arXiv subjects

Xiaolong Yin

Publications and source records attributed to Xiaolong Yin.

13 recordsLinked to original sources

arXiv2026

Vortex-mediated spin current injection into two-dimensional superconductor NbSe2

Injection of pure spin current into superconductors remains a major challenge in superconducting spintronics. Previous studies have primarily focused on spin-polarized quasiparticles and spin-triplet supercurrents, while vortices, ubiquitous topological defects in type-2 superconductors, have been theoretically proposed as alternative carriers of spin angular momentum, yet direct experimental evidence is still lacking. Here, we report the vortex-mediated spin current injection in NbSe2/LiAl2Fe3O8(LAFO) bilayer, an Ising superconductor/ferrimagnetic insulator heterostructure. Under an out-of-plane temperature gradient and an in-plane magnetic field, the NbSe2/LAFO bilayer shows a pronounced thermoelectric peak near the upper critical magnetic field, which has the opposite sign to the conventional vortex Nernst signal observed in a single-layer NbSe2. The sign reversal suggests that the vortex flow induced by spin current injection is opposite to the flow driven by the temperature gradient, which is consistent with theoretical mechanisms including spin-vorticity transmutation and the inverse vortex spin Hall effect. By mapping the field temperature phase diagram, we reveal that the spin current injection occurs exclusively in the vortex liquid phase of NbSe2. Absence of the thermoelectric signal above the superconducting transition temperature further rules out the quasiparticle contribution. Our results establish vortices as efficient carriers of spin information in superconductors, opening a new route towards vortex-mediated superconducting spintronic devices.

cond-mat.supr-con↗

arXiv2026

TraceCAD: Trace-Guided Repair for Agentic CAD Generation

LLM-based CAD agents produce executable parametric programs, but their correction loops may lose evidence about satisfied requirements, faulty operations, and prior repairs. We introduce TraceCAD, a recovery layer that links requested features, modeling steps, failure evidence, and candidate outcomes as persistent state. TraceCAD diagnoses likely faulty operations, searches bounded edits in their dependency regions, validates candidates through execution and preservation checks, and retains successful and failed repair outcomes in reusable skill memory. On DeepCAD-derived benchmarks with 200-model ablations and a 1K-model comparison, TraceCAD achieves competitive geometric quality in terms of IoU, Chamfer distance, and Hausdorff distance. Removing persistent state nearly halves recovery score; removing localized search more than doubles geometric regression and doubles code-agent invocations. Initializing the skill store on disjoint training models further reduces retries, token cost, and latency. These results demonstrate that persistent, localized, and reusable recovery improves final CAD quality and repair reliability.

cs.AI↗

arXiv2026

OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories

Task success can hide process anomalies in real-world agent executions. An agent may pass the final task oracle while still accumulating unresolved ambiguity, unsafe external writes, ignored errors, weakly grounded commitments, or capability-boundary overcommitment. We study this mismatch as the Outcome-Process Gap and introduce OpenClawBench, a large-scale dataset for measuring and supervising process-side anomalies in real agent execution processes. OpenClawBench is built from BFCL-driven OpenClaw sessions produced by 6 source models and contains 31,264 annotated trajectories. It aligns task-oracle outcomes with structured process evidence. FullTax converts the aligned trajectories into structured anomaly supervision: binary labels, supporting evidence, onset/span localization, severity, recoverability, and a 5-class anomaly taxonomy. Using OpenClawBench, we make the Outcome-Process Gap measurable. Among 31,135 oracle-passing executions, 2,904 are still labeled process-anomalous under FullTax. These results show that success-only evaluation misses a concrete class of process-side failures in real agent executions. A LoRA-fine-tuned Gemma 3 12B detector trained on the high-confidence FullTax supervised pool reaches binary F1=0.729 on the cleaner-labels held-out test split. Together, OpenClawBench turns real agent execution logs into auditable and reusable supervision for studying, diagnosing, and operationally monitoring runtime agent reliability.

cs.AI↗

arXiv2026

CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent

Computer-Aided Design (CAD) is widely used for conceptual design and parametric 3D modeling, but typically requires a high level of expertise from designers. To lower the entry barrier and facilitate early-stage CAD modeling, we present CADDesigner, an LLM-powered agent for conceptual CAD design. The agent accepts both textual descriptions and sketches as input, engaging in interactive dialogue with users to refine and clarify design requirements through comprehensive requirement analysis. Built upon a novel Explicit Context Imperative Paradigm (ECIP), the agent generates high-quality CAD modeling code. During the generation process, the agent incorporates iterative visual feedback to improve model quality. Generated design cases can be stored in a structured knowledge base, providing a mechanism for continual knowledge accumulation and future improvement of code generation. Experimental results show that CADDesigner achieves competitive performance and outperforms representative baselines on conceptual CAD model generation tasks.

