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University of Texas Southwestern Medical Center, Southern Methodist University
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GlaKG achieves near-perfect classification while providing a transparent reasoning framework that links biomarker evidence to clinical rules, addressing the black-box nature of traditional deep learning models in healthcare.
MVG-KAN achieves superior PM$_{2.5}$ forecasting by leveraging a novel Geo-Wind Graph that captures wind-driven pollutant transport dynamics.
Adaptive reduced-order models can now stay accurate for much longer by remembering their past, thanks to a clever incremental SVD trick.
Fact verification benchmarks may be teaching models the wrong thing: NEI labels are often gamed via dataset-specific shortcuts, meaning models don't actually learn to identify insufficient evidence.
LLM-powered autonomous hacking frameworks often hallucinate attacks rather than execute them, revealing a critical gap between theoretical potential and real-world effectiveness.
