arXiv subjects

Guodong Liu

Publications and source records attributed to Guodong Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Unusual Dual Flat Bands and two-dimensional Dirac-node Arc State in Kagome Metal Ni3In2S2

Kagome materials are at the frontier of condensed matter physics. An ideal kagome lattice features only one geometrically frustrated flat band spanning the entire momentum space and a single Dirac cone at the Brillouin-zone corners. However, for the first time, here we observe unusual flat-band and Dirac physics in the newly discovered "322" kagome material Ni3In2S2 by combining high-resolution synchrotron- and laser-based angle-resolved photoemission spectroscopy with a micro-focused beam, scanning tunneling microscopy, and first-principles calculations. We resolve two distinct electronic flat-band states located in close proximity to the Fermi level: a robust Topological Surface Flat Band at ~40 meV below the Fermi level on the Sulfur-terminated surface, originating from weak topological insulator states, and a kagome lattice-derived flat band at ~100 meV binding energy with an ultranarrow bandwidth (~5 meV). Instead of the single Dirac cone, the Indium-terminated surface hosts a rare two-dimensional Dirac-node arc state, where the gapless Dirac nodes extend along an open one-dimensional line crossing the Brillouin-zone boundary, exhibiting sharp linear dispersion, exceptionally high Fermi velocity, and pronounced circular dichroism. These findings establish Ni3In2S2 as a unique topological kagome metal in which multiple flat-band states of different physical origin coexist with an unusual Dirac-node arc, opening an avenue for discovering flat-band--driven and topology-enabled quantum phenomena.

cond-mat.mtrl-sci↗

arXiv2025

R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression

Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing multimodal approaches struggle to align these heterogeneous signals. We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting to jointly model structural MRI and single nucleotide polymorphisms (SNPs) variations. By representing each anatomically parcellated brain region as a visual token and encoding SNP profiles as structured text, the framework enables cross-modal attention that links regional atrophy patterns to underlying genetic factors. Applied to the ADNI cohort, R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition (NC), subjective memory concerns (SMC), mild cognitive impairment (MCI), and AD. Beyond predictive accuracy, the model yields biologically meaningful explanations by identifying stage-specific brain regions and gene signatures, as well as coherent ROI-Gene association patterns across the disease continuum. Attention-based attribution revealed genes consistently enriched for established GWAS-supported AD risk loci, including APOE, BIN1, CLU, and RBFOX1. Stage-resolved neuroanatomical signatures identified shared vulnerability hubs across disease stages alongside stage-specific patterns: striatal involvement in subjective decline, frontotemporal engagement during prodromal impairment, and consolidated multimodal network disruption in AD. These results demonstrate that interpretable multimodal AI can synthesize imaging and genetics to reveal mechanistic insights, providing a foundation for clinically deployable tools that enable earlier risk stratification and inform precision therapeutic strategies in Alzheimer's disease.

cs.LG↗

arXiv2025

Vidar: Embodied Video Diffusion Model for Generalist Manipulation

Scaling general-purpose manipulation to new robot embodiments remains challenging: each platform typically needs large, homogeneous demonstrations, and end-to-end pixel-to-action pipelines may degenerate under background and viewpoint shifts. Based on previous advances in video-based robot control, we present Vidar, consisting of an embodied video diffusion model as the generalizable prior and a masked inverse dynamics model (MIDM) as the adapter. We leverage a video diffusion model pre-trained at Internet scale, and further continuously pre-train it for the embodied domain using 750K multi-view trajectories collected from three real-world robot platforms. For this embodied pre-training, we introduce a unified observation space that jointly encodes robot, camera, task, and scene contexts. The MIDM module learns action-relevant pixel masks without dense labels, grounding the prior into the target embodiment's action space while suppressing distractors. With only 20 minutes of human demonstrations on an unseen robot (1% of typical data), Vidar outperforms state-of-the-art baselines and generalizes to unseen tasks, backgrounds, and camera layouts. Our results suggest a scalable recipe for "one prior, many embodiments": strong, inexpensive video priors together with minimal on-robot alignment.

cs.LG↗

arXiv2025

Step-GUI Technical Report

Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.