cs.AI↗

arXiv2026

Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion

Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images remains challenging due to the complexity of topological constraints and operation sequences. We present Img2CADSeq, a multi-stage pipeline that overcomes these limitations by encoding CAD sequences into a three-level hierarchical codebook. Guided by an importance prioritization, this strategy values profiles over details, compressing long sequences into a stable discrete latent space. To bridge the modality gap, we leverage a coarse-to-fine point cloud intermediate, aligning 2D visual features with 3D CAD sequences via contrastive learning to condition a VQ-Diffusion model. Supported by newly introduced CAD-220K and PrintCAD datasets, our approach ensures robust industrial domain adaptation. Extensive experiments demonstrate that Img2CADSeq significantly outperforms state-of-the-art methods, producing standard STEP files that can be directly used in commercial CAD software.

cs.CV↗

arXiv2026

Magnetoresistance Oscillations in Few-Layer NbSe2 in Superconducting Fluctuation Regime

Quantum interference phenomena in superconductors, such as Josephson interference and Little-Parks oscillations, serve as powerful probes of phase coherence, symmetry breaking and vortex dynamics. However, they are typically observed in well-defined mesoscopic structures, and their behavior in the two-dimensional limit remains largely unexplored. Here, we report periodic magnetoresistance oscillations, superconducting interference patterns, and interfering diode effect in unpatterned few-layer NbSe2. These phenomena emerge exclusively within the superconducting fluctuation regime of thin samples, consistent with the enhanced anomalous metallic behavior of atomically thin NbSe2. The non-monotonic temperature dependence of both the oscillation amplitude and the diode efficiency can be captured by a model in which thermally activated vortices traverse intrinsic supercurrent loops. Our results reveal that the observed interference phenomena originate from the lost of global phase coherence, providing a new route to accessing interference effects in unpatterned superconductors.

cond-mat.supr-con↗

arXiv2025

RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation

A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations. Although there is growing academic interest in the automatic generation of command sequences, existing methods and datasets only support operations such as 2D sketching, extrusion,and Boolean operations. This limitation makes it challenging to represent more complex geometries. In this paper, we present a reinforcement learning (RL) training environment (gym) built on a CAD geometric engine. Given an input boundary representation (B-Rep) geometry, the policy network in the RL algorithm generates an action. This action, along with previously generated actions, is processed within the gym to produce the corresponding CAD geometry, which is then fed back into the policy network. The rewards, determined by the difference between the generated and target geometries within the gym, are used to update the RL network. Our method supports operations beyond sketches, Boolean, and extrusion, including revolution operations. With this training gym, we achieve state-of-the-art (SOTA) quality in generating command sequences from B-Rep geometries.

cs.LG↗

arXiv2024

Observation of Complete Orbital Two-channel Kondo Effect in van der Waals Ferromagnet Fe3GaTe2

Orbital two-channel Kondo (2CK) effect is one of the crucial systems with non- Fermi liquid (NFL) behaviors. But the full three-regime transport evidence has never been observed in one sample. Here, all three-resistive regimes for the orbital 2CK effect induced by two-level systems (TLSs) have been observed in the van der Waals ferromagnet Fe3GaTe2. The electron behavior undergoes a continuous transition from electron scattering to the NFL behavior, and subsequently to Fermi liquid behavior. The magnetic field does not affect any regimes, indicating the non-magnetic origin of the TLSs in Fe3GaTe2. In addition, the slope of linear negative magnetoresistance, rather than the topological Hall effect, has been found to be related to spin-magnon scattering and can be used to infer the emergence of spin textures. Our findings indicate Fe3GaTe2 may be an ideal platform to study electron-correlation and topological phenomena.

cond-mat.str-el↗

arXiv2024

Near-Room-Temperature Field-Controllable Exchange Bias in 2D van der Waals Ferromagnet Fe3GaTe2

Exchange bias (EB) is a cornerstone of modern magnetic memory and sensing technologies. Its extension to the realm of two-dimensional (2D) van der Waals (vdW) magnets holds promise for revolutionary advancements in miniaturized and efficient atomic spintronic devices. However, the blocking temperature of EB in 2D vdW magnets is currently well below room temperature ~130 K. This study reports a robust EB phenomenon in Fe3GaTe2 thin-layer devices, which significantly increases the blocking temperature to a near-room-temperature record of 280 K. Both the bias direction and magnitude can be isothermally tuned by adjusting the field sweep range, in striking contrast to the conventional EB in ferromagnetic/antiferromagnetic (FM/AFM) bilayers. We propose an exchange spring model in which crystal defects with higher coercivity act as the pivotal pinning source for the observed EB phenomenon, deviating from the conventional FM/AFM interface mechanism. Cumulative growth of minor loops and multiple magnetization reversal paths are observed in field cycles below the saturation field, consistent with the hard FM defects behavior of our exchange spring model. These findings provide insights into the complex magnetic order in 2D ferromagnets and open new avenues for developing practical ultrathin vdW spintronic devices with EB-like properties at room temperature.

