Search arXiv⌕ Search

arXiv subjects

Xiaoyu Wang

Publications and source records attributed to Xiaoyu Wang.

At least 19 recordsLinked to original sources
arXiv2026

Wonder 4: A Mixture-of-Experts Recipe for Image Quality Enhancement

A real photograph is often blurry, grainy, compressed, scratched, and low-resolution at the same time. Each defect has its own severity and its own place in the image. The corrections for these defects conflict with each other. Exist- ing models describe the whole image with one description, so they cannot say where each correction belongs. To solve this problem, we present Wonder 4, a mixture-of-experts (MoE) model for image quality enhancement. It corrects several defects of a real photograph at the same time, each in the region where it occurs. Every block of the model holds a mixture of N = 13 experts. A router, trained with the model, sends every image patch to k = 5 of them. No degradation classifier, mask, prompt, detector, or task label is used. Another issue is that standard MoE training, taken from language models, pushes every expert toward equal use. Equal use applies every correction everywhere, even where it is not needed. We replace this rule with a bias that only sets a minimum share of use. Across all patches, an expert used less than this minimum gets higher router scores until it is selected again. An expert above the minimum is not changed, so no expert is forced to be used as often as the others. The bias needs no extra loss. The method adds no extra module and no extra processing step. We evaluate Wonder 4 on DIV2K and Flickr2K, against five published methods and three open-weight pipelines. It reaches the best LPIPS, DISTS, and NIQE on all three test settings (DIV2K x4, DIV2K x8, and Flickr2K x4).

cs.CV↗
arXiv2026

Chemical potentials from structure factors: I. Neutral multi-component mixtures

The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemical potentials from equilibrium molecular dynamics simulations, by leveraging the thermodynamic relationship between particle number fluctuations and derivatives of chemical potentials, followed by numerical integration along different compositions. Here we generalize the S0 method from two-component mixtures to neutral multi-component mixtures. We first extend the statistical mechanical formalism to high-dimensional compositional space, and then introduce a Gaussian process integration scheme combined with active learning to efficiently integrate chemical potentials and sample diverse compositions. We use this method to compute the mixing free energies of a molten metal alloy, and the solubilities of two paracetamol polymorphs in water-ethanol solvents. The extended S0 method provides a practical and scalable route for computing chemical potentials in neutral bulk multi-component mixtures from atomistic simulations.

physics.chem-ph↗
arXiv2026

Chemical potentials from structure factors: II. Charged multi-component mixtures

The chemical potentials of charged multi-component mixtures are central to electrolyte thermodynamics, but remain difficult to compute from atomistic simulations. The S0 method enables computing chemical potentials of mixtures from equilibrium molecular dynamics simulations. Here we extend the S0 method to charged mixtures by combining the composition-space framework developed in Part I: Neutral Multi-component Mixtures with a Coulombic treatment of the small-wavenumber limits of static structure factors. This approach separates thermodynamically relevant neutral composition fluctuations from forbidden macroscopic charge fluctuations, and further accounts for charge-neutrality constraints. We use the method to compute the chemical potentials of multiple-halide aqueous salt solutions, elucidating the ion-specific thermodynamic effects. We also calculate the mixing free energies of molten salt mixtures, demonstrating the importance of correctly describing long-wavelength electrostatic correlations.

cond-mat.stat-mech↗
arXiv2026

Testing the Gaussian Ansatz for TMD PDFs Through CMS Measurements

We investigate the differential cross section of the $pp$ Drell-Yan process within the framework of Transverse Momentum Dependent (TMD) factorization. We perform phenomenological calculations of the differential cross section as a function of $q_T$ in different dilepton invariant mass bins and compare it with the measured data from the CMS experiment at $13~$TeV, employing two distinct transverse momentum dependencies of the parton distribution functions: the Collins-Soper-Sterman (CSS) evolution method and the Gaussian Ansatz. The results indicate that the CSS evolution method agrees well with experimental data across the entire energy range, demonstrating its strong predictive capability for the evolution behavior of TMD distribution functions. In contrast, the Gaussian Ansatz of the TMD PDFs is only applicable in the low-energy region and exhibits significant limitations at high energies. This study verifies the effectiveness of the TMD factorization theorem in describing high-energy processes and provides theoretical support for an in-depth exploration of the nucleon's internal structure and non-perturbative QCD dynamics.

