arXiv subjects

Chuhang Liu

Publications and source records attributed to Chuhang Liu.

8 recordsLinked to original sources

arXiv2026

ProbPlug: A Plugin Uncertainty Network for Reliable Confidence in LLM Binary Classification

Large language models (LLMs) have achieved strong performance across a broad range of classification settings, yet the reliability of their predictions remains a major obstacle to deployment in high-stakes scenarios. Although confidence estimation for LLMs has been widely studied, confidence calibration for LLM-based classification remains underexplored. We introduce ProbPlug, a lightweight confidence estimation framework for LLM-based binary classification, which predicts whether an output is correct using internal token features extracted from a frozen LLM. ProbPlug employs a self-attention module to aggregate hidden representations and can be integrated into the original inference pipeline without modifying the base model. Experiments across multiple tasks involving both text-based and multimodal large models show that ProbPlug provides more reliable confidence estimates, improves classification performance with negligible additional overhead, and exhibits strong generalization across tasks. These results indicate that ProbPlug serves as a practical solution for confidence estimation in LLM-based classification. Our code is publicly available at Github.

cs.CL↗

arXiv2026

Visualizing Microwave-Driven Dynamics of Antiskyrmions and Surface Skyrmions

Microwaves provide coherent access to low-energy excitations and serve as effective probes of high-frequency spin dynamics in quantum and magnetic systems. For topological spin textures, microwave excitation is expected to generate rich collective responses, yet direct real-space observation of ultrafast dynamics remains limited. Here we use time-resolved Lorentz transmission electron microscopy to visualize microwave-driven dynamics in a hybrid antiskyrmion structure composed of a central antiskyrmion and surface skyrmions. We resolve the picosecond evolution of antiskyrmion area and second-harmonic signals, evidencing nonlinear responses of spin textures under microwave excitations. We track the core motions of the antiskyrmion and surface skyrmions, which follow distinct trajectories while sharing the same rotational sense. Micromagnetic simulations reproduce the key observations and associate the dynamic modes with the spatial modulation of the core profile along the thickness. These achievements establish ultrafast electron microscopy as a powerful real-space approach for probing high-frequency microwave-driven dynamics of topological magnetic solitons.

cond-mat.mtrl-sci↗

arXiv2026

Multiferroicity in the two-dimensional limit in hexagonal LuFeO3 films

Multiferroic oxides, which combine coupled ferroelectric and magnetic orders, are central to understanding correlated quantum phenomena. Yet, as thickness approaches the two-dimensional (2D) limit, both ferroelectricity and magnetism are conventionally expected to vanish due to depolarization fields and finite-size effects, respectively. Here, we demonstrate that hexagonal LuFeO3 (h-LuFeO3) retains coupled ferroelectricity and magnetism at the 2D limit, with a thickness of just one and a half unit cells. Remarkably, the ferroelectric polarization remains comparable to bulk values at room temperature, while long-range magnetism and magnetoelectric coupling persist at low temperatures. We further show that the K3 phonon mode, which mediates the polarization-magnetism coupling, is stable down to the 2D limit. Our results establish h-LuFeO3 as the first oxide system to exhibit genuine 2D-limit multiferroicity, providing a fundamental breakthrough in the long-standing quest to understand and control coupled ferroic orders at the atomic scale.

cond-mat.mtrl-sci↗

arXiv2026

VLA-InfoEntropy: A Training-Free Vision-Attention Information Entropy Approach for Vision-Language-Action Models Inference Acceleration and Success

Vision-Language-Action (VLA) models integrate visual perception, language understanding, and action decision-making for cross-modal semantic alignment, exhibiting broad application potential. However, the joint processing of high-dimensional visual features, complex linguistic inputs, and continuous action sequences incurs significant computational overhead and low inference efficiency, thereby hindering real-time deployment and reliability. To address this issue, we use image entropy to quantify the grayscale distribution characteristics of each visual token and introduce attention entropy to capture the distribution of attention scores over task-related text. Visual entropy identifies texture-rich or structurally informative regions, while attention entropy pinpoints semantically relevant tokens. Combined with timestep information, these metrics enable a dynamic transition strategy that shifts the model's focus from global visual features to attention-guided local informative regions. Thus, the resulting VLA-InfoEntropy method integrates spatial, semantic, and temporal cues to reduce redundancy while preserving critical content. Extensive experiments show that our method reduces inference parameters, accelerates inference speed, and outperforms existing approaches.

