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Guodong Liu

Publications and source records attributed to Guodong Liu.

At least 19 recordsLinked to original sources
arXiv2026

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cross-sectional imaging. In this work, we propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that continuously estimates disease severity from longitudinal diffusion tensor imaging (DTI). Specifically, DCP models disease severity as a low-dimensional probabilistic latent variable by jointly integrating longitudinal observations with weak clinical supervision, from which the proposed Disease Continuum Score (DCS) is derived to quantify an individual's position along the Alzheimer's disease continuum together with its associated uncertainty. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods. More importantly, comprehensive validation analyses show that DCS accurately characterizes disease severity, exhibits strong clinical relevance, preserves longitudinal disease evolution, and predicts future disease conversion. These results suggest that DCS provides a quantitative imaging-derived representation for continuous assessment of Alzheimer's disease progression beyond conventional diagnostic labels and clinical scores.

cs.LG↗
arXiv2026

Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet

Translating complex biomedical data into patient-friendly narratives is central to modern biomedical informatics. This study presents a comparative analysis of training small language models (SLMs) in specialized biomedical datato-text generation tasks. We explore widely adopted post-training methods including supervised fine-tuning (SFT), direct preference optimization (DPO), odds ratio preference optimization (ORPO), and group relative policy optimization (GRPO) with Qwen-based SLMs on a medicine package leaflets dataset. To assess cross-dataset generalizability, we also curated drug label data from openFDA. We evaluate models using both standard lexical overlap metrics like ROUGE as well as semantic similarity measures. Across our experiments, the results show that (1) the aligned SLMs outperform proprietary models like GPT-5; (2) ORPO outperforms the SFTbaselines; (3) GRPO yields the most robust cross-dataset performance among the alignment methods tested as well as GPT-5.

cs.CL↗
arXiv2026

Confidence-feedback-weighted graph matching network: online-offline laser-induced damage site matching under complex interference

Online inspection images of final optics in high-power laser facilities contain pseudo-damage sites that closely resemble true damage sites. Determining the authenticity of online-detected sites is therefore difficult and requires accurate matching to offline ground-truth sites. However, this matching remains highly challenging due to limited match-discriminative features, local geometric distortions, and numerous distractor sites. Existing matching models mainly suppress distractors implicitly through loss-function supervision. We propose a confidence-feedback-weighted graph matching network that requires only damage-site centroid coordinates as input. It estimates node matchability confidence from each round of matching scores and feeds it back as a reliability weight to guide subsequent edge-feature aggregation, thereby suppressing distractor propagation and enhancing cross-graph discriminability. Within this framework, a geometric consistency constraint calibrates spurious high-confidence matchability estimates, while a hard-example mining loss improves discrimination between structurally similar sites. Experiments on our Complex-Scene dataset show that the proposed method achieves a matching F1-score of 96.36$\%$ with robust and efficient performance.

cs.CV↗
arXiv2026

Coexistence of High Temperature Superconductivity and Antiferromagnetic Order in a Cuprate with Multiple Hole Fermi Pockets

The intricate relationship between high temperature superconductivity and antiferromagnetic order in cuprates, and the fundamental origin of electron pairing remain open questions. By utilizing high-resolution laser-based spatially-resolved angle-resolved photoemission spectroscopy, we investigate the seven-layer $Bi_{2}Sr_{2}Ca_{6}Cu_{7}O_{18+\delta}$ (Bi2267) and identify a cuprate system that consists of multiple hole Fermi pockets. The observed Fermi pockets exhibit pronounced momentum-, temperature- and Fermi surface-dependent energy gaps. Crucially, high temperature superconductivity with a critical temperature ($T_{\mathrm{c}}$) of $\sim$75 K emerges in a system with multiple Fermi pockets and the presence of strong antiferromagnetic order and correlations. In particular, substantial electron pairing is observed along the Fermi pocket with an energy gap up to $\sim$42 meV in lightly-doped CuO$_{2}$ planes ($p\sim$0.05). These findings challenge the conventional understanding of the roles of the nodal and antinodal electronic states in driving high-temperature superconductivity. They show that superconductivity and antiferromagnetism can coexist in a cuprate with multiple Fermi pockets, offering further insights into the pairing mechanism in cuprate superconductors.