hep-ph↗
arXiv2026

WaitLens: Temporal VLM-Based Adaptive Waiting for Industrial GUI Testing Agents

Improving the efficiency of GUI testing agents is important for industrial deployment. Beyond LLM inference, fixed waiting between consecutive interactions introduces substantial overhead. Our industrial study reveals that fixed waiting accounts for 42.3% of the time within the sampled interaction intervals, comparable to the 54.8% spent on model calls. Moreover, 62.9% of the waiting time is spent after the GUI has already finished loading. Although prior studies have proposed adaptive waiting strategies, their reliance on app-specific data or platform-specific information limits their applicability across diverse industrial environments. To this end, we propose WaitLens, which fine-tunes a lightweight VLM on GUI loading trajectories annotated with waiting end times. We introduce temporal resampling to construct diverse training examples, enabling the model to learn varying GUI loading behaviors from temporal observations. Experiments show that WaitLens achieves 86.08% accuracy in deciding whether to keep waiting, compared with 54.13% for the backbone VLM. Our evaluation using recordings from Meituan's industrial testing pipeline shows that WaitLens reduces mean extra waiting after the interface is ready by 94.98% compared with a 20-second fixed wait.

cs.SE↗
arXiv2026

Herculean: An Agentic Benchmark for Financial Intelligence

As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional workflows. Existing benchmarks cannot answer this question because they evaluate either static competencies or agents within a single interaction structure. We introduce Herculean, a benchmark of end-to-end agentic financial intelligence that crosses agent architecture, backbone model, and four structurally distinct workflows: Trading, Hedging, Market Insights, and Auditing. Each workflow is implemented as an MCP skill that preserves its native state, actions, constraints, and outcome criteria, allowing intact agent systems to be evaluated under matched external conditions. Across 30 architecture--backbone configurations, we find that agent capability is not globally ordered. Workflow rankings are weakly aligned, changing the architecture frequently reverses performance under the same backbone, and each architecture concentrates failure in different forms of workflow control. These results establish that agent performance is an interaction between architecture and workflow demand, rather than an intrinsic property of the underlying model. Consequently, success on a single financial workflow provides little evidence of dependable, general financial intelligence.

cs.AI↗
arXiv2026

Fully First-Order Optimization for Implicit Multilevel Compositions via Recursive Perturbations

We study optimization over a finite composition of implicit response maps generated by smooth potentials with strongly monotone signed gradients. This class includes chains mixing strongly convex minimization and strongly convex--strongly concave saddle responses. We propose a fully first-order method based on recursively perturbed response solves. The central issue is stability under inexact response solves: a downstream adjoint error changes the perturbation vector of the next upstream perturbed problem. We establish coupled bounds for the forward response errors and backward errors in the scaled response differences whose coefficients remain bounded as the perturbation parameter $δ$ decreases. A triangular Lyapunov construction combines these bounds with the $O(δ)$ bias of the exact perturbation estimator. For the warm-started method with finitely many inner iterations, this gives $\frac{1}{K}\sum_{k=0}^{K-1}\|\nablaΦ(x^k)\|^2 \leq O(K^{-1})+O(δ^2).$ Thus, for fixed problem data and finite depth, $δ=O(ε)$ yields an $O(ε^{-2})$ outer-iteration dependence. For strongly convex minimization chains, we also derive condition-number-dependent first-order oracle complexity bounds.

