arXiv subjects

Yongsheng Zhao

Publications and source records attributed to Yongsheng Zhao.

8 recordsLinked to original sources

arXiv2026

TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulation

Vision-Language-Action (VLA) models have demonstrated strong performance across diverse robotic manipulation tasks, yet their predominantly vision-centric perception and position-controlled execution remain insufficient for contact-rich manipulation. Visual observations alone often provide limited evidence of contact onset and interaction magnitude, while position-control policies cannot respond compliantly to rapidly changing contact dynamics. To bridge both the perception and control gaps, we propose TAO-Force, a force-conditioned VLA framework that combines force-aware policy learning with contact-regulated execution. For force-aware perception, TAO-Force introduces Force-conditioned Feature-wise Linear Modulation (F-FiLM) to inject encoded force feedback into the representations of a frozen pretrained visual-language backbone while preserving its semantic priors. For responsive control, it employs a contact-gated fast-slow architecture, with a slow position-control branch tracking nominal trajectories during non-contact phases and a fast admittance-control branch regulating physical interaction during contact phases. Detailed analyses on a force-perception task and real-world evaluations across four contact-rich manipulation tasks validate the effectiveness and robustness of TAO-Force.

cs.RO↗

arXiv2026

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient. Existing tabular generation approaches, such as generative adversarial networks (GANs) and fine-tuned Large Language Models (LLMs), typically require sufficient reference data, limiting their effectiveness in domain-specific datasets with scarce records. While prompt-based LLMs offer flexibility without parameter tuning, they often generate distributionally drifted data with localized redundancy, leading to degradation in downstream task performance. To overcome these issues, we propose ReFine, a framework that (i) extracts symbolic if-then rules from interpretable models and embeds them into prompts to explicitly guide the generation process toward the domain-specific distribution, and (ii) applies dual-granularity filtering that mitigates over-sampling patterns while preserving rare but informative samples to reduce localized redundancy. Extensive experiments on diverse benchmarks demonstrate that ReFine provides robust downstream utility, achieving a top-tier average rank across datasets and data regimes, with an average relative improvement of 7.48% in extreme low-data regimes.

cs.LG↗

arXiv2026

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction-graph datasets suffer from two pervasive limitations: (i) they provide sparse node-level semantics beyond anonymized identifiers, and (ii) they rely on template-driven anomaly injection, which biases benchmarks toward static structural motifs and yields overly optimistic assessments of model robustness. We propose TransXion, a benchmark ecosystem for Anti-Money Laundering (AML) research that integrates profile-aware simulation of normal activity with stochastic, non-template synthesis of illicit subgraphs.TransXion jointly models persistent entity profiles and conditional transaction behavior, enabling evaluation of "out-of-character" anomalies where observed activity contradicts an entity's socio-economic context. The resulting dataset comprises approximately 3 million transactions among 50,000 entities, each endowed with rich demographic and behavioral attributes. Empirical analyses show that TransXion reproduces key structural properties of payment networks, including heavy-tailed activity distributions and localized subgraph structure. Across a diverse array of detection models spanning multiple algorithmic paradigms, TransXion yields substantially lower detection performance than widely used benchmarks, demonstrating increased difficulty and realism. TransXion provides a more faithful testbed for developing context-aware and robust AML detection methods. The dataset and code are publicly available at https://github.com/chaos-max/TransXion.

cs.LG↗

arXiv2025

VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots

Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models.

cs.RO↗

arXiv2023

Fluctuation, insulation and superconductivity: the pressure-dependent phase-diagram of Rb$_2$Mo$_6$Se$_6$

The quasi-one-dimensional (q1D) material Rb$_2$Mo$_6$Se$_6$ has been proposed to display a nontrivial combination of low-dimensional fluctuations and a dynamical charge density wave (CDW) at ambient pressure. This may lead to a progressive metal to insulator cross over at low temperature. To explore the link between the crystal dimensionality and this insulating instability, we have performed hydrostatic pressure-dependent electrical transport measurements on single crystals of Rb$_2$Mo$_6$Se$_6$. At low pressure, we observe thermally-activated behaviour consistent with a temperature-dependent gap $E_g(T)$ opening below a characteristic temperature $T_{Rmin}$. Upon increasing the pressure $T_{Rmin}$ initially rises, indicating a reinforcement of the low temperature insulating state despite a continuous reduction in $E_g(P)$. We interpret this as a signature of suppressed fluctuations as the dimensionality of the electronic structure rises. However, $T_{Rmin}$ drops above 8.8 GPa and superconductivity emerges at 12 GPa. Between 12-24.2 GPa the superconducting and insulating instabilities coexist, with superconductivity surviving up to the maximum attained pressure (52.8 GPa). Analysis of the magneto-transport reveals two distinct regions: at high pressures the anisotropy gradually falls and the superconducting state appears unremarkable. In contrast, coexistence with the gapped insulating phase creates a superconducting dome. The emergence of a peak in the critical temperature Tc despite the depleted density of states is indicative of enhanced coupling. Our journey from the extreme 1D to 3D limits in this prototypical q1D metal reveals an intriguing relationship between superconducting and insulating ground states which is simultaneously competitive and symbiotic.

