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Yongxiang Liu

Publications and source records attributed to Yongxiang Liu.

At least 19 recordsLinked to original sources
arXiv2026

GeoJeV: Unified Spatial Probability for Training-Free Efficient Evidence Acquisition in Ultra-High-Resolution Remote Sensing

Ultra-high-resolution (UHR) remote sensing imagery provides rich spatial details but poses challenges for accurate and efficient multimodal understanding. Existing evidence acquisition methods selectively explore relevant image regions, yet largely rely on generative multimodal large language models (MLLMs) for intermediate search control. However, reinforcement learning from human feedback (RLHF) primarily aligns these models with human-preferred responses rather than explicitly optimizing region-level relevance judgments, while repeated autoregressive decoding introduces additional computational overhead. To address these limitations, we propose GeoJeV, a training-free method that formulates evidence acquisition as spatial probability-driven decision making. At each evaluated region, a frozen JeV model produces a probability distribution over candidate regions. Specifically, Region Rank prioritizes candidate regions, Sparse Branch adaptively determines branching width through sparse probability support, and Depth Gate controls refinement depth based on branch sparsity and distribution concentration. By coordinating search priority, branching width, and refinement depth through a shared distribution, GeoJeV reduces reliance on RLHF-aligned generation and eliminates intermediate decoding. Experiments on LRS-GRO, MME-RealWorld-RS, and LRS-VQA achieve accuracies of 60.59%, 52.79%, and 34.32%, respectively. Across the three benchmarks, GeoJeV improves the average accuracy of Qwen3-VL-8B by +5.32pp with only 1.12 additional seconds per question, while achieving a 7.8-fold inference speedup over the recent method.

cs.CV↗
arXiv2026

MMATR-X: High-Resolution Optical-SAR Joint Pretraining via Conditioned Alignment and Degraded-Target Prediction

Joint optical and Synthetic Aperture Radar (SAR) pretraining seeks cross-modal gains over representations learned from either modality alone. This is challenging because the modalities share only partial information, and their correspondence varies across pairs with scene context, acquisition timing, sensor configuration, and spatial resolution. In particular, finer resolution exposes more detailed modality-specific optical appearance and SAR backscatter, further complicating cross-modal supervision. Existing methods encourage cross-modal agreement through representation alignment or cross-modal prediction. Under simplified models of partial, pair-dependent correspondence, we show why a fixed alignment relation can be insufficient and characterize prediction ambiguity arising from target variation unresolved by the source representation. These analyses motivate CoDe-MAE, a masked-autoencoding framework for high-resolution optical-SAR joint pretraining. Its Conditioned Contrastive Learning (CCL) aligns cross-attention-conditioned representations, while Cross-modal Degraded-Target Prediction (CDP) predicts transformed targets to balance conditional predictability and retained supervision. We curate OSPretrain-1M, a 1M-sample optical-SAR corpus, and use CoDe-MAE to pretrain MMATR-X, which achieves leading transfer performance across optical-only, SAR-only, and dual-modal tasks. Controlled experiments on remote-sensing optical-SAR, optical-DEM, and indoor RGB-depth pairs further support these design principles across heterogeneous modality pairs. Code: \url{https://github.com/scenarri/MMATR-X}.

cs.CV↗
arXiv2026

Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts

Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions should be combined during sampling. Learning a global router is particularly challenging when the server has limited, potentially unrepresentative images. We study two router-learning strategies for a fixed expert pool. Server-Only learns input- and timestep-dependent expert assignments from server images with component labels. Anchor-Delta (AD) uses the same images and frozen experts to obtain additional evidence from denoising losses. It distills targets that combine this evidence with the current router's assignments, while a proxy gate controls a subsequent refinement step. Component labels describe data origin; expert losses measure empirical prediction performance. We evaluate these distinct signals through routing analyses and paired medical image generation. An AD-trained router improves generation metrics over Server-Only in a heterogeneous four-domain setting, and a separate server-data study shows gains in held-out routing over a corrected-routing comparator. Additional paired evaluations show that the effect of inference-time evidence mixing varies across benchmarks. These findings establish server-data availability and expert performance as distinct considerations in learning routers for multi-domain diffusion models.

cs.CV↗
arXiv2026

T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representations

Earth observation (EO) data provide rich temporal supervision, yet existing remote sensing foundation models mainly exploit sequential observations through imposing predefined pairwise relations or aggregating holistic reconstruction context. We seek to further exploit the sparse and nonuniform temporal sampling inherent in EO sequences as supervisory signals. To this end, we propose T-JEPA, a temporal joint-embedding predictive architecture that learns time-gap-conditioned latent transitions. A shared single-frame encoder processes each observation, while a temporal predictor estimates the complete target latent field from a masked source latent representation and the actual elapsed time. Across multiple temporal intervals, these predictive constraints organize observed states into structured latent trajectories. Asymmetric metadata injection mitigates shortcut learning, and direct supervision across multiple temporal scales proves more effective than recursively rolling out intermediate states. In parallel, masked pixel reconstruction provides complementary supervision for preserving spatial details. Under matched pre-training data and throughput, T-JEPA achieves leading transfer performance on both static and temporal tasks. Analyses further reveal that T-JEPA learns representations with time-gap-dependent transition predictability and coherent latent dynamics, while maintaining strong cross-period consistency, representation diversity, and semantic discriminability.