math.OC↗
arXiv2026

Manifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification

Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small trainable modules to modify the output of each layer, while keeping the original residual connections unchanged. Building on our preliminary study of manifold-constrained Hyper-Connections in speaker encoders, we explore parameter-efficient adaptation of both residual paths and feature selection across layers. We first propose a Manifold-Constrained Residual Adapter (MCRA), which splits the hidden dimensions of each Transformer residual state into N groups and uses a small Sinkhorn-normalized matrix to exchange information among them. A learnable gate controls the balance between the original and mixed residual paths. The gate adds only one scalar parameter at each residual connection and initially favors the original path, allowing gradual adaptation. We further propose Frame-Adaptive Layer Aggregation (FALA), an attention-based aggregation method that uses a single learned query shared across all utterances to compute layer weights separately for each speech frame. Because the layer representations depend on the input, the resulting weights vary across both utterances and frames, unlike the fixed weights of a conventional layer-weighted sum. Compared with a frozen WavLM baseline using a fixed layer-weighted sum, our method achieves relative EER reductions of 22.4% on VoxCeleb1-O and 2.9% on CN-Celeb, with trainable adaptation parameters equivalent to only 0.0009% and 0.0016% of the WavLM-Large parameters, respectively.

cs.SD↗
arXiv2026

ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation

With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far surpass traditional deepfake techniques, posing new challenges for manipulation detection. Existing image manipulation detection and localization (IMDL) benchmarks suffer from limited content diversity, narrow generative-model coverage, and insufficient interpretability, which hinders the generalization and explanation capabilities of current manipulation detection methods. To address these limitations, we introduce \textbf{ManipBench}, a large-scale benchmark for image manipulation detection and localization focusing on AI-edited images. ManipBench contains over 450K manipulated images produced by 25 state-of-the-art image editing models across 12 manipulation categories, among which 100K images are further annotated with bounding boxes, judgment cues, and textual explanations to support interpretable detection. Building upon ManipBench, we propose \textbf{ManipShield}, an all-in-one model based on a Multimodal Large Language Model (MLLM) that leverages contrastive LoRA fine-tuning and task-specific decoders to achieve unified image manipulation detection, localization, and explanation. Extensive experiments on ManipBench and several public datasets demonstrate that ManipShield achieves state-of-the-art performance and exhibits strong generality to unseen manipulation models. The project is available at https://github.com/IntMeGroup/ManipShield.

cs.CV↗
arXiv2026

MODAIC: Coordinated Asynchrony for Decentralized Multimodal Learning

Multimodal decentralized learning enables collaboration across heterogeneous edge devices without sharing raw data, but differing modality sets create coupled computation and communication bottlenecks. Multimodal agents support cross-modal alignment while training more model components and sharing bandwidth across multiple modality overlays. Under bandwidth limits, these agents can become hot spots: transfers miss aggregation windows, and their updates become stale or underrepresented, degrading worst-agent accuracy. Asynchrony removes global blocking but does not by itself coordinate local cross-modal interaction with timely peer-update exchange. We present MODAIC (Modality-Decoupled Asynchrony with Interleaved Coordination), a wait-free system that coordinates local modality progress, peer-to-peer update delivery, and aggregation under heterogeneous compute and bandwidth. Hierarchical asynchrony lets modalities progress independently while enforcing reciprocal feature exchange; load-aware update fetching overlaps training with communication, limits concurrent serving load, and reuses late arrivals; and consensus-aware peer selection uses lightweight metadata to account for update availability, peer participation, and loss similarity without active probing. We implement MODAIC as a containerized multi-agent framework and evaluate it on local and cross-region cloud testbeds. Across AVE and KU-HAR, MODAIC improves average accuracy and multimodal worst-agent performance. On AVE at 200 Mbps per agent, it improves multimodal worst-agent accuracy by 11.0 percentage points at 1000 seconds over the strongest baseline. On the cloud testbed, it reduces time-to-accuracy by up to 30%. Ablations show that simpler load-balancing and delayed-aggregation variants leave a worst-agent accuracy gap, and stress tests show tolerance to WAN variability and partial tracker unavailability.

cs.DC↗
arXiv2026

Accelerating Non-Smooth and Heavy-Tailed Sampling

Anchored Langevin dynamics (ALD) is useful for non-smooth sampling where the density of the target distribution is possibly non-differentiable and heavy-tailed; reflected anchored Langevin dynamics (RALD) can sample possibly non-differentiable target density on a constrained domain. In this paper, we propose and study non-reversible anchored Langevin dynamics (NALD) for sampling possibly non-differentiable and heavy-tailed target density in the Euclidean space and the non-reversible reflected anchored Langevin dynamics (NRALD) for sampling possibly non-differentiable target density in the constrained space. Our construction adds a circulation drift generated by a possibly state-dependent divergence-free skew-symmetric matrix field and a stream potential. It preserves the target distribution without requiring derivatives of target density, admits a random-time-change representation, and applies both on the whole Euclidean space and on bounded domains with normal reflection. By breaking reversibility, we show that NALD and NRALD can converge to their target distributions faster than their reversible counterparts via finite-time non-asymptotic convergence analysis, a large deviations analysis and asymptotic variance reduction. Numerical experiments demonstrate the efficiency of the proposed algorithms.

