Selected work

Representative Papers

Adaptive Repulsive Pheromone Clustering for Foraging Robot Swarms

Aug 17, 2026

This study addresses the inefficiency caused by redundant exploration in robotic swarm foraging by proposing an Adaptive Repellent Pheromone Clustering method. This approach innovatively integrates bio-inspired pheromone deposition with nest-centric clustering estimation to mark explored areas and steer agents away from low-value zones, thereby achieving a dynamic balance between resource exploitation and exploratory redundancy. Extensive validation across multiple scenarios using ARGoS simulations demonstrates that the proposed method significantly outperforms traditional strategies. Specifically, it improves early resource discovery rates by 10% and increases late-stage collection efficiency by up to 60%. These results confirm that the method effectively enhances search diversity and overall system performance in swarm foraging tasks.

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SynthGuard-ReleaseBench: Locked-Audit Evidence for Synthetic Tabular Data Releases

Aug 13, 2026

This study addresses the lack of purpose-specific compliance verification in evaluating synthetic tabular data by proposing a Locked Audit Framework. This approach fixes intended use and tolerance prior to evaluation, decoupling utility, privacy, and human authorization through pre-audit rules, variance adaptation, and anytime-valid certificates. It establishes query budget lower bounds to generate reproducible release evidence. Experiments across multiple datasets demonstrate that the framework effectively discriminates between model performance while tightening certificate bounds by two- to ten-fold. These results validate both the discriminative power and theoretical necessity of the proposed criteria, establishing a rigorous auditing paradigm for safe, context-aware data release in specific application scenarios.

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Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods

Jul 30, 2026

This work addresses the limited adaptivity of stochastic gradient methods in the absence of gradient history by introducing a novel parallel optimization framework. The proposed approach concurrently executes multiple instances of static gradient descent with varying iteration counts and allocates computational resources according to a geometric sequence, thereby automatically identifying effective hyperparameters without manual tuning. By uniquely integrating parallel computation with static gradient methods, the algorithm achieves adaptive optimization while maintaining theoretical convergence guarantees. Experimental results demonstrate that this method significantly enhances adaptability to unknown problem structures compared to existing approaches.

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Energy Market and Carbon Emission Spillovers in Critical Minerals Investment: A Dynamic Connectedness Approach

Jul 29, 2026

This study investigates financial risk spillovers in critical mineral investments and their dynamic linkages with energy markets, carbon emissions, and other macroeconomic variables. Utilizing daily data from 2013 to 2023, it pioneers the use of exchange-traded funds (ETFs) for critical minerals—rather than physical commodity prices—to construct a time-varying parameter vector autoregressive (TVP-VAR) model. Integrating dynamic connectedness and net spillover measures, the analysis uncovers time-varying interaction mechanisms among seven mineral ETFs, energy markets, carbon markets, and investor sentiment. The findings reveal that high-ESG-rated assets act predominantly as net transmitters of risk, while cobalt and aluminum ETFs serve as primary sources of shocks; conversely, WTI crude oil and carbon emission futures largely function as net receivers. The COVID-19 pandemic triggered a structural shift in these spillover roles, offering investors actionable insights for hedge strategies grounded in financial network positions.

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Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

Jul 07, 2026

This study addresses the widespread neglect of computer algebra systems (CAS) integration in current AI-for-math research. The authors propose a ReAct agent framework that synergistically combines large language models with SageMath, enhanced by Context7-powered real-time documentation retrieval and a multi-stage verification mechanism, to emulate authentic mathematical research workflows on a newly introduced RealMath benchmark. This work presents the first systematic evaluation of performance gains when mainstream models are augmented with CAS access: all models exhibit an average accuracy improvement of 9.7 percentage points (up to 27.8 pp), substantially narrowing the gap between open- and closed-source models. Notably, Qwen3.7-Max shows the most significant gain, while GPT-5.5 achieves the highest problem-solving rate at 75.2% with minimal token consumption, advancing automated mathematical conjecture discovery.

