Selected work

Representative Papers

Search-Based Generation of Undetected Quantum Circuit Mutants

Aug 10, 2026

This work addresses the limitations of existing quantum mutation analysis tools, which rely on fixed gate-level mutations that produce easily detectable mutants and thus inadequately evaluate test suite quality. To overcome this, the authors propose QUMUG, a novel approach that integrates search-based optimization with parameterized quantum gates. By employing a genetic algorithm to automatically tune gate parameters, QUMUG generates challenging, non-equivalent quantum circuit mutants capable of evading detection by current test suites. Experimental results across 30 quantum programs demonstrate that QUMUG’s mutants are, on average, three times harder to detect than those from existing tools, yielding 494 undetected mutants per program with a success rate of 99.67% and a non-equivalence ratio of 94.3%. Furthermore, these mutants prompted a fivefold expansion of test suites, substantially enhancing the effectiveness of quantum program testing.

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Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA

Jul 16, 2026

This work addresses the limitations of current medical multimodal AI systems, which often prioritize answer accuracy at the expense of clinical reasoning reliability and interpretability. Leveraging the MediaEval Medico 2025 gastrointestinal endoscopy dataset, the study systematically evaluates nine multimodal visual question answering (VQA) systems and introduces an evaluation paradigm that transcends conventional leaderboard metrics. The proposed framework emphasizes structured reasoning, explicit visual-textual evidence alignment, mechanisms to prevent data leakage, and lightweight robustness calibration. Experimental results demonstrate that merely improving accuracy does not necessarily enhance clinical trustworthiness; in contrast, approaches incorporating structured reasoning and explicit grounding exhibit greater robustness on heterogeneous clinical questions. These findings underscore the necessity and efficacy of the new evaluation dimensions in advancing trustworthy medical AI.

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Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization

Jun 29, 2026

This work addresses the unreliability of explanations in original Interpretable Mesoscopic Networks (IMNs), which lack constraints and are prone to degenerate solutions—such as weight collapse onto a single dimension. To mitigate this, the authors propose Local Fidelity Regularization (LFR), which enforces alignment between the weights of the linear output layer and local data variations, combined with an L1 sparsity penalty. This approach simultaneously prevents degenerate solutions, enhances predictive performance, and improves the trustworthiness of model explanations. On OpenML benchmark datasets, the regularized IMN significantly outperforms its unregularized counterpart in terms of AUROC, yields reliable interpretations, and achieves predictive accuracy comparable to state-of-the-art black-box models, thereby effectively overcoming the traditional trade-off between accuracy and interpretability.

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Fuzzing REST APIs in Industry: Necessary Features and Open Problems

Apr 02, 2026

This work addresses the high cost and heavy reliance on manual effort in industrial REST API testing, where existing academic fuzzing tools struggle to achieve practical deployment. Drawing on Volkswagen Group’s real-world practices from 2023 to 2026, we systematically evaluate and enhance the open-source search-based fuzzer EvoMaster. For the first time, we identify the critical features required for its successful industrial adoption and uncover several practical challenges. Our approach integrates empirical feedback from real-world API testing with insights from a user study involving 11 testing professionals across four companies. This dual perspective informs targeted improvements to the tool’s design, clarifies its industrial value proposition, and outlines a concrete pathway for future enhancements to bridge the gap between academic research and industrial applicability.

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A phase-field framework for anisotropic viscoelastic-viscoplastic fracture in short fiber-reinforced polymers in hygrothermal environments

Mar 03, 2026

This study addresses the challenge of modeling the anisotropic viscoelastic–viscoplastic fracture behavior of short-fiber-reinforced polymers (SFRPs) under hygrothermal conditions by proposing a unified phase-field fracture framework. For the first time, this framework couples hygrothermal effects, a finite-deformation viscoelastic–viscoplastic constitutive model, and a structural-tensor-based anisotropic fracture energy formulation. By multiplicatively decomposing the deformation gradient, incorporating hygrothermal expansion, and introducing environment-sensitive material parameters, the model accurately captures the dominant influence of multiple fiber orientations on crack driving force distribution and path deflection. Numerical simulations successfully reproduce the complex crack evolution in SFRPs under hygrothermal loading, quantifying the effects of fiber orientation and hygrothermal degradation on peak load, fracture energy, and crack trajectories, thereby significantly enhancing damage prediction accuracy.