cond-mat.supr-con↗
arXiv2026

Suppression of p-Wave Altermagnetism by Localized 4f Electrons in CeNiAsO

Altermagnetism, characterized by momentum-dependent spin splitting and zero net magnetization, has so far been explored mainly in weakly or moderately correlated d-electron systems. How symmetry-allowed altermagnetic band splitting manifests in heavy-fermion materials, where magnetic exchange competes with Kondo correlations, remains unclear. Here we use high-resolution angle-resolved photoemission spectroscopy (ARPES) to investigate CeNiAsO, a Kondo-lattice system that was predicted to be a candidate for p-wave altermagnetism. Fermi surface mapping and polarization-dependent ARPES show that the experimentally observed itinerant bands are mainly derived from Ni 3d orbitals, while resonant photoemission reveals that the Ce 4f states remain predominantly localized with residual c-f hybridization. Ultra-low-temperature measurements reveal no resolvable near-Fermi-level p-wave-like exchange splitting on the Ni 3d-derived conduction bands across the successive antiferromagnetic transitions. These experimental observations cannot be captured by an itinerant-4f band-structure description, which predicts a sizable p-wave splitting in the itinerant bands. When the localized Ce 4f character is incorporated, our band structure calculations indicate that the itinerant Ce 4f band weight is shifted away from the Fermi level and the p-wave-like splitting on the Ni 3d-derived bands is reduced to the few-meV scale. These results establish CeNiAsO as a strongly correlated f-electron setting in which the magnetic symmetry allows p-wave-like band splitting, but localized 4f electrons strongly suppress its observable itinerant single-particle signature.

cond-mat.str-el↗
arXiv2026

Termination-Dependent Surface States and Magnetic Fingerprints of Chiral Helimagnet Cr1/3TaS2

Chiral helimagnets based on intercalated transition-metal dichalcogenides, characterized by nano-scale spin ordering, provide a powerful route to engineer chiral spin textures (e.g. the topologically protected magnetic solitons) and emergent electronic functionality at reduced dimensions, where surface and interface states often dominate device operation. However, despite growing interest, direct experimental studies of termination-dependent surface electronic structures and their temperature-driven magnetic evolution remain largely unexplored, hindering a microscopic understanding of the electronic states that is crucial for the development of low-dimensional spintronic devices. Here, for the first time, taking Cr1/3TaS2 as a representative example, we systematically investigate the termination-dependent surface electronic states of the chiral helimagnets and uncover their distinct temperature evolution across the magnetic transition (TC~142K) by combining high-resolution ARPES with a micro-focused beam and surface-state-resolved first-principles calculations. The TaS2-terminated surface hosts folded monolayer-like TaS2 bands under the $\sqrt3\times\sqrt3$ superlattice potential and a shallow triangular electron pocket at the superlattice $\bar K$ point arising from Cr-Ta orbital hybridization. In contrast, the Cr-terminated surface exhibits reconstructed hole pockets with pronounced magnetic band splitting. This splitting disappears above TC and closely follows the chiral helimagnetic order parameter, providing a direct spectroscopic fingerprint of chiral helimagnetic order. In addition, multiple ultranarrow Cr-d-derived surface flat bands are resolved. These findings establish Cr1/3TaS2 as a model system in which surface electronic states are strongly coupled to chiral magnetism, opening new opportunities for chiral spintronic and valleytronic micro/nanodevices.

cond-mat.mtrl-sci↗
arXiv2026

Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection

Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead to performance bottlenecks and limited trustworthiness in real-world applications. To address these challenges, this paper proposes a novel knowledge-guided loss function that seamlessly integrates model interpretability into the training process without incurring any additional inference cost. Our method operates in two phases: first, a primary classification network is trained, and its explanations, in the form of saliency maps, are generated as prior knowledge. Second, a multi-task learning framework is established, where the main task performs classification, and an auxiliary task imposes consistency between the saliency maps of the final model and the primary model. This consistency is enforced by a dedicated knowledge-guided loss term, effectively acting as a powerful regularizer to steer the model towards robust feature representations. Extensive experiments on multiple public defect datasets demonstrate that our approach consistently enhances the performance of baseline models in terms of accuracy and AP. Moreover, visual analysis reveals that the proposed method yields more concentrated and human-intelligible saliency maps. This work presents a simple yet effective paradigm for bridging the gap between model performance and interpretability, paving the way for more reliable and high-performing vision systems in industrial quality inspection.

cs.CV↗
arXiv2026

Persistent Fermi Pockets and Robust Electron Pairing in Lightly Doped CuO$_2$ Planes of Cuprate Superconductors

High temperature superconductivity in cuprate superconductors is generally considered to be generated from doping the Mott insulators. The fundamental nature of the doped parent compounds as well as the microscopic origin of electron pairing remain critical issues in understanding the emergence of superconductivity. Here, using high-resolution spatially-resolved laser angle-resolved photoemission spectroscopy, we investigate the intrinsic electronic structures of the CuO$_2$ planes in multilayer cuprates Bi$_2$Sr$_2$Ca$_{n-1}$Cu$_n$O$_{2n+4+\delta}$ (n=5$\sim$8). The inner CuO$_2$ planes are well shielded from the disorders and provide a rare and ideal platform to probe the intrinsic electronic phase diagram. We observe well-defined Fermi pockets with hole doping levels as low as 0.007, demonstrating an abrupt transition from the parent Mott insulator to a metallic state upon the introduction of an infinitesimal amount of doping. The innermost CuO$_2$ planes (IP$_0$) display gapless Fermi pockets, while the second innermost planes (IP$_1$) exhibit anisotropic superconducting gaps up to $\sim$33$\,$meV, indicative of robust electron pairing coexisting with strong antiferromagnetic order. Our findings provide a revised framework for understanding the doping-driven transitions and pairing mechanisms in cuprate superconductors.