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Recent publications

Latest Papers

Adaptive Repulsive Pheromone Clustering for Foraging Robot Swarms

Aug 17, 2026

This study addresses the inefficiency caused by redundant exploration in robotic swarm foraging by proposing an Adaptive Repellent Pheromone Clustering method. This approach innovatively integrates bio-inspired pheromone deposition with nest-centric clustering estimation to mark explored areas and steer agents away from low-value zones, thereby achieving a dynamic balance between resource exploitation and exploratory redundancy. Extensive validation across multiple scenarios using ARGoS simulations demonstrates that the proposed method significantly outperforms traditional strategies. Specifically, it improves early resource discovery rates by 10% and increases late-stage collection efficiency by up to 60%. These results confirm that the method effectively enhances search diversity and overall system performance in swarm foraging tasks.

0 citationsRead paper

SynthGuard-ReleaseBench: Locked-Audit Evidence for Synthetic Tabular Data Releases

Aug 13, 2026

This study addresses the lack of purpose-specific compliance verification in evaluating synthetic tabular data by proposing a Locked Audit Framework. This approach fixes intended use and tolerance prior to evaluation, decoupling utility, privacy, and human authorization through pre-audit rules, variance adaptation, and anytime-valid certificates. It establishes query budget lower bounds to generate reproducible release evidence. Experiments across multiple datasets demonstrate that the framework effectively discriminates between model performance while tightening certificate bounds by two- to ten-fold. These results validate both the discriminative power and theoretical necessity of the proposed criteria, establishing a rigorous auditing paradigm for safe, context-aware data release in specific application scenarios.

0 citationsRead paper

Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods

Jul 30, 2026

This work addresses the limited adaptivity of stochastic gradient methods in the absence of gradient history by introducing a novel parallel optimization framework. The proposed approach concurrently executes multiple instances of static gradient descent with varying iteration counts and allocates computational resources according to a geometric sequence, thereby automatically identifying effective hyperparameters without manual tuning. By uniquely integrating parallel computation with static gradient methods, the algorithm achieves adaptive optimization while maintaining theoretical convergence guarantees. Experimental results demonstrate that this method significantly enhances adaptability to unknown problem structures compared to existing approaches.

0 citationsRead paper

Energy Market and Carbon Emission Spillovers in Critical Minerals Investment: A Dynamic Connectedness Approach

Jul 29, 2026

This study investigates financial risk spillovers in critical mineral investments and their dynamic linkages with energy markets, carbon emissions, and other macroeconomic variables. Utilizing daily data from 2013 to 2023, it pioneers the use of exchange-traded funds (ETFs) for critical minerals—rather than physical commodity prices—to construct a time-varying parameter vector autoregressive (TVP-VAR) model. Integrating dynamic connectedness and net spillover measures, the analysis uncovers time-varying interaction mechanisms among seven mineral ETFs, energy markets, carbon markets, and investor sentiment. The findings reveal that high-ESG-rated assets act predominantly as net transmitters of risk, while cobalt and aluminum ETFs serve as primary sources of shocks; conversely, WTI crude oil and carbon emission futures largely function as net receivers. The COVID-19 pandemic triggered a structural shift in these spillover roles, offering investors actionable insights for hedge strategies grounded in financial network positions.

0 citationsRead paper

Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

Jul 07, 2026

This study addresses the widespread neglect of computer algebra systems (CAS) integration in current AI-for-math research. The authors propose a ReAct agent framework that synergistically combines large language models with SageMath, enhanced by Context7-powered real-time documentation retrieval and a multi-stage verification mechanism, to emulate authentic mathematical research workflows on a newly introduced RealMath benchmark. This work presents the first systematic evaluation of performance gains when mainstream models are augmented with CAS access: all models exhibit an average accuracy improvement of 9.7 percentage points (up to 27.8 pp), substantially narrowing the gap between open- and closed-source models. Notably, Qwen3.7-Max shows the most significant gain, while GPT-5.5 achieves the highest problem-solving rate at 75.2% with minimal token consumption, advancing automated mathematical conjecture discovery.

0 citationsRead paper