arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Stringent Constraints on Spin-Spin-Velocity-Dependent Exotic Interactions with a Levitated Magnet Force Sensor

Exotic spin-spin-velocity-dependent interactions, predicted in extensions of the Standard Model involving new bosonic fields, could resolve fundamental puzzles from dark matter to cosmic asymmetry. However, exploring these weak potential interactions at centimeter scales presents formidable challenges, primarily due to the overwhelming dominance of electromagnetic backgrounds that can easily obscure the weak exotic signals. Here, we utilize a levitated magnet force sensor with ultrahigh electron spin density to probe these interactions. We constrain two interactions individually through a designed spin source and a multi-layer magnetic shielding system that suppresses electromagnetic backgrounds. In this study, we constrain two types of interactions: the V_6 potential at force ranges from $10^{-3}$ m to $6 \times 10^{-2}$ m and the V_{14} potential at ranges greater than $10^{-3}$ m. Our measurements establish 95% confidence-level bounds of $|f_6| \leq 2.12 \times 10^{-13}$ and $|f_{14}| \leq 2.34 \times 10^{-23}$ at $λ= 1.6 \times 10^{-2}$ m, improving prior limits by up to 12 and 13 orders of magnitude, respectively. Our result demonstrates the levitated magnet as a highly sensitive probe for detecting new bosonic fields in extensions of the Standard Model.

physics.app-ph↗

arXiv2026

DRRG: A Discrete Diffusion Framework for Radiology Report Generation

Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most existing methods rely on autoregressive (AR) frameworks that generate reports token by token and cannot revise earlier content, making them prone to error propagation and inconsistent with the iterative refinement process of radiological reporting. In contrast, discrete diffusion large language models (DLLMs) generate text through iterative denoising, naturally enabling report refinement. However, DLLMs have not been extensively investigated for RRG. In this study, we developed and evaluated a discrete diffusion framework for RRG that enables iterative refinement rather than conventional left-to-right autoregressive decoding. Materials and methods: We developed DRRG, a DLLM-based framework that formulates RRG as iterative masked-token denoising. DRRG incorporates a clinical-entities-aware complementary mask to improve token supervision coverage and emphasize clinically important entities, together with a concept-conditioning module that injects image-derived clinical concepts into visual representations. DRRG was trained and evaluated on MIMIC-CXR and CheXpert Plus. Results: On MIMIC-CXR, DRRG achieved BLEU-4 of 0.210, CheXpert-F1 of 0.549, RadGraph-F1 of 0.281, GREEN of 0.360, and RaTEScore of 0.604, outperforming the compared methods on most reported metrics, despite employing a substantially smaller LLM decoder. On CheXpert Plus, DRRG achieved the highest BLEU-4 (0.119) and CheXpert-F1 (0.347) among the compared methods. Conclusion: Discrete diffusion provides an effective alternative to autoregressive radiology report generation by enabling iterative, bidirectional report refinement. Incorporating clinically focused masking and image-derived concept conditioning improves report quality and clinical consistency.

cs.CV↗

arXiv2026

Exact autoregressive sampling of planar Ising spin glasses via the Kac--Ward theory

Exact sampling from the Boltzmann distribution of spin glasses remains an outstanding challenge: Markov chain Monte Carlo methods suffer from critical slowing down and metastable trapping, while modern neural autoregressive samplers such as variational autoregressive networks are approximate and, in the absence of exact reference samples, cannot be rigorously benchmarked. Here we present an exact autoregressive sampling algorithm for planar Ising spin glasses based on the Kac--Ward theory. Under the chain-rule factorization, sequentially fixing spins induces boundary-localized external fields, which destroy the zero-field structure required for exact evaluation. By encoding these fields with a planarity-preserving auxiliary spin construction, the conditional partition functions are mapped to an extended zero-field Ising model and exactly evaluated using the Kac--Ward determinant formula. The method generates strictly independent and identically distributed samples with exact normalized likelihoods at a computational cost of $\mathcal{O}(N^{5/2})$ for $N$ spins, thereby providing an exact baseline for benchmarking neural autoregressive samplers.

