arXiv subjects

Wenjun Liu

Publications and source records attributed to Wenjun Liu.

At least 19 recordsLinked to original sources

arXiv2026

Third-order nonlinear transport in a percolative two-dimensional superconductor

Percolative superconductivity frequently arises in two-dimensional van der Waals materials due to reduced dimensionality, enhanced quantum fluctuations, and complex electron-phonon interactions, providing a unique platform where normal electrons coexist with Cooper pairs. We report the observation of substantial third-order nonlinear transport in a trilayer $1T^\prime$-MoTe$_2$ superconductor within its percolative transition regime. The third-harmonic longitudinal voltage ($V_{\|}^{3\omega}$) exhibits a clear cubic dependence on excitation current below a threshold, with both its magnitude and nonlinear coefficient strongly correlated with the superconducting state. This nonlinear response is semiquantitatively captured by the superconducting fluctuation within the time-dependent Ginzburg-Landau theory, where nonlinear transport arises due to fluctuating Cooper pairs. Our results demonstrate that third-order nonlinear transport serves as a sensitive probe of superconducting transitions in percolative systems and establish a foundation for exploring higher-order transport phenomena in strongly correlated systems.

cond-mat.mes-hall↗

arXiv2026

Phase singularity enabled polarization switchable analog spatial differentiation in an atomic MoS$_2$ planar Fabry-P\'erot cavity

Reconfigurable analog optical computing requires rapid and efficient switching between core mathematical operations, such as first- and second-order spatial differentiation. Here, we demonstrate a monolayer MoS$_2$ integrated a planar Fabry-P\'erot (F-P) cavity that performs polarization switchable analog spatial differentiation under oblique incidence. By exploiting polarization dependent phase singularities, the device satisfies distinct optical transfer functions for different-order differentiation at the same operating condition. As a result, first-order and second-order derivatives of input images are experimentally realized by simply switching the incident polarization. These results establish the planar cavity as a compact reconfigurable spatial differentiator, whose computational order is controlled solely by light polarization. This approach provides a fast, convenient, and integration friendly strategy for tunable optical computing, and enables polarization switchable edge detection for image processing, with potential applications in real-time object recognition, feature extraction, and optical data compression.

physics.optics↗

arXiv2026

Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization

Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation. However, achieving this under multiple competing objectives remains challenging. We introduce BiCompoDiff, a compositional diffusion and energy-based framework that jointly optimizes grasp selection, handover, regrasp, and motion planning under multiple constraints. By combining a pretrained grasp diffusion model with bimanual planning energy-based models (EBMs), our method injects gradient guidance during reverse diffusion to enforce collision avoidance, trajectory smoothness (via differentiable inverse kinematics), handover feasibility, and regrasp safety. Annealed MCMC sampling further refines grasp poses over the composite energy landscape. Experiments across diverse simulated household reorientation tasks demonstrate that BiCompoDiff achieves over 20% higher success rates and up to 37% smoother trajectories (measured by joint displacement) compared to strong sampling-based baselines. Real-world validation confirms effective sim-to-real transfer and robust performance on challenging scenes.

cs.RO↗

arXiv2026

Development of a Reduced Multi-Fluid Equilibrium Model and Its Application to Proton-Boron Spherical Tokamaks

Proton-Boron fusion requires extreme ion temperatures and robust confinement, making Spherical Tokamaks (ST) with high-power neutral beam injection primary candidates. In these devices, strong toroidal rotation and the large mass disparity between protons and boron ions drive complex multi-fluid effects - specifically centrifugal species separation and electrostatic polarization - that standard single-fluid magnetohydrodynamic (MHD) models fail to capture. While comprehensive multi-fluid models are often numerically stiff, we develop a reduced model balancing physical fidelity with computational robustness. By retaining dominant toroidal rotation and self-consistent potential while neglecting poloidal inertia and pressure anisotropy, the model couples a generalized Grad-Shafranov equation with species-specific Bernoulli relations and a quasi-neutrality constraint. The model is applied to two representative p-B ST configurations: the experimental EHL-2 and reactor-scale EHL-3B. Simulation results demonstrate that equilibrium modifications are governed by the ion Mach number ($M$). In the low-rotation regime ($M < 0.5$), multi-fluid effects are weak and solutions approach the single-fluid limit. However, at $M > 2$, strong centrifugal forces drive significant boron accumulation at the low-field side (LFS) and generate an internal electrostatic potential on the order of 10 kV. These findings confirm the necessity of multi-fluid modeling for accurate p-$^{11}$B reactor design and establish a theoretical foundation for future investigations into stability, transport, and free-boundary dynamics.