cs.CV↗
arXiv2026

On the Convergence of Muon and Beyond

The Muon optimizer has demonstrated strong empirical performance in training neural networks with matrix-structured parameters. A central theoretical question is how momentum variance reduction interacts with practical orthogonalization and weight decay. We analyze two variants: the one-batch Muon-MVR1 and the two-batch Muon-MVR2. To the best of our knowledge, this is the first \textbf{horizon-free} convergence analysis of variance-reduced Muon that jointly covers constant mini-batches, finite practical fixed-coefficient Newton--Schulz iterations, and decoupled weight decay in unconstrained stochastic nonconvex optimization. {Muon-MVR1 and Muon-MVR2 attain anytime stationarity rates of $\widetilde{\mathcal{O}}(T^{-1/4})$ and $\widetilde{\mathcal{O}}(T^{-1/3})$, respectively, over $T$ iterations. Under mean-square smoothness, the Muon-MVR2 rate matches the stochastic first-order lower bound up to logarithmic factors. The analysis provides alignment and norm bounds uniform in the step count, together with a nuclear-norm alignment bound that improves with the number of steps.} Under the Polyak--Łojasiewicz (PL) condition and a uniform gradient bound along the iterates, Muon-MVR1 and Muon-MVR2 achieve last-iterate objective-gap rates of $\mathcal{O}(T^{-1/4})$ and $\mathcal{O}(T^{-1/3})$, respectively, with finite NS steps and decoupled weight decay under the prescribed horizon-free schedules. Experiments on CIFAR-10 and C4 support the practical effectiveness of the proposed variance-reduced Muon variants. Code is available at the \href{https://github.com/MaeChd/MUON-MVR}{Muon-MVR repository}.

cs.LG↗
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 supervision by nearly two orders of magnitude relative to pixel-space supervision. Taken together, these results support 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

FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks

Fairness-aware distributed learning prioritizes clients with large losses to reduce performance disparities, but label poisoning can create large losses, thereby inducing a fairness--robustness conflict. We propose FairMean to manage this conflict. FairMean weights client gradients using a bounded, nondecreasing function of local loss. The increasing weights prioritize high-loss clients to promote fairness, while the upper bound prevents excessive loss-induced amplification of poisoned-client gradients. In the absence of label poisoning, we show that minimizing the FairMean objective is more conducive to solution fairness than minimizing the standard average-loss objective. Under label poisoning, we establish an average-stationarity bound whose attack-dependent term is proportional to the square of the poisoned-client fraction. Experiments show that FairMean promotes fairness by reducing accuracy variance while improving worst-client accuracy.

cs.LG↗
arXiv2026

SGEMM-cube: Precision-Recovery FP32 GEMM Approximation on Ascend NPUs with FP16 Matrix Engines

Modern AI accelerators provide high-throughput low-precision matrix engines, but often lack efficient support for FP32 GEMM. This paper presents SGEMM-cube, an FP32-accuracy GEMM approximation for Ascend NPUs built on FP16 Cube units. Following the fixed-length two-word splitting of Ootomo and Yokota, each FP32 operand is represented by an FP16 high component and a scaled FP16 residual. The product is reconstructed from three FP16 GEMMs while omitting the residual-residual term; the method therefore targets FP32-level accuracy rather than bit-exact emulation. Under an RN FP32-accumulation model, we provide a componentwise error analysis showing that the omitted term is no larger than the rounding error of a short FP32 inner product and that, for practically relevant inner-product lengths, the overall error is dominated by ordinary FP32 accumulation. We further analyze residual underflow and scaling under round-to-nearest conversion, compare two accumulation orders, and adapt L1-aware blocking and double buffering to Ascend's software-managed memory hierarchy. On Ascend 910A, SGEMM-cube is substantially more accurate than native FP16 GEMM, is comparable to the tested OpenBLAS FP32 SGEMM baseline for the evaluated input distributions and exponent range, and reaches 65.3\,TFLOP/s, or 77\% of the three-GEMM FP32-equivalent peak.