cond-mat.stat-mech↗

arXiv2026

Vector Akhmediev Breathers and State Transitions in the Degenerate and Nondegenerate Regimes for the Coupled Sasa-Satsuma System

We investigate vector Akhmediev breathers (ABs) in the degenerate and nondegenerate regimes for the coupled Sasa-Satsuma system describing two coupled ultrashort optical pulse envelopes. Based on the carrier-wavenumber relations of the two-component background, we examine three configurations: a single vanishing wavenumber, a pair of opposite wavenumbers, and two nonzero wavenumbers of unequal magnitudes. These configurations admit distinct maximal degrees of nondegeneracy. For each configuration, we derive the reduced spectral equations, construct the existence diagrams, and compare the resulting branches with the modulation-instability gain spectra. Owing to the distinct carrier-wavenumber symmetries, nondegenerate-type modes can occur in spectrally degenerate regions, whereas degenerate-type ones may also appear in spectrally nondegenerate regions. Moreover, all admissible branches in the degenerate regimes satisfy the state-transition condition and correspond to periodic-wave states, while such branches are also present in nondegenerate regimes. Direct numerical simulations of representative solutions further confirm the analytical predictions.

nlin.PS↗

arXiv2026

Distributed Nesterov Flows for Multi-agent Optimization

Various distributed gradient descent algorithms for multi-agent optimization have incorporated the Nesterov accelerated gradient method, where the use of momentum enhances convergence rates. These algorithms have found broad applications in large-scale machine learning and optimization owing to their simplicity and low communication complexity. In this paper, we establish a continuous-time approximation of distributed Nesterov gradient descent. The convergence properties and convergence rate of the resulting distributed Nesterov flow are analyzed using Lyapunov methods. Building on these insights, we design new parameter choices within the flow, from which we derive flow-inspired discrete-time algorithms for multi-agent optimization. Surprisingly, the resulting algorithms achieve faster convergence compared to existing distributed gradient descent methods: they require fewer iterations to reach the same accuracy for strongly convex functions and exhibit an improved convergence rate for general convex functions without incurring additional communication rounds. Furthermore, we investigate the influence of the network topology on algorithm performance and derive an explicit relationship between the convergence rate and the graph condition number. Numerical simulations are presented to validate the effectiveness of the proposed approach.

eess.SY↗

arXiv2026

Edge-Native Embodied Intelligence for Action-Aware Wireless Edge Networks

Embodied intelligence is shifting artificial intelligence from passive digital perception toward active physical interaction. However, foundation-model-enabled embodied agents face a fundamental tension between open-world cognition and resource-constrained deployment. On-device models are limited by computation, memory, and energy budgets, whereas cloud-centric solutions introduce latency and reliability risks over dynamic wireless links. Edge general intelligence provides a promising cognitive backbone, but existing frameworks still lack physical grounding, action awareness, and mechanisms for actively acquiring useful physical experience. To address these limitations, this article introduces edge-native embodied intelligence (ENEI), an action-aware wireless edge framework that integrates embodied agents, the 6G communication and networking fabric, and edge cognitive services into a 6G-mediated bidirectional edge-embodiment loop. Along the edge-to-embodiment axis, confidence-aware assistance and edge-driven generative adaptation enhance local autonomy under out-of-distribution (OOD) conditions. Along the embodiment-to-edge axis, value-of-experience guided active embodied federated learning enables physical actions to generate informative experience for continuous edge model evolution. The 6G fabric supports both directions through goal-oriented transmission and programmable radio-resource allocation. Two case studies on OOD drone navigation and mobility-driven federated learning illustrate the feasibility and communication efficiency of the proposed mechanisms. ENEI provides a unified perspective in which edge cognition strengthens embodied action, while embodied agency actively enriches edge cognition, laying the foundation for scalable, adaptive, and self-evolving embodied wireless systems.

