arXiv subjects

Weijie Li

Publications and source records attributed to Weijie Li.

At least 19 recordsLinked to original sources

arXiv2026

Candidate for a Fractional Topological Insulator in Twisted MoTe2

The interplay among electronic correlation, topology, and time-reversal-symmetry (TRS) often leads to exotic quantum states of matter, as highlighted by the discoveries of fractional Chern insulators (FCIs) in twisted bilayer MoTe2 (tMoTe2). Among the FCIs in tMoTe2, the most robust is at a hole filling factor of v=-2/3 per moiré unit cell. Here, employing pump-probe circular dichroism (CD) measurement on tMoTe2 at twist angles (3.9 and 3.7 degrees), we show that a correlated state at v =-4/3 exhibits an unusual Ising antiferromagnet behavior. The v =-4/3 state with no net magnetization undergoes first order phase transitions at extremely low magnetic fields of ~ 2-6 mT to partially valley polarized (PVP) states. This behavior is notably absent for all other correlated states in tMoTe2 and also disappears for v =-4/3 at higher or lower twist angles (4.0 or 3.3 degree). The observed magnetic signature is consistent with a theoretically proposed fractional topological insulator (FTI), consisting of two copies of v =-2/3 FCIs with opposite chirality in the two K valleys. The experimental results are supported by interacting continuum model calculations that reveal the extreme closeness in energy ( < 1 meV) between the putative FTI and PVP states. Our findings present a candidate FTI with TRS and call for advanced transport and imaging measurements to establish the quantized helical edge modes.

cond-mat.str-el↗

arXiv2026

Nonequilibrium dynamics of doped Chern ferromagnets: a case study for false vacuum decay

Even though metastable false vacuum decay is ubiquitous in physics, its underlying dynamics are still not well understood. Dissipative state preparation in moiré quantum materials provides an exceptional setting for exploring this physics since it allows the possibility of generating exotic quantum states that are not the ground state of the system Hamiltonian. Motivated by recent experiments demonstrating steady-state optical orientation of the spin-valley degree of freedom of holes, here we investigate dynamics of itinerant and Chern ferromagnets in the presence of an opposing magnetic field. Optical pumping using a circularly polarized Laguerre-Gauss beam allows us to deterministically prepare a true vacuum bubble embedded inside a metastable state. Depending on its initial size controlled by the pump power, we observe that the bubble collapses or expands due to an interplay between domain wall and bulk dynamics. For external magnetic fields comparable to the coercive field of ferromagnetism, we observe up to two-orders-of-magnitude prolongation of the spin polarization decay time at commensurate fillings corresponding to integer and fractional Chern insulator states. Our experiments reveal that the nonequilibrium dynamics of the ferromagnetic domains is substantially more sensitive to the precise filling factor around Chern insulator states than standard transport or optical measurements.

cond-mat.str-el↗

arXiv2026

Observation of metastable chiral domain walls in a topological magnet

The interplay between topology and correlation can give rise to exotic collective excitations. The integer and fractional quantum anomalous Hall (QAH) magnets recently discovered in two-dimensional (2D) flatband systems are predicted to host spin excitations distinct from those in conventional magnets. Experimentally, nevertheless, these new excitations remain largely unexplored. Here we investigate spin-valley excitations in a twisted MoTe2 moiré superlattice using resonant ultrafast pump-probe spectroscopy. We observe a metastable spin-valley excitation in the QAH magnet below T ~ 3.7 K that survives reverse magnetic field several times larger than the saturation field. The behavior of this excitation is sharply distinct from ordinary domain walls and magnons, indicating a new type of spin-valley textures unique to topological magnets. We propose that these textures are chiral domain walls with an in-plane winding of the pseudospin order parameter along the domain wall. Their metastability arises from the interplay between the topological winding in real space and the quantum geometry of the parent bands in momentum space through a universal mechanism. These chiral domain walls govern the nonequilibrium dynamics of QAH magnets and may play a central role in their stability. Our study highlights intrinsic quantum geometry effects on spin excitations in topological magnets; and provides key insights into the fundamental mechanism limiting stability of topological protection.