cs.CV↗

arXiv2025

Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects

Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects spatially. In real production lines, extreme foreground-background imbalance, defect sparsity with a long-tailed scale distribution, and low contrast are common. As a result, pixel-centric training and evaluation are easily dominated by large homogeneous regions, making it difficult to drive models to attend to small or low-contrast defects-one of the main bottlenecks for deployment. Empirically, existing models achieve strong pixel-overlap metrics (e.g., mIoU) but exhibit insufficient stability at the sample level, especially for sparse or slender defects. The root cause is a mismatch between the optimization objective and the granularity of QC decisions. To address this, we propose a sample-centric multi-task learning framework and evaluation suite. Built on a shared-encoder architecture, the method jointly learns sample-level defect classification and pixel-level mask localization. Sample-level supervision modulates the feature distribution and, at the gradient level, continually boosts recall for small and low-contrast defects, while the segmentation branch preserves boundary and shape details to enhance per-sample decision stability and reduce misses. For evaluation, we propose decision-linked metrics, Seg_mIoU and Seg_Recall, which remove the bias of classical mIoU caused by empty or true-negative samples and tightly couple localization quality with sample-level decisions. Experiments on two benchmark datasets demonstrate that our approach substantially improves the reliability of sample-level decisions and the completeness of defect localization.

cs.CV↗

arXiv2025

RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Simulation-based data synthesis has emerged as a powerful paradigm for advancing real-world robotic manipulation. Yet existing datasets remain insufficient for robust bimanual manipulation due to (1) the lack of scalable task generation methods and (2) oversimplified simulation environments. We present RoboTwin 2.0, a scalable framework for automated, large-scale generation of diverse and realistic data, together with unified evaluation protocols for dual-arm manipulation. At its core is RoboTwin-OD, an object library of 731 instances across 147 categories with semantic and manipulation-relevant annotations. Building on this, we design an expert data synthesis pipeline that leverages multimodal language models (MLLMs) and simulation-in-the-loop refinement to automatically generate task-level execution code. To improve sim-to-real transfer, RoboTwin 2.0 applies structured domain randomization along five axes: clutter, lighting, background, tabletop height, and language, enhancing data diversity and policy robustness. The framework is instantiated across 50 dual-arm tasks and five robot embodiments. Empirically, it yields a 10.9% gain in code generation success rate. For downstream policy learning, a VLA model trained with synthetic data plus only 10 real demonstrations achieves a 367% relative improvement over the 10-demo baseline, while zero-shot models trained solely on synthetic data obtain a 228% gain. These results highlight the effectiveness of RoboTwin 2.0 in strengthening sim-to-real transfer and robustness to environmental variations. We release the data generator, benchmark, dataset, and code to support scalable research in robust bimanual manipulation. Project Page: https://robotwin-platform.github.io/, Code: https://github.com/robotwin-Platform/robotwin/.

cs.RO↗

arXiv2025

Doping Evolution of Nodal Electron Dynamics in Trilayer Cuprate Superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ Revealed by Laser-Based Angle-Resolved Photoemission Spectroscopy

The doping evolution of the nodal electron dynamics in the trilayer cuprate superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ (Bi2223) is investigated using high-resolution laser-based angle-resolved photoemission spectroscopy (ARPES). Bi2223 single crystals with different doping levels are prepared by controlled annealing which cover the underdoped, optimally-doped and overdoped regions. The electronic phase diagram of Bi2223 is established which describes the T$_\mathrm{c}$ dependence on the sample doping level. The doping dependence of the nodal Fermi momentum for the outer (OP) and inner (IP) CuO$_2$ planes is determined. Charge distribution imbalance between the OP and IP CuO$_2$ planes is quantified, showing enhanced disparity with increasing doping. Nodal band dispersions demonstrate a prominent kink at $\sim$94$\,$meV in the IP band, attributed to the unique Cu coordination in the IP plane, while a weaker $\sim$60$\,$meV kink is observed in the OP band. The nodal Fermi velocity of both OP and IP bands is nearly constant at $\sim$1.62$\,$eVÅ independent of doping. These results provide important information to understand the origin of high T$_\mathrm{c}$ and superconductivity mechanism in high temperature cuprate superconductors.

cond-mat.supr-con↗

arXiv2025

TOP: Trajectory Optimization via Parallel Optimization towards Constant Time Complexity

