arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 19 recordsLinked to original sources

arXiv2026

Fractional Chern Insulators in Twisted Bilayer Optical Lattices

Twisted bilayer materials provide a versatile platform for realizing novel topological states. Motivated by recent experimental realization of atomic Bose-Einstein condensates in twisted bilayer optical lattices, we theoretically investigate topological states for cold atoms trapped in such a system. At single-particle level, the system hosts nearly flat moir\'{e} bands under appropriate experimental parameters. Although these noninteracting bands are topologically trivial in the Altland-Zirnbauer classification, we find that atomic interactions can induce a flat Chern band based on self-consistent Hartree-Fock calculations. Furthermore, exact diagonalization identifies a fractional Chern insulator phase at fractional filling. Our work thus paves the way for exploring strongly correlated topological phases in highly tunable twisted bilayer optical lattices.

cond-mat.quant-gas↗

arXiv2026

Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials

Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, perturbative treatments of electron-phonon interactions and the harmonic phonon picture often break down, necessitating non-perturbative approaches. The ab initio Kubo-Greenwood(aiKG) formalism provides a rigorous framework for evaluating temperature-dependent carrier transport beyond the harmonic approximation. Nevertheless, its practical application is computationally demanding because it requires large supercells, extensive statistical sampling, and extrapolation to the zero-frequency limit. In this work, we introduce an artificial-intelligence(AI)-assisted aiKG framework that incorporates the deep-learning Hamiltonian model. By predicting the Kohn-Sham Hamiltonian with sub-meV accuracy for supercells of up to 250 atoms, the model bypasses the costly iterative self-consistent field calculations while retaining first-principles reliability within the scope of effects captured by the training data. Using a strongly anharmonic thermal insulator, potassium iodide(KI) as a benchmark system, we demonstrate that the proposed approach enables efficient simulations of electronic structure and transport properties from a large supercell. The framework reproduces temperature-dependent carrier mobilities, spectral functions, and effective masses in close agreement with the underlying density functional theory while reducing computational cost to 10%. These results suggest that the AI-assisted aiKG framework can make non-perturbative transport calculations tractable for strongly anharmonic materials, opening a scalable route towards realistic simulations and accelerated discovery of new functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike conventional uncertainty estimates, the GMD employs full covariance matrices to explicitly reveal parameter couplings and multi-modal likelihood structures. The network is trained by minimizing the negative log-likelihood via a surjective parameterization that hard-encodes all GMD constraints, thereby approximating the true likelihood. We validate the method on five numerical examples with increasing complexity, including systems driven by fractional Gaussian and L\'evy noises, oscillators with colored noise, coupled neurons under different observability, and an aeroelastic airfoil with unidentifiable stochastic disturbances. Results demonstrate that PENN-GMD accurately recovers likelihood distributions, captures parameter couplings, and naturally diagnoses non-identifiability through variance broadening or mode splitting. These capabilities establish PENN-GMD as a practical tool for uncertainty-aware parameter identification in complex stochastic systems where conventional likelihood-based methods are infeasible.

stat.ML↗

arXiv2026

StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation

Generalist robot policies aim to map multimodal observations and linguistic task instructions to actions across diverse tasks. However, existing methods typically represent the future as a fixed, short video-action chunk. This short-term future captures local scene evolution for action execution, but it does not explicitly describe the stage-level future that specifies how a task should progress from its current stage to the next. We therefore distinguish two complementary futures for robot manipulation: a short-term physical future to capture local scene evolution and a stage-level semantic future to represent task progress. We introduce StageWAM, which augments a Motus-based World Action Model (WAM) with Stage-JEPA, a goal-conditioned Joint-Embedding Predictive Architecture (JEPA) predictor. Given the current observation and task instruction, Stage-JEPA uses a frozen V-JEPA2 encoder to extract the current-state representation and predicts the latent target of the next inferred stage. Across 50 RoboTwin 2.0 tasks in clean and randomized environments, StageWAM achieves 90.25% overall success and reduces the mean number of execution steps in successful rollouts by 5.97% relative to the strongest baseline.

