Smart contract vulnerabilities have caused billions in financial losses, raising questions about whether programming language paradigms can reduce security overhead. While imperative languages like Solidity require developers to manually implement security checks, resource-oriented languages like Move encode safety guarantees in type systems. We present a preliminary mixed-methods study analyzing 12 functionally-equivalent contract pairs implemented in both Solidity and Move by the same development team, complemented by a survey of 11 developers experienced in both languages. Quantitative analysis reveals that Move reduces explicit security overhead by 60\% (security check density: 6.7% vs. 16.8%, p=0.002, Cohen's d=-1.75) at the cost of 47% larger code size (p=0.002, d=1.90), while maintaining identical cyclomatic complexity. Developer surveys show moderate learning difficulty but higher safety confidence in Move (Median=6/7, 10 of 11 above neutral), with 55% preferring Move for security-critical applications despite ecosystem maturity gaps. These preliminary findings suggest resource-oriented paradigms shift security from runtime validation to compile-time guarantees, though adoption requires investment in learning and tooling. The controlled comparison provides initial evidence for paradigm effects on smart contract development, informing language selection decisions and identifying opportunities for improved developer resources.

Analysis of 28 years of OGLE project data for the 0.404 day period binary OGLE BLG ELL-006503 (V1231 Sco) shows a superposition of tidal-interaction variations of about 0.07 magnitude in the I-band onto a time-scale 5-10 year, 0.15-0.17 magnitude brightness drift. Seasonal light-curves suggest that this long-term variability is driven by large photospheric spots (approx 0.3 stellar radii) that exhibit a tendency to orient themselves relative to the tidal interaction line. The eclipse time O-C residuals reveal an orbital period change characterized by a timescale of approximately 2x10^6 years, suggesting a mild mass redistribution, potentially linked to global magnetic fields reconfiguration. Gaia parallax and absolute magnitude estimates indicate a system consisting of solar-type stars. The lack of eclipses and extreme spot activity suggest the binary may consist of late-type, tidally-deformed stars rather than a typical W UMa-type system.

Near-infrared (near-IR) spectroscopy provides critical constraints on the surface composition of trans-Neptunian objects (TNOs), but spectroscopic observations remain limited compared to broadband photometry. We develop a probabilistic latent-space framework to quantify how much spectral information is retained in sparse photometric measurements. Using a principal component representation trained on a sample of near-IR spectra, we model the spectral manifold of TNOs and perform Bayesian inference in this reduced space to reconstruct full spectra from photometry while propagating uncertainties. Leave-one-out cross-validation demonstrates that the dominant modes of spectral variability are low-dimensional: 4 to 5 principal components capture the structure relevant for taxonomic classification, while 8-10 components improve spectral reconstruction fidelity and uncertainty calibration. For most objects, the reconstructed spectra achieve empirical credible-interval coverage of 95 percent across wavelength. This suggests the diversity of near-IR spectral shapes is governed by structured, correlated surface processes rather than stochastic variation. Practically, we apply this framework to survey optimization, quantifying the information content of JWST/NIRCam filters to identify optimal configurations (e.g., F090W, F115W, F410M, F460M) for TNO taxonomy. Additionally, we demonstrate the pipeline's capability to detect and reconstruct rare spectral types, such as the peculiar Neptune Trojans 2006 RJ103 and 2011 SO277, by allowing constraining photometry to select low-probability intermediate models from the continuous topological manifold. Ultimately, this framework bridges the gap between sparse photometry and spectroscopy, providing a statistically rigorous tool to map the compositional structure of minor planets in upcoming large-scale surveys.

