arXiv subjects

Xiaotian Wang

Publications and source records attributed to Xiaotian Wang.

At least 19 recordsLinked to original sources

arXiv2026

Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT). However, direct Doc2Doc translation remains prone to n-gram repetition and progressive quality degradation. A common remedy is to segment the document into finer-grained chunks. Nonetheless, conventional rule-based chunking approaches fail to handle the length distribution mismatch between training and inference. To address this, we introduce Fixed-Range Chunking (FRC), utilizing dynamic programming to partition documents into chunks within a predefined length interval. By consistently applying FRC during training and inference, the input documents of any length are mapped to the same length distribution, substantially reducing train-test length mismatch. Centered on FRC, we propose a lightweight dual-boundary matching algorithm for chunk alignment, alongside four distinct training strategies. Experimental results show that FRC-based fine-tuning substantially improves 7B LLMs over direct Doc2Doc fine-tuning and outperforms existing DocMT methods on IWSLT2017. We further construct GlobVDoc, a 10-language test set independent of mainstream DocMT training sources, and show that FRC improves out-of-distribution document translation.

cs.CL↗

arXiv2026

Hybrid-parity sliding multiferroics

In this work, we introduce a class of hybrid-parity sliding multiferroics in which the spontaneous ferroelectric polarization is coupled to certain nonrelativistic spin splitting components through interlayer sliding, allowing these components to be reversibly switched in an electrical way. Symmetry analysis identifies coplanar magnets as natural platforms for realizing this form of sliding multiferroicity. First-principles calculations establish bilayer VBr$_2$ as a representative example, demonstrating the coupled reversal of the out-of-plane ferroelectric polarization and the signs of both even- and odd-parity nonrelativistic spin splitting components via an interlayer-sliding pathway. The signs of these nonrelativistic spin splitting components are locked to the sliding-switchable ferroelectric polarization and encoded in the spin-current responses, providing a signature of the coupled ferroic switching. Our findings expand the scope of sliding multiferroics and the functionality of sliding ferroelectrics for low-energy, nonvolatile logic devices.

cond-mat.mtrl-sci↗

arXiv2026

Ferroelectric Band Twinning from Pair-State Symmetry

Ferroelectric switching provides a nonvolatile way to control electronic structures, but a general symmetry rule connecting the full Bloch bands of two switchable polarization states is still lacking. Here, we introduce ferroelectric band twinning, a pair-state relation in which the bands of two opposite-polarization states are mapped onto each other by a non-inversion state-exchange symmetry. Using dichromatic groups, we derive the band-twinning rule and identify 11 ferroelectric band-twinning point-group classes. Screening the Ferroelectric Materials Database yields 16 candidate compounds, of which the two lattice-metric-preserving candidates, bulk gamma-Ag3SI and BaAl2O4, are selected for first-principles validation. For gamma-Ag3SI, we further show that the same pair-state symmetry controls the transformation of shift-current tensor components under polarization reversal. These results establish ferroelectric band twinning as a general symmetry framework for nonvolatile control of momentum-dependent electronic structures in ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2026

One-dimensional electronics with edge states in two-dimensional altermagnets

The coupling between real-space inhomogeneities coordinates and spin (r-s) provides an alternative route to achieve efficient spin manipulation in spintronics beyond the conventional momentum-spin (k-s) coupling paradigm. Here we demonstrate an unexpected manifestation of one-dimensional (1D) r-s coupling in two-dimensional (2D) altermagnetic second-order topological insulators, where the spin-split floating edge states -- energetically isolated within the bulk band gap -- emerge and exhibit both Neel-vector-dependent and electrically tunable behaviors. The 1D edge-spin r-s coupling ensures carrier transport to be exclusively carried by the edge states with quantized spin conductance, giving rise to an unconventional edge tunnel magnetoresistance (edge-TMR) effect that can be switched On or Off. As a proof of concept, we computationally design an edge-TMR device based on Cr_2Se_2O monolayer to demonstrate its edge transportation and controllability via the Néel order or electric field. Our findings propose a general prototype altermagnetic device for next-generation low-dimensional spintronics.