eess.SY↗

arXiv2026

MusicLayout: Explicit Structural Planning for Controllable Text-to-Music Generation

Text-to-music generation has advanced rapidly, but current systems still rely primarily on global text prompts, leaving the structural organization of generated music implicit and difficult to inspect, control, or revise before audio generation. To address this issue, we introduce MusicLayout, an explicit intermediate representation for controlling musical structure in text-to-music generation. MusicLayout describes a musical piece as a time-aligned layout of sections, textures, repetitions, variations, and instrument-level arrangements, serving as an interpretable planning layer between textual intent and the generated music. We integrate MusicLayout into a text-to-music framework built on a unified autoregressive formulation, where the model first generates a MusicLayout representation and subsequently predicts audio tokens conditioned on this representation within a single sequence. The resulting MusicLayout can be inspected and modified prior to audio generation, providing a mechanism for layout-level structural control. We evaluate MusicLayout through layout-conditioned generation, layout manipulation experiments, and matched-data ablations, providing evidence that explicit layout planning can improve long-range structural organization and support layout-level control. We have released the implementation as open source on GitHub at https://github.com/XaryLee/MusicLayout.

cs.SD↗

arXiv2026

ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models

Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step video synthesis, extending it to interactive world models remains challenging, as discrete keyboard states and continuous mouse motion must remain aligned with temporally compressed latent chunks during causal training and autoregressive rollout. We introduce ForgeWM, a progressive framework that transforms a bidirectional action-conditioned video generator into efficient few-step world models through domain adaptation, teacher-forced causal training, causal consistency distillation, and on-policy distribution matching with a bidirectional teacher. The resulting budget-specialized students operate at steady-state denoising budgets of 1, 2, and 4 steps. ForgeWM further supports a dual-path deployment protocol combining latency-critical interaction with optional replay-time refinement, where the one-step student re-noises and refines its saved draft. On paired Minecraft trajectories, ForgeWM leads the evaluated systems in Imaging Quality, reference-aligned motion-profile agreement, action-sign accuracy, and mouse-control accuracy, while achieving the lowest reference LPIPS; the same four-stage recipe transfers to gamepad-controlled FPS gameplay. Replay-time refinement matches four-step reference quality while remaining roughly three times closer to the experienced trajectory than regeneration from noise. These results demonstrate ForgeWM's effectiveness for controllable few-step video generation.

cs.CV↗

arXiv2026

Distributed Nash Equilibrium Seeking with Logarithmic Bit Rates over Digital Channels

This paper introduces quantization techniques to reduce the communication complexity in the distributed Nash equilibrium (NE) seeking problem, achieving an exponential reduction in bit rates over digital channels. The goal of distributed NE seeking algorithms is to coordinate agents in a network game toward equilibrium through iterative message exchanges among them via a communication network. The computational complexity of this distributed algorithm critically depends on network communication overhead in the digital channel, motivating the development of communication reduction mechanism. Regarding this, we proposed some quantizers based on sparsification and uniform quantization through a general class of ultimate-boundedness-based quantizers. Based on this, we propose a Passivity-Based NE seeking Algorithm with Time-varying scaling Error state Quantization (PBA-TEQ), and show that the linear convergence can be achieved under a sufficient condition. Moreover, when employing either the scalarization quantizer or the greedy quantizer, both belonging to the ultimate-boundedness-based quantizers, within the PBA-TEQ framework, we establish a lower bound on communication complexity of $\log_2(\mathcal{O}(nd))$ bit rates per transmission to achieve unbiased linear convergence, with $n$ being the number of agents and $d$ being the dimension of the decision state of the network game. Numerical simulation examples are provided to validate our theoretical results.