cond-mat.mes-hall↗

arXiv2026

Fifty Years of SAR Automatic Target Recognition: The Road Forward

Synthetic Aperture Radar (SAR) imaging is capable of observing objects in nearly all weather and illumination conditions, and has become an indispensable means of information acquisition for analysis and recognition of objects and scenes. SAR Automatic Target Recognition (SAR ATR) has been one of the most fundamental and challenging problems in remote sensing image analysis. Nowadays, the artificial intelligence (AI) technology, represented by large models and AI agents, has transformed the research paradigm, profoundly influenced various research fields, and continues to evolve at an unprecedented pace. However, the huge potential of AI for SAR image analysis remains locked. To unlock the potential of AI in SAR image understanding, the research community should rethink how to enable bidirectional empowerment between AI and SAR image understanding and strive to achieve substantial breakthroughs at critical bottlenecks. Given this period of remarkable evolution, this paper offers the first comprehensive review of SAR ATR, tracing its development and milestones over the past five decades and providing the research community with a clear roadmap. This survey includes approximately 260 research contributions, covering critical aspects of SAR ATR: pivotal challenges, important datasets, the merits and limitations of representative methods, evaluation metrics, and state-of-the-art performance. Finally, we finish the survey by identifying promising directions for future research. Looking ahead, we call for significant attention on three fundamental pillars: the curation of high-quality large-scale datasets, the design of fair and comprehensive evaluation benchmarks, and the fostering of safe open-source ecosystems.

eess.IV↗

arXiv2026

SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models

Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsettled. This article argues that a SAR pre-training target should satisfy two conditions to produce transferable representations: (i) physics-grounded stability, i.e., approximate invariance of the target operator to multiplicative speckle inherent in coherent imaging; and (ii) semantic scale compatibility, i.e., coverage of the heterogeneous spatial scales that downstream tasks demand. These two conditions are individually achievable but jointly difficult: physics-grounded stability favors fixed operators, while semantic scale compatibility favors data-driven composition. To this end, SARATR-X-v2 reconciles both within a single design. The target is constructed through fixed structural extractors spanning six receptive fields, from blind-spot local aggregation to directional log-ratio region contrast, and fused via learnable weights into one unified supervision signal for masked reconstruction. On twelve SAR benchmarks across classification, detection, and segmentation, SARATR-X-v2 achieves state-of-the-art transfer performance. Under synthetic speckle variation, the proposed target reduces perturbation drift in the learned representation by nearly two orders of magnitude relative to pixel-space supervision. Taken together, these results establish physics-grounded stability and semantic scale compatibility as a principled framework for pre-training target design under coherent imaging, and suggest that effective SAR pre-training is not about reconstructing more signal, but about reconstructing the right structural target.

cs.CV↗

arXiv2026

AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning

Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce Agora, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model's context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4% accuracy, with notable variation across domains.

cs.CL↗

arXiv2026

Electrically Programmable Correlated Topology and Magnetism in a Moiré Trilayer

Strong electron-electron interactions underlie a wide range of quantum many-body phenomena, including magnetism, superconductivity, and charge fractionalization. A central goal is to achieve in situ control over lattice geometry, bandwidth, and band topology within a single platform. Here we realize such an electrically programmable quantum many-body system in an alternating twisted trilayer MoTe$_2$, where an out-of-plane displacement field continuously modifies the layer polarization, effective lattice, and topology of the moiré bands. At zero displacement field, the system realizes a triangular lattice hosting a correlated insulator at one hole per moiré unit cell ($ν= -1$). Doping this state produces strongly asymmetric magnetic responses: double-exchange-like ferromagnetism for $|ν| > 1$, and signatures of spin polarons and antiferromagnetism for $|ν| < 1$. At large displacement field, interlayer hybridization reconstructs the electronic structure into a honeycomb lattice with a flat Chern band, supporting integer and fractional Chern insulators. Magneto-optical measurements further reveal the signatures of gap closure and Landau-level formation from a spin-polarized Fermi surface near the crossover between the two regimes. These results establish a unified, electrically tunable platform in which correlated magnetism and topological states emerge from a single controllable band structure.