cs.DC↗
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

Ascend to Science: Exploration of AI Chips for Scientific Computing

The rapid rise of AI-oriented accelerators has reshaped compute systems around low-precision tensor engines, raising a practical question for the HPC community: under what conditions can such hardware support scientific workloads that demand numerical robustness, irregular memory access, and scalability? Using the Ascend 910 NPU series as a representative tensor-centric platform, we characterize precision, execution, and memory-hierarchy bottlenecks that hinder the direct deployment of scientific codes. We then develop and evaluate workload-specific mappings across five application studies -- HPL-MxP, LRSVD, SGEMM-cube, PQSim, and SMC-X -- combining heterogeneous execution, mixed-precision numerical formulations, precision emulation, hierarchical memory orchestration, and communication--computation overlap. These studies show that AI-native NPUs can achieve numerical robustness, competitive performance, and satisfactory scalability when numerical formulation, execution placement, and data movement are addressed in a coordinated manner. Our results provide a state-of-the-practice case study of how scientific workloads can be adapted to tensor-centric architectures, while distinguishing transferable optimization principles from Ascend-specific implementation details.

cs.DC↗
arXiv2026

Muse: Representation Geometry of Muon Beyond Normalized Momentum

Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this representation choice as a form of optimizer geometry and introduce {\method}, a family of Muon-style optimizers that shares the same momentum rule and Newton--Schulz backend across native, nearest-square, skinny, and vector representations. Each Frobenius-isometric representation induces a distinct polar steepest-descent geometry, in which the shorter matrix dimension determines the number of supported singular channels, the pullback scaling, and the constants in stochastic nonconvex convergence bounds. In a teacher--student model, curvature collapse and an isotropic Marchenko--Pastur spectral profile connect early-stage dissipation to the represented nuclear-to-squared-Frobenius norm ratio. Pretraining experiments on LLaMA2-130M and LLaMA2-600M, together with fixed-momentum diagnostics, show that balanced non-native representations can match the performance of the native representation, whereas reducing the shorter dimension weakens the scaling and singular-channel support, leading to behavior that increasingly resembles normalized momentum.

cs.LG↗
arXiv2026

Toward Active Object Detection for UAVs in the Wild: A Large-Scale Dataset, Benchmark and Method

Object detection is a fundamental component in numerous Unmanned Aerial Vehicle (UAV) applications, yet it has long been plagued by hindrances like occlusion or target pixel scarcity. Active Object Detection (AOD) provides a novel paradigm to address these challenges via active vision, while UAV-based AOD research remains scarce due to the lack of high-quality datasets and benchmarks for algorithm development and evaluation. To fill this gap, this paper presents ATRNet-LUDO, the first large-scale real-world dataset for UAV-Ground Active Object Detection (UGAOD). It contains 121,000 multi-view panoramic multi-target aerial images and 1.21 million local single-target slices, covering 10 vehicle targets across 40 scenarios. It enables the construction of diverse training and testing environments for UAV agent interaction and active observation policy learning. Based on this dataset, we establish a comprehensive evaluation benchmark for AOD policy learning methods. Most existing AOD policies rely on Deep Reinforcement Learning (DRL) but suffer from poor generalization. Evaluations on our benchmark reveal a significant generalization gap between training and testing performance, highlighting an urgent need for solutions. To this end, we leverage the Joint Embedding Predictive Architecture (JEPA) to construct a world model that enhances state representation learning, and propose AOD-JEPA by incorporating AOD-specific prior knowledge. Extensive experiments validate its effectiveness and superiority. We hope ATRNet-LUDO and the benchmark will advance research in the UGAOD field. The dataset and code are soon available at https://github.com/Leo000ooo/LUDO_dataset.

cs.CV↗
arXiv2026

RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction

Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuity, restricting their generalization across environments. To address these challenges, we introduce WorldRoadSeg-360K, the largest and most diverse road segmentation dataset to date, comprising 366,947 high-resolution images collected from 38 countries and 223 cities across various terrains and continents. WorldRoadSeg-360K serves as a comprehensive benchmark and reveals key challenges in handling diverse and structurally complex scenes. Automated approaches often struggle to preserve road connectivity, while current interactive methods lack efficient, topology-sensitive tools for real-world road editing. To this end, we present RoadGIE, establishing a novel interactive paradigm for road extraction in remote sensing. Unlike prior point- or box-based prompting strategies, RoadGIE supports connectivity-aware prompts, including clicks and scribbles, which inherently align with the topology of road networks. To improve structural consistency and mitigate performance degradation during iterative interactions, RoadGIE integrates an expert-guided prompting strategy and adapts the skeleton-based recall loss for interactive scenarios. RoadGIE achieves state-of-the-art performance in both segmentation accuracy and topological consistency on WorldRoadSeg-360K and other benchmarks, while maintaining efficient operation with only 3.7M parameters. The code are publicly available at: https://github.com/chaineypung/RoadGIE