eess.SY↗

arXiv2026

Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields

Forward uncertainty propagation in complex physical systems can induce structured covariance across field-valued outputs. For a probabilistic surrogate, the total predictive covariance comprises the covariance of conditional means across input realizations and the average conditional predictive covariance. Probabilistic DeepONet (Prob-DeepONet) provides lightweight uncertainty quantification by predicting pointwise Gaussian means and variances in a single forward pass, but its conditional predictive covariance is restricted to a diagonal form. To represent cross-location conditional dependence without explicitly parameterizing a full high-dimensional covariance matrix, we develop a two-step mean-variance DeepONet (two-step MV-DeepONet) through two principal modifications. First, two-step training is used to decouple output-basis learning from the input-to-coefficient mapping, together with basis orthogonalization and subspace rotation. Second, Gaussian probabilistic modeling is transferred from the high-dimensional physical output space to the low-dimensional rotated coefficient space. Mapping these probabilistic coefficients through the shared basis induces a generally non-diagonal conditional predictive covariance in the physical output space while retaining single-pass inference. A Frobenius-norm error decomposition and corresponding upper bound identify low-rank covariance compressibility, trunk-subspace approximation, finite-sample statistical error, and coefficient-space covariance estimation as the principal factors governing covariance recovery. Numerical experiments on three representative problems governed by partial differential equations (PDEs) and a hypersonic blunt-body aerothermal problem show improved generalization, more structured uncertainty bands, and accurate recovery of off-diagonal correlation patterns compared with Prob-DeepONet.

math.NA↗

arXiv2026

GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views

Fine-grained cross-modal understanding in drone views is essential for aerial vision-language navigation. However, the inherent wide field of view and overhead perspective of drone scenarios impose dual challenges on vision-language understanding. At the macro level, overwhelming background clutter in visual representations leads to Cross-Modal Focus Misalignment, where the model prioritizes global environmental similarities over specific object details. At the micro level, Visual Isomorphism creates ambiguity, where candidates share similar geometric structures yet differ only in subtle attributes. To address these challenges, we propose the Granularity-Aware Region Alignment and Semantic Prototype (GRASP) learning framework, enhancing discriminative capability through two synergistic strategies. Specifically, we introduce Region-Focused Alignment (RFA) to promote object-centric cross-modal alignment while suppressing background interference. Concurrently, to tackle visual isomorphism, we propose Semantic Perturbation Enhanced Matching (SPEM), which leverages a foreground-purified Semantic Prototype Codebook (SPC) to construct semantically perturbed negatives for fine-grained semantic discrimination. Extensive experiments on the GeoText-1652 benchmark and the unseen ERA dataset demonstrate that GRASP achieves competitive performance in drone-view fine-grained image-text retrieval, validating its effectiveness for cross-modal understanding in aerial scenarios. Our code implementation is available at https://github.com/UCAS-JC/GRASP.

cs.CV↗

arXiv2026

A Computable Stochastic Riccati Equations Framework for Mean--Variance Portfolio Selection with Multifactor Stochastic Volatility Model

We investigate a computable and empirically implementable framework for continuous-time mean--variance optimal portfolio selection with random market coefficients. The market model is built on a tractable multifactor stochastic volatility structure, which captures state-dependent risk premia, stochastic volatility, and dynamic cross-asset dependence. The optimal control is characterized by stochastic Riccati equations. On the computational side, we design an iterative BSDE-based procedure to approximate the stochastic Riccati equation from above and below, where the initial upper and lower bounds are obtained by solving two linear BSDEs. We then apply a logarithmic transformation to remove the singularity in the SRE, and solve the resulting transformed equation using both Deep BSDE and DBDP methods. The linear BSDE bounds also provide effective initial-value estimates, improving the convergence speed and training stability of the Deep BSDE solver. Empirical experiments based on sector ETF data show that the proposed multifactor mean--variance strategy produces smooth target-return wealth dynamics, with improved drawdown control and downside-risk protection relative to benchmark strategies. These results demonstrate the practical potential of combining stochastic Riccati equations, neural BSDE solvers, and multifactor market modeling for dynamic asset allocation.