cond-mat.supr-con↗
arXiv2026

ATRS: Adaptive Trajectory Re-splitting via a Shared Neural Policy for Parallel Optimization

Parallel trajectory optimization via the Alternating Direction Method of Multipliers (ADMM) has emerged as a scalable approach to long-horizon motion planning. However, existing frameworks typically decompose the problem into parallel subproblems based on a predefined fixed structure. Such structural rigidity often causes optimization stagnation in highly constrained regions, where a few lagging subproblems delay global convergence. A natural remedy is to adaptively re-split these stagnating segments online. Yet, deciding when, where, and how to split exceeds the capability of rule-based heuristics. To this end, we propose ATRS, a novel framework that embeds a shared Deep Reinforcement Learning policy into the parallel ADMM loop. We formulate this adaptive adjustment as a Multi-Agent Shared-Policy Markov Decision Process, where all trajectory segments act as homogeneous agents and share a unified neural policy network. This parameter-sharing architecture endows the system with size invariance, enabling it to handle dynamically changing segment counts during re-splitting and generalize to arbitrary trajectory lengths. Furthermore, our formulation inherently supports zero-shot generalization to unseen environments, as our network relies solely on the internal states of the numerical solver rather than on the geometric features of the environment. To ensure solver stability, a Confidence-Based Election mechanism selects only the most stagnating segment for re-splitting at each step. Extensive simulations demonstrate that ATRS accelerates convergence, reducing the number of iterations by up to 26.0% and the computation time by up to 19.1%. Real-world experiments further confirm its applicability to both large-scale offline global planning and real-time onboard replanning within 35 ms per cycle, with no sim-to-real degradation.

cs.RO↗
arXiv2026

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reliability. A single undetected erroneous prediction can lead to catastrophic outcomes. Unfortunately, there is often no alternative but to place trust in the outputs of a trained AI system, which operates without an internal safeguard to flag unreliable predictions, even in cases of high accuracy. We propose a post-hoc explanation-based indicator to detect false negatives in binary defect detection networks. To our knowledge, this is the first method to proactively identify potentially erroneous network outputs. Our core idea leverages the difference between class-specific discriminative heatmaps and class-agnostic ones. We compute the difference in their intersection over union (IoU) as a reliability score. An adversarial enhancement method is further introduced to amplify this disparity. Evaluations on two industrial defect detection benchmarks show our method effectively identifies false negatives. With adversarial enhancement, it achieves 100\% recall, albeit with a trade-off for true negatives. Our work thus advocates for a new and trustworthy deployment paradigm: data-model-explanation-output, moving beyond conventional end-to-end systems to provide critical support for reliable AI in real-world applications.

cs.CV↗
arXiv2026

Mott-Derived Local Moments and Kondo Hybridization in a d-electron Kagome lattice

Unlike canonical Kondo lattices in f-electron systems, where localized f orbitalsnaturally provide local moments, d-electron Kondo lattices require a distinct mechanism for local-moment formation. However, the study of d-electron Kondo lattices in bulk materials remains far from settled, particularly with regard to the microscopic origin of the local moments. Here, we report a microscopic mechanism for this process in the bilayer kagome metal CsCr6Sb6, where strong correlations drive a Mott splitting of the kagome flat band to supply the requisite local moments. By combining STM/STS and ARPES, we resolve a spectroscopic hierarchy between high-energy correlation effects and low temperature hybridization. Low-temperature STS reveals a robust asymmetric suppression of the density of states near EF that is well captured phenomenologically by a Fano-type lineshape, while ARPES detects a sharp quasiparticlepeak near EF. These low-energy signatures evolveon the same temperature scale and disappear upon warming, consistent with the onset of Kondo hybridization. At the same time, STS resolves symmetric humps at approximately +-50 mV and ARPES identifies a weakly dispersive feature around 50 meV below EF; unlike the near-EF hybridization signatures, these features persist to substantially higher temperatures. This separation of energy and temperature scales supports a two-stage picture in which a kagome flat band first undergoes correlation-driven splitting into lower and upper Hubbard bands, and the occupied lower Hubbard band supplies the local moments that later hybridize with itinerant electrons at lower temperature. Our results therefore move beyond the phenomenology of a kagome Kondo lattice candidate and instead provide a microscopic spectroscopic picture linking Mottness to Kondo hybridization in a frustrated d-electron system.