cond-mat.mes-hall↗

arXiv2026

Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation

Robotic manipulation requires the effective integration of heterogeneous inputs, including visual observations, language instructions, and trajectory representations, to generate accurate actions. Existing transformer-based policies typically process these heterogeneous modalities within a shared parameter space, which often leads to modality interference and inefficient representation learning, especially in data-scarce scenarios. While Mixture-of-Experts (MoE) offers a scalable solution through expert specialization, conventional routing mechanisms are often sensitive to such cross-modal representation discrepancies, resulting in unstable expert assignment and expert collapse. In this work, we propose MATE (Multi-ModAl TrajEctory Policies), a novel trajectory prediction framework built upon MoE. Specifically, we introduce a Multi-Modal MoE architecture to achieve fine-grained sub-token feature decoupling, and design a cross-modal cosine router for stable and scale-invariant expert assignment across heterogeneous modalities. We further employ temperature-controlled routing and stochastic noise injection to improve expert balance and prevent premature routing collapse under scarce demonstrations. Experiments on the LIBERO benchmark show that our MATE consistently outperforms prior work under data scarcity. It achieves a 4.75% improvement in average success rate over the trajectory-guided counterpart. Real-world experiments on robotic ping-pong also suggest that the predicted trajectories can provide useful guidance for downstream robotic execution, further indicating the practical feasibility of our algorithm.

cs.RO↗

arXiv2026

Robust Lightweight Crack Classification for Real-Time UAV Bridge Inspection

With the widespread application of Unmanned Aerial Vehicles (UAVs) in bridge structural health monitoring, deep learning-based automatic crack detection has become a major research focus. However, practical UAV inspections still face four key challenges: weak crack features, degraded imaging conditions, severe class imbalance, and limited computational resources for practical UAV inspection workflows. To address these issues, this paper proposes a unified lightweight convolutional neural network framework composed of four synergistic components: a lightweight backbone network, a Convolutional Block Attention Module (CBAM) for channel and spatial enhancement, a directed robust augmentation strategy based on inspection-scene priors, and Focal Loss for hard-sample learning under class imbalance. Experiments on the SDNET2018 bridge deck dataset show that the proposed method achieves an inference speed of 825 FPS with only 11.21M parameters and 1.82G FLOPs. Compared with the baseline model, the complete framework improves the F1-score by 2.51% and recall by 3.95%. In addition, Grad-CAM visualizations indicate that the introduced attention module shifts the model's focus from scattered regions to precise tracking along crack trajectories. Overall, this study achieves a strong balance among accuracy, speed, and robustness, providing a practical solution for ground-station assisted real-time deployment in UAV bridge inspections. The source code is available at: https://github.com/skylynf/AttXNet .

cs.CV↗

arXiv2026

Van Hove Singularity-Driven Topological Magnetism in Twisted MoTe2

Van Hove singularities (vHSs) strongly amplify electron interactions and can stabilize correlated phases in topological bands. Here we report signatures of topological magnetism in large-angle twisted bilayer MoTe2 driven by the interplay of vHSs, strong correlations, and valley topology. In a 4.8 degree device, electrostatic tuning to a vHS produces a spontaneous anomalous Hall hot spot near nu = -1. Combined transport and reflective magnetic circular dichroism measurements indicate that this regime is not governed by magnetization alone, but instead emerges from a correlated intervalley-coherent antiferromagnetic state that evolves with doping into a canted phase. With increasing magnetic field, the Hall response develops an additional finite-field component consistent with a topological Hall effect from a noncoplanar spin texture, before transitioning into a C = -1 Chern insulator. Our results establish tunable vHSs in moire topological bands as a route to chiral magnetism and engineering topological phase transitions.

