularly in rare-event probability estimation. To overcome this limitation, the authors propose a sampling-free algorithm that leverages the asymptotic properties of wide networks to analytically propagate activation distributions and compute expected outputs. By integrating cumulant analysis, Hermite polynomial expansions, and Gaussian integration theory, the method achieves accurate estimates without Monte Carlo sampling. Theoretical analysis and empirical experiments demonstrate that the proposed approach significantly reduces FLOPs compared to traditional sampling while attaining comparable mean squared error. Moreover, it outperforms Monte Carlo methods in both rare-event estimation and model training, effectively mitigating catastrophic tail risks.

0 citationsRead paper

On the Foundations of Trustworthy Artificial Intelligence

Mar 25, 2026

This work addresses the fundamental challenge that cross-platform nondeterminism undermines the reliability of AI system verification, thereby eroding the foundation of trustworthy AI. We propose the “Determinism Thesis,” asserting that deterministic inference is both necessary and sufficient for achieving key trustworthiness properties—including fairness, robustness, privacy, security, and alignment. To realize this, we develop a pure integer inference engine that eliminates nondeterminism inherent in IEEE 754 floating-point arithmetic and introduce “trust entropy” to quantify the cost of nondeterminism. Implemented in Rust, our system integrates hash-consistency validation with on-chain blockchain proofs. Across 82 cross-architecture (ARM/x86) tests—using models up to 6.7B parameters—it achieves zero hash mismatches, perfect output consistency across four geographically distributed nodes, and successful verification via 356 on-chain transactions.

0 citationsRead paper

Multi-Agent-Based Simulation of Archaeological Mobility in Uneven Landscapes

Mar 03, 2026

This study addresses the reconstruction of ancient human movement, interaction, and transport behaviors in complex terrains from static archaeological evidence. To this end, we propose a multi-agent simulation framework that integrates high-fidelity 3D terrain modeling, heterogeneous agents—including humans, pack animals, and wheeled vehicles—and a hybrid navigation mechanism combining global path planning with reinforcement learning–based local dynamic adjustments. The framework incorporates empirically derived mobility parameters such as load capacity and slope tolerance, enabling efficient and interpretable large-scale simulations. We demonstrate its utility through applications in pursuit-evasion scenarios and comparative analyses of transport modes, revealing the significant influence of terrain morphology, visibility, and agent heterogeneity on ancient mobility patterns.

0 citationsRead paper