arXiv subjects

Shun Wang

Publications and source records attributed to Shun Wang.

At least 19 recordsLinked to original sources

arXiv2026

Learning the Energy Landscapes of Dynamical Systems via Energetic Variational Optimal Transport under Data Quantity--Quality Trade-offs

Dynamic optimal transport unifies optimal transport, fluid mechanics, and gradient-flow theory within a continuous dynamical framework, offering a geometry-aware language for applications across physics, biology, and machine learning. However, conventional formulations cast it as a constrained optimization problem that must explicitly satisfy the continuity equation, hindering the reconstruction of the underlying dynamics directly from data. We propose the energetic variational method for dynamic optimal transport (EVMDOT), which reformulates it within an energetic variational framework by combining the flow map, the least action principle, and the maximum dissipation principle. The flow map recasts the constrained problem as an unconstrained one by automatically enforcing the continuity equation, while the balance between the conservative and dissipative forces determines the velocity field. Applied to the Fokker--Planck equation, the EVMDOT reconstructs both the energy landscape and the Waddington landscape directly from time-series density data. Through numerical experiments, we reveal that the EVMDOT achieves an intrinsic balance between data quantity and data quality: a sufficient data quantity compensates for limited data quality, making the reconstruction robust to the choice of the observation window. We further apply the EVMDOT to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to infer the potential landscape of amyloid-$\beta$ and tau, revealing two wells corresponding to the cognitively normal and Alzheimer's disease stages and the transition pathway between them.

math.DS↗

arXiv2026

Electron Delocalization versus Emission Coherence of Quantum Dot Superlattices

Cooperative emission is a collective quantum optical process that requires macroscopic phase coherence among coupled emitters. Recent observations of cooperative emission in QD superlattices have renewed interest in how such coherence emerges in nanostructured solids. Meanwhile, theoretical studies have long discussed the relationship between electronic delocalization and coherence, particularly whether delocalized states necessarily give rise to cooperative emission. This study addresses this question through power-dependent steady-state PL and time-resolved PL decay measurements. The findings indicate that, although the quantum resonance peak exhibits delocalized excitonic characteristics, it shows no signatures of cooperative radiation. In particular, neither superlinear intensity scaling nor power-dependent emission delay was observed, indicating the absence of cooperative-radiation signatures. This can be understood from two disorder-related aspects. Temperature-dependent spectroscopy reveals pronounced inhomogeneous broadening and low-temperature dark-exciton participation, pointing to intra-domain static disorder and exciton-state mixing. These effects collectively hinder the establishment of macroscopic coherence. The temperature dependence of the quantum resonance peak decay lifetime is consistent with two-dimensional exciton dynamics. This work provides direct experimental evidence that electronic delocalization can be decoupled from cooperative coherence in CdSe quantum dot superlattices.

cond-mat.mes-hall↗

arXiv2026

LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning

We introduce LongCat-Flash-Prover, a flagship 560-billion-parameter open-source Mixture-of- Experts (MoE) model that advances Native Formal Reasoning in Lean4 through agentic tool-integrated reasoning (TIR). We decompose the native formal reasoning task into three independent formal capabilities, i.e., auto-formalization, sketching, and proving. To facilitate these capabilities, we propose a Hybrid-Experts Iteration Framework to expand high-quality task trajectories, including generating a formal statement based on a given informal problem, producing a whole-proof directly from the statement, or a lemma-style sketch. During agentic RL, we present a Hierarchical Importance Sampling Policy Optimization (HisPO) algorithm, which aims to stabilize the MoE model training on such long-horizon tasks. It employs a gradient masking strategy that accounts for the policy staleness and the inherent train-inference engine discrepancies at both sequence and token levels. Additionally, we also incorporate theorem consistency and legality detection mechanisms to eliminate reward hacking issues. Extensive evaluations show that our LongCat-Flash-Prover sets a new state-of-the-art for open-weights models in both auto-formalization and theorem proving. Demonstrating remarkable sample efficiency, it achieves a 97.1% pass rate on MiniF2F-Test using only 72 inference budget per problem. On more challenging benchmarks, it solves 70.8% of ProverBench and 41.5% of PutnamBench with no more than 220 attempts per problem, significantly outperforming existing open-weights baselines.

cs.AI↗

arXiv2026

Electrically tunable circular photocurrent via local-field induced symmetry breaking at a metal-MoTe2 interface