math.OC↗

arXiv2026

SemDINO: Foundation Prior-Guided Cross-Temporal Semantic Alignment Network for Remote Sensing Change Detection

Semantic change detection (SCD) in remote sensing aims to identify land-cover transitions between bi-temporal observations while suppressing pseudo-changes caused by illumination variations, seasonal differences, and registration errors. Although Vision Foundation Models (VFMs) provide transferable semantic priors, their application to SCD remains challenging due to the mismatch between foundation-model representations and task-specific spatial features, as well as temporal-order sensitivity. To address these issues, this paper proposes SemDINO, a foundation prior-guided framework that integrates transferable vision foundation model priors with hierarchical convolutional representations for cross-temporal semantic reasoning. Specifically, a Gated Pyramid Fusion (PyFu) module is developed to adaptively combine foundation-model semantics with CNN spatial details while reducing domain noise. A Multi-scale Temporal Bi-directional Transformer (M-TBTT) is introduced to achieve symmetric cross-temporal feature interaction and alleviate temporal-order bias. Furthermore, a Feature Change Enhancement (FeaCE) flow is designed to refine aligned representations and distinguish genuine semantic transitions from pseudo variations. Finally, a multi-branch decoupled prediction head jointly generates change masks, bi-temporal semantic maps, and edge constraints. Extensive experiments across five benchmark datasets demonstrate that SemDINO consistently outperforms state-of-the-art methods on both semantic and binary change detection tasks. The results validate the effectiveness of alignment-oriented representation learning for robust remote sensing change analysis.

cs.CV↗

arXiv2026

Onsager-variational-principle-based Lattice Boltzmann Model For Three-phase Dielectric Fluid Flows

Multiphase electrohydrodynamic (EHD) flows play a crucial role in various engineering applications. However, existing numerical studies on three-phase electrohydrodynamic systems predominantly rely on phenomenological models, often neglecting thermodynamic consistency and critical surface charge convection mechanisms. To address these fundamental gaps, this paper proposes a thermodynamically consistent three-phase EHD model derived strictly from the Onsager variational principle. This theoretical framework intrinsically guarantees thermodynamic consistency and accurately captures complex multiphysics interactions without requiring a priori assumptions. Furthermore, a mesoscopic lattice Boltzmann method is developed to solve the proposed model, enabling the natural capture of interfacial evolution and charge transport. The accuracy of the numerical framework are rigorously validated against several benchmark cases, including electroosmotic flow in microchannels, the spreading of a three-phase liquid lens, the equilibrium of static compound droplet, and the deformation of compound droplet under uniform electric field. Using this validated framework, we investigate EHD applications, specifically simulating the complex dynamics of double droplet coalescence and separation under electric field, as well as the behavior of droplets subjected to combined EHD and shear flow. Overall, this work provides a robust, thermodynamically reliable numerical tool for exploring the highly nonlinear behaviors of multiphase EHD systems.

physics.flu-dyn↗

arXiv2026

CompanionBench: A Theory-Anchored, Real-World-Grounded Benchmark for AI Emotional Companionship

LLM companions are deployed at scale in personally consequential settings, yet poorly evaluated. Existing benchmarks use hand-authored scenarios and prompted simulators, aggregate empathy into one score, and overlook judge biases such as same-family favoritism and scale drift. We introduce CompanionBench, an interactive bilingual benchmark. To our knowledge, it is the first companion benchmark to ground both its scenarios and a trained user simulator in de-identified real-world data. A hidden disclosure gate branches each persona's trajectory on the agent's own behavior, controlling the interaction state space without scripting dialogue. We operationalize ten capabilities derived from 25 theories across psychology and counseling, four of them not graded explicitly by prior work: holding ambiguity, selfobject responsiveness, positive resonance and calibrated challenge. Agents are assessed on two complementary axes: a subjective ten-capability rubric and a deterministic measure of whether deeper disclosure was earned. A cross-family panel dilutes same-family favoritism; an Item Response Theory model separates agent quality from judge severity. Theory fixes what to measure and how personas are structured; real data supply events, history, and profiles -- coverage from theory, authenticity from data. Rankings are reproducible in both languages (rho = 0.996 ZH / 0.953 EN). Evaluating 28 agents reveals capability-level differences obscured by aggregate scores. Emotion regulation and calibrated challenge remain common weaknesses; holding ambiguity discriminates most. Role-play agents rank near the bottom: immersion does not imply relational competence. Across agents, the dominant failure mode is substituting surface warmth for substantive relational support. We will release 500 Chinese-English parallel pairs and the evaluation code.

