arXiv subjects

Gang Cao

Publications and source records attributed to Gang Cao.

At least 19 recordsLinked to original sources

arXiv2026

Emergent Electronic Bond-Order Wave in a Quasi-One-Dimensional Chain

Collective order is notoriously difficult to stabilize in one dimension, where strong fluctuations suppress symmetry-breaking states. Here we report the emergence of an electronic bond-order wave (BOW) in the quasi-one-dimensional (1D) material Ba9Rh8O24, in which structural bonds become active electronic degrees of freedom that overcome 1D fluctuations to establish long-range order. Single-crystal X-ray diffraction uncovers a striking inversion of inequivalent Rh-Rh bonds across a transition TA = 180 K, where short and long bonds interchange their identities rather than undergoing conventional Peierls dimerization. This bond inversion coincides with a heat-capacity anomaly and a profound reorganization of the dielectric response with strong suppression of dielectric loss upon cooling. The BOW exhibits strongly nonlinear, frequency-dependent I-V characteristics, clockwise hysteresis, and nonvolatile memristive switching. These findings uncover an unprecedented transformation from a dynamic bond liquid to a rigid yet electrically reconfigurable BOW, establishing bond-centered electronic order as a new organizing principle for 1D quantum matter.

cond-mat.str-el↗

arXiv2026

Phonon-assisted transport and hole-phonon coupling in GaAs double quantum dots

Hole-phonon interactions play an important role in transport and decoherence processes in semiconductor quantum dots. Here we investigate hole-phonon coupling in a gate-defined GaAs double quantum dot integrated with a quantum point contact charge sensor. Under finite source-drain bias, pronounced oscillatory stripe patterns appear near specific charge transition regions in the charge stability diagram. We attribute these oscillations to phonon emission during inelastic interdot tunneling. A theoretical model including piezoelectric hole-phonon coupling reproduces the observed patterns. Furthermore, our analysis shows that the oscillations emerge only in particular charge configurations. Our results provide direct insight into phonon-assisted transport and coherent hole-phonon interactions in semiconductor quantum dots.

cond-mat.mes-hall↗

arXiv2026

Effective Synthetic Image Detection via Noise Residual Clustering

The rapid advancement of generative artificial intelligence (AI) has made synthetic images remarkably realistic, posing security threats such as misinformation and fraud. It is significant to detect the synthetic image in the manner of passive and blind image authentication. Most existing detectors rely on supervised training with large labeled datasets, leading to high costs and degraded performance on unknown generative models. To attenuate such deficiencies, we propose a training-free detection method. Specifically, noise residual fingerprints are first extracted by a simple yet effective pre-trained Noiseprint++ model. Then multi-scale features are further extracted from such residual by a frozen Vision Transformer (ViT), followed by adaptive weighted fusion. Only a few real image samples are used needed to initialize the clustering centers for unsupervised K-Means, distinguishing real and synthetic images without training. Extensive evaluations on four benchmark datasets show that our proposed scheme achieves an average accuracy of 82.2%, outperforming the state-of-the-art detectors on generalization ability. Superior performance is gained on the popular diffusion type of synthetic images, and the effectiveness of each module is validated by ablation studies. Source code will be publicly available at https://github.com/multimediaFor/NoiseCluSID.

cs.CV↗

arXiv2026

Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning

The rapid advancement of large-scale generative models has accelerated the spread of highly deceptive AI-generated images, making generalized synthetic image detection a critical imperative. Existing forensic networks often struggle with cross-model generalization and realworld degradations due to their reliance on single-domain representations and conventional binary classification optimization. To overcome these limitations, we propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning. Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate highfrequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module. To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy. By explicitly penalizing challenging training samples, HSCL reshapes the latent feature space to maximize the discriminative margin between pristine and synthetic domains. Extensive experiments across eight public benchmark datasets verify that our model achieves state-of-the-art performance, delivering superior generalization ability, robustness, and computational efficiency. Code and dataset will be publicly available on https://github.com/multimediaFor/RNSIDNet.

cs.CV↗

arXiv2026

MR-LiDAR: A Multi-Resolution Roadside LiDAR Benchmark for Perception Diagnostics and Deployment Guidance

