This paper investigates a robust incentive Stackelberg stochastic differential game problem for a linear-quadratic mean field system, where the model uncertainty appears in the drift term of the leader's state equation. Moreover, both the state average and control averages enter into the leader's dynamics and cost functional. Based on the zero-sum game approach, mean field approximation and duality theory, firstly the representation of the leader's limiting cost functional and the closed-loop representation of decentralized open-loop saddle points are given, via decoupling methods. Then by convex analysis and the variational method, the decentralized strategies of the followers' auxiliary limiting problems and the corresponding consistency condition system are derived. Finally, applying decoupling technique, the leader's approximate incentive strategy set is obtained, under which the asymptotical robust incentive optimality of the decentralized mean field strategy is verified. A numerical example is given to illustrate the theoretical results.

In this study, we investigated the propagation pattern and the site-to-site correlation function in a PT-symmetric waveguide array with different input quantum states. Recognizing the stark difference in propagation pattern before and after the PT symmetry-breaking point, we have developed a novel, straightforward intensity-based criterion to determine the exceptional point (EP). This new criterion shows excellent agreement with those obtained by directly computing the Hamiltonian's eigenvalues. Given the computational complexity of Hamiltonian diagonalization, our proposed criterion provides a highly efficient and valuable alternative for identifying the PT symmetry-breaking point. Importantly, the proposed criterion is not restricted to the specific system studied here, but is generally applicable to a wide class of systems that can be described within the tight-binding framework.

A gauge-theoretic framework for spacetime and gravitation is proposed, in which a Cartan khronon field breaks the symmetry between space and time, enabling the emergence of temporality within a fundamentally Euclidean setting. Based on a $Spin(4)$ gauge structure, the theory provides a real-valued formulation of chiral spacetime, wherein the effects typically attributed to dark matter may instead be accounted for by the dynamics of gravitation. New results are presented that are relevant to a broad range of phenomena, including cosmology, large-scale structure, gravitational waves, black holes, and potential signatures accessible to laboratory experiments.

We investigate preference domains under which every unanimous and locally strategy-proof social choice function (scf) satisfies dictatorship. We identify a condition on domains called connected with distinct neighbours which is necessary for dictatorship under unanimity and local strategy-proofness. Further, we show that this condition is sufficient within the class of domains where every unanimous and locally strategy-proof scf satisfies tops-onlyness. While a complete characterization remains open, we also show that on domains that are connected with distinct neighbours, unanimity and strategy-proofness (a stronger requirement) imply dictatorship.

We analyze the quasinormal modes (QNMs) of scalar, electromagnetic, and Dirac test fields in the background of a black hole immersed in a galactic dark matter halo. The analytic black hole solution considered here is sourced by a physically motivated halo density profile that leads to a flat galactic rotation curve. Using the sixth-order WKB method with Padé approximants, we compute the QNM spectra for various field spins and parameter values, and provide numerical data in tabulated form. In addition to the numerical analysis, we derive analytic expressions for the quasinormal frequencies in the eikonal limit and beyond, by means of an expansion in inverse powers of the multipole number. We also calculate the Unruh temperature perceived by a static observer in the halo-modified spacetime. Our results demonstrate that the presence of the dark matter halo leads to observable modifications in the QNM spectra only if the density or compactness of the galactic halo is extraordinary high, so that quasinormal ringing a reliable observable for testing the black hole geometry, even in the presence of galactic environments.

This paper addresses the approximation of the local volatility function in the Cheyette interest rate model. Its main contribution is an explicit analytical formula for approximating local volatility, derived by extending the classical Dupire framework to interest rate models. In particular, an implicit Dupire-like expression for local volatility is first derived for options written on the short rate. This expression is then approximated using a combination of perturbation methods and probabilistic techniques, resulting in a formula expressed in terms of time and strike derivatives of the Bachelier implied variance. The final formula naturally extends to multi-factor Cheyette models and provides a practical tool for model calibration.

We show that the Fourier extension conjecture on the paraboloid in three dimensions is equivalent to a local single scale smooth Alpert trilinear inequality, which is an improvement of an analogous multiscale trilinear inequality in arXiv:2506.03992.