We present a new implementation of the SFUMATO code, called SFUMATO#, for solving self-gravitational radiation hydrodynamics problems using adaptive mesh refinement (AMR) with the CUDA/HIP programming frameworks. The code incorporates a multigrid solver for self-gravity, radiation transfer with M1 closure and reduced speed of light approximation, non-equilibrium chemistry, thermal evolution, and sink particle schemes. We develop new non-equilibrium chemistry and thermal solvers based on a linearized implicit method, whose accuracy is validated through a series of test problems by comparison with solutions obtained using the Newton-Raphson method. By incorporating the heat capacity of dust grains, the dust temperature can be evolved without iterative energy-balance calculations. From the perspective of computational cost, we demonstrate that adopting an increased pseudo dust heat capacity accelerates the chemistry solver while preserving accuracy, even when the value is increased by up to three orders of magnitude relative to the realistic value. In addition, we perform a suite of test problems to confirm the validity of the other components of our implementation. The code supports multi-GPU execution via MPI-based parallelization. We measure the strong-scaling performance of the hydrodynamics and self-gravity solvers on both uniform and AMR grids, as well as the overall code performance using a giant molecular cloud simulation. We find that the computational cost of the self-gravity solver increases with the number of MPI processes, indicating that efficient parallel performance is achieved only when the number of devices is chosen such that the cost of the self-gravity solver remains comparable to that of the other components.

The nonlinear Hall effect is a new member of the Hall effect family, which attracts intense research interests, and it is closely related to the quantum geometry of quantum materials. The previous studies primarily concentrate on the second-order and third-order nonlinear Hall effect. However, the experimental study of higher-order nonlinear Hall effect is scarce at present. In this work, we report the observations of the higher odd-order (third-, fifth-, seventh-order) nonlinear Hall effect in magnetic topological insulator Mn(Bi1-xSbx)2Te4 thin flakes. The higher odd-order nonlinear Hall voltage exhibits a twofold angular dependence and exists only below the Néel temperature. It reaches its maximum near the charge neutral point and decays exponentially as the order of the nonlinear Hall effect increases. Furthermore, such higher odd-order nonlinear Hall effect is observed in both odd- and even-layer samples with comparable magnitudes. Theoretical analysis indicates that the higher odd-order nonlinear Hall effect responses may arise from the Berry curvature multipoles. Our work paves the way for the study of the higher-order nonlinear transport phenomena.

We investigate the effects of finite-range interatomic interactions on the equation of state (EoS) of a weakly interacting Bose gas. Within the Cornwall-Jackiw-Tomboulis effective action approach, we show that finite-range effects influence not only the EoS but also the thermodynamic properties of the system at zero temperature, leading to nonuniversal behavior.

Let $U$ be a Lucas sequence, $p$ be prime, and $ρ_U(p)$ be the rank of appearance of $p$ in $U$. We derive closed-form formulas for the Dirichlet density of primes $p$ for which $d\mid ρ_U(p)$, where $d\geq 1$ is a fixed integer. Our results complete the work of Sanna ($2022$) by covering all $U$ and all $d\geq 1$.

We investigate the Langevin dynamics for Wigner matrices with a spherical spike, in the regime where the signal-to-noise ratio $θ$ is large, but order one. For large, order-$1$, signal-to-noise, the (worst-case) mixing time undergoes a sharp transition around the critical inverse temperature $β_c(θ) = \frac{1}θ$. Namely, if $β= α/θ$, and $α<1$ then at large $θ$ the mixing time is $O(\log N)$, and if $α>1$ it is exponential in $N$. We show that initialized from the uniform-at-random spherical prior, however, the mixing time in the low-temperature $α>1$ regime circumvents the exponential bottleneck and the mixing time is $O(\log N)$. In fact, this fast mixing holds for any initialization that is symmetric with respect to the top eigenvector of the spiked matrix. Using this, we are able to show a low-temperature metastability picture, pinning down the exact exponential rate of the (worst-case initialization) mixing time for low temperatures, showing it is given by the difference of the free energies of the spiked and null models.

