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University of Dayton

Academic institutionnorthamerica · us
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Research library16linked papers
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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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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