Research

Publications and archived preprints on AI agent evaluation, code benchmarks, computer vision, biomedical imaging, and scientific modeling.

Chen, D. (2026). RefactorBench-JS: Evaluating LLM agents on behavior-preserving code decomposition. Preprint. Visual explainer · Zenodo · Code and data.

RefactorBench-JS result table preview

Chen, D. (2026). Fluorescence distributions in combinatorial models of amyloid fibrils composed of split-YFP, Sup35p, and CFP. Preprint. Zenodo · Code.

Amyloid model plot preview

Chen, D., Zinn, Z., & Lowe, M. (2026). Parameter-efficient fine-tuning of DINOv2 for large-scale font classification. arXiv preprint arXiv:2602.13889.

DINOv2 font samples preview

Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., ... Chen, D. T., ... & Maier-Hein, L. (2022). Biomedical image analysis competitions: The state of current participation practice. arXiv preprint arXiv:2212.08568.

Biomedical competition image montage preview

Chen, D. T., Chen, A. T., & Wang, H. (2022). Simple and fast convolutional neural network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021), Lecture Notes in Computer Science (Vol. 12962). Springer. Presented at the BrainLes workshop at MICCAI 2021. Slides · Presentation video.

MGMT CNN MRI sampling preview

In Progress

Working manuscripts not yet archived as preprints.

Chen, D. (2026). MergeConflictBench: Evaluating LLM agents on semantically correct merge conflict resolution. In progress. PDF · Code and data.

MergeConflictBench dataset table preview

Chen, D., & Surve, A. (2026). laint: Lint rules for AI agents. In progress. PDF · Code and data.

laint benchmark tables preview

Jiha, A., & Chen, D. (2026). FileRerankingBench: A benchmark for file selection in code-editing agents. In progress. PDF · Code and data.

FileRerankingBench task table preview

Chen, D. (2026). A testable prediction for anomalous consciousness research: That better-designed studies will favour the production model where proponent-cited anomalies now favour the filter model. In progress. PDF · Source.

Anomalous consciousness model fit plot preview