cs.CL↗

arXiv2026

What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills

Agent skills are increasingly optimized by automated feedback loops, producing long structured artifacts whose internal value remains unclear. We study skill valuation: assigning credit to the internal units of a fixed skill, such as rules, examples, scripts, and heuristics, under a fixed agent and held-out task distribution. Skill valuation differs from data or prompt-span valuation because skill units are structured: they may depend on other units, belong to a document hierarchy, trigger agent behavior, and consume limited prompt context. We introduce SkillSV, a structure-aware Shapley-style framework for skill valuation. SkillSV compiles a skill into units, dependencies, and hierarchy, so that only valid counterfactual skills are evaluated. It uses paired deletion and length-neutral padding to separate content value from context cost, and estimates the resulting values with a rollout-budgeted estimator for noisy agent evaluations. On four agentic benchmarks, we assess the faithfulness, actionability, and explanation of SkillSV: it recovers unit interactions, preserves aggregate skill lift, and guides safe pruning and compression.

cs.AI↗

arXiv2026

Thermal Evolution and Hydrodynamic Filtering of Pseudoscalar Dark Matter

Filtered dark matter provides a mechanism for producing massive dark matter particles during a first order phase transition. The resulting abundance can be modified by plasma hydrodynamics. We investigate this effect in a complex singlet extension of the Standard Model, focusing on the deflagration regime. The entropy normalized dark matter abundance $Y_χ$ is calculated using both analytic and numerical methods. As the dark matter mass increases, we identify three patterns of abundance evolution before nucleation, ranging from thermal equilibrium to thermal suppression and freeze-out. For $ξ_w=0.01$, we find that shock heating increases $Y_χ$ by factors of approximately $4.3$, $5.8$, and $32$ for $m_χ=1.78$, $2.30$, and $5.03~\mathrm{TeV}$, respectively. For the two lighter masses, the numerical and analytic results agree within $5\%$, and the difference is reduced to about $1\%$ or less when hydrodynamic effects are included. The heaviest case requires a numerical treatment because dark matter freeze-out before nucleation. Our results show that hydrodynamic reheating can enhance $Y_χ$ even when dark matter has already frozen out before the phase transition. In this regime, $Y_χ$ is determined by the dark matter abundance established before nucleation and hydrodynamic filtering at the bubble wall.

hep-ph↗

arXiv2026

Mechanism of State Transitions for the Vector Kuznetsov-Ma Breathers

We study two types of mechanisms of state transitions for vector Kuznetsov-Ma breathers (KMBs) in the coupled Fokas-Lenells framework on unequal backgrounds. The amplitude imbalance breaks spectral reflection symmetry and generates richer breather morphologies. In the degenerate sector, KMBs approaching a non-self-conjugate degeneration curve from opposite sides yield different limiting solitons, revealing a discontinuous KMB-to-soliton transition. In the nondegenerate sector, the background plane waves become frequency matched at a special wavenumber, converting the KMB into a single- or two-soliton state. We determine the exact transition threshold and characterize how nearby solutions vary with the parameter. We further investigate special limiting mechanisms arising on the self-conjugate degeneration curve and on the real spectrum at the state-transition wavenumber. Numerical excitation provides further evidence for these results.

nlin.PS↗