physics.plasm-ph↗

arXiv2026

Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm

As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This paper presents a novel RAG framework called Bridge-RAG. To overcome the accuracy challenge, we introduce the concept of abstract to bridge query entities and document chunks, providing robust semantic understanding. We organize the abstracts into a tree structure and design a multi-level retrieval strategy to ensure the inclusion of sufficient contextual information. While this hierarchical organization substantially improves answer quality, traversing the tree to locate the abstracts that contain a query entity inevitably introduces additional retrieval overhead. To restore retrieval efficiency, we further integrate the Cuckoo Filter in CFT-RAG, which provides O(1) entity lookup and naturally fits the entity-to-abstract pathway of our framework. Extensive experiments show that Bridge-RAG achieves consistent accuracy improvements across all metrics and up to $1.9\times$ faster retrieval compared to structured RAG baselines.

cs.IR↗

arXiv2025

Non-Inductive Current Start-Up Using Multi-Harmonic Electron Cyclotron Wave and Current Ramp-Up Through Combined Electron Cyclotron Wave and Ohmic Heating in EXL-50U Spherical Torus

The non-inductive current start-up by multi-harmonic electron cyclotron wave has been systematically investigated in the EXL-50U spherical torus. Significant enhancements of the driven current with increasing number of resonance layers have been demonstrated by variation of the number of harmonic resonance layers of the ECW through adjustment of the magnetic field or plasma cross section. The critical role of multi-harmonic ECW in enhancing the driven current has been experimentally verified for the first time. To explain the related experimental observations, a physical mechanism involving multi-harmonic heating, multiple reflections, and multi-pass absorption - leading to the generation of high-energy electrons via X-mode wave or electron Bernstein wave has been proposed. The current drive capacity of the first harmonic extraordinary mode of the ECW has also been experimentally confirmed for the first time. After the application of Ohmic heating during the current ramp-up phase, the current drive efficiency of ECW is further enhanced. Leveraging the synergistic effect between ECW and Ohmic heating, EXL-50U achieved a plasma current of 1 MA, with the non-inductively driven current fraction reaching 70%.

physics.plasm-ph↗

arXiv2025

SELECT: Detecting Label Errors in Real-world Scene Text Data

We introduce SELECT (Scene tExt Label Errors deteCTion), a novel approach that leverages multi-modal training to detect label errors in real-world scene text datasets. Utilizing an image-text encoder and a character-level tokenizer, SELECT addresses the issues of variable-length sequence labels, label sequence misalignment, and character-level errors, outperforming existing methods in accuracy and practical utility. In addition, we introduce Similarity-based Sequence Label Corruption (SSLC), a process that intentionally introduces errors into the training labels to mimic real-world error scenarios during training. SSLC not only can cause a change in the sequence length but also takes into account the visual similarity between characters during corruption. Our method is the first to detect label errors in real-world scene text datasets successfully accounting for variable-length labels. Experimental results demonstrate the effectiveness of SELECT in detecting label errors and improving STR accuracy on real-world text datasets, showcasing its practical utility.

