arXiv subjects

Chi Wu

Publications and source records attributed to Chi Wu.

5 recordsLinked to original sources

arXiv2026

Annular Majorana mode in a superconducting topological insulator

When the surface states of a topological insulator becomes superconducting, topological superconductivity can be obtained, and each vortex on the surface can host one single Majorana zero-energy mode which is usually a wave packet decaying exponentially off the vortex core. Here, we predict stable Majorana zero-energy mode whose wave function is ring-shape, dubbed as annular Majorana mode, in the superconducting vortex in topological insulators respecting $3$-fold or $6$-fold rotational symmetry. Such topological insulators are featured with a single nonlinear Dirac cone located at $\bar{\Gamma}$ or three linear Dirac cones at $\bar{\text{M}}$ in the surface Brillouin zone. The annular Majorana mode originates from the effective chiral $f$-wave superconductivity on the nonlinear Dirac cone in the former case and the interference of the effective chiral $p$-wave superconductivity on the three linear Dirac cones in the latter. In both cases, the annular Majorana mode is stabilized by the rotational symmetry and the winding number $3$ carried by the surface states. Candidate materials supporting the annular Majorana mode are predicted. Our work provides new insights into the topological superconductivity in superconducting topological insulators.

cond-mat.supr-con↗

arXiv2026

Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields

Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple applications, and short-horizon tasks, leaving it largely unknown whether modern agents can follow user instructions to autonomously operate domain-specific professional software and accomplish economically valuable work in an end-to-end manner. To bridge this gap, we introduce Workflow-GYM, a benchmark for long-horizon GUI tasks centered on professional domains and specialized software environments. Through extensive experiments on state-of-the-art models, we find that even the strongest models achieve only slightly above 30% success rates, highlighting that professional long-horizon GUI workflows remain highly challenging for current GUI agents. Further analysis reveals that current agents struggle to maintain long-horizon workflow consistency, frequently exhibiting workflow stage omission, error propagation, objective drift, and insufficient understanding of professional software environments. Our findings provide important insights into the limitations of current agent systems and suggest key directions for the next generation of GUI-agent research.

cs.AI↗

arXiv2026

Direct probing the quantum geometric tensor for bosonic collective excitations

The quantum geometric tensor (QGT), whose real and imaginary parts define the quantum metric and Berry curvature, encodes the intrinsic geometry of quantum states. While electronic QGT has recently become experimentally accessible and linked to diverse physical phenomena, its bosonic counterpart remains largely unexplored. Here we show that the dynamical structure factor encodes the momentum-space structure of bosonic wave functions and thereby provides direct access to the full bosonic QGT throughout the Brillouin zone. Applying this framework, we uncover clear geometric signatures in the twofold quadrupole-Weyl phonon of BaPtGe and the nodal-line magnon in Gd, and further generalize the formalism to multiband systems. Our results establish a general route to measuring (non-)Abelian quantum geometry in bosonic systems, a crucial step toward elucidating its impact on condensed matter phenomena.

cond-mat.mtrl-sci↗

arXiv2025

Spin-Orbit Driven Topological Phases in Kagome Materials

Kagome materials have garnered substantial attention owing to their diverse physical phenomena, yet canonical systems such as the AV$_3$Sb$_5$ family exhibit poor $Z_{2}$-type topological properties, spurring an urgent quest for kagome platforms hosting ideal topological states. Recently, Zhou et al. proposed the kagome-type IAMX family, which exhibits distinctive ideal topological states; however, their analysis is primarily restricted to the spinless approximation. In this work, we model relativistic effects in the IAMX family, demonstrating that tuning the spin-orbit coupling (SOC) strength drives topological phase transitions and induces novel topological states, resulting in a rich phase diagram. The configuration of topological surface states evolves continuously as the SOC strength is modulated, consistent with the evolution of the topological phase transition. This suggests a viable route toward designing multi-functional topological devices. First-principles calculations performed on three specific IAMX compounds confirm that SOC governs their topological phases, in complete accord with our model analysis.

cond-mat.mtrl-sci↗

arXiv2019

Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems

In this paper, we consider the user positioning problem in the massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking advantage of the UPA array geometry and wide bandwidth, we advocate the use of the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to replace the traditional ones. The ADCPM embeds the stable and stationary multipath characteristics, e.g. delay, power, and angle in the vertical and horizontal directions, which are beneficial to positioning. Taking ADCPM fingerprints as the inputs, we propose a novel three-dimensional (3D) convolution neural network (CNN) enabled learning method to localize users' 3D positions. In particular, such a 3D CNN model consists of a convolution refinement module to refine the elementary feature maps from the ADCPM fingerprints, three extended Inception modules to extract the advanced feature maps, and a regression module to estimate the 3D positions. By intensive simulations, the proposed 3D CNN-enabled positioning method is demonstrated to achieve higher positioning accuracy than the traditional searching-based ones, with reduced computational complexity and storage overhead, and the ADCPM fingerprints are more robust to noise contamination.

eess.SP↗