cond-mat.mes-hall↗

arXiv2026

Echo: Learning from Experience Data via User-Driven Refinement

Static "human data" faces inherent limitations: it is expensive to scale and bounded by the knowledge of its creators. Continuous learning from "experience data" - interactions between agents and their environments - promises to transcend these barriers. Today, the widespread deployment of AI agents grants us low-cost access to massive streams of such real-world experience. However, raw interaction logs are inherently noisy, filled with trial-and-error and low information density, rendering them inefficient for direct model training. We introduce Echo, a generalized framework designed to operationalize the transition from raw experience to learnable knowledge, effectively "echoing" environmental feedback back into the training loop for model optimization. In today's agent ecosystem, user refinement serves as a primary source of such feedback: driven by responsibility for the outcome, users rigorously transform flawed agent proposals into verified solutions. These user-driven refinement sequences inherently distill agents' crude attempts into high-quality training signals. Echo systematically harvests these signals to continuously align the agent with real-world needs. Large-scale validation in a production code completion environment confirms that Echo effectively harnesses this pipeline, breaking the static performance ceiling by increasing the acceptance rate from 25.7% to 35.7%.

cs.AI↗

arXiv2026

Chiral Altermagnetic Magnetoelectrics

In this work, we introduce a new class of chiral altermagnetic magnetoelectrics in structurally chiral, nonpolar altermagnetic systems and identify the experimentally well-characterized three-dimensional metal-organic framework K[Co(HCOO)$_3$] as a promising material platform. K[Co(HCOO)$_3$] exhibits chirality-locked \emph{g}-wave altermagnetic spin splitting together with dual-mode switchable electric polarization controlled by Néel-vector reorientation and structural chirality. Specifically, Néel-vector reorientation generates a finite electric polarization and reverses its sign, whereas chirality switching between left- and right-handed enantiomers produces an additional sign reversal. The associated electronic and optical responses provide effective readout channels for these switchable states. Our results establish chiral altermagnetic magnetoelectrics as a promising route to chirality- and Néel-vector-controlled nonvolatile multifunctional spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Interstitial-Electron Altermagnetism in Two Dimensions

Altermagnetism has so far been associated with compensated magnetic moments carried by atoms. Here we introduce Stoner instability induced interstitial-electron altermagnetism, a distinct mechanism in which altermagnetic order is carried instead by interstitial anionic electrons in electrides. We show that, owing to the quasi-nucleus-free nature of interstitial electrons, the Stoner instability in electrides hosting two interstitial electrons can naturally stabilize an altermagnetic state rather than the conventional ferromagnetic one. This mechanism leads to a practical design principle for two-dimensional materials, from which we identify monolayers Zr2N and Ti2N as representative candidates. The strong sensitivity of interstitial electrons to cavity size enables efficient strain control of the altermagnetic order and a pronounced piezo-altermagnetic effect. Moreover, we investigate the evolution of the magnetism in Zr2N under ultrafast laser excitation, which exhibits dynamics distinct from those in all previously reported magnetic materials where magnetism is carried by real atoms. Our work not only offers a novel pathway to realize altermagnetism but also reveals an efficient non-magnetic route for its control.

cond-mat.str-el↗

arXiv2026

Small Object Detection in Complex Backgrounds with Multi-Scale Attention and Global Relation Modeling

Small object detection under complex backgrounds remains a challenging task due to severe feature degradation, weak semantic representation, and inaccurate localization caused by downsampling operations and background interference. Existing detection frameworks are mainly designed for general objects and often fail to explicitly address the unique characteristics of small objects, such as limited structural cues and strong sensitivity to localization errors. In this paper, we propose a multi-level feature enhancement and global relation modeling framework tailored for small object detection. Specifically, a Residual Haar Wavelet Downsampling module is introduced to preserve fine-grained structural details by jointly exploiting spatial-domain convolutional features and frequency-domain representations. To enhance global semantic awareness and suppress background noise, a Global Relation Modeling module is employed to capture long-range dependencies at high-level feature stages. Furthermore, a Cross-Scale Hybrid Attention module is designed to establish sparse and aligned interactions across multi-scale features, enabling effective fusion of high-resolution details and high-level semantic information with reduced computational overhead. Finally, a Center-Assisted Loss is incorporated to stabilize training and improve localization accuracy for small objects. Extensive experiments conducted on the large-scale RGBT-Tiny benchmark demonstrate that the proposed method consistently outperforms existing state-of-the-art detectors under both IoU-based and scale-adaptive evaluation metrics. These results validate the effectiveness and robustness of the proposed framework for small object detection in complex environments.