cond-mat.mes-hall↗

arXiv2026

Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition

Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learning deployment on resource-constrained edge devices, and existing lightweight models struggle to balance high-precision feature extraction with low computational requirements. The emerging Kolmogorov-Arnold Network (KAN) enhances fitting by replacing fixed activations with learnable ones, reducing parameters and computation. Inspired by KAN, we propose Light-ResKAN to achieve a better balance between precision and efficiency. First, Light-ResKAN modifies ResNet by replacing convolutions with KAN convolutions, enabling adaptive feature extraction for SAR images. Second, we use Gram Polynomials as activations, which are well-suited for SAR data to capture complex non-linear relationships. Third, we employ a parameter-sharing strategy: each kernel shares parameters per channel, preserving unique features while reducing parameters and FLOPs. Our model achieves 99.09%, 93.01%, and 97.26% accuracy on MSTAR, FUSAR-Ship, and SAR-ACD datasets, respectively. Experiments on MSTAR resized to $1024 \times 1024$ show that compared to VGG16, our model reduces FLOPs by $82.90 \times$ and parameters by $163.78 \times$. This work establishes an efficient solution for edge SAR image recognition.

cs.CV↗

arXiv2026

ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples, 10 times larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Secondly, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

cs.CV↗

arXiv2026

PCoKG: Personality-aware Commonsense Reasoning with Debate

Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structured dataset comprising 521,316 quadruples. We begin by employing three evaluators to score and filter events from the ATOMIC dataset, selecting those that are likely to elicit diverse reasoning patterns across different personality types. For knowledge graph construction, we leverage the role-playing capabilities of large language models (LLMs) to perform reasoning tasks. To enhance the quality of the generated knowledge, we incorporate a debate mechanism consisting of a proponent, an opponent, and a judge, which iteratively refines the outputs through feedback loops. We evaluate the dataset from multiple perspectives and conduct fine-tuning and ablation experiments using multiple LLM backbones to assess PCoKG's robustness and the effectiveness of its construction pipeline. Our LoRA-based fine-tuning results indicate a positive correlation between model performance and the parameter scale of the base models. Finally, we apply PCoKG to persona-based dialogue generation, where it demonstrates improved consistency between generated responses and reference outputs. This work bridges the gap between commonsense reasoning and individual cognitive differences, enabling the development of more personalized and context-aware AI systems.

cs.AI↗

arXiv2025

Optical Control of Integer and Fractional Chern Insulators

Optical control of topology, particularly in the presence of electron correlations, is a fascinating topic with broad scientific and technological impact. Twisted MoTe$_2$ bilayer (tMoTe$_2$) is a newly discovered zero-field fractional Chern insulator (FCI), exhibiting the fractionally quantized anomalous Hall (FQAH) effect. Since the chirality of the edge states and sign of the Chern number are determined by the underlying ferromagnetic polarization, manipulation of ferromagnetism would realize control of the CI/FCI states. Here, we demonstrate control and switching of ferromagnetic polarization, and thus the CI and FCI states by circularly polarized optical pumping in tMoTe$_2$. At low optical excitation power, we achieve on-demand preparation of ferromagnetic polarization by optical training, i.e., electrically tuning the system from non-ferromagnetic to desirable ferromagnetic states accompanied with helicity-selective optical pumping. With increased excitation power, we further realize direct optical switching of ferromagnetic polarization at a temperature far below the Curie temperature. Both optical training and direct switching of ferromagnetism are most effective near CI/FCI states, which we attribute to a gap enhanced valley polarization of photo-injected holes. We show that the magnetization can be dynamically switched by modulating the helicity of optical excitation. Spatially resolved measurements further demonstrate optical writing of a ferromagnetic, and thus a CI (or FCI) domain. Our work realizes precise optical control of a topological quantum many-body system with potential applications in topological spintronics, quantum memories, and creation of exotic edge states by programmable patterning of integer and fractional QAH domains.

cond-mat.mes-hall↗

arXiv2025

Optical control over topological Chern number in moiré materials

Controlling quantum matter with light offers a promising route to dynamically tune its many-body properties, ranging from band topology to superconductivity. However, achieving such optical control for strongly correlated electron systems in the steady-state has remained elusive. Here, we demonstrate all-optical switching of the spin-valley degree of freedom of itinerant ferromagnets in twisted MoTe2 homobilayers. This system uniquely features flat valley-contrasting Chern bands and exhibits a range of strongly correlated phases at various moiré lattice fillings, including Chern insulators and ferromagnetic metals. We show that the spin-valley orientation of all of these phases can be dynamically reversed by resonantly exciting the attractive polaron transition with circularly-polarized light. These findings not only constitute the first direct evidence for non-thermal switching of a ferromagnetic spin state at zero magnetic field, but also demonstrate the possibility of dynamical control over topological order parameter, paving the way for all-optical generation of chiral edge modes and topological quantum circuits.

