Selected work

Representative Papers

Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence

Sep 24, 2026

This study addresses the core computational bottleneck of evaluating normalization constants in parameter estimation for discrete models. To overcome this challenge, we propose a novel normalization-free estimation framework that integrates empirical localization with deformed Bregman divergence. By leveraging empirical localization techniques, the proposed method effectively circumvents the computation of global normalization constants, substantially reducing computational overhead. Furthermore, the incorporation of deformed Bregman divergence reconstructs the objective function, endowing the estimator with rigorous statistical properties. Both theoretical analysis and empirical evaluations demonstrate that our approach significantly lowers computational costs and enhances estimation efficiency while exhibiting superior robustness against anomalous noise. Ultimately, this work establishes a new paradigm for efficient parameter estimation in large-scale discrete probabilistic models.

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Rare States and Long-Run Pricing

Sep 22, 2026

研究解决了宏观经济危机对资产价格的影响问题，通过图论和谱分析方法探讨了在可约及近似可约动态下的长期定价机制。

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An LLM Agent-based Framework for Whaling Countermeasures

Jan 21, 2026

This study addresses the growing threat of high-precision generative AI-powered whaling attacks targeting senior university personnel, for which existing defenses lack personalization and contextual awareness. The work proposes the first defense framework based on large language model (LLM) agents, which constructs individualized vulnerability profiles by mining publicly available information, identifies high-risk scenarios, and generates contextually coherent, interpretable, and personalized defensive strategies. By innovatively deploying LLM agents for whaling protection tailored to academic staff, the approach demonstrates in preliminary experiments its ability to produce realistic risk assessments and strategy explanations aligned with actual professional contexts. These findings validate the framework’s feasibility while also highlighting key challenges for real-world deployment.

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Logarithmic scaling and stochastic criticality in collective attention

Jan 18, 2026

This study uncovers universal scaling laws and an underlying stochastic critical mechanism governing collective attention dynamics. By analyzing large-scale Wikipedia pageview data, the authors formulate a stochastic differential equation model driven by fractional Brownian motion and demonstrate that the logarithmic variance of collective attention grows slowly with the logarithm of time, deviating from conventional power-law behavior. They introduce a critical boundary defined by a single exponent ξ = H − η, which separates power-law growth from saturation at ξ = 0, thereby classifying collective attention as a non-Markovian process characterized by long-range memory and ultraslow dynamics. Integrating a Gaussian mixture model, the framework successfully reproduces the observed logarithmic variance scaling and accurately reconstructs the cumulative attention distribution, revealing a profound connection to aging dynamics in glassy systems.

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Recent publications

Latest Papers

Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence

Sep 24, 2026

This study addresses the core computational bottleneck of evaluating normalization constants in parameter estimation for discrete models. To overcome this challenge, we propose a novel normalization-free estimation framework that integrates empirical localization with deformed Bregman divergence. By leveraging empirical localization techniques, the proposed method effectively circumvents the computation of global normalization constants, substantially reducing computational overhead. Furthermore, the incorporation of deformed Bregman divergence reconstructs the objective function, endowing the estimator with rigorous statistical properties. Both theoretical analysis and empirical evaluations demonstrate that our approach significantly lowers computational costs and enhances estimation efficiency while exhibiting superior robustness against anomalous noise. Ultimately, this work establishes a new paradigm for efficient parameter estimation in large-scale discrete probabilistic models.

0 citationsRead paper

Rare States and Long-Run Pricing

Sep 22, 2026

研究解决了宏观经济危机对资产价格的影响问题，通过图论和谱分析方法探讨了在可约及近似可约动态下的长期定价机制。

0 citationsRead paper

An LLM Agent-based Framework for Whaling Countermeasures

Jan 21, 2026

This study addresses the growing threat of high-precision generative AI-powered whaling attacks targeting senior university personnel, for which existing defenses lack personalization and contextual awareness. The work proposes the first defense framework based on large language model (LLM) agents, which constructs individualized vulnerability profiles by mining publicly available information, identifies high-risk scenarios, and generates contextually coherent, interpretable, and personalized defensive strategies. By innovatively deploying LLM agents for whaling protection tailored to academic staff, the approach demonstrates in preliminary experiments its ability to produce realistic risk assessments and strategy explanations aligned with actual professional contexts. These findings validate the framework’s feasibility while also highlighting key challenges for real-world deployment.

0 citationsRead paper

Logarithmic scaling and stochastic criticality in collective attention

Jan 18, 2026

This study uncovers universal scaling laws and an underlying stochastic critical mechanism governing collective attention dynamics. By analyzing large-scale Wikipedia pageview data, the authors formulate a stochastic differential equation model driven by fractional Brownian motion and demonstrate that the logarithmic variance of collective attention grows slowly with the logarithm of time, deviating from conventional power-law behavior. They introduce a critical boundary defined by a single exponent ξ = H − η, which separates power-law growth from saturation at ξ = 0, thereby classifying collective attention as a non-Markovian process characterized by long-range memory and ultraslow dynamics. Integrating a Gaussian mixture model, the framework successfully reproduces the observed logarithmic variance scaling and accurately reconstructs the cumulative attention distribution, revealing a profound connection to aging dynamics in glassy systems.

0 citationsRead paper