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Xueyang Li

Xueyang Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Artificial Intelligence cond-mat.mes-hall cond-mat.mtrl-sci

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preprint2026arXiv

An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance

Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings. Agent skills, self-contained directories that package reusable procedures for AI agents, are emerging as a procedural layer for adapting healthcare agents across diverse healthcare settings. We present the first empirical analysis of healthcare agent skills, drawing on 557 healthcare-related skills filtered from 58,159 public skills on ClawHub and annotated along ten dimensions covering function, deployment context, autonomy, and safety. We find that public healthcare skills emphasize patient-facing workflow automation and monitoring rather than the diagnostic and treatment-oriented tasks foregrounded in healthcare-agent research; coverage of the healthcare lifecycle and specialized clinical inputs remains uneven; and general technical risk does not reliably capture clinical risk. These findings position healthcare skills as a procedural layer not yet addressed by current benchmarks and risk frameworks.

preprint2024arXiv

Origin of zigzag antiferromagnetic orders in XPS3 (X= Fe, Ni) monolayers

Recently, two monolayer magnetic materials, i.e., FePS3 and NiPS3, have been successfully fabricated. Despite that they have the same atomic structure, the two monolayers exhibit distinct magnetic properties. FePS3 holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while NiPS3 exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model which can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies which failed to fully consider the spin-orbit coupling effect, we find that the AFM-ZZ ground state in FePS3 is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor exchange interactions, and combining single-ion anisotropy. Whereas, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in NiPS3. We additionally calculate spin-wave spectrum of AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of AFM-ZZ ground state in two-dimensional materials.