cs.CV↗

arXiv2026

Attention-weighted Centered Kernel Alignment for Knowledge Distillation in Large Audio-Language Models Applied to Speech Emotion Recognition

The emergence of Large Audio-Language Models (LALMs) has advanced Speech Emotion Recognition (SER), but their size limits deployment in resource-constrained environments. While Knowledge Distillation is effective for LALM compression, existing methods remain underexplored in distilling the cross-modal projection module (Projector), and often struggle with alignment due to differences in feature dimensions. We propose PL-Distill, a KD framework that combines Projector-Level Distillation (PDist) to align audio embeddings and Logits-Level Distillation (LDist) to align output logits. PDist introduces Attention-weighted Centered Kernel Alignment, a novel approach we propose to highlight important time steps and address dimension mismatches. Meanwhile, LDist minimizes the Kullback-Leibler divergence between teacher and student logits from audio and text modalities. On IEMOCAP, RAVDESS, and SAVEE, PL-Distill compresses an 8.4B-parameter teacher to a compact 1.1B-parameter student, consistently outperforming the teacher, state-of-the-art pretrained models, and other KD baselines across all metrics.

cs.SD↗

arXiv2025

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation

Cd$_3$As$_2$ is a prototypical Dirac semimetal that hosts a chiral anomaly and thereby functions as a platform to test high-energy physics hypotheses and to realize energy efficient applications. Here we use a combination of accelerator-based fast ion implantation and theory-driven planning to enhance the negative longitudinal magnetoresistance (NLMR)--a signature of a chiral anomaly--in Nb-doped Cd$_3$As$_2$ thin films. High-energy ion implantation is commonly used to investigate semiconductors and nuclear materials but is rarely employed to study quantum materials. We use electrical transport and transmission electron microscopy to characterize the NLMR and the crystallinity of Nb-doped Cd$_3$As$_2$ thin films. We find surface-doped Nb-Cd$_3$As$_2$ thin films display a maximum NLMR around $B = 7$ T and bulk-doped Nb-Cd$_3$As$_2$ thin films display a maximum NLMR over $B = 9$ T--all while maintaining crystallinity. This is more than a 100% relative enhancement of the maximum NLMR compared to pristine Cd$_3$As$_2$ thin films ($B = 4$ T). Our work demonstrates the potential of high-energy ion implantation as a practical route to realize chiralitronic functionalities in topological semimetals.

cond-mat.mtrl-sci↗

arXiv2024

Electric-Field Control of Magnetic Skyrmion Chirality in a Centrosymmetric 2D van der Waals Magnet

Two-dimensional van der Waals magnets hosting topological magnetic textures, such as skyrmions, show promise for applications in spintronics and quantum computing. Electrical control of these topological spin textures would enable novel devices with enhanced performance and functionality. Here, using electron microscopy combined with in situ electric and magnetic biasing, we show that the skyrmion chirality, whether left-handed or right-handed, in insulating Cr2Ge2Te6, is controlled by external electric field direction applied during magnetic field cooling process. The electric-field-tuned chirality remains stable, even amid variations in magnetic and electric fields. Our theoretical investigation reveals that nonzero Dzyaloshinskii-Moriya interactions between the nearest neighbors, induced by the external electric field, change their sign upon reversing the electric field direction, thereby facilitating chirality selection. The electrical control of magnetic chirality demonstrated in this study can be extended to other non-metallic centrosymmetric skyrmion-hosting magnets, opening avenues for future device designs in topological spintronics and quantum computing.

cond-mat.mes-hall↗

arXiv2020

Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data

Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.

physics.data-an↗