cs.CV↗

arXiv2026

Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge

In this work, we develop, for the first time, a supervised classification framework with class-dependent rewards (CDR) to predict $\geq$MM flares within 24 hr. We construct multiple datasets, covering knowledge-informed features and line-of sight (LOS) magnetograms. We also apply three deep learning models (CNN, CNN-BiLSTM, and Transformer) and three CDR counterparts (CDR-CNN, CDR-CNN-BiLSTM, and CDR-Transformer). First, we analyze the importance of LOS magnetic field parameters with the Transformer, then compare its performance using LOS-only, vector-only, and combined magnetic field parameters. Second, we compare flare prediction performance based on CDR models versus deep learning counterparts. Third, we perform sensitivity analysis on reward engineering for CDR models. Fourth, we use the SHAP method for model interpretability. Finally, we conduct performance comparison between our models and NASA/CCMC. The main findings are: (1)Among LOS feature combinations, R_VALUE and AREA_ACR consistently yield the best results. (2)Transformer achieves better performance with combined LOS and vector magnetic field data than with either alone. (3)Models using knowledge-informed features outperform those using magnetograms. (4)While CNN and CNN-BiLSTM outperform their CDR counterparts on magnetograms, CDR-Transformer is slightly superior to its deep learning counterpart when using knowledge-informed features. Among all models, CDR-Transformer achieves the best performance. (5)The predictive performance of the CDR models is not overly sensitive to the reward choices.(6)Through SHAP analysis, the CDR model tends to regard TOTUSJH as more important, while the Transformer tends to prioritize R_VALUE more.(7)Under identical prediction time and active region (AR) number, the CDR-Transformer shows superior predictive capabilities compared to NASA/CCMC.

cs.LG↗

arXiv2026

Operational Solar Flare Forecasting System Using an Explainable Large Language Model

This study focuses on forecasting major (>=M-class) solar flares that can severely impact the near-Earth environment. We construct two types of datasets using the Space Weather HMI Active Region Patches (SHARP), and develop a flare prediction network based on large language model (LLMFlareNet). We apply SHapley Additive exPlanations (SHAP) to explain the model predictions. We develop an operational forecasting system based on the LLMFlareNet model. We adopt a daily mode for performance comparison across various operational forecasting systems under identical active region (AR) number and prediction date, using daily operational observational data. The main results are as follows. (1) Through ablation experiments and comparison with baseline models, LLMFlareNet achieves the best TSS scores of 0.720 +/- 0.040 on the ten cross-validation (CV) dataset with mixed ARs. (2) By both global and local SHAP analyses, we identify that R_VALUE is the most influential physical feature for the prediction of LLMFlareNet, aligning with flare magnetic reconnection theory. (3) In daily mode, LLMFlareNet achieves TSS scores of 0.680/0.571 (0.689/0.661, respectively) on the dataset with single/mixed ARs, markedly outperforming NASA/CCMC (SolarFlareNet, respectively). This work introduces the first application of a large language model as a universal computation engine with explainability method in this domain, and presents the first comparison between operational flare forecasting systems in daily mode. The proposed LLMFlareNet-based system demonstrates substantial improvements over existing systems.

astro-ph.SR↗

arXiv2025

Magnetic-Field and Strain Engineering of Modulated Transverse Transport in Altermagnetic Topological Materials

Here, we explore the role of inherent altermagnetic topology in transverse transport phenomena (such as crystal/anomalous Hall, Nernst, and thermal Hall effects) in several famous altermagnets, including tetragonal \textit{X}V$_2$\textit{Y}$_2$O (\textit{X} = K, Rb, Cs; \textit{Y} = S, Se, Te), RuO$_2$, MnF$_2$, as well as hexagonal CrSb and MnTe. Notably, in \textit{X}V$_2$\textit{Y}$_2$O, the first experimentally realized layered altermagnets, transverse transport is governed by altermagnetic pseudonodal surfaces, emphasizing the purely topological contributions to transverse transport. Interestingly, we demonstrate that strain engineering and magnetic field, two unique methods for selectively controlling crystal and anomalous transport, can substantially enhance the magnitude of these phenomena while preserving the alternating spin characteristics in both real and momentum space. Moreover, due to the spin symmetry breaking via shear strain, a new magnetic phase, fully compensated ferrimagnetism, with isotropic spin splitting, can be induced. Our findings provide effective strategies not only for manipulating transverse transport in altermagnets but also for controlling magnetic phase transitions, offering valuable insights for their potential applications in spintronics and spin caloritronics.

