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20242026
most citedNavigating LLM Ethics: Advancements, Challenges, and Future Directions

26 citations · 27 across the 10 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.RO2026

RoboPhys-3D: A Comprehensive Embodied World Model Evaluation via 3D Reconstruction

Tianyi Wang, Jiazhou Chen, Yiming Xu +7

Video world models increasingly serve as data engines, action planners, and simulators for embodied AI, but conventional embodied world model (EWM) benchmarks lack a unified 3D-gro…

cs.DL2026

From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines

Saleh Afroogh, Yasser Pouresmaeil, Yiming Xu +3

Large Language Models (LLMs) are rapidly reshaping academic research across the natural sciences, social sciences, and humanities, yet the scientific community lacks a comprehensiv…

eess.SY2026

Solar phased arrays-based wireless power transfer for commercial airlines can reduce energy costs and carbon emissions in the United States

Tianyi Wang, Yiming Xu, Jiseop Byeon +5

Decarbonizing aviation remains challenging because energy-dense jet fuels dominate beyond short-range operations, while batteries impose severe range and payload penalties. Here we…

eess.SY2026

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

Tianyi Wang, Tianyi Zeng, Zimo Zeng +8

Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS cr…

eess.SY2026

ARCAS: An Augmented Reality Collision Avoidance System with SLAM-Based Tracking for Enhancing VRU Safety

Ahmad Yehia, Jiseop Byeon, Tianyi Wang +4

Vulnerable road users (VRUs) face high collision risks in mixed traffic, yet most existing safety systems prioritize driver or vehicle assistance over direct VRU support. This pape…

cs.LG2026

PILD: Physics-Informed Learning via Diffusion

Tianyi Zeng, Tianyi Wang, Jiaru Zhang +7

Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability in engineering and scie…