Transition metal dichalcogenides (TMDCs) constitute a promising platform for symmetry-engineered responses to circularly polarized light. The high crystal symmetry of centrosymmetric 2H-phase TMDCs inherently forbids the circular photogalvanic effect, thereby necessitating external stimuli such as electric fields or strain to lower the symmetry for its activation. While Schottky junctions provide a ubiquitous built-in field for potentially inducing circular photocurrents, the mechanism for the generation and control of circular photocurrents in TMDCs is not understood. In this study, we fabricated a localized gold-MoTe2 heterostructure and demonstrate a pronounced circular photocurrent at the interface under normal incidence. The photocurrent is attributed to circular photogalvanic effect governed by the strength and direction of the built-in electric field, enabling continuous modulation via an external bias. First-principles calculations show that the gold interface induces a spin splitting in the valence bands of MoTe2, establishing a valley-dependent spin ordering. The observed circular photocurrent from multilayer 2H-MoTe2 under normal incidence indicates the breaking of C3 rotational symmetry by the local in-plane field. These results establish an effective strategy for developing voltage-tunable circularly polarized photodetectors and valleytronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Unveiling Scaling Laws of Parameter Identifiability and Uncertainty Quantification in Data-Driven Biological Modeling

Integrating high-dimensional biological data into data-driven mechanistic modeling requires rigorous practical identifiability to ensure interpretability and generalizability. However, coordinate identifiability analysis often suffers from numerical instabilities near singular local minimizers. We present a computational framework that uncovers fundamental scaling laws governing practical identifiability through asymptotic analysis. By synthesizing Fisher information with perturbed Hessian matrices, we establish a hierarchical approach to quantify coordinate identifiability and inform uncertainty quantification within non-identifiable subspaces across different orders. Supported by rigorous mathematical analysis and validated on synthetic and real-world data, our framework was applied to HIV-host dynamics and spatiotemporal amyloid-beta propagation. These applications demonstrate the framework's efficiency in elucidating critical mechanisms underlying HIV diagnostics and Alzheimer's disease progression. In the era of large-scale mechanistic digital twins, our framework provides the scaling laws for data-driven modeling in terms of both parameter identifiability and uncertainty, ensuring that data-driven inferences are grounded in verifiable biological reality.

q-bio.QM↗

arXiv2025

Deciphering the lattice vibrational behaviors of CuInP2S6 by angle-resolved polarized Raman scattering

The layered van der Waals (vdW) ferroelectric CuInP2S6 (CIPS) exhibits unique cation hopping-driven phenomena that bring about unconventional properties with intriguing mechanisms and hold promises for advanced applications in nanoelectronics. However, an explicit analysis of its lattice dynamics and vibrational symmetries, pivotal for understanding the material's peculiar ferroelectric and ferroionic behaviors, remains incomplete. Here, we employ angle-resolved polarized Raman spectroscopy in concert with first-principles calculations to systematically unravel the anisotropic lattice vibrations of CIPS single crystals. By analyzing the polarization-dependent Raman intensities, we determine the symmetry assignments and Raman tensors of all major vibrational modes, revealing good agreement with theoretical predictions. Furthermore, we demonstrate the utility of Raman spectroscopy as a sensitive and non-invasive probe for structural and ferroelectric order evolution, by examining temperature-driven phase transitions and thickness-dependent polarization suppression in CIPS. Our findings establish a foundational framework for correlating lattice dynamics with functional properties in CIPS and provide a methodological blueprint for studying other vdW ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

A Symmetric Superconducting Dome Hosts Non-Fermi Liquid Behavior at Optimal Doping in MoS2

The similarities between the phase diagrams of ionic liquid-gated transition metal dichalcogenides (TMDCs) and high-temperature superconductors have garnered considerable interest due to the presence of a superconducting dome with a non-monotonic dependence of the superconducting order parameter as a function of charge carrier density. However, the lack of a complete superconducting dome and insights into the normal state in ionic liquid-gated TMDCs prevents a detailed mapping between the two systems. In this work, we obtain a symmetric superconducting dome that extends from deep underdoped regime all the way to deep overdoped regime in ionic liquid gated MoS2 with a refined gating protocol. We demonstrate that the dome is anticorrelated with the evolution of non-Fermi liquid behavior in the normal states. The scattering rate in the non-Fermi liquid regime can reach Planckian limit. The results may shed light on the emergence of superconductivity in TMDCs.

cond-mat.supr-con↗

arXiv2025

Convergence of long-time stable variable-step arbitrary order ETD-MS scheme for gradient flows with Lipschitz nonlinearity

We analyze a variable-step extension of a family of arbitrarily high-order exponential time differencing multistep (ETD-MS) schemes recently developed by the authors. We prove that the schemes are unconditionally stable in the sense that a modified energy-representing a slight perturbation of the original energy-decreases monotonically over time, provided the nonlinearity is Lipschitz continuous in some appropriate sense. Moreover, we establish optimal-order convergence under mild conditions on the time-step size and local time-step ratio. Numerical experiments on the thin film epitaxial growth model without slope selection, employing a novel variable-step second-order scheme, validate the theoretical findings as well as its potential in developing highly efficient time-adaptive solution.