cond-mat.mtrl-sci↗

arXiv2025

Sliding Ferroelectric Metal with Ferrimagnetism

Two-dimensional (2D) sliding ferroelectric (FE) metals with ferrimagnetism represent a previously unexplored class of spintronic materials, featuring out-of-plane FE polarization, metallic conductivity, and a finite net magnetization, which together enable electrically tunable spintronic functionalities via FE switching. Here, based on antiferromagnetic (AFM) metallic bilayers, we propose a general strategy for constructing 2D sliding FE ferrimagnetic (FiM) metals that can achieve triply-coupled switching, in which the FE polarization, spin splitting, and net magnetization are reversed simultaneously through FE switching. As a prototypical realization, we design a bilayer sliding FE metal with FiM order, derived from monolayer Fe$_5$GeTe$_2$ -- a van der Waals metal with intrinsic ferromagnetic order close to room temperature. The system exhibits a FE transition from a nonpolar (NP) AFM phase to a FE FiM phase via interlayer sliding. The in-plane mirror symmetry breaking in FE metallic states lifts the nonrelativistic spin degeneracy that exists in the NP phase, leading to a sizable net magnetic moment. Furthermore, the interplay between metallicity, ferroelectricity, and ferrimagnetism gives rise to pronounced sign-reversible transport responses near the Fermi level, all of which can be electrically controlled by FE switching. Our results establish sliding FE metals with FiM as a promising platform for electrically reconfigurable, high-speed, and low-dissipation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Lithium and Vanadium Intercalation into Bilayer V2Se2O: Ferrimagnetic-Ferroelastic Multiferroics and Anomalous and Spin Transport

Spin splitting in emerging altermagnets is non-relativistic and momentum-dependent, yet energy-independent, and localized in momentum space, posing challenges for practical applications. Here, we propose an intercalation-driven paradigm for altermagnets to attain ameliorative electronic structures, multiferroic characteristics, and anomalous and spin transport functionalities. As a representative system, we investigate electrochemistry- and self-intercalated V2Se2O bilayers, building on the recently reported room-temperature K- and Rb-intercalated V2Se2O family [Nat. Phys. 2025, 21, 754; Nat. Phys. 2025, 21, 760], utilizing density functional theory, Wannier function analyses, Monte Carlo simulations, and non-equilibrium Green function methods. Intercalation induces room-temperature intralayer ferrimagnetic and interlayer ferromagnetic order (358 K for Li-intercalation and 773 K for V-intercalation), ferroelasticity (~1 % signal intensity), in-plane uniaxial magnetic anisotropy, and metallization, while also modifying the anomalous Hall effect. Notably, Li- and V-intercalated V2Se2O bilayers exhibit enhanced spin splitting and half-metallic behavior, respectively, yielding near-perfect spin filtering efficiency. Intercalation substantially enhances spin transport in V2Se2O-based devices, enabling giant magnetoresistance (877 %), ultra-high thermal tunneling magnetoresistance (~12000 %), and observable spin Seebeck and temperature negative differential resistance effects. This intercalation-driven paradigm expands altermagnetic functionalities through multifunctional integration, offering promising avenues for advanced, miniaturized, room-temperature exploitation of anomalous, electron, and spin transport properties.

cond-mat.mtrl-sci↗

arXiv2025

BiMax: Bidirectional MaxSim Score for Document-Level Alignment

Document alignment is necessary for the hierarchical mining (Bañón et al., 2020; Morishita et al., 2022), which aligns documents across source and target languages within the same web domain. Several high precision sentence embedding-based methods have been developed, such as TK-PERT (Thompson and Koehn, 2020) and Optimal Transport (OT) (Clark et al., 2019; El-Kishky and Guzmán, 2020). However, given the massive scale of web mining data, both accuracy and speed must be considered. In this paper, we propose a cross-lingual Bidirectional Maxsim score (BiMax) for computing doc-to-doc similarity, to improve efficiency compared to the OT method. Consequently, on the WMT16 bilingual document alignment task, BiMax attains accuracy comparable to OT with an approximate 100-fold speed increase. Meanwhile, we also conduct a comprehensive analysis to investigate the performance of current state-of-the-art multilingual sentence embedding models. All the alignment methods in this paper are publicly available as a tool called EmbDA (https://github.com/EternalEdenn/EmbDA).