LiDAR model selection is a critical issue in roadside sensing systems, as it directly determines both perception capability and deployment cost. However, the lack of empirical benchmarks for comparing perception performance across different LiDAR configurations has greatly constrained scientific sensor selection and deployment planning. To address this gap, we present MR-LiDAR, a controlled multi-resolution LiDAR benchmark for roadside perception diagnostics. Using 16-, 32-, 80-, and 128-beam LiDARs in identical roadside scenarios, we collect point clouds and ground-truth annotations for diverse traffic participants, including vehicles and vulnerable road users (VRUs), across varying distances. This controlled design isolates intrinsic LiDAR specifications, particularly beam count and beam distribution, as the key variables for precise performance diagnostics. Based on MR-LiDAR, we conduct systematic empirical analyses to examine how beam count, beam distribution, target distance, object category, and vehicle occlusion affect LiDAR perception performance. The results reveal that all of these factors have substantial impacts. In particular, contrary to the common assumption that higher beam counts always yield better perception, we show that an 80-beam LiDAR with optimized beam distribution can match or even outperform a 128-beam LiDAR with uniform beam distribution. In addition, we provide a practical reference guide for LiDAR selection, including target point-count statistics and detection performance comparisons based on two widely used detection algorithms. This work offers a diagnostic benchmark and practical guidance for determining cost-effective LiDAR configurations in roadside perception applications.

cs.RO↗

arXiv2026

Inductance Meets Memory in the Quantum Magnet Mn3Si2Te6

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn3Si2Te6, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.

cond-mat.str-el↗

arXiv2026

Cs$_4$Cr$_7$Te$_{10}$: Interwoven Reconstructed Archimedean and Kagome Lattices with a Possible Phase Transition near 130 K

Chromium-based materials with complex lattice geometries provide an important platform for investigating correlated electronic and magnetic states. However, Cr-based compounds with unusual crystal geometries are still rarely reported. Here, we report a new Cr-based compound, Cs$_4$Cr$_7$Te$_{10}$, featuring interwoven Cr and Te sublattices that can be viewed as reconstructed networks derived from Archimedean (3.4.6.4) tiling and the kagome lattice, respectively. Transport measurements reveal the semiconducting nature in Cs$_4$Cr$_7$Te$_{10}$. Magnetization measurements show a weak anisotropy between H//b and H//ac planes, and uncover an anomaly near 130 K that is insensitive to the applied magnetic fields. Specific-heat measurements further confirm this transition, indicating its bulk thermodynamic nature. The associated entropy change is as small as 0.41 J mol^-1 K^-1, ruling out a structural phase transition and pointing to a possible electronic and/or magnetic phase transition. These results provide a new route for designing complex crystal geometries and exploring their associated emergent phenomena.

cond-mat.mtrl-sci↗

arXiv2026

On-chip Parametric Amplification in a Double Quantum Dots Circuit

In microwave-based quantum circuits, including double quantum dots (DQDs), superconducting qubits and spin qubits, parametric amplifiers are indispensable in achieving high-fidelity qubit readouts. Despite its importance, the application of parametric amplifiers is hampered by several challenges, such as high insertion losses, constrained tunability, and a pronounced vulnerability to magnetic fields. Here, we demonstrate an on-site single-atom parametric amplifier (SAPA) within a reconfigurable quantum circuit, which consists of a superconducting microwave cavity and two GaAs gate-defined DQDs. Leveraging the inherent nonlinearity of the DQD, a parametric gain exceeding 11 dB is achieved. This gain contributes to enhance the qubit readout, as evidenced by exceeding two times improvement in the signal-to-noise ratio (SNR) when employing the DQD-based amplifier for reading out another DQD. Our work not only presents a versatile experimental platform with enhanced readout capabilities in quantum computing, but also introduces alternative choices of parametric amplifiers for a variety of microwave-based quantum circuits.

quant-ph↗

arXiv2026

Coexisting Paramagnetic Spins and Long-Range Magnetic Order in Ba$_4$(Ru$_{0.92}$Ir$_{0.08}$)$_3$O$_{10}$

We investigate the effect of dilute Ir substitution on the magnetism of the trimer-based ruthenate Ba$_4$Ru$_3$O$_{10}$ using neutron diffraction, magnetic susceptibility measurements, atomistic simulations, and first-principles calculations. Neutron diffraction shows that Ir doping preserves the zigzag antiferromagnetic structure and the ordered-moment magnitude of the parent compound, in which the moments reside exclusively on the two outer Ru(2) sites of each $\rm Ru_3O_{12}$ trimer, while the central Ru(1) site remains nonmagnetic. The N\'eel temperature is reduced from $\approx\!105$ K to 84.0(1) K upon 8% Ir substitution, while magnetic susceptibility reveals a pronounced low-temperature Curie-like upturn, indicating the coexistence of paramagnetic spins with long-range antiferromagnetic order. Density-functional calculations shows that Ir preferentially occupies the central Ru(1) site, where its extended $5d$ orbitals disrupt the Ru-Ru molecular-orbital network and intra/inter-trimer exchange pathways. Atomistic simulations incorporating this paramagnetic dilution reproduce the suppressed ordering temperature and the coexistence of ordered and paramagnetic components.

cond-mat.str-el↗

arXiv2026

Magnetic excitations in the Kitaev material Na$_2$IrO$_3$ studied by neutron scattering