stat.ML↗
arXiv2026

Reflected Anchored Langevin Algorithms

First order Langevin algorithms for constrained sampling in machine learning, such as projected Langevin Monte Carlo which are based on discretizations of reflected Langevin dynamics, require differentiable log densities that limits their applicability. This paper introduces reflected anchored Langevin dynamics (RALD), a reflected diffusion that converges to non-differentiable targets on constrained domains. The method uses a smooth anchored reference potential and multiplies the drift and noise covariance of its reflected Langevin dynamics by the same state dependent scaling factor. Its Euler-Maruyama discretization with projection gives reflected anchored Langevin Monte Carlo (RALMC) algorithm. We prove explicit convergence bounds and iteration complexity for RALMC in the 2-Wasserstein distance to the target distribution. Numerical experiments are provided to illustrate the theoretical predictions and the empirical performance of the method.

stat.ML↗
arXiv2026

Residual Learning in Empirical Asset Pricing

Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models. Residual learning allows neural network models in asset pricing to go deeper by preserving and refining their shallow counterparts. The out-of-sample Sharpe ratio for value-weighted long-short portfolios of deep residual models (2.07) is higher than that for the corresponding shallow ones (1.92) and more than twice that of the deep feedforward models (0.89). We show that model depth is a source of additional economic value in asset pricing. Residual learning can be used to deepen other neural-network-based asset pricing models if they contain intermediate layers. Our design also provides one way to scale asset pricing models, making native "large asset pricing models" more feasible.

q-fin.ST↗
arXiv2026

Bulk universality of random regular graphs

We consider the adjacency matrix of a uniformly random simple $d$-regular graph on $N$ vertices. For every fixed $d\geq3$ and every fixed bulk energy, we prove that the rescaled eigenvalue point process converges to the $\mathrm{Sine}_1$ process with intensity $1/π$. We also establish universality of consecutive gaps at deterministic bulk labels and of fixed-energy correlation measures. The eigenvector input consists of polynomial estimates for mixed fourth moments on a uniformly sampled star. These estimates prevent loss of variance in the qualitative joint Gaussian limits of Backhausz--Szegedy, yielding independent variance-one Gaussian waves for fixed tuples at nearby energies. Exact switching identities then give a stationary marked flow and the microscopic loop hierarchy, which identifies the point-process limit. The appendices prove the stronger correlation and gap statements using counting-moment bounds and conditional log-gas laws for tagged limits.

math.PR↗
arXiv2026

Projection of purification performance for the RELICS experiment

The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low impurity concentration in the detector. This work presents a comprehensive purity evolution model developed to describe impurity migration inside the detector. Utilizing measured material outgassing rates as input parameters, the model incorporates non-uniform transport mechanisms of the impurities, including circulation, vaporization, and condensation. The model is validated using data from a dedicated prototype detector. Based on this validated model, projections for the purification performance of the upcoming RELICS-10 and RELICS-50 detectors are provided.

