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University of Dayton

Academic institutionnorthamerica · us
Official website
Research library15linked papers
Opportunities0open roles
Selected work

Representative Papers

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks

Aug 12, 2026

This work addresses the problem of selecting the latent space dimension in randomized low-dimensional reparameterization to ensure neural networks can be efficiently trained into low-loss regions. By characterizing accessibility phase transitions through conic geometry, the authors propose a directionally resolved quadratic theoretical framework that accurately predicts residual errors in random slices. Integrating structured random projections—such as Hadamard or recycled Gaussian mappings—with matrix-free curvature approximations and optimizer state compression, they develop a memory-efficient training framework. The method automatically determines the optimal dimensionality without exhaustive scanning, and empirical results on both vision and language models reveal training phase transitions that align closely with theoretical predictions, substantially outperforming existing approximation strategies that neglect directional information.

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Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs

Jun 07, 2026

Current evaluations of large code models predominantly rely on a single pass rate metric, which fails to capture their true performance across multiple programming languages, problem types, and error categories. This work proposes a multilingual, fine-grained evaluation framework that integrates both execution-based testing and static code analysis. We conduct a large-scale assessment of nine open-source models across 12 programming languages and 2,707 LeetCode problems. Results reveal that even the best-performing model, Yi-Coder-9B-Chat, achieves only a 23.64% average accuracy—substantially lower than the human baseline of 57.2%. Notably, 63.25% of failures stem from compilation errors, and static code quality shows a significant disconnect from functional correctness, exposing critical performance limitations obscured by conventional single-metric evaluations.

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LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation

May 08, 2026

This work addresses the challenge of achieving both physical consistency and strong nonlinear modeling capability in vehicle dynamics prediction under highly dynamic autonomous driving scenarios, where existing models often suffer from low accuracy and poor generalization. To overcome this limitation, the authors propose a hybrid vehicle dynamics model that integrates physical priors with data-driven learning. The approach embeds four key physical components—load-sensitive tire forces, longitudinal load transfer, lateral coupling effects, and actuator rate limits—into a neural network architecture constrained by physics. By combining the Pacejka tire model, end-to-end training, and fused simulation–real telemetry data, the method achieves significant improvements: displacement error is reduced by 16.1%–20.6%, yaw rate RMSE drops by 91.3%, inference speed increases by 1.5×, computational cost decreases by 21.6%, and closed-loop lap times improve by 9.5%–17.4% without any track excursions.

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Recent publications

Latest Papers

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks

Aug 12, 2026

This work addresses the problem of selecting the latent space dimension in randomized low-dimensional reparameterization to ensure neural networks can be efficiently trained into low-loss regions. By characterizing accessibility phase transitions through conic geometry, the authors propose a directionally resolved quadratic theoretical framework that accurately predicts residual errors in random slices. Integrating structured random projections—such as Hadamard or recycled Gaussian mappings—with matrix-free curvature approximations and optimizer state compression, they develop a memory-efficient training framework. The method automatically determines the optimal dimensionality without exhaustive scanning, and empirical results on both vision and language models reveal training phase transitions that align closely with theoretical predictions, substantially outperforming existing approximation strategies that neglect directional information.

0 citationsRead paper

Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs

Jun 07, 2026

Current evaluations of large code models predominantly rely on a single pass rate metric, which fails to capture their true performance across multiple programming languages, problem types, and error categories. This work proposes a multilingual, fine-grained evaluation framework that integrates both execution-based testing and static code analysis. We conduct a large-scale assessment of nine open-source models across 12 programming languages and 2,707 LeetCode problems. Results reveal that even the best-performing model, Yi-Coder-9B-Chat, achieves only a 23.64% average accuracy—substantially lower than the human baseline of 57.2%. Notably, 63.25% of failures stem from compilation errors, and static code quality shows a significant disconnect from functional correctness, exposing critical performance limitations obscured by conventional single-metric evaluations.

0 citationsRead paper

LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation

May 08, 2026

This work addresses the challenge of achieving both physical consistency and strong nonlinear modeling capability in vehicle dynamics prediction under highly dynamic autonomous driving scenarios, where existing models often suffer from low accuracy and poor generalization. To overcome this limitation, the authors propose a hybrid vehicle dynamics model that integrates physical priors with data-driven learning. The approach embeds four key physical components—load-sensitive tire forces, longitudinal load transfer, lateral coupling effects, and actuator rate limits—into a neural network architecture constrained by physics. By combining the Pacejka tire model, end-to-end training, and fused simulation–real telemetry data, the method achieves significant improvements: displacement error is reduced by 16.1%–20.6%, yaw rate RMSE drops by 91.3%, inference speed increases by 1.5×, computational cost decreases by 21.6%, and closed-loop lap times improve by 9.5%–17.4% without any track excursions.

0 citationsRead paper