Inelastic neutron scattering experiments with a large set of comounted Na$_2$IrO$_3$ crystals reveal the low-energy magnon dispersion in this candidate material for Kitaev physics. The magnon gap amounts to 1.7(1) meV and can be interpreted similarly to the sister compound $\alpha$-RuCl$_{3}$ to stem from the zone boundaries in the antiferromagnetic zigzag structure. The neutron experiments find no evidence for low-energy excitations with ferromagnetic character, which contrasts to the findings in $\alpha$-RuCl$_{3}$. Our results are consistent with a recently proposed microscopic model that involves an antiferromagnetic Heisenberg nearest-neighbor exchange in Na$_2$IrO$_3$ in contrast to the ferromagnetic one considered for $\alpha$-RuCl$_{3}$. Although the magnetic response shows the signatures of bond-directional anisotropy in both materials the different relative signs of Kitaev and Heisenberg interaction result in different deviations from the initial Kitaev model. Low-energy ferromagnetic fluctuations cannot be considered as a fingerprint of ferromagnetic Kitaev interaction.

cond-mat.str-el↗

arXiv2026

GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training

Post-training GUI agents in interactive environments is critical for developing generalization and long-horizon planning capabilities. However, training on real-world applications is hindered by high latency, poor reproducibility, and unverifiable rewards relying on noisy visual proxies. To address the limitations, we present GUI-GENESIS, the first framework to automatically synthesize efficient GUI training environments with verifiable rewards. GUI-GENESIS reconstructs real-world applications into lightweight web environments using multimodal code models and equips them with code-native rewards, executable assertions that provide deterministic reward signals and eliminate visual estimation noise. Extensive experiments show that GUI-GENESIS reduces environment latency by 10 times and costs by over $28,000 per epoch compared to training on real applications. Notably, agents trained with GUI-GENESIS outperform the base model by 14.54% and even real-world RL baselines by 3.27% on held-out real-world tasks. Finally, we observe that models can synthesize environments they cannot yet solve, highlighting a pathway for self-improving agents.

cs.AI↗

arXiv2026

Hidden Density-Wave Instability in the Trimer Ruthenate Ba$_4$Ru$_3$O$_{10}$

We report a hidden density-wave instability in the trimer-based ruthenate Ba4Ru3O10, previously regarded as a pure antiferromagnet with a phase transition at TA=100 K. This transition is manifested in lattice parameters, transport, thermodynamics, and magnetic susceptibility, yet remains remarkably insensitive to magnetic fields up to at least 14 T, indicating an electronically driven reconstruction. At much lower temperatures T*= 20 K, charge transport becomes strongly nonlinear, exhibiting distinct depinning thresholds, negative differential resistance, pronounced current- and frequency-dependence, and slow collective dynamics in the Hertz range. While each feature is characteristic of density-wave transport, their simultaneous occurrence in an antiferromagnetic oxide is unprecedented. All nonlinear signatures vanish upon only 3% Ir substitution, which preserves the crystal structure and insulating state, ruling out Joule heating or extrinsic artifacts. The wide separation between the electronic reconstruction at TA and the emergence of nonlinear dynamics at T* identifies Ba4Ru3O10 as a rare correlated system hosting a strongly pinned collective electronic state intertwined with antiferromagnetism.

cond-mat.str-el↗

arXiv2026

UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics

Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and the "distillation ceiling" of synthetic teacher supervision. To transcend these limitations, we propose UI-Oceanus, a framework that shifts the learning focus from mimicking high-level trajectories to mastering interaction physics via ground-truth environmental feedback. Through a systematic investigation of self-supervised objectives, we identify that forward dynamics, defined as the generative prediction of future interface states, acts as the primary driver for scalability and significantly outweighs inverse inference. UI-Oceanus leverages this insight by converting low-cost autonomous exploration, which is verified directly by system execution, into high-density generative supervision to construct a robust internal world model. Experimental evaluations across a series of models demonstrate the decisive superiority of our approach: models utilizing Continual Pre-Training (CPT) on synthetic dynamics outperform non-CPT baselines with an average success rate improvement of 7% on offline benchmarks, which amplifies to a 16.8% gain in real-world online navigation. Furthermore, we observe that navigation performance scales with synthetic data volume. These results confirm that grounding agents in forward predictive modeling offers a superior pathway to scalable GUI automation with robust cross-domain adaptability and compositional generalization.

cs.LG↗

arXiv2026

The combined X-ray and $\gamma$-ray modeling of millisecond pulsars PSR J0030+0451 in the dissipative magnetospheres