physics.ins-det↗
arXiv2026

Penalized Nonreversible Langevin for Constrained Sampling

We propose penalized nonreversible Langevin algorithms for sampling from $π(x)\propto e^{-f(x)}\mathbf 1_{\mathcal C}(x)$, where $\mathcal C\subset\mathbb R^d$ is a compact convex set. The algorithms combine a squared distance penalty with constant or compatible state dependent skew symmetric perturbations that preserve the penalized Gibbs distribution. For smooth, possibly nonconvex $f$, we derive nonasymptotic total variation bounds for the full gradient algorithm under a log Sobolev inequality. When unbiased stochastic gradients are available, we establish $2$-Wasserstein bounds under global contraction and Lipschitz conditions on the full drift in an adapted quadratic metric. For a fixed penalty parameter, the error relative to the penalized Gibbs distribution decays exponentially to an $\mathcal{O}(\sqrtη)$ neighborhood, where $η$ is the stepsize. We also bound the discrepancy between the penalized Gibbs distribution and the constrained target. In a two dimensional quadratic model, we establish nonreversible acceleration by tuning the skew perturbation to the curvature imbalance induced by penalization. With the target accuracy and smaller curvature fixed and initial Wasserstein distances uniformly bounded, tuning the skew perturbation improves the sufficient Euler iteration bound from linear to logarithmic in the curvature ratio. Numerical experiments evaluate the algorithms on constrained Bayesian regression, classification, neural networks, and truncated sampling, and examine the acceleration mechanism in a stochastic quadratic model.

stat.ML↗
arXiv2026

Robust 3D Reconstruction from Multi-View Optical Satellite Imagery via Reliability-Aware Height-Evidence Fusion in Gaussian Splatting

Robust 3D reconstruction from multi-view optical satellite imagery requires fusing complementary but sometimes conflicting geometric evidence. Digital surface models (DSMs) are the primary elevation representations for satellite-based 3D reconstruction, making reliable height estimation essential. However, in a Gaussian scene representation jointly optimized from multiple views, Gaussian responses at different elevations can support competing height hypotheses at the same rendered location, while conventional alpha-weighted elevation aggregation may produce intermediate elevations that do not correspond to physical surfaces. To address this challenge, we formulate DSM reconstruction as a reliability-aware height-hypothesis fusion problem and propose HLC-GS, a reliability-aware Height-Layer Consistency Gaussian Splatting framework for multi-view satellite 3D reconstruction. HLC-GS organizes projected Gaussian responses into candidate height hypotheses and evaluates their relative support using layer competition and Gaussian footprint support. A continuous height-layer risk map guides dominant-layer reliability correction and secondary-layer suppression during optimization. The proposed training strategy regulates conflicting Gaussian responses within the shared representation to improve the reliability of reconstructed surface elevations. Experiments on seven scenes from the DFC2019 and IARPA2016 datasets demonstrate improved DSM reconstruction accuracy. Compared with EOGS, HLC-GS reduces the average DSM MAE from 1.46~m to 1.18~m and RMSE from 2.78~m to 2.58~m, while increasing PAG$_{2.5}$ from 86.09\% to 88.61\%, with comparable computational cost.

cs.CV↗
arXiv2026

PC-SubMax: Efficient Prompt Compression via Regularized Submodular Maximization

While large language models (LLMs) are increasingly deployed in long-context scenarios, lengthy prompts can increase inference costs and latency and exacerbate the ``lost-in-the-middle'' phenomenon. Selective prompt compression offers a model-agnostic approach to alleviating these issues. However, methods based on fixed token- or sentence-level importance scores may overlook how content contributions change with the selected subset, limiting their ability to account for inter-sentence redundancy. Compression procedures that rely on autoregressive LLM scoring can also introduce substantial overhead. We propose PC-SubMax, a theoretically grounded framework that formulates selective prompt compression as regularized monotone submodular maximization under a knapsack constraint. The objective is $U(S)-\ell(S)$, where the monotone submodular utility $U$ combines information coverage, query relevance, and log-determinant diversity, and the non-negative modular penalty $\ell$ captures token cost. Through diminishing marginal returns, the objective evaluates each sentence's contribution relative to the selected content. To optimize this objective, we develop the Regularized Greedy+Max (RGM) algorithm, which deterministically returns a feasible set $Q$ satisfying $U(Q)-\ell(Q)\geq \frac{1}{2}U(O)-\ell(O)$, where $O$ is an optimal feasible solution to the regularized problem. RGM uses $O(nκ)$ value-oracle queries, where $n$ is the number of candidate sentences and $κ$ is the maximum feasible subset size. PC-SubMax uses encoder representations and avoids autoregressive LLM scoring during compression. Experiments across seven diverse benchmarks demonstrate competitive downstream performance with low compression overhead.

cs.CL↗