Optimization has been widely used to generate smooth trajectories for motion planning. However, existing trajectory optimization methods show weakness when dealing with large-scale long trajectories. Recent advances in parallel computing have accelerated optimization in some fields, but how to efficiently solve trajectory optimization via parallelism remains an open question. In this paper, we propose a novel trajectory optimization framework based on the Consensus Alternating Direction Method of Multipliers (CADMM) algorithm, which decomposes the trajectory into multiple segments and solves the subproblems in parallel. The proposed framework reduces the time complexity to O(1) per iteration to the number of segments, compared to O(N) of the state-of-the-art (SOTA) approaches. Furthermore, we introduce a closed-form solution that integrates convex linear and quadratic constraints to speed up the optimization, and we also present numerical solutions for general inequality constraints. A series of simulations and experiments demonstrate that our approach outperforms the SOTA approach in terms of efficiency and smoothness. Especially for a large-scale trajectory, with one hundred segments, achieving over a tenfold speedup. To fully explore the potential of our algorithm on modern parallel computing architectures, we deploy our framework on a GPU and show high performance with thousands of segments.

cs.RO↗

arXiv2025

Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception

Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhanced the automation level of detection processes. However, conventional semantic segmentation and object detection models heavily rely on large-scale annotated datasets, which conflicts with the practical requirements of defect detection tasks. This paper proposes a novel weakly supervised semantic segmentation framework comprising two key components: a region-aware class activation map (CAM) and pseudo-label training. To address the limitations of existing CAM methods, especially low-resolution thermal maps, and insufficient detail preservation, we introduce filtering-guided backpropagation (FGBP), which refines target regions by filtering gradient magnitudes to identify areas with higher relevance to defects. Building upon this, we further develop a region-aware weighted module to enhance spatial precision. Finally, pseudo-label segmentation is implemented to refine the model's performance iteratively. Comprehensive experiments on industrial defect datasets demonstrate the superiority of our method. The proposed framework effectively bridges the gap between weakly supervised learning and high-precision defect segmentation, offering a practical solution for resource-constrained industrial scenarios.

cs.CV↗

arXiv2025

Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging

Medical image segmentation has achieved remarkable success through the continuous advancement of UNet-based and Transformer-based foundation backbones. However, clinical diagnosis in the real world often requires integrating domain knowledge, especially textual information. Conducting multimodal learning involves visual and text modalities shown as a solution, but collecting paired vision-language datasets is expensive and time-consuming, posing significant challenges. Inspired by the superior ability in numerous cross-modal tasks for Large Language Models (LLMs), we proposed a novel Vision-LLM union framework to address the issues. Specifically, we introduce frozen LLMs for zero-shot instruction generation based on corresponding medical images, imitating the radiology scanning and report generation process. {To better approximate real-world diagnostic processes}, we generate more precise text instruction from multimodal radiology images (e.g., T1-w or T2-w MRI and CT). Based on the impressive ability of semantic understanding and rich knowledge of LLMs. This process emphasizes extracting special features from different modalities and reunion the information for the ultimate clinical diagnostic. With generated text instruction, our proposed union segmentation framework can handle multimodal segmentation without prior collected vision-language datasets. To evaluate our proposed method, we conduct comprehensive experiments with influential baselines, the statistical results and the visualized case study demonstrate the superiority of our novel method.}

cs.CV↗

arXiv2025

Ultra-Stable Ferrimagnetic Second-Order Topological Insulator in 2D Metal-Organic Framework

Two-dimensional (2D) magnetic second-order topological insulators (SOTIs) exhibit distinct topological phases characterized by spin-polarized zero-dimensional (0D) corner states, which have garnered significant interest. However, 2D ferrimagnetic (FiM) SOTIs, particularly those that simultaneously exhibit ultra-stable corner states, are still lacking. Here, based on first-principles calculations and theoretical analysis, we reveal such SOTI state in a 2D metal-organic framework (MOF) material, Cr(pyz)2 (pyz = pyrazine). This material exhibits FiM ground state with an easy axis aligned along [001] direction. It hosts a nontrivial real Chern number in the spin-up channel, enabled by PT symmetry, with 0D corner states observable in disk. In contrast, the spin-down channel exhibits a trivial gapped bulk state. Notably, the topological corner states in monolayer Cr(pyz)2 show high robustness, even if the symmetries are broken by introducing defects, the corner states persist. We also considered other external perturbations, including uniaxial/biaxial strain, ligand rotation, and electric fields, the corner states still remain stable. Even more, the energy positions of the corner states are also nearly unchanged. This work is the first to identify ultra-stable FiM SOTI state in the MOF system, and provide an ideal platform for future experimental investigations and applications in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases

Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically rely on collaboration between computer scientists and domain experts, which can be both time-intensive and resource-demanding. To address these limitations, this paper presents a novel self-guided, knowledge-infused multimodal GNN that autonomously incorporates domain knowledge into the model development process. Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions. To evaluate our framework, we curated a comprehensive dataset of recent peer-reviewed papers on AD and integrated it with multiple real-world AD datasets. Experimental results demonstrate the ability of our method to extract relevant domain knowledge, provide graph-based explanations for AD diagnosis, and improve the overall performance of the GNN. This approach provides a more scalable and efficient alternative to inject domain knowledge for AD compared with the manual design from the domain expert, advancing both prediction accuracy and interpretability in AD diagnosis.

cs.LG↗

arXiv2024

Spin-layer coupling in altermagnets multilayer: a design principle for spintronics

The discovery of collinear symmetric-compensated altermagnets (AM) with intrinsic spin splitting provides a route towards energy-efficient and ultrafast device applications. Here, using first-principles calculations and symmetry analysis, we propose a series of AM Cr2SX (X=O, S, Se) monolayer and explore the spin splitting in Cr2SX multilayer. A general design principle for realizing the spin-layer coupling in odd/even-layer is mapped out based on the comprehensive analysis of spin group symmetry. The spin splitting behavior related with the MzUt, Mz and ML symmetries in AM multilayer can be significantly modulated by magnetic orders, crystal symmetry and external perpendicular gate field (Ez). Due to the spin-compensated bands of sublayers linked by overall Mz and interlayers ML symmetries, the Cr2S2 odd-layer exhibits the unique coexistence of spin splitting and spin degeneracy at high symmetric paths and X/Y valley, respectively. Furthermore, owing to the higher priority of overall ML symmetry compared to interlayers ML symmetry in AM even-layer, the spin-layer coupling of AM multilayer shows strong odd/even-layer dependence. Our work not only offer a new direction for manipulating spin splitting, but also greatly enrich the research on AM monolayer and multilayer.

physics.app-ph↗

arXiv2024

Piezoelectric Manipulation and Engineering for Layertronics in Two-Dimensional Materials

The electronic transport characteristics of two-dimensional (2D) systems have widespread application prospects in the fabrication of multifunctional nanodevices. However, the current research for basic transport phenomena, such as anomalous valley Hall effect (AVHE) and piezoelectric response, is limited to discrete discussion. Here, we theoretically propose a valley-piezoelectricity coupling strategy beyond the existing paradigm to realize AVHE and layer Hall effect (LHE) in ferrovalley (FV) systems, and its essential principle can be extended to general valleytronic materials. Through first-principles calculations, we demonstrate that the large polarized electric field of 2.8*106 (1.67*107) V/m can be induced by 0.1% uniaxial strain in FV 2H-LaHF (1T-LaHF) monolayers. In addition, the microscopic mechanism of interlayer antiferromagnetic (AFM) state of 2H-LaHF bilayer is uncovered by the spin Hamiltonian and super-superexchange (SSE) interaction. Our findings pave the way for new explorations of valley Hall-related effect involving piezoelectricity.

cond-mat.mes-hall↗

arXiv2024

Possible way to achieve anomalous valley Hall effect by tunable intrinsic piezoelectric polarization in FeO$_2$SiGeN$_2$ monolayer