cs.RO↗

arXiv2026

Spin-Chirality-Driven Bulk Photovoltaic Effect in van der Waals Magnet CrSBr

The bulk photovoltaic effect (BPVE) can be greatly enriched in magnetic materials. Here, we establish vector spin chirality as a tunable knob for generating an unconventional time-reversal-even magnetic BPVE, comprising the chiral shift current (CSC) and chiral injection current (CIC). Using bilayer antiferromagnetic (AFM) CrSBr as a prototype, we theoretically demonstrate the emergence of CSC and CIC. Compared with conventional photovoltaic currents arising from noncentrosymmetric crystal structures or collinear magnetic orderings, CSC and CIC not only possess comparable magnitudes but also exhibit exceptional tunability. Specifically, they can be switched on and off by magnetic-field-induced spin canting, reversed in direction upon canting-direction reversal, and continuously modulated in intensity via canting-angle variation. Furthermore, we reveal an unusual optical transition channel governing both currents in CrSBr. Our work establishes an unconventional magnetic BPVE with remarkable controllability, paving the way for applications in optoelectronics and magnetic sensing in noncollinear magnets.

cond-mat.mtrl-sci↗

arXiv2026

NCGR: Noise-Conditional Gated Rectification for Camera Extrinsic Perturbations in BEV 3D Object Detection

Camera-based bird's-eye-view (BEV) 3D detection typically assumes accurate and fixed camera extrinsics. In detectors using spatial cross-attention (SCA), extrinsic perturbations displace the image-plane projections of BEV reference points, causing queries to sample features from incorrect regions and degrading detection performance. To address this failure mode, Noise-Conditional Gated Rectification (NCGR) is proposed to compensate for projection errors without explicitly estimating a full six-degree-of-freedom extrinsic correction. For each query-camera pair, a 2D rectification offset is predicted and modulated by a camera-level gate to rectify the base projection before native deformable sampling. During training, the perturbation-derived quantities used to construct the condition and gate are gradually replaced through scheduled interpolation by counterparts generated from an auxiliary scalar predicted from camera features. This transition enables blind inference without perturbation metadata. During training, a weight-shared clean-teacher/perturbed-student pair is used, and the rectification module is supervised by a BEV-consistency objective between the two branches. NCGR is evaluated on nuScenes with simulated dynamic and static extrinsic perturbations. In a five-camera dynamic stress test, NCGR achieves 39.69% NDS, compared with 28.00% for BEVFormer and 33.23% for CAPE. Under clean extrinsics, NCGR maintains performance comparable to that of BEVFormer.

cs.CV↗

arXiv2026

FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models

Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization. For illumination recovery, we propose an illumination transfer attention (ITA), which re-weights the self-attention maps in diffusion model to transfer illumination cues from non-shadow to shadow regions. For content preservation, we analyze the effects of illumination variations on self-attention maps and latent high-frequency features in diffusion model, and selectively preserve illumination-invariant components to maintain content fidelity while suppressing residual shadows. We further propose local texture-preserving relighting (LTPR) to mitigate local texture misalignment caused by VAE compression. Extensive experiments demonstrate that our method achieves strong generalization and produces realistic shadow-free images.

cs.CV↗

arXiv2026

GMoT: Gated Motion-Aware Tokenization for Fine-Grained Micro-Gesture Video Reasoning with Multimodal LLMs

Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise. While Multimodal Large Language Models (MLLMs) excel at general video understanding, they inherently struggle with subtle kinematics and often rely on static posture priors. To this end, we propose GMoT, a Gated Motion-Aware Tokenization module that explicitly distills sparse kinematic evidence into a compact sequence prior to temporal modeling. GMoT dynamically spotlights action-relevant regions via spatially weighted pooling, extracts adjacent-frame temporal differencing to capture precise motion energy, and adaptively fuses these cues into the visual stream using a conservatively initialized semantic gate. To transition from simple classification to evidence-grounded reasoning, we further introduce a progressive reward-guided policy refinement paradigm, supported by a semi-supervised annotation pipeline that generates anatomically focused captions. Beyond achieving the best Top-1 accuracy among the compared methods on iMiGUE (67.32\%) and SMG (73.11\%), improving the Qwen3-VL-8B baseline by +6.80 and +3.11 points, our framework introduces Body-Region Grounding (BRG) Recall as an anatomical-grounding proxy conditioned on correct predictions, together with an overlapping-label cross-domain transfer protocol between iMiGUE and SMG. Extensive evaluations demonstrate that our GMoT-augmented model improves in-domain accuracy, retains clear gains under label-preserving corruptions, and improves accuracy-oriented cross-domain transfer under explicit small-split caveats while maintaining high anatomical grounding in its generated rationales.

cs.CV↗

arXiv2026

ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On

Image-based virtual try-on has emerged as a compelling task in e-commerce and augmented reality, yet existing methods struggle to simultaneously preserve fine garment semantics and adapt to diverse person body geometries under large clothing-body deformations. We present ModaFlow, a modality-aware flow-matching based framework for high-fidelity virtual try-on that achieves precise alignment between textual descriptions and garment appearance. Unlike prior methods that treat multimodal conditions uniformly, ModaFlow introduces a modality-aware guidance scheme: visual garment embeddings extracted by a pretrained image prompt adapter provide deterministic, persistent structural guidance, while textual embeddings generated from garment descriptions are controlled via classifier-free guidance (CFG) with adaptive scaling and zero-initialized velocity. To further enhance flow field accuracy, we propose two regularization losses, cosine similarity and perceptual flow discrimination, that jointly improve directional consistency and perceptual realism of the velocity field. Additionally, a mask manipulation strategy stochastically samples among box, transparent, and relaxed masks during training, simulating diverse occlusion scenarios and enabling robust inference under unpaired settings where only a box mask is available. Experiments show that ModaFlow achieves state-of-the-art results in both qualitative and quantitative evaluations, reducing FID by approximately 30% on paired and 20% on unpaired benchmarks.

cs.CV↗

arXiv2026

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning

Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, most existing methods rely on conventional deep learning architectures, and the limited model capacity constrains performance. Although large-model post-training techniques have achieved great success in general domains, their direct transfer to RSICC remains challenging due to data scarcity and the need for fine-grained change understanding. To address this, we propose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data generation paradigm, release the instruction dataset RSICI, and establish a task-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change representations and guide the model in perceiving and understanding temporal differences. In addition, we propose Dual-Negative Preference Optimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICP and further refine model performance. Extensive experiments validate the superior capability of RSICCLLM, which achieves outstanding results with only 7B parameters, surpassing models of substantially larger scales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.

cs.CV↗

arXiv2026

Navigating User Behavior toward Personalized Multimodal Generation

Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visual details, leaving generators misaligned with user demand. We study personalized content generation, which turns a user's interaction history into an executable instruction for downstream synthesis, and identify two obstacles: behavior must be encoded in a form legible to language reasoning, and the model must acquire instruction-writing skill absent from both pretraining and behavior data. We propose NaviGen, which represents each item with a dual identifier coupling a collaborative code and a textual code as a behavioral substrate and a semantic bridge in one token stream. On this representation, a two-stage SFT+RL pipeline first distills preference reasoning and instruction writing from evolutionarily searched supervision, then aligns generation with user intent through hierarchical and self-consistent rewards. Experiments across product, game, and short-video domains show that NaviGen improves personalized image and video generation, strengthens next-item prediction, and yields more specific, relevant, and visually generatable instructions. Our code is released at: https://github.com/iLearn-Lab/NaviGen.