cond-mat.mes-hall↗

arXiv2024

Potential quantum advantage for simulation of fluid dynamics

Numerical simulation of turbulent fluid dynamics needs to either parameterize turbulence-which introduces large uncertainties-or explicitly resolve the smallest scales-which is prohibitively expensive. Here we provide evidence through analytic bounds and numerical studies that a potential quantum exponential speedup can be achieved to simulate the Navier-Stokes equations governing turbulence using quantum computing. Specifically, we provide a formulation of the lattice Boltzmann equation for which we give evidence that low-order Carleman linearization is much more accurate than previously believed for these systems and that for computationally interesting examples. This is achieved via a combination of reformulating the nonlinearity and accurately linearizing the dynamical equations, effectively trading nonlinearity for additional degrees of freedom that add negligible expense in the quantum solver. Based on this we apply a quantum algorithm for simulating the Carleman-linearized lattice Boltzmann equation and provide evidence that its cost scales logarithmically with system size, compared to polynomial scaling in the best known classical algorithms. This work suggests that an exponential quantum advantage may exist for simulating fluid dynamics, paving the way for simulating nonlinear multiscale transport phenomena in a wide range of disciplines using quantum computing.

quant-ph↗

arXiv2018

Notes on the Onset of Clustering in Gas-Solid HCS

This study contributes to the body of work on instabilities in the homogeneous cooling system focusing on clustering in the multiphase gas-particle system. The critical system size for the onset of instability, $L^*_c$, is studied via three different numerical methods: i) particle resolved direct numerical simulation; ii) computational fluid dynamics-discrete element method; and iii) a two-fluid model derived from kinetic theory. In general, the $L^*_c$ results at several concentrations, inelasticities and initial thermal Reynolds numbers are in good qualitative agreement with one another. Additionally, most of the expected trends (i.e., general $L^*_c (ϕ)$ behavior) are observed. However, there is a larger level of quantitative discrepancy between the continuum and discrete particle methods than observed previous (simpler) granular results. While the level of agreement may be expected to decrease with the increased physical complexity of the gas-solid system, a significant time-dependence is revealed and shown to be responsible for some of the oddities in the numerical data.

physics.flu-dyn↗

arXiv2016

Transport coefficients of solid particles immersed in a viscous gas

Transport properties of a suspension of solid particles in a viscous gas are studied. The dissipation in such systems arises from two sources: inelasticity in particle collisions and viscous dissipation due to the effect of the gas phase on the particles. Here, we consider a simplified case in which the mean relative velocity between the gas and solid phases is taken to be zero, such that "thermal drag" is the only remaining gas-solid interaction. Unlike the previous more general treatment of the drag force [Garzó \emph{et al.}, J. Fluid Mech. \textbf{712}, 129 (2012)], here we take into account contributions to the (scaled) transport coefficients $η^*$ (shear viscosity), $κ^*$ (thermal conductivity) and $μ^*$ (Dufour-like coefficient) coming from the temperature-dependence of the (dimensionless) friction coefficient $γ^*$ characterizing the amplitude of the drag force. At moderate densities, the thermal drag model (which is based on the Enskog kinetic equation) is solved by means of the Chapman-Enskog method and the Navier-Stokes transport coefficients are determined in terms of the coefficient of restitution, the solid volume fraction and the friction coefficient. The results indicate that the effect of the gas phase on $η^*$ and $μ^*$ is non-negligible (especially in the case of relatively dilute systems) while the form of $κ^*$ is the same as the one obtained in the dry granular limit. Finally, as an application of these results, a linear stability analysis of the hydrodynamic equations is carried out to analyze the conditions for stability of the homogeneous cooling state. A comparison with direct numerical simulations shows a good agreement for conditions of practical interest.

cond-mat.soft↗

arXiv2012

Water-oil drainage dynamics in oil-wet random microfluidic porous media analogs

Displacement experiments carried out in microfluidic porous media analogs show that reduced surface tension leads to a more stable displacement, opposite to the process in Hele-Shaw cells where surface tension stabilizes the displacement of a more viscous fluid by a less viscous fluid. In addition, geometry of porous media is observed to play an important role. Three random microfluidic porous media analogs were made to study water-oil drainage dynamics, featuring a pattern of randomly connected channels with a uniform width, a pattern with Gaussian channel width distribution, and a pattern with large isolated pores. The microfluidic chips fabricated using Polydimenthylsiloxane with glass covers have the internal surface treated by Trichlorosilane to achieve a uniform oil-wet condition. The aqueous phase displaces the oil phase, with a viscosity ratio of about 1:40 and a density ratio of 1:0.85. Videos 1-3 show water flooding processes. It is observed that both channel size distribution (Video 2) and heterogeneity in pore size (Video 3) lead to stronger fingers and reduced displacement efficiency. Video 4 shows that meniscus in small channels retreat as water front moves into a nearby large cavity due to the disparity in the capillary force and contact angle hysteresis. Videos 5 and 6, both taken at 100X magnification in Chip 2, show the stabilizing effect of reduced interfacial tension.

physics.flu-dyn↗