Searcharxiv⌕ Search

arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 91 records · Page 5Linked to original sources
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↗
arXiv2026

Emergence of Topological Electron Crystals in Bilayer Graphene--Mott Insulator Heterostructures

The interplay between strong electron correlation and band topology offers a playground for discovering exotic quantum phases. Here, we predict the emergence of topological electron crystals in a charge-transfer bilayer graphene-Mott insulator heterostructure. In this system, interlayer charge transfer induces a charge-neutral electron-hole bilayer with strong mass asymmetry. While the extreme dilute limit favors a classical triangular dipolar Wigner crystal, we show that increasing the carrier density triggers a critical competition between the Coulomb interaction and the underlying topological band structure of bilayer graphene. This interplay destabilizes the triangular dipolar Wigner crystal and instead stabilizes intrinsic quantum electron crystals with spontaneously formed honeycomb and kagome geometries. Crucially, these new phases can host distinct topological responses including the quantum anomalous and quantum spin Hall effects, which inherit the nonlocal quantum geometry of the bilayer graphene wave functions. Our results establish this artificial heterostructure as a highly tunable platform for mimicking two-dimensional topological solids in a single device.

cond-mat.mes-hall↗
arXiv2026

Charge-Density-Wave Phase Selection by Janus-Induced Intrinsic Strain in Monolayer NbSSiAs$_2$

Controlling phase selection among competing charge-density-wave (CDW) instabilities remains challenging in two-dimensional materials. Here, first-principles calculations show that Janus-induced intrinsic tensile strain redirects the off-M soft-mode tendency of NbS$_2$ to the M point in NbSSiAs$_2$, selecting a $2\times2$ CDW reconstruction. Electron-phonon coupling analysis identifies momentum-selective coupling between Nb-derived states and a longitudinal acoustic mode as the origin of the M-point instability. The reconstructed phase hosts two nearly degenerate Nb-trimerized configurations whose relative stability is tuned by biaxial strain. Both configurations retain phonon-mediated superconductivity on the 6-7 K scale, indicating the coexistence of CDW order and superconductivity. Compressive strain favors the 1+3-hollow configuration and induces a band-inverted, $Z_2$-nontrivial state while preserving superconductivity. Together, these results identify Janus-induced intrinsic strain as an internal structural route for CDW phase selection, whereas external strain provides access to a regime in which CDW order, topology, and superconductivity coexist.

cond-mat.mtrl-sci↗
arXiv2026

Quadratic piezoelectricity from stacking-engineered interference in multilayer sliding ferroelectrics

Designing nonlinear piezoelectricity requires suppressing the linear piezoelectric coefficient without extinguishing higher-order electromechanical response, yet a general and reconfigurable route remains lacking. Here we introduce stacking-engineered piezoelectric interference as such a mechanism in multilayer sliding ferroelectrics. Combining first-principles calculations with a generalized Ginzburg--Landau framework, we show that each interlayer gap acts as a local piezoelectric channel whose sign and magnitude are determined by stacking. Constructive interference between same-signed channels produces a linear-dominated response, whereas destructive interference between oppositely signed channels suppresses the linear coefficient while preserving a finite quadratic response. Representative MoS$_2$ and NiTe$_2$ multilayers approach the parabolic limit, with BAAC-stacked MoS$_2$ reducing the linear-to-quadratic crossover strain by a factor of 25 relative to CBA-stacked MoS$_2$. Experimentally accessible tetralayer MoS$_2$ sliding pathways further connect linear-dominated, quadratic-dominated and sign-inverted states. Here, we identify stacking-engineered interference as a design principle for programmable nonlinear electromechanics in layered materials.

cond-mat.mtrl-sci↗
arXiv2026

Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents

GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows across platforms, combine GUI interaction with CLI execution, complete long-horizon tasks, proactively initiate useful services, and autonomously improve their capabilities with minimal human effort. Guided by this vision, we present Qwen-UI-Agent, a real-world centric foundation GUI agent spanning mobile, computer-use, web, and DeepSearch environments. Qwen-UI-Agent combines diverse sandbox environments with a large-scale real-device mobile runtime. Its unified action space interleaves GUI operations with CLI execution and generates batched actions in a single model turn. An AutoResearch-style data flywheel uses agents to construct tasks and environments, diagnose failures, and plan subsequent iterations. Online RL supports training on trajectories exceeding 100 turns, with over 10,000 concurrent environments accelerating rollout. A lightweight harness layer supports proactive service initiation and stateful workflows across mobile and computer. Across a broad suite of evaluations, Qwen-UI-Agent sets state-of-the-art performance on mobile-use benchmarks while delivering competitive performance on computer- and browser-use tasks against frontier models, including Opus 4.8, Gemini 3.1 Pro, and GPT-5.6 Sol. On mobile use, it achieves 82.1% on MobileWorld, 92.2% on MobileWorld-Real, and 97.5% on AndroidDaily. On computer use, it achieves 79.5% on OSWorld-Verified and a 40.0% partial-progress score on OSWorld-v2. On browser use and GUI grounding, it achieves 73.6% on WebArena and 81.5% on ScreenSpot-Pro, respectively.