We propose a novel computational strategy to study the glass transition of molecular fluids. Our approach combines the construction of simple yet realistic models with the development of Monte Carlo algorithms to accelerate equilibration and sampling. Inspired by the well-studied ortho-terphenyl glass-former, we construct a molecular model with an analogous triangular geometry and construct a `flip' Monte Carlo algorithm. We demonstrate that the flip Monte Carlo algorithm achieves a sampling speedup of about $10^9$ at the experimental glass transition temperature $T_g$. This allows us to systematically analyze the equilibrium structure and molecular dynamics of the model over a temperature regime previously inaccessible. We carefully compare the observed physical behavior to earlier studies that used atomistic models. In particular, we find that the glass fragility and the departure from the Stokes-Einstein relation are much closer to experimental observations. We characterize the development and temperature evolution of spatial correlations in the relaxation dynamics, using both orientational and translational degrees of freedom. Excess wings emerge at intermediate frequencies in dynamic rotational spectra, and we directly visualize the corresponding molecular motion near $T_g$. Our approach can be generalized to a \rev{broad range of molecular geometries and paves the way to a deeper} understanding of how molecular details may affect more universal physical aspects characterizing molecular liquids approaching their glass transition.

We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. Our approach incorporates data preprocessing techniques such as SMOTE, correlation analysis, and PCA, and achieves high accuracy and recall in the task of wide binary classification. The resulting publicly available code enables rapid, scalable, and customizable analysis of wide binaries, complementing conventional analyses and providing a valuable resource for future astrophysical studies.

How does the endogenous selection of information shape belief formation? In observational settings, individuals only consume information they choose, making it impossible to observe how they would respond to information they actively avoid. We address this identification challenge using a randomized experiment on COVID-19 vaccines in Taiwan. After eliciting subjects' preferences over vaccine-specific reports, we randomly assign them to receive either their chosen or unchosen information, orthogonalizing selection from exposure. We find subjects are more likely to select information about vaccines they already perceive as more effective. Conditional on receiving information, belief updating is substantially larger when the information was self-selected, even after controlling for prior-posterior disagreement. These findings highlight endogenous information demand as a central determinant of persuasion, suggesting that increasing information availability alone may be insufficient when individuals rationally filter out options they perceive as less relevant to their decision-making.

In this work, we study a scotogenic extension of the Standard Model featuring two inert scalar doublets and three singlet Majorana fermions, where neutrino masses are generated radiatively at one loop. The lightest among the Majorana fermions and neutral scalars can serve as dark matter candidates. We explore the parameter space, considering theoretical constraints (perturbativity, unitarity, vacuum stability) and experimental limits (lepton flavor violation, Higgs measurements, electroweak precision observables). Our analysis identifies regions where sizable Yukawa couplings naturally arise due to constructive interference in the scalar sector. Additionally, we estimate the oblique parameters, finding that only $ΔT$ is sensitive to charged mass splittings, while $ΔS$ and $ΔU$ remain small across the viable parameter space. However, 60\% of the viable parameter space is excluded by the recent CMS measurement of the $W$ boson mass, since the shift $ΔM_W$ depends on the oblique parameters, particularly $ΔT$ that is sensitive to scalar mass splittings.

We propose a new and simple method for determining the renormalized quark masses from lattice simulations. Renormalized quark masses are an important input to many phenomenological applications, including searching and modeling physics beyond the Standard Model. The non-perturbative renormalization is performed using gradient flow combined with the short-flow-time expansion that is improved by renormalization-group (RG) running to match to the $\overline{\text{MS}}$-scheme. Implementing the RG running perturbatively, we demonstrate this method works reliably at least up to the charm-quark mass and exhibits an easily-attainable ``windowing condition''. Using RBC/UKQCD's (2+1)-flavor Shamir domain-wall fermion ensembles with Iwasaki gauge action, we find $m_s^\overline{\text{MS}}(μ=2 \text{ GeV}) = 90(3)$ MeV and $m_c^\overline{\text{MS}}(μ=3 \text{ GeV}) = 972(16)$ MeV. These results predict the scale-independent ratio $m_c/m_s= 12.1(4)$. Generalization to other observables is possible, providing an efficient approach to determine non-perturbatively renormalized fermionic observables like form factors or bag parameters from lattice simulations.

Considering a range of candidate quantum phases of matter, half-integer thermal conductance ($κ_{\text{th}}$) is believed to be an unambiguous evidence of non-Abelian states. It has been long known that such half-integer values arise due to the presence of Majorana edge modes, representing a significant step towards topological quantum computing platforms. Here, we challenge this prevailing notion by presenting a comprehensive theoretical and experimental study where half-integer two-terminal thermal conductance plateau is realized employing Abelian phases. Our proposed setup features a confined geometry of bilayer graphene, interfacing distinct particle-like and hole-like integer quantum Hall states. Each segment of the device exhibits full charge and thermal equilibration. Our approach is amenable to generalization to other quantum Hall platforms, and may give rise to other values of fractional (electrical and thermal) quantized transport. Our study demonstrates that the observation of robust non-integer values of thermal conductance can arise as a manifestation of mundane equilibration dynamics as opposed to underlying non-trivial topology.