cs.CV↗
arXiv2026

MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration

Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates after orthogonalization or use heavier whitening-based preconditioners before it. We introduce {\method}, a lightweight family of pre-orthogonalization equilibration schemes for Muon with three forms: two-sided row/column normalization (RC), row normalization (R), and column normalization (C). By rebalancing the momentum matrix before finite-step Newton--Schulz orthogonalization, {\method} improves the geometry seen by orthogonalization. We show that finite-step orthogonalization is governed by the input spectrum, especially stable rank and condition number, and that row/column normalization acts as a zeroth-order surrogate for whitening. For hidden matrix weights, R is the default variant. Theoretically, {\method} (R) retains the standard $\widetilde{\mathcal O}(T^{-1/4})$ Muon-type nonconvex stationarity guarantee with decoupled weight decay and a horizon-free diminishing learning-rate schedule, and extends it to finite-step NS5 up to an explicit inexactness constant. In LLaMA2 pretraining on C4, {\method} (R) consistently outperforms Muon on 130M, 350M, and 1B models, with faster convergence and lower validation perplexity. The code is available at the \href{https://github.com/MaeChd/muon-eq}{MuonEq codebase}.

cs.LG↗
arXiv2026

Adaptive Subspace Signal Detection and Performance Analysis in Nonzero-Mean Clutter

To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is the loss of degree of freedom (DOF), which is reduced by 1 compared with the zero-mean clutter scenario. The second is the loss of signal-to-clutter (SCR) ratio. Simulation and measured data verify the effectiveness of the proposed detectors and demonstrate their practical value in real-world radar systems.

stat.OT↗
arXiv2026

HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation

Synthetic Aperture Radar (SAR) imagery generation is essential for deepening the study of scattering mechanisms, establishing trustworthy electromagnetic scene models, and fundamentally alleviating the data scarcity bottleneck that constrains development in this field. However, existing methods find it difficult to simultaneously ensure high fidelity in both global geospatial semantics and microscopic scattering mechanisms, resulting in severe challenges for global generation. To address this, we propose \textbf{HuiYanEarth-SAR}, the first foundational SAR imagery generation model based on AlphaEarth and integrated scattering mechanisms. By injecting geospatial priors to control macroscopic structures and utilizing implicit scattering characteristic modeling to ensure the authenticity of microscopic textures, we achieve the capability of generating high-fidelity SAR images for global locations solely based on geographic coordinates. This study not only constructs an efficient SAR scene simulator but also establishes a bridge connecting geography, scatter mechanism, and artificial intelligence from a methodological standpoint. It advances SAR research by expanding the paradigm from perception and understanding to simulation and creation, providing key technical support for constructing a high-confidence digital twin of the Earth.

cs.CV↗
arXiv2026

SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators

The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Here we present SMC-AI, a general algorithmic framework that extends the SMC-X method for efficient canonical Monte Carlo simulation on AI accelerators, including GPUs and NPUs, while maintaining extreme scalability. The implementation of SMC-AI on an NPU cluster reaches unprecedented performance, achieving MC simulation of 4 trillion atoms on 4096 NPU dies. This represents the largest ML-accelerated atomistic simulation reported, delivering 32X system size and 1.3X throughput than previous records, with a relatively small computational budget. Excellent strong and weak scaling efficiency are reached for both the NPU and GPU implementation. By decoupling ML models from simulation, SMC-AI creates an abstraction that facilitates integration and porting of diverse ML models, laying a foundation for the future development of scalable scientific software.

physics.comp-ph↗
arXiv2026

S$^4$ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack

Transferable Targeted Attacks (TTAs) face significant challenges due to severe overfitting to surrogate models. Recent breakthroughs heavily rely on large-scale training data of victim models, while data-free solutions, \textit{i.e.}, image transformation-involved gradient optimization, often depend on black-box feedback for method design and tuning. These dependencies violate black-box transfer settings and compromise threat evaluation fairness. In this paper, we propose two blind estimation measures, self-alignment and self-transferability, to analyze per-transformation effectiveness and cross-transformation correlations under strict black-box constraints. Our findings challenge conventional assumptions: (1) Attacking simple scaling transformations uniquely enhances targeted transferability, outperforming other basic transformations and rivaling leading complex methods; (2) Geometric and color transformations exhibit high internal redundancy despite weak inter-category correlations. These insights drive the design and tuning of S$^4$ST (Strong, Self-transferable, faSt, Simple Scale Transformation), which integrates dimensionally consistent scaling, complementary low-redundancy transformations, and block-wise operations. Extensive evaluations across diverse architectures, training distributions, and tasks show that S$^{4}$ST achieves state-of-the-art effectiveness-efficiency balance without data dependency. We reveal that scaling's effectiveness stems from visual data's multi-scale nature and ubiquitous scale augmentation during training, rendering such augmentation a double-edged sword. Further validations on medical imaging and face verification confirm the framework's strong generalization.

cs.CR↗