cond-mat.supr-con↗

arXiv2021

Pressure-induced charge orders and their coupling to magnetism in hexagonal multiferroic LuFe2O4

Hexagonal LuFe2O4 is a promising charge-order (CO) driven multiferroic material with high charge and spin ordering temperatures. The coexisting charge and spin orders on Fe3+/Fe2+ sites result in novel magnetoelectric behaviors, but the coupling mechanism between the charge and spin orders remains elusive. Here, by tuning external pressure, we reveal three correlated spin-charge ordered phases in LuFe2O4: i) a centrosymmetric incommensurate three-dimensional CO with ferrimagnetism, ii) a non-centrosymmetric incommensurate quasi-two-dimensional CO with ferrimagnetism, and iii) a centrosymmetric commensurate CO with antiferromagnetism. Experimental in-situ single-crystal X-ray diffraction and X-ray magnetic circular dichroism measurements combined with density functional theory calculations suggest that the charge density redistribution caused by pressure-induced compression in the frustrated double-layer [Fe2O4] cluster is responsible for the correlated spin-charge phase transitions. The pressure-enhanced effective Coulomb interactions among Fe-Fe bonds drive the frustrated (1/3, 1/3) CO to a less frustrated (1/4, 1/4) CO, which induces the ferrimagnetic to antiferromagnetic transition. Our results not only elucidate the coupling mechanism among charge, spin and lattice degrees of freedom in LuFe2O4 but also provide a new way to tune the spin-charge orders in a highly controlled manner.

cond-mat.mtrl-sci↗

arXiv2021

High-Tc superconductivity in clathrate calcium hydride CaH6

Recent discovery of superconductive rare earth/actinide superhydrides has ushered in a new era of superconductivity research at high pressures. This distinct type of clathrate metal hydrides was first proposed for alkaline-earth-metal hydride CaH6 that, however, has long eluded experimental synthesis, impeding an understanding of pertinent physics. Here, we report successful synthesis of CaH6 and its measured superconducting critical temperature Tc of 215 K at 172 GPa, which is evidenced by a sharp drop of resistivity to zero and a characteristic decrease of Tc under a magnetic field up to 9 T.An estimate based on the Werthamer-Helfand-Hohenberg model gives a giant zero-temperature upper critical magnetic field of 203 T. These remarkable benchmark superconducting properties place CaH6 among the most outstanding high-Tc superhydrides, marking it as the hitherto only clathrate metal hydride outside the family of rare earth/actinide hydrides. This exceptional case raises great prospects of expanding the extraordinary class of high-Tc superhydrides to a broader variety of compounds that possess more diverse material features and physics characteristics.

cond-mat.supr-con↗

arXiv2019

Pressure enhanced interplay among lattice, spin and charge in La2FeMnO6 mixed perovskite

Spin crossover plays a central role in the structural instability, net magnetic moment modification, metallization, and even in superconductivity in corresponding materials. Most reports on the pressure-induced spin crossover with a large volume collapse so far focused on compounds with single transition metal. Here we report a comprehensive high-pressure investigation of a mixed Fe-Mn perovskite La2FeMnO6. Under pressure, the strong coupling between Fe and Mn leads to a combined valence/spin transition: Fe3+(S = 5/2) to Fe2+(S = 0) and Mn3+(S = 2) to Mn4+(S = 3/2), with an isostructural phase transition. The spin transitions of both Fe and Mn are offset by ~ 20 GPa of the onset pressure, and the lattice collapse occurs in between. Interestingly, Fe3+ ion shows an abnormal behavior when it reaches a lower valence state (Fe2+) accompanied by a + 0.5 eV energy shift in Fe K-absorption edge at 15 GPa. This process is associated with the charge-spin-orbital state transition from high spin Fe3+ to low spin Fe2+, caused by the significantly enhanced t2g-eg crystal field splitting in the compressed lattice under high pressure. Density Functional Theory calculations confirm the energy preference of the high-pressure state with charge redistribution accompanied by spin state transition of Fe ions. Moreover, La2FeMnO6 maintains semiconductor behaviors even when the pressure reached 144.5 GPa as evidenced by the electrical transport measurements, despite the huge resistivity decreasing 7 orders of magnitude compared with that at ambient pressure. The investigation carried out here demonstrates high flexibility of double perovskites and their good potentials for optimizing the functionality of these materials.

cond-mat.str-el↗