cs.AI↗

arXiv2026

AI-accelerated metallized $\sigma$-bonding screening for superconductor discovery

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized $\sigma$-bonding picture, we introduce the $\sigma$-bonding density of states ($\sigma$DOS) as an efficient physical descriptor to identify high-transition-temperature ($T_{\mathrm{c}}$) superconductors from density functional theory (DFT)-level electronic structure without explicit DFPT calculations. The evaluation of $\sigma$DOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B$_{13}$Se as an ambient-pressure superconductor candidate with predicted $T_{\mathrm{c}} > 40$~K, together with a family of high-$T_{\mathrm{c}}$ B$_{13}X$ candidates, supporting the effectiveness of this discovery strategy. By bridging physics priors with AI acceleration, this study delivers an efficient and generalizable route for computational materials discovery in the AI era.

physics.comp-ph↗

arXiv2026

Topological Codes from Space Groups: A Route beyond Translation Invariance

Translation invariance underlies all algebraic constructions of topological codes with geometrical locality. It has remained an open question whether codes that generically break this invariance can still be topological and simultaneously possess geometrical locality. Resolving this question is important both fundamentally---deepening our understanding of topological phases---and practically, as relaxing translation invariance could vastly expand the design space and potentially reduce resource overhead in fault-tolerant architectures. Here we introduce space-group codes, in which crystallographic point-group operations enter the bulk stabilizer algebra; bivariate bicycle (BB) codes arise as the translation-only limit. The key insight is that the point-group orbit resolves topology and locality together: it yields a computable algebraic criterion for topological order and a folded geometry in which point-group operations become local. We identify space-group codes whose code parameters exceed the reported same-blocklength, same-check-weight BB benchmarks. In five parameter-matched neutral-atom comparisons, reflection codes reduce the optimized movement cost in every case, by up to $60\%$, while folded placements also enable lower-overhead multilayer superconducting layouts. Treating spatial operations as a code-design variable therefore opens a route to topological codes jointly optimized for information protection and hardware geometry.

quant-ph↗

arXiv2026

Saturated and Anisotropic Magnetostriction in an Altermagnet

Magnetostriction, a fundamental phenomenon bridging magnetism and mechanics, has enabled a broad spectrum of applications. For almost two centuries, it has been mainly investigated for ferromagnets. Regarding the magnetostriction of antiferromagnets (AFMs), limitedly known examples for both conventional collinear AFMs and noncollinear AFMs predominantly exhibit non-saturating magnetic-field dependence. Herein, we report an easily saturated magnetostriction effect in a prototypical altermagnet - MnTe, which is an emerging class of collinear AFMs with special crystal symmetries. For high-quality MnTe single crystals, the magnetostriction saturates under a moderate field of ~0.7 T with an intriguing two-fold-symmetry anisotropy. First-principles calculations reveal that the saturated and anisotropic magnetostriction originates from symmetry-allowed coupling between elastic strain and its N\'eel order parameter. These findings break the traditional wisdom on antiferromagnetic magnetostriction.

cond-mat.mtrl-sci↗

arXiv2026

An EFT Map of Axion Dark Radiation from Reheating

Light, weakly coupled sectors can retain information about the cosmological background in which they are produced. We study light axions produced during reheating and their contribution to dark radiation, $\Delta N_{\rm eff}$. We develop a shift-symmetric EFT in which an inflaton-dependent axion kinetic term systematically organizes the leading production channels. The same kinetic function generates both direct inflaton decay and inflaton annihilation from the oscillating inflaton background. Direct decay is described by an invisible inflaton branching fraction, while annihilation is a genuinely reheating-sensitive source controlled by a coherent combination of Wilson coefficients. We derive the contribution to $\Delta N_{\rm eff}$ from both channels and show that they scale oppositely with the reheating temperature: the decay contribution falls as $T_{\rm rh}^{-2}$, whereas the annihilation contribution grows approximately as $T_{\rm rh}^{4/3}$. Their crossing is missed by treatments that keep only one production channel. We translate current and projected $\Delta N_{\rm eff}$ sensitivities into constraints on the Wilson coefficients of the kinetic function, obtaining a two-dimensional EFT map of axion dark radiation from reheating. This map can imply both lower and upper bounds on the reheating temperature, showing that light axion relics can turn dark radiation measurements into constraints on reheating.