The Worldvolume Hybrid Monte Carlo (WV-HMC) method [arXiv:2012.08468] is a reliable and versatile algorithm for addressing the numerical sign problem. It resolves the ergodicity issues commonly encountered in Lefschetz thimble-based approaches while maintaining low computational costs. In this paper, as a general framework for applying WV-HMC to lattice gauge theories, we extend the algorithm to systems defined on compact group manifolds. The key is to introduce a symplectic structure on the tangent bundle of the worldvolume and formulate molecular dynamics upon it. The validity of the proposed algorithm is demonstrated using the one-site model with a purely imaginary coupling constant.

Every natural number greater than $2$ can be written as the sum of a prime and a square-free number, and recent work has imposed additional divisibility conditions on the square-free number. We overcome limitations in these works to prove new results on square-free numbers co-prime to any integer with up to two prime factors, which make the expected asymptotic results explicit.

Recently, there has been a lot of interest in Carroll black holes and in particular whether or not one could find a Carrollian analogue of a rotating black hole spacetime. Here we show that every stationary and axisymmetric solution (and thence also a black hole) of Carrollian general relativity in any number of $d>3$ dimensions is necessarily also static (up to a "topological rotation"). The case of $d=3$ dimensions is special. There, the topological rotation is important and one can have a rotating Carroll BTZ black hole, obtained from a static one by the Carroll boost accompanied by the re-identification of the angular coordinate, similar to what happens in the Lorentzian case. We also find a Carrollian analogue of an accelerating black hole, showing that Schwarzschild is not the only possible stationary and axisymmetric Carroll black hole in four dimensions. A generalization of the no go theorem to include Maxwell, dilatonic, and axionic matter fields is also discussed.

Light dark matter with sub-eV masses has a high number density in our galaxy, and its scattering cross section with macroscopic objects can be significantly enhanced by coherence effects. Repeated scattering with a target object can induce a measurable acceleration. Torsion balance experiments with geometric asymmetry are, in principle, capable of detecting such signals. Our analysis shows that existing torsion balances designed to test the Equivalence Principle already place the most stringent constraints on DM-nucleon scattering in the $(10^{-2}, 1)\,$eV mass range.

We investigate gravitational wave signals in a non-supersymmetric grand unified model where the group $SO(10)$ is broken in two steps to the Standard Model gauge group. We calculate the analytical form of the one-loop effective potential responsible for the first step of symmetry breaking and show that it can lead to a first-order phase transition with gravitational wave production. We also determine the gravitational wave background produced by the primordial plasma of relativistic particles. The present experimental sensitivity is still far from the expected signals, but could be in reach of novel detector concepts.

The von Neumann equation with delta self-interaction kernel serves as a statistical model for nonlinear waves, and it exhibits a bifurcation between stable and unstable regimes. In oceanography it is known as the Alber equation, and its bifurcation is important for understanding rogue waves, a key problem in marine safety. Despite its significance, only one first-order-in-time numerical method exists in the literature. In this paper, we propose a structure-preserving, linearly implicit, second-order-in-time scheme for its numerical solution. We employ fourth-order finite differences for the spatial discretization. As an illustrative example, we explore the onset of modulation instability. We verify that the linear stability analysis accurately predicts the initial growth phase, but fails to forecast the maximum amplitude, the formation of a coherent structure in the nonlinear regime, or the relevant timescales. Monte Carlo simulations with Gaussian background spectra reveal that the maximum amplitude depends mainly on the homogeneous background rather than the initial inhomogeneity. For weak instabilities, the inhomogeneity grows substantially from its initial condition, but remains small compared to the background. On the other hand, strong instability leads to recurrent hotspots of increased variance. This provides a possible explanation of how modulation instability makes rogue waves more likely in unidirectional sea states.

We are concerned with the existence of $T$-periodic solutions to an equation of type $$\left (|u'(t))|^{p(t)-2} u'(t) \right )'+f(u(t))u'(t)+g(u(t))=h(t)\quad \mbox{ in }[0,T]$$ where $p:[0,T]\to(1,\infty)$ with $p(0)=p(T)$ and $h$ are continuous on $[0,T]$, $f,g$ are also continuous on $[0,\infty)$, respectively $(0,\infty)$. The mapping $g$ may have an attractive singularity (i.e. $g(x) \to +\infty$ as $x\to 0+$). Our approach relies on a continuation theorem obtained in the recent paper M. García-Huidobro, R. Manásevich, J. Mawhin and S. Tanaka, J. Differential Equations (2024), a priori estimates and method of lower and upper solutions.