In this paper we study the existence and summability of the solutions to the following parabolic-elliptic system of partial differential equations with discontinuous coefficients: \begin{equation*} \begin{cases} u_t - \operatorname{div}(A(x, t) \nabla u) = -\operatorname{div}(u M(x) \nabla ψ) + f(x, t) & \text{in } Ω_T, \\ -\operatorname{div}(M(x) \nabla ψ) = |u|^θ& \text{in } Ω_T, \\ ψ(x, t) = 0 & \text{on } \partial Ω\times (0, T), \\ u(x, t) = 0 & \text{on } \partial Ω\times (0, T), \\ u(x, 0) = 0 & \text{in } Ω. \end{cases} \end{equation*} Here, $Ω$ is an open and bounded subset of $\mathbb R^N$, $N>2$, $θ\in(0,\frac{2}{N})$, $0<T<+\infty$ and $Ω_T=Ω\times(0,T)$. We prove existence results for data $f\in L^1(Ω_T)$ and a corresponding increase in summability that obeys the $L^p$-regularity theorems for parabolic equations proved by Aronson-Serrin and by Boccardo-Dall'Aglio-Gallouët-Orsina. In particular, despite the term $u M(x)\nablaψ$ not being regular enough (since it only belongs to $L^2(Ω_T)$), the solution $u$ belongs to $L^s(Ω_T)\cap L^q(0,T;W^{1, q}_0(Ω))$ for suitable $s>1$ and $q>1$.

We design and deploy in production the first flat datacenter networks. Our design, called RNG, is based on quasi-random graphs. While the cost and fault-tolerance benefits of such topologies have been long known, their practical realization has been hampered by a lack of scalable routing and cabling approaches. RNG has a new distributed routing protocol that exploits the properties of random graphs to find a large number of edge disjoint paths between pairs of endpoints. It uses a novel passive optical device that internally shuffles cables, which makes its cabling complexity similar to that of fat trees. We show that RNG matches or exceeds the performance of fat trees for a range of traffic patterns, despite being up to 45% cheaper. RNG is now the default datacenter network for most workloads at Amazon.

We investigated the structure of the optical F-corona, i.e., inner zodiacal light, using a publicly released wide-field image of a total solar eclipse that was obtained during the Artemis~II crewed lunar flyby. In this image, the solar disk is fully occulted by the Moon, providing a rare view of diffuse circumsolar emission over a wide angular extent. Although the dataset is derived from a rendered RGB JPEG image without full photometric calibration, the gamma correction inherent to the image format was explicitly accounted for and the instrumental response was validated using field stars. The stellar calibration demonstrates a linear response within the unsaturated regime relevant to our measurements, enabling a reliable analysis of the relative morphology and brightness profiles of the F-corona. The observed F-corona exhibits a flattened, nearly elliptical morphology aligned with the ecliptic plane, with flattening indices of 0.52, 0.54, and 0.56 for the red, green, and blue channels, respectively. Radial intensity profiles along ecliptic longitude and latitude are well described by power laws in solar elongation, although the derived slopes are systematically steeper than previous observations. Comparison with the ZodiSURF zodiacal light model indicates that the observed radial profile of the F-corona along the ecliptic longitude is modestly reproduced by the model, supporting a radial dust number-density power-law index of $α$ ~1.3, even in regions near the Sun. In a broader historical context, these results provide an empirical proof-of-concept that supports future solar coronal occultation observations from lunar orbit.

Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for 6G mobile systems. This integration enables the ML BLER prediction model to dynamically adapt to previously unseen channel conditions in real-time. Our extensive results show a substantial reduction in the average BLER prediction error of up to 48.8% with online fine-tuning. Furthermore, we leverage this BLER prediction algorithm for link adaptation and demonstrate average throughput improvements of up to 15.5% compared to a conventional table-based outer loop link adaptation (OLLA) algorithm.

We study jet schemes and arc spaces in the context of derived algebraic geometry. Explicitly, we consider the jet and arc functors in the category of schemes and study their animations to the category of derived schemes -- what we call the derived jet and arc spaces. We show that the derived constructions agree with the classical versions when the base scheme is smooth, or more generally for local complete intersection log canonical singularities, giving a derived interpretation to a theorem of Mustaţă. For more singular spaces we get new singularity invariants in the form of higher homotopy groups. We also study cotangent complexes for derived jet and arc spaces, generalizing previous formulas for sheaves of differentials of classical jet and arc spaces. Several applications are obtained. Specifically, we revisit recent results on the local structure of arc spaces from the lens of cotangent complexes, giving more unified proofs and removing unnecessary hypotheses. In particular, we extend a version of Reguera's curve selection lemma for arc spaces to the case of non-perfect base fields.