Modeling of the NICER X-ray light curves of millisecond pulsars PSR J0030+0451 provides a strong evidence for the existence of non-dipole magnetic fields. We study the X-ray and $\gamma$-ray emission of PSR J0030+0451 in the dissipative dipole plus off-centred quadrupole magnetospheres. The dissipative FF+AE dipole magnetospheres by combining force-free (FF) and Aristotelian electrodynamics (AE) are solved by a 3D pseudo-spectral method in the rotating coordinate system. We use the FF+AE dipole plus off-centred quadrupole fields with minimum free parameters to reproduce two hotspot configurations found by the NICER observations. The X-ray and $\gamma$-ray emission from PSR J0030+0451 are simultaneously computed by using a ray-tracing method and a particle trajectory method. The modelled X-ray and $\gamma$-ray emission is then directly compared with those of PSR J0030+0451 from the NICER and Fermi observations. Our results can well reproduce the observed trends of the NICER X-ray and Fermi $\gamma$-ray emission for PSR J0030+0451.

astro-ph.HE↗

arXiv2026

Cryogenic interface-state filling and tunneling mechanisms in strained Ge/SiGe heterostructures

Traps at the semiconductor-oxide interface are considered as a major source of instability in strained Ge/SiGe quantum devices, yet the quantified study of their cryogenic behavior remains limited. In this work, we investigate interface-state trapping using Hall-bar field-effect transistors fabricated on strained Ge/SiGe heterostructures. Combining transport measurements with long-term stabilization and Schr\"odinger-Poisson modelling, we reconstruct the gradual filling process of interface states at cryogenic condition. Using the calculated valence band profiles, we further evaluate the tunneling current density between the quantum well and the semiconductor-oxide interface. Our calculation demonstrates that the total tunneling current is consistent with a crossover from trap-assisted-tunneling-dominated transport to Fowler-Nordheim-tunneling-dominated transport under different gate bias regimes. These results refine the conventional Fowler-Nordheim-based picture of interface trapping in strained Ge/SiGe heterostructures and provide guidelines for improving Ge-based quantum device performance by improving barrier crystalline qualities and reducing dislocation-related trap densities.

cond-mat.mes-hall↗

arXiv2025

From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents

While Large Language Model (LLM) agents show great potential for automated UI navigation such as automated UI testing and AI assistants, their efficiency has been largely overlooked. Our motivating study reveals that inefficient UI representation creates a critical performance bottleneck. However, UI representation optimization, formulated as the task of automatically generating programs that transform UI representations, faces two unique challenges. First, the lack of Boolean oracles, which traditional program synthesis uses to decisively validate semantic correctness, poses a fundamental challenge to co-optimization of token efficiency and completeness. Second, the need to process large, complex UI trees as input while generating long, compositional transformation programs, making the search space vast and error-prone. Toward addressing the preceding limitations, we present UIFormer, the first automated optimization framework that synthesizes UI transformation programs by conducting constraint-based optimization with structured decomposition of the complex synthesis task. First, UIFormer restricts the program space using a domain-specific language (DSL) that captures UI-specific operations. Second, UIFormer conducts LLM-based iterative refinement with correctness and efficiency rewards, providing guidance for achieving the efficiency-completeness co-optimization. UIFormer operates as a lightweight plugin that applies transformation programs for seamless integration with existing LLM agents, requiring minimal modifications to their core logic. Evaluations across three UI navigation benchmarks spanning Android and Web platforms with five LLMs demonstrate that UIFormer achieves 48.7% to 55.8% token reduction with minimal runtime overhead while maintaining or improving agent performance. Real-world industry deployment at WeChat further validates the practical impact of UIFormer.

cs.SE↗

arXiv2025

Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector

Recent AI-generated image (AIGI) detectors achieve impressive accuracy under clean condition. In view of antiforensics, it is significant to develop advanced adversarial attacks for evaluating the security of such detectors, which remains unexplored sufficiently. This letter proposes a Dual-domain Feature Importance Attack (DuFIA) scheme to invalidate AIGI detectors to some extent. Forensically important features are captured by the spatially interpolated gradient and frequency-aware perturbation. The adversarial transferability is enhanced by jointly modeling spatial and frequency-domain feature importances, which are fused to guide the optimization-based adversarial example generation. Extensive experiments across various AIGI detectors verify the cross-model transferability, transparency and robustness of DuFIA.

cs.CV↗

arXiv2025

Spatio-temporal migration of antiferromagnetic domain walls in Sr2IrO4

By laser pump-probe time-resolved coherent magnetic X-ray diffraction imaging, we have measured the migration velocity of antiferromagnetic domain walls in the Mott insulator Sr2IrO4 at 100 K. During the laser-induced demagnetization, we observe domain walls moving at 3x10^6 m/s, significantly faster than acoustic velocities. This is understood to arise from a purely electronic spin contribution to the magnetic structure without any role for coupling to the crystal lattice.

cond-mat.str-el↗