math.NA↗

arXiv2025

Prospects of $|V_{us}|$ and axial vector form factors in $\Lambda\to pe^{-}{\bar\nu}_{e}$ decay at STCF

We report a feasibility study of the semileptonic decay $\Lambda\to pe^{-} {\bar\nu}_{e}$ by using a fast simulation software package at STCF. With an anticipated integrated luminosity of 3.4 trillion $J/\psi$ per year at a center-of-mass energy at 3.097 GeV, the statistical sensitivity of the branching fraction is determined to be 0.15\%. The statistical sensitivities of form factors $g_{av}$ and $g_w$ are determined to be 0.4\% and 2.15\%, respectively. Combining this result with $g_1(0)$ from Lattice QCD, we can obtain the projected sensitivity of $|V_{us}|$, to be 0.9\%, which is comparable to the precision obtained through meson decay measurements. The precise measurement to be obtained at STCF will provide a rigorous test of Standard Model.

hep-ex↗

arXiv2025

Realization of large magnetocaloric effect in the Kagome antiferromagnet Gd3BWO9 for Sub-Kelvin cryogenic refrigeration

Rare-earth (RE) based frustrated magnets have attracted great attention as excellent candidates for magnetic refrigeration at sub-Kelvin temperatures, while the experimental identification on systems exhibiting both large volumetric cooling capacity and reduced working temperatures far below 1 K remain to be a challenge. Here, through the ultra-low temperature magnetism and thermodynamic characterizations, we unveil the large magnetocaloric effect (MCE) realized at sub-Kelvin temperatures in the frustrated Kagome antiferromagnet Gd3BWO9 with TN~1.0 K. The isothermal magnetization curves indicate the existence of field (B) induced anisotropic magnetic phase diagrams, where four distinct magnetic phases for B // c-axis and five magnetic phases for B // ab-plane are identified at T< TN. The analysis of magnetic entropy S(B, T) data and direct adiabatic demagnetization tests reveal a remarkable cooling performance at sub-Kelvin temperatures featured by a large volumetric entropy density 502.2 mJ/K/cm3 and a low attainable minimal temperature Tmin~168 mK from the initial cooling condition of 2 K and 6 T, surpassing most of Gd-based refrigerants previously documented in temperature ranges of 0.25-4 K. The realized Tmin~168 mK far below TN ~ 1.0 K in Gd3BWO9 is related to the combined effects of magnetic frustration and criticality-enhanced MCE, which together leave a substantial magnetic entropy at reduced temperatures by enhancing spin fluctuations.

cond-mat.str-el↗

arXiv2025

Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders

Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on downstream tasks. In this work, we apply an effective LLM decomposition method using a dictionary-learning approach with sparse autoencoders. This helps extract monosemantic features from polysemantic LLM neurons. Remarkably, our work identifies model-internal misunderstanding, allowing the automatic reformulation of the prompts with additional annotations to improve the interpretation by LLMs. Moreover, this approach demonstrates a significant performance improvement in downstream tasks, such as mathematical reasoning and metaphor detection.

cs.CL↗

arXiv2025

Dual-mode superconducting diode effect enabled by in-plane and out-of-plane magnetic field

The discovery of the superconducting diode effect (SDE) has been cherished as a milestone in developing superconducting electronics. Tremendous efforts are being dedicated to realizing SDE in a wide variety of material platforms. Despite the diversity in the hosting materials and device designs, SDE is usually operated in a single mode which is enabled by either out-of-plane or in-plane magnetic field/magnetization. In this work, we report the realization of a dual-mode SDE in 2H-$\mathrm{NbS_2}$/2H-$\mathrm{NbSe_2}$ heterostructures where both the out-of-plane magnetic field $B_{\perp}$ and in-plane magnetic field $B_{||}$ can independently generate and manipulate SDE. The two modes share similar diode efficiency but differ in two aspects: 1. $B_{\perp}$-induced SDE is activated by a field on the order of 1 mT while $B_{||}$-induced SDE requires a field on the order of 100 mT; 2. $\eta$ of $B_{\perp}$-induced SDE exhibits a square-root like temperature dependence while $\eta$ of $B_{||}$-induced SDE takes a more linear-like one. We demonstrate that the dual-mode SDE is most likely a result of mirror symmetry breaking along multiple orientations. Thanks to the two orders difference in the operational field for the two modes, we propose a dual-functionality device scheme to showcase the potential of the dual-mode SDE in realizing advanced superconducting architecture, where fast polarity-switching functionality is implemented with $B_{\perp}$-induced SDE and high-fidelity functionality is enabled with $B_{\perp}$-induced SDE.