cs.CL↗

arXiv2025

Electric-Field Control of Terahertz Response via Spin-Corner-Layer Coupling in Altermagnetic Bilayers

Electric field control of electron charge and spin degrees of freedom is fundamental to modern semiconductor and spintronic devices. Yet controlling electromagnetic waves with an electric field, particularly in the terahertz (THz) band, remains a challenge. Here, we propose a spin-corner-layer coupling (SCLC) mechanism in second-order topological altermagnetic bilayers. By using an electric field to influence electrons between different layers, the SCLC mechanism enables simultaneous control over corner and spin degrees of freedom, thereby allowing electric-field tuning of the absorption, emission intensity, and even polarization of THz waves. Taking bilayer NiZrI$_6$ nanodisks as a prototype, we demonstrate that an ultralow electrostatic field can switch both the spin and the layer polarizations of corner states. This dual switching modulates transition dipole moments and oscillator strengths between different corner states, thereby enabling the manipulation of THz waves. This study establishes a mechanism for the electric-field control of spin and THz waves through SCLC, yielding important implications for the advancement of THz spintronics.

cond-mat.mtrl-sci↗

arXiv2025

SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior

As a key research direction in the field of multi-object tracking (MOT), UAV-based multi-object tracking has significant application value in the analysis and understanding of urban intelligent transportation systems. However, in complex UAV perspectives, challenges such as small target scale variations, occlusions, nonlinear crossing motions, and motion blur severely hinder the stability of multi-object tracking. To address these challenges, this paper proposes a novel multi-object tracking framework, SocialTrack, aimed at enhancing the tracking accuracy and robustness of small targets in complex urban traffic environments. The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism. The Velocity Adaptive Cubature Kalman Filter (VACKF) improves the accuracy of trajectory prediction by incorporating a velocity dynamic modeling mechanism. The Group Motion Compensation Strategy (GMCS) models social group motion priors to provide stable state update references for low-quality tracks, significantly improving the target association accuracy in complex dynamic environments. Furthermore, the Spatio-Temporal Memory Prediction (STMP) leverages historical trajectory information to predict the future state of low-quality tracks, effectively mitigating identity switching issues. Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics. Significant improvements in MOTA and IDF1, among other core performance indicators, highlight its superior robustness and adaptability. Additionally, SocialTrack is highly modular and compatible, allowing for seamless integration with existing trackers to further enhance performance.

cs.CV↗

arXiv2025

Altermagnetic Multiferroics: Symmetry-Locked Magnetoelectric Coupling

Multiferroics exhibit significant potential for low-power spintronic devices due to magnetoelectric coupling. Here, we discuss an emerging class of altermagnetic multiferroics, a system demonstrating distinct advantages including zero net magnetization (eliminating stray fields), momentum-dependent spin splitting (enabling electric-field control of spin currents), and intrinsic strong magnetoelectric coupling originating from spin-space symmetry.

cond-mat.mtrl-sci↗

arXiv2025

Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition

In recent years, numerous neuroscientific studies demonstrate that specific areas of the brain are connected to human emotional responses, with these regions exhibiting variability across individuals and emotional states. To fully leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (APAGNN), which dynamically captures the spatial relationships among brain regions during emotional processing. The APAGNN employs three specialized experts that progressively analyze brain topology. The first expert captures global brain patterns, the second focuses on region-specific features, and the third examines emotion-related channels. This hierarchical approach enables increasingly refined analysis of neural activity. Additionally, a weight generator integrates the outputs of all three experts, balancing their contributions to produce the final predictive label. Extensive experiments conducted on SEED, SEED-IV and MPED datasets indicate that our method enhances EEG emotion recognition performance, achieving superior results compared to baseline methods.

eess.SP↗