cs.CV↗

arXiv2025

Virtual Community: An Open World for Humans, Robots, and Society

The rapid progress in AI and Robotics may lead to a profound societal transformation, as humans and robots begin to coexist within shared communities, introducing both opportunities and challenges. To explore this future, we present Virtual Community-an open-world platform for humans, robots, and society-built on a universal physics engine and grounded in real-world 3D scenes. With Virtual Community, we aim to enable the study of embodied social intelligence at scale. To support these, Virtual Community features: 1) An open-source multi-agent physics simulator that supports robots, humans, and their interactions within a society; 2) A large-scale, real-world aligned community generation pipeline, including vast outdoor space, diverse indoor scenes, and a community of grounded agents with rich characters and appearances. Leveraging Virtual Community, we propose two novel challenges. The Community Planning Challenge evaluates multi-agent reasoning and planning ability in open-world settings, such as cooperating to help agents with daily activities and efficiently connecting other agents. The Community Robot Challenge requires multiple heterogeneous robots to collaborate in solving complex open-world tasks. We evaluate various baselines on these tasks and demonstrate the challenges in both high-level open-world task planning and low-level cooperation controls. We hope that Virtual Community will unlock further study of human-robot coexistence within open-world environments.

cs.CV↗

arXiv2025

Characterisation of Irradiation Damage in Fe3Cr and Fe5Cr: A Study on the Effects of Chromium Content and Temperature

Fe-Cr binary alloys serve as simplified model systems to study irradiation damage relevant to fusion structural materials. Here, Fe-3%Cr and Fe-5%Cr samples were irradiated with 4 MeV Fe ions under a dose rate of 4x10^5 dpa/s across a linear thermal gradient (120C to 480C) in a single experiment, enabling direct comparison of temperature and Cr content effects under identical conditions. Depth-resolved Laue micro-diffraction (~10^4 strain sensitivity), nanoindentation, and AFM reveal non-monotonic evolution of lattice strain and hardness: both decrease with temperature up to ~300C, then increase beyond. This turning point reflects a shift from enhanced defect mobility and partial recovery to solute-defect clustering and cavity formation, which stabilize damage. Fe-3%Cr shows consistently higher strain and hardening than Fe-5%Cr, especially at lower temperatures. Minimal change in post-indentation pile-up indicates limited softening or localization. These results highlight how Cr content and temperature jointly affect irradiation response, offering new insights into defect evolution in fusion-relevant alloys.

cond-mat.mtrl-sci↗

arXiv2025

Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge

LLM-as-Judge has emerged as a scalable alternative to human evaluation, enabling large language models (LLMs) to provide reward signals in trainings. While recent work has explored multi-agent extensions such as multi-agent debate and meta-judging to enhance evaluation quality, the question of how intrinsic biases manifest in these settings remains underexplored. In this study, we conduct a systematic analysis of four diverse bias types: position bias, verbosity bias, chain-of-thought bias, and bandwagon bias. We evaluate these biases across two widely adopted multi-agent LLM-as-Judge frameworks: Multi-Agent-Debate and LLM-as-Meta-Judge. Our results show that debate framework amplifies biases sharply after the initial debate, and this increased bias is sustained in subsequent rounds, while meta-judge approaches exhibit greater resistance. We further investigate the incorporation of PINE, a leading single-agent debiasing method, as a bias-free agent within these systems. The results reveal that this bias-free agent effectively reduces biases in debate settings but provides less benefit in meta-judge scenarios. Our work provides a comprehensive study of bias behavior in multi-agent LLM-as-Judge systems and highlights the need for targeted bias mitigation strategies in collaborative evaluation settings.

cs.AI↗

arXiv2025

Scaling Autonomous Agents via Automatic Reward Modeling And Planning

Large language models (LLMs) have demonstrated remarkable capabilities across a range of text-generation tasks. However, LLMs still struggle with problems requiring multi-step decision-making and environmental feedback, such as online shopping, scientific reasoning, and mathematical problem-solving. Unlike pure text data, collecting large-scale decision-making data is challenging. Moreover, many powerful LLMs are only accessible through APIs, which hinders their fine-tuning for agent tasks due to cost and complexity. To address LLM agents' limitations, we propose a framework that can automatically learn a reward model from the environment without human annotations. This model can be used to evaluate the action trajectories of LLM agents and provide heuristics for task planning. Specifically, our approach involves employing one LLM-based agent to navigate an environment randomly, generating diverse action trajectories. Subsequently, a separate LLM is leveraged to assign a task intent and synthesize a negative response alongside the correct response for each trajectory. These triplets (task intent, positive response, and negative response) are then utilized as training data to optimize a reward model capable of scoring action trajectories. The effectiveness and generalizability of our framework are demonstrated through evaluations conducted on different agent benchmarks. In conclusion, our proposed framework represents a significant advancement in enhancing LLM agents' decision-making capabilities. By automating the learning of reward models, we overcome the challenges of data scarcity and API limitations, potentially revolutionizing the application of LLMs in complex and interactive environments. This research paves the way for more sophisticated AI agents capable of tackling a wide range of real-world problems requiring multi-step decision-making.