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Recent publications

Latest Papers

Search-Based Generation of Undetected Quantum Circuit Mutants

Aug 10, 2026

This work addresses the limitations of existing quantum mutation analysis tools, which rely on fixed gate-level mutations that produce easily detectable mutants and thus inadequately evaluate test suite quality. To overcome this, the authors propose QUMUG, a novel approach that integrates search-based optimization with parameterized quantum gates. By employing a genetic algorithm to automatically tune gate parameters, QUMUG generates challenging, non-equivalent quantum circuit mutants capable of evading detection by current test suites. Experimental results across 30 quantum programs demonstrate that QUMUG’s mutants are, on average, three times harder to detect than those from existing tools, yielding 494 undetected mutants per program with a success rate of 99.67% and a non-equivalence ratio of 94.3%. Furthermore, these mutants prompted a fivefold expansion of test suites, substantially enhancing the effectiveness of quantum program testing.

0 citationsRead paper

Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA

Jul 16, 2026

This work addresses the limitations of current medical multimodal AI systems, which often prioritize answer accuracy at the expense of clinical reasoning reliability and interpretability. Leveraging the MediaEval Medico 2025 gastrointestinal endoscopy dataset, the study systematically evaluates nine multimodal visual question answering (VQA) systems and introduces an evaluation paradigm that transcends conventional leaderboard metrics. The proposed framework emphasizes structured reasoning, explicit visual-textual evidence alignment, mechanisms to prevent data leakage, and lightweight robustness calibration. Experimental results demonstrate that merely improving accuracy does not necessarily enhance clinical trustworthiness; in contrast, approaches incorporating structured reasoning and explicit grounding exhibit greater robustness on heterogeneous clinical questions. These findings underscore the necessity and efficacy of the new evaluation dimensions in advancing trustworthy medical AI.

0 citationsRead paper

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization

Jun 29, 2026

This work addresses the unreliability of explanations in original Interpretable Mesoscopic Networks (IMNs), which lack constraints and are prone to degenerate solutions—such as weight collapse onto a single dimension. To mitigate this, the authors propose Local Fidelity Regularization (LFR), which enforces alignment between the weights of the linear output layer and local data variations, combined with an L1 sparsity penalty. This approach simultaneously prevents degenerate solutions, enhances predictive performance, and improves the trustworthiness of model explanations. On OpenML benchmark datasets, the regularized IMN significantly outperforms its unregularized counterpart in terms of AUROC, yields reliable interpretations, and achieves predictive accuracy comparable to state-of-the-art black-box models, thereby effectively overcoming the traditional trade-off between accuracy and interpretability.

0 citationsRead paper

Fuzzing REST APIs in Industry: Necessary Features and Open Problems

Apr 02, 2026

This work addresses the high cost and heavy reliance on manual effort in industrial REST API testing, where existing academic fuzzing tools struggle to achieve practical deployment. Drawing on Volkswagen Group’s real-world practices from 2023 to 2026, we systematically evaluate and enhance the open-source search-based fuzzer EvoMaster. For the first time, we identify the critical features required for its successful industrial adoption and uncover several practical challenges. Our approach integrates empirical feedback from real-world API testing with insights from a user study involving 11 testing professionals across four companies. This dual perspective informs targeted improvements to the tool’s design, clarifies its industrial value proposition, and outlines a concrete pathway for future enhancements to bridge the gap between academic research and industrial applicability.

0 citationsRead paper

A phase-field framework for anisotropic viscoelastic-viscoplastic fracture in short fiber-reinforced polymers in hygrothermal environments

Mar 03, 2026

This study addresses the challenge of modeling the anisotropic viscoelastic–viscoplastic fracture behavior of short-fiber-reinforced polymers (SFRPs) under hygrothermal conditions by proposing a unified phase-field fracture framework. For the first time, this framework couples hygrothermal effects, a finite-deformation viscoelastic–viscoplastic constitutive model, and a structural-tensor-based anisotropic fracture energy formulation. By multiplicatively decomposing the deformation gradient, incorporating hygrothermal expansion, and introducing environment-sensitive material parameters, the model accurately captures the dominant influence of multiple fiber orientations on crack driving force distribution and path deflection. Numerical simulations successfully reproduce the complex crack evolution in SFRPs under hygrothermal loading, quantifying the effects of fiber orientation and hygrothermal degradation on peak load, fracture energy, and crack trajectories, thereby significantly enhancing damage prediction accuracy.

0 citationsRead paper