cs.AI↗
arXiv2026

A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints

In this paper, we investigate optimization problems with nonnegative and orthogonal constraints, where any feasible matrix of size $n \times p$ exhibits a sparsity pattern such that each row accommodates at most one nonzero entry. Our analysis demonstrates that, by fixing the support set, the global solution of the minimization subproblem for the proximal linearization of the objective function can be computed in closed form with at most $n$ nonzero entries. Exploiting this structural property offers a powerful avenue for dramatically enhancing computational efficiency. Guided by this insight, we propose a support-set algorithm preserving strictly the feasibility of iterates. A central ingredient is a strategically devised update scheme for support sets that adjusts the placement of nonzero entries. We establish the convergence of the support-set algorithm to a first-order stationary point, and show that its iteration complexity required to reach an $ε$-approximate first-order stationary point is $O (ε^{-2})$. Numerical results are strongly in favor of our algorithm in real-world applications, including nonnegative PCA, clustering, and community detection.

math.OC↗
arXiv2026

Distributed Nonlinear Equality-Constrained Optimization via Feedback Linearization and Singular Perturbation

Distributed optimization becomes particularly challenging when nonconvex objectives are combined with nonlinear equality constraints: feasibility is network coupled, while most existing exponentially convergent methods rely on convexity or affine constraints. This paper introduces a feedback-linearization and singular-perturbation framework for two representative problem classes involving, respectively, distributed local nonlinear equality constraints and nonlinear aggregate equality constraints. The framework separates the regulation of consensus and feasibility residuals from optimization along the feasible manifold. Specifically, an ideal feedback-linearized dynamics is constructed whose output behavior can be explicitly assigned, while its zero-output dynamics coincides with the projected gradient flow of the aggregate objective on the feasible manifold. The ideal feedback-linearizing input, however, is determined by a state-dependent, network-coupled algebraic equation and is therefore not directly implementable in a distributed manner. To overcome this obstruction, we replace the nonlocal algebraic solution with a fast residual-tracking dynamics, yielding a singular-perturbation realization that uses only local and neighboring information. Under a local quadratic-growth condition imposed only on the feasible manifold, we establish local exponential convergence of both the ideal and distributed dynamics. For sufficiently strong time-scale separation, the convergence rate of the distributed realization can be chosen arbitrarily close to that of the ideal dynamics. Explicit Euler discretizations are also proved to preserve local exponential convergence. Under stronger manifold regularity conditions, the region of attraction extends to a tubular neighborhood of the feasible manifold and, for affine local equality constraints, to the whole admissible state space.

math.OC↗
arXiv2026

MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback

Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting can perform well under favorable model specification but may become unstable in complex settings. Machine-learning and representation-learning methods provide greater flexibility, but joint optimization may allow outcome information to alter treatment representations and compromise the intended causal structure. We propose MOCA (Modular One-way Causal Attention), a transformer-based framework that separates treatment and outcome modeling through a modular architecture. This design preserves directional information flow while retaining the flexibility of transformer architectures. Using a Markov-kernel formulation, we show that MOCA learns an autonomous treatment representation and is predictively KL-optimal within its Gaussian model class. Under correct specification, MOCA recovers the true average treatment effect, while additional two-way feedback does not further reduce average treatment effect estimation error. We also propose a conformal inference procedure for individual treatment effects. Across multiple simulation scenarios, MOCA achieved competitive or improved average treatment effect estimation compared with IPW, AIPW, the X-learner, TARNet, DragonNet, BART, Causal Forest, and Do-PFN. Ablation studies supported the contributions of the proposed architectural components. We further evaluated MOCA on the Infant Health and Development Program benchmark and the observational Dehejia-Wahba dataset. Overall, modular attention with one-way information flow provides an effective and interpretable framework for causal inference using modern deep-learning models.