cs.AI↗

arXiv2025

Overview of EXL-50 Research Progress and Future Plan

XuanLong-50 (EXL-50) is the first medium-size spherical torus (ST) in China, with the toroidal field at major radius at 50 cm around 0.5T. CS-free and non-inductive current drive via electron cyclotron resonance heating (ECRH) was the main physics research issue for EXL-50. Discharges with plasma currents of 50 kA - 180 kA were routinely obtained in EXL-50, with the current flattop sustained for up to or beyond 2 s. The current drive effectiveness on EXL-50 was as high as 1 A/W for low-density discharges using 28GHz ECRH alone for heating power less than 200 kW. The plasma current reached Ip>80 kA for high-density (5*10e18m-2) discharges with 150 kW 28GHz ECRH. Higher performance discharge (Ip of about 120 kA and core density of about 1*10e19m-3) was achieved with 150 kW 50GHz ECRH. The plasma current in EXL-50 was mainly carried by the energetic electrons.Multi-fluid equilibrium model has been successfully applied to reconstruct the magnetic flux surface and the measured plasma parameters of the EXL-50 equilibrium. The physics mechanisms for the solenoid-free ECRH current drive and the energetic electrons has also been investigated. Preliminary experimental results show that 100 kW of lower hybrid current drive (LHCD) waves can drive 20 kA of plasma current. Several boron injection systems were installed and tested in EXL-50, including B2H6 gas puffing, boron powder injection, boron pellet injection. The research plan of EXL-50U, which is the upgrade machine of EXL-50, is also presented.

physics.plasm-ph↗

arXiv2025

CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter

Although retrieval-augmented generation(RAG) significantly improves generation quality by retrieving external knowledge bases and integrating generated content, it faces computational efficiency bottlenecks, particularly in knowledge retrieval tasks involving hierarchical structures for Tree-RAG. This paper proposes a Tree-RAG acceleration method based on the improved Cuckoo Filter, which optimizes entity localization during the retrieval process to achieve significant performance improvements. Tree-RAG effectively organizes entities through the introduction of a hierarchical tree structure, while the Cuckoo Filter serves as an efficient data structure that supports rapid membership queries and dynamic updates. The experiment results demonstrate that our method is much faster than naive Tree-RAG while maintaining high levels of generative quality. When the number of trees is large, our method is hundreds of times faster than naive Tree-RAG. Our work is available at https://github.com/TUPYP7180/CFT-RAG-2025.

cs.LG↗

arXiv2025

Guiding polaritonic energy and momentum through two-dimensional Bravais lattices

The strong exciton absorption in monolayer transition metal dichalcogenides provides a promising platform for studying polaritons with tunable dispersions, which are crucial for controlling polaritonic energy and momentum, but remain underexplored. In this work, monolayer MoS$_2$ is coupled with a Fabry-P\'erot microcavity to form polaritons. Five types of Bravais lattices with sub-wavelength periods, based on polymethyl methacrylate (PMMA) nanopillars, are intentionally designed. The energy overlap between the periodic PMMA scattering wave and the polariton establishes a coupling channel that controls the directional flow of polaritonic energy, as demonstrated through angle-resolved reflectance measurements. Back-space image measurements further demonstrate that the dispersion in reciprocal space can be directly and manually tuned, allowing for control over their number and their positions. The coupling between the polariton and PMMA scattering wave is further demonstrated by analyzing the reflectance using the two-port two-mode model. The symmetries of 2D Bravais lattices allow the angle between energy and momentum flow to vary widely, from 90{\deg}, 60{\deg}, 45{\deg}, and 30{\deg} to arbitrary values. By adjusting the lattice vector lengths, the position of the dispersion branch in a specific direction can be fine-tuned, enabling full-range control over polariton dispersion. This work presents the first theoretical and experimental demonstrations of guiding the direction of polaritonic energy and momentum through Bravais lattice design.