We discuss a basis for the nonperturbative Hilbert space of quantum gravity with one asymptotic boundary. We use this basis to show that the Hilbert space for gravity with two disconnected boundaries factorizes into a product of two copies of the single boundary Hilbert space.

Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users' daily lives. However, existing personalization approaches often fail to distinguish which components of model predictions and training data truly reflect user preferences, leading to superficial personalization alignment. In this paper, we introduce NextQuill, a novel LLM personalization alignment framework grounded in causal preference modeling. We approach personalization from a causal perspective, treating both model predictions and ground-truth data generation as outcomes influenced by user preferences, along with other factors. We define the true preference effect as the causal impact of user history (which reflects preferences) on each token prediction or data generation instance, estimated through causal intervention techniques. Building on this insight, NextQuill introduces two complementary alignment strategies: (1) aligning model-internal causal preference effects on predictions with those reflected in ground-truth data, rather than indiscriminately fitting predictions, and (2) focusing on fitting preference-bearing tokens identified via ground-truth data preference effects, rather than treating all tokens uniformly. By integrating these strategies, NextQuill shifts the alignment process toward learning from causal preference effects, facilitating more effective and personalized adaptation. Experiments across multiple personalization benchmarks demonstrate that NextQuill significantly improves personalization quality, offering a principled, causal foundation for LLM personalization. Our codes are available on https://github.com/juntaoyou/NextQuill.

We propose an inverse-design approach for computational spectrometers in which the scattering media are topology-optimized to achieve better performance in inference of unknown spectra. Unlike traditional end-to-end approaches, our inverse design of the scattering media does not need a training set of spectra, a distribution of detector noise, or an inference algorithm. Our approach allows the selection of the inference algorithm to be decoupled from that of the scatterer. For smooth spectra, we additionally devise a regularized reconstruction algorithm based on Chebyshev interpolation, which yields higher accuracy compared with conventional methods in which the spectra are sampled at equally spaced frequencies or wavelengths with equal weights. Our approaches are numerically demonstrated via inverse design of integrated computational spectrometers and reconstruction of example spectra. The inverse-designed spectrometers exhibit significantly better performance in the presence of noise than their counterparts with random scatterers. Our method provides a useful complement to end-to-end co-design methods.

Heterotic string models in $4$-dimensions are the hybrid theories of a left-moving $N=1$ fermionic string whose additional $6$-dimensions are compactified on a $N=2$ SCFT theory with the central charge $9$, and a right-moving bosonic string, whose additional dimensions are also compactified on $N=2$ SCFT theory with the central charge $9$, and the remaining $13$ dimensions compactified on the torus of $E(8)\times SO(10)$ Lie algebra. The important class of exactly solvable Heterotic string models considered earlier by D. Gepner corresponds to the products of $N=2$ minimal models with the total central charge $c=9$. These models are known to describe Heterotic string models compactified on Calabi-Yau manifolds, which belong a special subclass of general CY manifolds of Berglund-Hubsch type. We generalize this construction to all cases of compactifications on Calabi-Yau manifolds of general Berglund-Hubsch type, using Batyrev-Borisov combinatorial approach. In particular, starting from the mirror pair of Batyrev lattices corresponding to a given CY manifold, we construct vertex operators of the complete physical theory as cohomology of Borisov differentials that correspond to points of reflexive Batyrev polyhedra. In particular, we show how the number of $27$, $\overline{27}$ and Singlet representations of $E(6)$ is determined by the data of reflexive Batyrev polytope that determines this CY-manifold.

Bragg Diffraction of matter waves is an established technique used in the most accurate quantum sensors. It is also the method of choice to operate large-momentum-transfer, high-sensitivity atom interferometers. It suffers, however, from an intrinsic multi-path character. Optimal control theory (OCT) has recently led to an improved robustness of atom interferometers to a range of challenging environmental effects such as vibrations or platform accelerations. In this theoretical work, we apply OCT protocols to control the Bragg diffraction phase shifts thereby enhancing the metrological accuracy of the interferometer. We show a minimization of the diffraction phase for realistic conditions of finite temperature of the incoming wavepacket in a multi-path, high-order Bragg interferometer in a Mach-Zehnder configuration. We study input states with different momentum widths and find that our approach mitigates diffraction phases below the microradian level in the case of $1\%$ of the photon recoil, thereby eliminating one of the leading systematic effects in atom interferometry.

Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood. Here, we investigate three paradigmatic random-organizing particle systems drawn from distinct domains: models from soft matter physics (random organization, biased random organization) and machine learning (stochastic gradient descent), each characterized by distinct sources of noise. We discover universal long-range behavior across all systems, namely the suppression of long-range density fluctuations, governed solely by the noise correlation between particles. Furthermore, we establish a connection between the emergence of long-range order and the tendency of stochastic gradient descent to favor flat minima -- a phenomenon widely observed in machine learning. To rationalize these findings, we develop a fluctuating hydrodynamic theory that quantitatively captures all observations. Our study resolves long-standing questions about the microscopic origin of noise-induced hyperuniformity, uncovers striking parallels between stochastic gradient descent dynamics on particle system energy landscapes and neural network loss landscapes, and should have wide-ranging applications -- from the self-assembly of hyperuniform materials to ecological population dynamics and the design of generalizable learning algorithms.

A periodic tangle is a one-dimensional submanifold in $\mathbb{R}^3$ that has translational symmetry in one, two or three transverse directions. A periodic tangle can be seen as the universal cover of a link in the solid torus, the thickened torus, or the three-torus, respectively. Our goal is to study equivalence relations of such periodic tangles. Since all finite covers of a link lift to the same periodic tangle, it is necessary to prove that isotopies between different finite covers are preserved. In this paper, we show that if two links have isotopic lifts in a common finite cover, then they are isotopic. To do so, we employ techniques from 3-manifold topology to study the complements of such links.

We present some new Poisson bivectors that are invariants by the flow of the nonholonomic Suslov problem. Two rank four invariant Poisson bivectors have globally defined Casimir functions and, therefore, define cubic Poisson brackets on the five dimensional state space with standard symplectic leaves. For the Suslov gyrostat in the potential field we found rank two Poisson bivectors having only two globally defined Casimir functions and, therefore, we say about formal Hamiltonian description in these cases.

Consider an $n\times k$ matrix of i.i.d. Bernoulli random numbers with $p=1/2$. Dual RSK algorithm gives a bijection of this matrix to a pair of Young tableaux of conjugate shape, which is manifestation of skew Howe $GL_{n}\times GL_{k}$-duality. Thus the probability measure on zero-ones matrix leads to the probability measure on Young diagrams proportional to the ratio of the dimension of $GL_{n}\times GL_{k}$-representation and the dimension of the exterior algebra $\bigwedge\left(\mathbb{C}^{n}\otimes\mathbb{C}^{k}\right)$. Similarly, by applying Proctor's algorithm based on Berele's modification of the Schensted insertion, we get skew Howe duality for the pairs of groups $Sp_{2n}\times Sp_{2k}$. In the limit when $n,k\to\infty$ $GL$-case is relatively easily studied by use of free-fermionic representation for the correlation kernel. But for the symplectic groups there is no convenient free-fermionic representation. We use Christoffel transformation to obtain the semiclassical orthogonal polynomials for $Sp_{2n}\times Sp_{2k}$ from Krawtchouk polynomials that describe $GL_{2n}\times GL_{2k}$ case. We derive an integral representation for semiclassical polynomials. The study of the asymptotic of this integral representation gives us the description of the limit shapes and fluctuations of the random Young diagrams for symplectic groups.

Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical liquidity data. We address these challenges through a novel methodological framework that reconstructs historical liquidity states from swap transaction data, enabling rigorous backtesting of dynamic liquidity provision strategies. Our parametric reconstruction method achieves high accuracy (approximation errors averaging around 2\%) without relying on historical liquidity snapshots, addressing a critical data gap in decentralized finance research. We apply this framework to evaluate tau-reset strategies--dynamic liquidity reallocation approaches that respond to market movements--across multiple Uniswap v3 pools. Using machine learning to optimize strategy parameters based on market conditions, we identify consistent outperformance (13--23\% higher fees) compared to uniform allocation benchmarks. Our analysis reveals important insights into the risk-return tradeoffs in automated market making, including the critical role of impermanent loss as a dominant risk factor and the effectiveness of asymmetric strategy modifications for capital preservation. These findings contribute to the broader understanding of market microstructure in decentralized exchanges, providing both methodological innovations for performance evaluation and practical insights for liquidity providers navigating this evolving financial landscape.