stat.ML↗
arXiv2026

Organizing Principles for Moiré Quantum Matter

Moiré flat bands in van der Waals bilayers are usually discussed through a small set of mechanisms associated with the $Γ$ and $K$ valleys of hexagonal crystals, and more recently with $M$-valleys systems. Here we show that this view is incomplete. The momentum-space location and effective local orbital character of the monolayer's band edge, in conjunction with the moiré symmetry and the symmetry representations of the resulting bands, provide a general set of organizing variables for the emergent low-energy moiré Hamiltonian. Applying fully relaxed first-principles calculations, band unfolding and symmetry-representation analysis to more than 600 commensurate twisted bilayers spanning all 2D lattice classes, we identify several routes to moiré quantum matter beyond the conventional single-orbital paradigm. The resulting flat bands realize trigonal, honeycomb, square, checkerboard and kagome-like Hubbard models with single-orbital, multi-orbital and multi-site Hilbert spaces; spin-orbit-coupled multi-orbital flat bands exhibit symmetry-indicated topology beyond the conventional $K$-valley setting; and nonsymmorphic moiré symmetries enforce semimetallic flat-band connectivity. Analogous quasi-one-dimensional flat-band structures are found in $M$-valley hexagonal systems and $X$-valley square or rectangular systems resulting from emergent momentum-space nonsymmorphic symmetries. Separately, coupled multi-valley manifolds with kagome-like connectivity are identified in several systems whose parent band edges lie at non-high-symmetry points. These results establish a valley-orbital-symmetry framework for connecting parent-material electronic structure to emergent moiré Hamiltonians relevant to correlated, topological and symmetry-enforced moiré phases.

cond-mat.mtrl-sci↗
arXiv2026

Analyzing and Correcting Benevolence Bias in Large Language Models

Large language models (LLMs) are increasingly used as stand-ins for human respondents, from opinion polls and simulated survey participants to agent-based social simulations. These uses rest on one assumption: that conditioning a model on who a person is yields answers resembling those of real people from that group. Here we identify and measure benevolence bias, a small but consistent tendency for aligned LLMs to lean toward the kinder, safer, more socially approved answer on value-laden survey questions. Across 18 widely used models, four social-science datasets (ANES, GSS, WVS, and a cross-cultural prospect-theory replication) and six psychological categories, we find that the bias is a stable model property, not a quirk of any one system: it points the same way across models, grows with model size, and traces to the post-training stage. Prompt language and framing change its size but never its direction, and a "malicious persona" stress test shows a one-sided limit: aligned models struggle to play people who are less kind, less prosocial or more harm-tolerant than average. The issue is thus not only a shifted average, but a narrowed range of people the model can imitate. The bias sits in the middle of the answer distribution rather than its tails, and survives changes in sampling temperature and simple prompted reflection. The encouraging news is that it is easy to diagnose and straightforward to fix: a light-touch contrastive calibration, which needs no retraining and works on black-box APIs, brings all six categories back to the human baseline. Our results give researchers a clear map of where aligned LLMs can already be trusted as human stand-ins, where they need care, and a ready-to-use method for closing the gap.

cs.HC↗
arXiv2026

Corner Reflector Array Jamming Discrimination Using Multi-Dimensional Micro-Motion Features with Frequency Agile Radar

This paper introduces a robust discrimination method for distinguishing real ship targets from corner-reflector-array jamming with frequency-agile radar. The key idea is to exploit the multidimensional micro-motion signatures that separate rigid ships from non-rigid decoys. From Range-Velocity maps we derive two new hand-crafted descriptors-mean weighted residual (MWR) and complementary contrast factor (CCF) and fuse them with deep features learned by a lightweight CNN. An XGBoost classifier then gives the final decision. Extensive simulations show that the hybrid feature set consistently outperforms state-of-the-art alternatives, confirming the superiority of the proposed approach.

cs.LG↗
arXiv2026

TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters

Recent GPU programming frameworks such as Triton, TileLang, and CUDA Tile adopt tiles as first-class primitives, making tile-centric programming the prevailing approach for high-performance GPU kernels. Performance-analysis tooling has not followed: programmers still rely on coarse roofline bounds, opaque ML predictors, or post-hoc profilers to understand kernel execution. This gap is acute for modern AI workloads, where kernel fusion and distributed inference depend on tensor cores, CUDA cores, cache hierarchies, memory pipelines, and inter-GPU networks. We present TileSight, a tile-centric performance-modeling tool that elevates the tile from a programming primitive to an analysis primitive. Within a GPU core, TileSight models compute-memory pipeline overlap; across cores, it models the cache hierarchy; across GPUs, it models inter-node communication. All layers share the tile abstraction: the intra-tile layer expresses work as a resource vector spanning network, memory, and compute pipelines; the inter-tile layer schedules dependent and ordered actions to expose legal overlap and infers multi-level cache hit rates from tile reuse distance; and the cross-device layer maps remote tensor accesses to placements and routes them through an alpha-beta stage cost. On A100, H200, B200, and B6000, TileSight predicts single-GPU kernel latency with 12.35% pooled mean absolute percentage error (MAPE), outperforming state-of-the-art baselines and transferring better across architectures. Its L2 cache-hit-rate predictions are within roughly one percentage point of measurements on every GPU. At up to 32 GPUs, TileSight achieves 16.18% weighted MAPE (wMAPE) on fused distributed kernels and 13.52% wMAPE on end-to-end vLLM serving. In optimization, TileSight selects tile configurations competitive with strong vendor and expert baselines. TileSight will be open-sourced upon publication.

cs.DC↗