cond-mat.mes-hall↗

arXiv2025

SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection

Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tackle these challenges, we establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets, providing a large-scale and diverse dataset for research purposes. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created. With this high-quality dataset, we conducted comprehensive experiments and uncovered a crucial challenge in SAR object detection: the substantial disparities between the pretraining on RGB datasets and finetuning on SAR datasets in terms of both data domain and model structure. To bridge these gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA) pretraining framework that tackles the problems from the perspective of data input, domain transition, and model migration. The proposed MSFA method significantly enhances the performance of SAR object detection models while demonstrating exceptional generalizability and flexibility across diverse models. This work aims to pave the way for further advancements in SAR object detection. The dataset and code is available at https://github.com/zcablii/SARDet_100K.

cs.CV↗

arXiv2025

Universal Magnetic Phases in Twisted Bilayer MoTe$_2$

Twisted bilayer MoTe$_2$ (tMoTe$_2$) has emerged as a robust platform for exploring correlated topological phases, notably supporting fractional Chern insulator (FCI) states at zero magnetic field across a wide range of twist angles. The evolution of magnetism and topology with twist angle remains an open question. Here, we systematically map the magnetic phase diagram of tMoTe$_2$ using local optical spectroscopy and scanning nanoSQUID-on-tip (nSOT) magnetometry. We identify spontaneous ferromagnetism at moiré filling factors $ν= -1$ and $-3$ over a twist angle range from 2.1$^\circ$ to 3.7$^\circ$, revealing a universal, twist-angle-insensitive ferromagnetic phase. At 2.1$^\circ$, we further observe robust ferromagnetism at $ν= -5$, absent in the devices with larger twist angle -- a signature of the flattening of higher bands in this twist angle range. Temperature-dependent measurements reveal a contrasting twist-angle dependence of the Curie temperatures between $ν= -1$ and $ν= -3$, indicating distinct interplay between exchange interaction and bandwidth for the two Chern bands. Despite spontaneous time-reversal symmetry breaking, we find no evidence of a topological gap at $ν= -3$; however, fragile correlated topological phases could be obscured by the device disorder evident in our spatially resolved measurements. Our results establish a global framework for understanding and controlling magnetic order in tMoTe$_2$ and highlight its potential for accessing correlated topological phases in higher energy Chern band.

cond-mat.mes-hall↗

arXiv2025

Observation of High-Temperature Dissipationless Fractional Chern Insulator

The fractional quantum anomalous Hall effect has recently been experimentally observed in zero-field fractional Chern insulators (FCI). However, an outstanding challenge is the presence of a substantial longitudinal resistance $R_{xx}$ (a few k$Ω$), even though the anomalous Hall resistance $R_{xy}$ is quantized. This dissipative behavior is likely linked to imperfect sample quality. Here, we report transport measurements of a drastically improved twisted $\text{MoTe}_2$ bilayer device, which exhibits quantized $R_{xy}$ and vanishing $R_{xx}$ for the $-2/3$ state, marking a dissipationless FCI. Contrary to fractional quantum Hall states where the energy gap increases with magnetic field, we find that the thermal activation gap of the observed FCI states decreases rapidly as the magnetic field rises from zero, then plateaus above a few teslas. This observation is attributed to the interplay between spin and charge gaps. Due to the spontaneous ferromagnetism, the spin gap dominates at low field, while the charge gap becomes appreciable once the magnetic field freezes spin fluctuations. For the $-2/3$ state, we estimate the spin and FCI gap of about 55 and 20 K, respectively. Our results provide insights into the energy scale of FCI and offer a pathway for quantum engineering of exotic correlated topological states.

cond-mat.mes-hall↗