hep-ph↗

arXiv2026

Irreducible Graviton Floor from Reheating

Inflaton decay inevitably emits gravitons through bremsstrahlung during reheating. We show that the soft part of this emission amplitude, fixed by Weinberg's soft-graviton theorem, becomes an irreducible stochastic gravitational-wave (GW) background after accounting for cosmological evolution. The theorem fixes the infrared branch of the spectrum, $\Omega_{\rm GW}\propto f$, independently of the microscopic operator responsible for inflaton decay, while the normalization is controlled by the hard inflaton decay rate and by a phase-space factor. We carry this out for inflaton $n$-body decays, including the phase-space integrals, finding that the maximum of the spectrum scales as $2/n$ relative to the $n=2$ case. The signal can reach $\Omega_{\rm GW}h^2\sim \mathcal O(10^{-17})$ at frequencies above the GHz scale. This predicts a stochastic graviton floor from perturbative reheating: a larger signal would require either other processes beyond perturbative bremsstrahlung or inflationary scenarios beyond conventional single-field slow roll.

hep-ph↗

arXiv2026

Exciton-roton mode in moir\'e fractional Chern insulators

Moir\'e fractional Chern insulators (FCIs) are a novel class of quantum matter that realizes fractional quantum Hall (FQH) physics in zero magnetic field and provides a platform for exploring unconventional collective excitations. Here we show that hybridization between the magneto-roton and moir\'e interband excitations gives rise to an exciton-roton mode absent in continuum FQH systems in the long-wavelength limit. Using exact diagonalization and a variational Bethe-Salpeter equation for twisted MoTe$_2$, we demonstrate that this hybridization is controlled by the quantum geometry and yields a mode that combines excitonic optical response with the characteristic FCI roton minimum. The resulting exciton-roton remains low-lying, with excitation energy below the interband transition, and acquires optical activity, leading to a double-peak spectroscopic signature. These results identify optical spectroscopy as a direct probe of collective excitations in moir\'e FCIs.

cond-mat.str-el↗

arXiv2026

Central limit theorem for slow-fast system under mixed fractional Brownian motion

This work considers a type of slow-fast system, where the slow component is driven by fractional Brownian motion (FBM) with $H > 1/2$ and the fast component is a Markovian stationary process. Our solution mapping is defined based on the Young-Wiener sense, which is constructed via the stochastic sewing lemma. Then, we aim to show the fluctuation from the averaging limit by applying the Poisson PDE method. Unlike the case of standard Brownian motion, the Poisson PDE method must be developed to a non-Markovian fractional setting. The first part addresses the central limit theorem problem for a slow-fast system under small FBM, which is fully coupled with the fast varying one. Firstly, a Wiener-Young-Ito formula is constructed for the Wiener-Young-Ito integral. The behavior of the deviation component is related to solutions of Poisson PDEs associated with the Laplace operator of the fast process. The fast one is assumed to satisfy "stricter" Holder conditions related to the time scale parameter. The tightness is then derived through the Holder semi-norm of the fast one, the properties of the Poisson PDE solution, and a Gronwall-type result for a linear Young differential equation. The weak limit shown includes an extra Gaussian process. The second part is dedicated to the problem under a general FBM. In contrast to the former one, here the FBM integral term will be more difficult to bound due to the coupling between the regularity of the Poisson PDE solution and dependence on the unbounded Holder seminorm of the fast one.

math.PR↗