cond-mat.str-el↗
arXiv2026

Electronic Structure and Superconducting Gap of HgBa$_2$Ca$_2$Cu$_3$O$_{8+\delta}$ Revealed by Laser-Based Angle-Resolved Photoemission Spectroscopy

The spatially-resolved laser-based high resolution angle resolved photoemission spectroscopy (ARPES) measurements have been performed on the optimally-doped HgBa$_2$Ca$_2$Cu$_3$O$_{8+\delta}$ (Hg1223) superconductor with a $T_c$ at 133 K. Two distinct regions are identified on the cleaved surface: the single Fermi surface region where only one Fermi surface is observed, and the double Fermi surface region where two Fermi surface sheets are resolved coming from both the inner (IP) and outer (OP) CuO$_2$ planes. The electronic structure and superconducting gap are measured on both of these two regions. In both cases, the observed electronic states are mainly concentrated near the nodal region. The momentum dependence of superconducting gap deviates from the standard d-wave form. These results indicate that the surface electronic structure of Hg1223 behaves more like that of underdoped cuprates.

cond-mat.supr-con↗
arXiv2026

Electronic Origin of Density Wave Orders in a Trilayer Nickelate

The discovery of superconductivity in Ruddlesden-Popper nickelates has established a new frontier in the study of high-temperature superconductors. However, the underlying pairing mechanism and its relationship to the material's electronic and magnetic ground states remain elusive. Since unconventional superconductivity often emerges from a complex interplay of magnetic correlations, elucidating the magnetic ground state of the nickelates at ambient pressure is crucial for understanding the emergence of superconductivity under high pressure. Here, we combine high-resolution angle-resolved photoemission spectroscopy with tight-binding model simulation to investigate the electronic structure of the representative trilayer Ruddlesden-Popper nickelate La$_4$Ni$_3$O$_{10}$. We provide the first experimental evidence of band splitting induced by interlayer coupling and further resolve the momentum-dependent density wave gap structures along all the Fermi surfaces. Our findings identify the mirror-selective Fermi surface nesting as the origin of the interlayer antiferromagnetic spin density wave and demonstrate the dominant role of Ni-3d$_{z^2}$ orbitals in the low-energy physics of La$_4$Ni$_3$O$_{10}$. These results provide a fundamental framework for understanding the magnetic interactions and high-temperature superconductivity mechanism in the Ruddlesden-Popper nickelate family.

cond-mat.supr-con↗
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↗
arXiv2026

HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize

Multimodal Large Language Models (MLLMs) have substantially advanced Radiology Report Generation (RRG), yet aligning them through reinforcement learning (RL) remains challenging due to heterogeneous medical supervision. Vanilla Group Relative Policy Optimization (GRPO) assigns uniform credit across the entire generation, leading to segment interference, token dilution, and evidence--diagnosis decoupling, which exacerbates clinical hallucinations. We propose HERO (Hierarchical Evidential Reasoning Optimization), a factorized policy optimization framework that aligns heterogeneous supervision with three optimization granularities. HERO separately optimizes reasoning, diagnosis, and evidence grounding through complementary segment-, token-, and completion-level optimization with a heterogeneous reward formulation covering diagnostic accuracy, reasoning quality, and think--answer consistency. Experiments on MIMIC-CXR and IU-Xray show that HERO outperforms strong supervised and reinforcement learning baselines, achieving state-of-the-art clinical efficacy while producing more evidence-grounded and think--answer-consistent reports, thereby substantially mitigating clinical hallucinations.

cs.LG↗
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

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

Expansion of Momentum Space and Full 2$\pi$ Solid Angle Photoelectron Collection in Laser-Based Angle-Resolved Photoemission Spectroscopy by Applying Sample Bias

Angle-resolved photoemission spectroscopy (ARPES) directly probes the energy and momentum of electrons in quantum materials, but conventional setups capture only a small fraction of the full 2$\pi$ solid angle. This limitation is acute in laser-based ARPES, where the low photon energy restricts momentum space despite ultrahigh resolution. Here we present systematic studies of bias ARPES, where applying a sample bias expands the accessible momentum range and enables full 2$\pi$ solid angle collection in two dimension using our 6.994 eV laser source. An analytical conversion relation is established and validated to accurately map the detector angle to the emission angle and the electron momentum in two dimensions. A precise approach is developed to determine the sample work function which is critical in the angle-momentum conversion of the bias ARPES experiments. Energy and angular resolutions are preserved under biases up to 100 V, and minimizing beam size is shown to be crucial. The technique is effective both near normal and off-normal geometries, allowing flexible Brillouin zone access with lower biases. Bias ARPES thus elevates laser ARPES to a new level, extending momentum coverage while retaining high resolution, and is applicable across a broad photon-energy range.

cond-mat.supr-con↗