cond-mat.supr-con↗

arXiv2025

Geography-Aware Large Language Models for Next POI Recommendation

The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.

cs.IR↗

arXiv2025

ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling

Traditional entropy-based methods - such as cross-entropy loss in classification problems - have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multi-source data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multi-source heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBCNews, and AGNews. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe$_3$Pt using data generated from density functional theory (DFT) and capture key material behaviors, including negative thermal expansion and the critical point in the temperature-pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

cs.LG↗

arXiv2025

Evolution of quantum criticality in underdoped cuprates

Quantum criticality, with both static and dynamic information of the system intrinsically encoded in characteristic length scales, serves as one of the most sensitive and universal probes to monitor quantum phase transition. Qualitatively different quantum criticality behaviours have been widely observed even in the same condensed matter system. The discrepancy is attributed to sample specificity but has not been systemically addressed. Here we report a single-parameter driven three-stage evolution of quantum criticality unveiled in superconductor-insulator transition in underdoped Bi2Sr2CaCu2O8+{\delta} flakes. The evolution starts with a single quantum critical point emerging at the boundary between the superconducting and antiferromagnetic phases, then evolving into anomalous quantum Griffiths singularity at the medium doping levels and eventually being replaced by quantum Griffiths singularity in the deep superconducting regime. A puddle model that incorporates the developments of antiferromagnetic correlation can capture the evolution. The results offer a new aspect to examine previous seemingly sample-specific quantum critical behavior and lay the foundation for further exploring complex quantum criticality in strongly correlated systems; meanwhile they shed light on the detailed interaction between superconductivity and antiferromagnetism in cuprates.

cond-mat.supr-con↗

arXiv2025

Enhanced Backgate Tunability on Interfacial Carrier Concentration in Ionic Liquid-Gated MoS2 Devices

The periodic spatial modulation potential arising from the zig-zag distribution of ions at large gate voltage in an ionic liquid gated device may enable functionalities in a similar way as nanopatterning and moir\'e engineering. However, the inherent coupling between periodic modulation potential and carrier concentration in ionic liquid devices has hindered further exploration. Here, we demonstrate the feasibility of decoupling manipulation on periodic modulation potential and carrier density in an ionic liquid device by using a conventional backgate. The backgate is found to have a tunability on carrier concentration comparable to that of ionic gating, especially at large ionic liquid gate voltage, by activating the bulk channels mediated back tunneling between the trapped bands and interfacial channel.

cond-mat.mes-hall↗

arXiv2025

High-frequency magnetic response measurement of test mass with a fluxgate magnetometer for gravitational wave detection

For space-borne gravitational wave detectors,such as LISA and TianQin ,the disturbance caused by the coupling of test masses and the external magnetic fields is one of the main sources of the residual acceleration noise. Although the detection frequency band is from 0.1 mHz to 1 Hz, magnetic fields with frequencies higher than 1 Hz can still contribute to the noise through down conversion effect. Therefore, it is necessary to measure the AC magnetic susceptibility or magnetic response of the test mass at higher frequency for the evaluation of the magnetic noise. In this work, we propose a magnetic field response measurement method by directly probing the induced magnetic field of the test mass placed in a spatially uniform magnetic field. The frequency can be measured up to 1500 Hz, satisfying the requirement of space-borne gravitational wave detection.

physics.ins-det↗

arXiv2025

Progress of the TianQin project

TianQin is a future space-based gravitational wave observatory targeting the frequency window of $10^{-4}$ Hz $\sim 1$ Hz. A large variety of gravitational wave sources are expected in this frequency band, including the merger of massive black hole binaries, the inspiral of extreme/intermediate mass ratio systems, stellar-mass black hole binaries, Galactic compact binaries, and so on. TianQin will consist of three Earth orbiting satellites on nearly identical orbits with orbital radii of about $10^5$ km. The satellites will form a normal triangle constellation whose plane is nearly perpendicular to the ecliptic plane. The TianQin project has been progressing smoothly following the ``0123" technology roadmap. In step ``0", the TianQin laser ranging station has been constructed and it has successfully ranged to all the five retro-reflectors on the Moon. In step ``1", the drag-free control technology has been tested and demonstrated using the TianQin-1 satellite. In step ``2", the inter-satellite laser interferometry technology will be tested using the pair of TianQin-2 satellites. The TianQin-2 mission has been officially approved and the satellites will be launched around 2026. In step ``3", i.e., the TianQin-3 mission, three identical satellites will be launched around 2035 to form the space-based gravitational wave detector, TianQin, and to start gravitational wave detection in space.

gr-qc↗