physics.optics↗

arXiv2025

Fabry-Perot resonance modes in a MoS$_2$-based vertical stacking cavity for strong light-matter coupling and topological phase singularity

Rich dielectric properties in atomic transition metal dichalcogenides (TMDs) enhance light-matter interactions and contribute to a variety of optical phenomena. The direct transfer of TMDs onto photonic crystals facilitates optical field confinement and modifies photon dispersion through the generation of polaritons. However, light-matter interaction is severely limited by this stacking method. This limitation can be significantly improved by constructing a vertical stacking cavity with alternating layers of dielectric material and monolayer MoS$_2$. This multilayer structure is proven to be a compact, versatile, and customizable platform for controlling Fabry-Perot cavity resonance mode. Angle-resolved reflectance further aids in studying resonance mode dispersion. Moreover, the strong light-matter interaction results in multiple perfect absorptions, with the monolayer MoS$_2$ significantly contributing to the absorption in this system, as schematically revealed by the electric field distribution. The multiple perfect absorptions produce an unusual amounts of phase singularities with topological pairs, whose generation, evolution, and annihilation can be controlled by adjusting cavity parameters. Our findings provide a flexible and consistent framework for optimizing light-matter interactions and supporting further studies on wavefront shaping, optical vortices, and topological variants.

physics.optics↗

arXiv2024

Interpretable Image Classification with Adaptive Prototype-based Vision Transformers

We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that.'' In our model, a prototype consists of \textit{parts}, which can deform over irregular geometries to create a better comparison between images. Unlike existing models that rely on Convolutional Neural Network (CNN) backbones and spatially rigid prototypes, our model integrates Vision Transformer (ViT) backbones into prototype based models, while offering spatially deformed prototypes that not only accommodate geometric variations of objects but also provide coherent and clear prototypical feature representations with an adaptive number of prototypical parts. Our experiments show that our model can generally achieve higher performance than the existing prototype based models. Our comprehensive analyses ensure that the prototypes are consistent and the interpretations are faithful.

cs.CV↗

arXiv2024

Optimizing Coded-Apertures for Depth-Resolved Diffraction

Coded apertures, traditionally employed in x-ray astronomy for imaging celestial objects, are now being adapted for micro-scale applications, particularly in studying microscopic specimens with synchrotron light diffraction. In this paper, we focus on micro-coded aperture imaging and its capacity to accomplish depth-resolved micro-diffraction analysis within crystalline specimens. We study aperture specifications and scanning parameters by assessing characteristics like size, thickness, and patterns. Numerical experiments assist in assessing their impact on reconstruction quality. Empirical data from a Laue diffraction microscope at a synchrotron undulator beamline supports our findings. Overall, our results offer key insights for optimizing aperture design in advancing micro-scale diffraction imaging at synchrotrons. This study contributes insights to this expanding field and suggests significant advancements, especially when coupled with the enhanced flux anticipated from the global upgrades of synchrotron sources.

eess.SP↗

arXiv2024

BugBlitz-AI: An Intelligent QA Assistant

The evolution of software testing from manual to automated methods has significantly influenced quality assurance (QA) practices. However, challenges persist in post-execution phases, particularly in result analysis and reporting. Traditional post-execution validation phases require manual intervention for result analysis and report generation, leading to inefficiencies and potential development cycle delays. This paper introduces BugBlitz-AI, an AI-powered validation toolkit designed to enhance end-to-end test automation by automating result analysis and bug reporting processes. BugBlitz-AI leverages recent advancements in artificial intelligence to reduce the time-intensive tasks of manual result analysis and report generation, allowing QA teams to focus more on crucial aspects of product quality. By adopting BugBlitz-AI, organizations can advance automated testing practices and integrate AI into QA processes, ensuring higher product quality and faster time-to-market. The paper outlines BugBlitz-AI's architecture, discusses related work, details its quality enhancement strategies, and presents results demonstrating its effectiveness in real-world scenarios.

cs.SE↗