Valley-related multiple Hall effect and piezoelectric response are novel transport characteristics in low-dimensional system, however few studies have reported their coexistence in a single system as well as their coupling relationships. By first-principles calculations, we propose a multifunctional Janus semiconductor, i.e. FeO$_2$SiGeN$_2$ monolayer with large valley polarization of about 120 meV and in-plane piezoelectric polarization with d11 of -0.714.03 pm/V. The magnetic anisotropy energy can be significantly regulated by electronic correlation strength and strain, which can be attributed to the change of competition relationship about Fe-3d-resolved magnetic anisotropy energy brought about by external regulatory means. Electronic correlation strength can induce phase transitions in Janus FeO$_2$SiGeN$_2$ monolayer from ferrovalley to quantum anomalous Hall phase, while the half-valley metallic state as the boundary of the phase transition can gererate 100% spin- and valley polarization. The related phase transition mechanism is analyzed based on the two-band strained kp model. The presence of piezoelectric strain coefficients d11 in valleytronic material makes the coupling between charge degrees of freedom and valley degrees of freedom possible, and the intrinsic electric field caused by the in-plane piezoelectric response provide the way to realize piezoelectric anomalous valley Hall effect. This work may pave a way to find a new member of materials with valley-related multiple Hall effect and stimulate further experimental works related to valleytronics and piezotronics.

cond-mat.mes-hall↗

arXiv2024

Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class Priors

Recent advances in unsupervised representation learning often rely on knowing the number of classes to improve feature extraction and clustering. However, this assumption raises an important question: is the number of classes always necessary, and do class labels fully capture the fine-grained features within the data? In this paper, we propose Contrastive Disentangling (CD), a framework designed to learn representations without relying on class priors. CD leverages a multi-level contrastive learning strategy, integrating instance-level and feature-level contrastive losses with a normalized entropy loss to capture semantically rich and fine-grained representations. Specifically, (1) the instance-level contrastive loss separates feature representations across samples; (2) the feature-level contrastive loss promotes independence among feature heads; and (3) the normalized entropy loss ensures feature diversity and prevents feature collapse. Extensive experiments on CIFAR-10, CIFAR-100, STL-10, and ImageNet-10 demonstrate that CD outperforms existing methods in scenarios where class information is unavailable or ambiguous. The code is available at https://github.com/Hoper-J/Contrastive-Disentangling.

cs.CV↗

arXiv2024

Quasi-one-dimensional spin transport in altermagnetic $Z^3$ nodal net metals

In three dimensions, quasi-one-dimensional (Q1D) transport has traditionally been associated with systems featuring a Q1D chain structure. Here, based on first-principle calculations, we go beyond this understanding to show that the Q1D transport can also be realized in certain three-dimensional (3D) altermagnetic (AM) metals with a topological nodal net in momentum space but lacking Q1D chain structure in real space, including the existing compounds $β$-Fe$_2$(PO$_4$)O, Co$_2$(PO$_4$)O, and LiTi$_2$O$_4$. These materials exhibit an AM ground state and feature an ideal crossed $Z^3$ Weyl nodal line in each spin channel around Fermi level, formed by three straight and flat nodal lines traversing the entire Brillouin zone. These nodal lines eventually lead to an AM $Z^3$ nodal net. Surprisingly, the electronic conductivity $σ_{xx}$ in these topological nodal net metals is dozens of times larger than $σ_{yy}$ and $σ_{zz}$ in the up-spin channel, while $σ_{yy}$ dominates transport in the down-spin channel. This suggests a distinctive Q1D transport signature in each spin channel, and the principal moving directions for the two spin channels are orthogonal, resulting in Q1D direction-dependent spin transport. This novel phenomenon cannot be found in both conventional 3D bulk materials and Q1D chain materials. In particular, the Q1D spin transport gradually disappears as the Fermi energy moves away from the nodal net, further confirming its topological origin. Our work not only enhances the comprehension of topological physics in altermagnets but also opens a new direction for the exploration of topological spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Absence of BCS-BEC Crossover in FeSe0.45Te0 55 Superconductor

In iron-based superconductor Fe(Se,Te), a flat band-like feature near the Fermi level was observed around the Brillouin zone center in the superconducting state. It is under debate whether this is the evidence on the presence of the BCS-BEC crossover in the superconductor. High-resolution laser-based angle-resolved photoemission measurements are carried out on high quality single crystals of FeSe0.45Te0.55 superconductor to address the issue. By employing different polarization geometries, we have resolved and isolated the dyz band and the topological surface band, making it possible to study their superconducting behaviors separately. The dyz band alone does not form a flat band-like feature in the superconducting state and the measured dispersion can be well described by the BCS picture. We find that the flat band-like feature is formed from the combination of the dyz band and the topological surface state band in the superconducting state. These results reveal the origin of the flat band-like feature and rule out the presence of BCS-BEC crossover in Fe(Se,Te) superconductor.

cond-mat.supr-con↗