We propose a hardware efficient quantum residual neural network which implements residual connections through a deterministic mixture of the identity operation and variational unitaries, enabling fully differentiable training. In contrast to the previous implementation of residual connections, our architecture avoids post-selection while preserving residual learning. Furthermore, we highlight circuit constructions where barren plateaus could be mitigated, which are considered as a major limitation of variational quantum learning models. In order to show the working of our model, we report its application to image classification tasks by training it for MNIST, CIFAR, and SARFish datasets, achieving accuracies of 99\% and 80\% for binary and multi-class classifications, respectively. These accuracies are comparable to previously achieved from the standard variational models, however our model requires 10x fewer gates making it better suited for resource constraint near-term quantum processors. In addition to high accuracies, the proposed architecture also demonstrates adversarial robustness which is another desirable parameter for quantum machine learning models. Overall our architecture offers a new pathway for developing accurate, robust, trainable and hardware efficient quantum machine learning models.

We establish new Liouville-type theorems for the stationary Navier-Stokes equations in $\mathbb{R}^3$. A central open problem in this context is whether the classical $L^{9/2}(\mathbb{R}^3)$ condition of G.Galdi can be relaxed. In this note we show that this global integrability requirement can indeed be weakened. More precisely, we prove that triviality already follows under assumptions of the form $u \in L^{9/2 + \varepsilon(\cdot)}(\mathbb{R}^3)$, where $\varepsilon(\cdot)>0$. As a consequence, we obtain a localized Liouville theorem: it is sufficient to impose this integrability condition only at infinity, with no additional assumptions on the behavior of $u$ inside a compact set. This highlights that the mechanism enforcing triviality is purely asymptotic. Our approach relies on a general uniqueness result in the framework of Lebesgue spaces with variable exponents, which naturally captures the coexistence of different integrability regimes across the domain.

It has long remained open whether smoothing Newton methods (SNMs) for symmetric cone programming (SCP) admit polynomial iteration complexity. A key difficulty lies in the lack of an analogue of the self-concordant convex framework underlying interior-point methods (IPMs). In this paper, inspired by Nemirovski's self-concordant convex-concave theory, we address this open problem by introducing a reduced barrier augmented Lagrangian (BAL) function. We prove that the reduced BAL function is self-concordant convex-concave and establish that the parameterized smooth system arising in SNMs coincides with the first-order optimality conditions of an associated minimax problem. Motivated by this equivalence, we propose a path-following smoothing Newton method (PFSNM). The reduced BAL function induces a central path and an associated neighborhood, which provide estimates for the Newton decrement needed for the path-following analysis. As a result, the method achieves an iteration complexity of $\mathcal{O}(\sqrtν\ln(1/\varepsilon))$, matching the best-known short-step complexity for IPMs. Numerical results on standard benchmarks show that PFSNM is competitive with several well-known interior-point solvers, and the observed performance is consistent with the theoretical development.

We show that every Vaisman manifold with large first Betti number and vanishing first basic Chern class is diffeomorphic to a Kodaira-Thurston manifold. Furthermore, its complex structure is left-invariant, the characteristic foliation is regular, and the associated fibration is given by the Albanese map. Under the additional assumption that the LCK rank is $1$, the Vaisman structure is also left-invariant. We further prove that if all basic harmonic $1$-forms have constant length, then the Vaisman manifold with large first Betti number is diffeomorphic to a Kodaira-Thurston manifold and its complex structure is the standard complex structure. Finally, we discuss the relationship of this condition with transverse geometric formality in this setting.

The group-theoretic approach is used to construct exact solutions to perfect fluid equations invariant under the Schrodinger group, or the l-conformal Galilei group, or the Lifshitz group. In each respective case, the velocity vector field looks similar to the Bjorken flow. It is shown that one can reach an arbitrarily high density (and hence pressure) for a short period of time by adjusting the value of l and other free parameters available.

We develop Wasserstein-based hypothesis tests for empirical-measure convergence in stationary dependent sequences. For a known candidate invariant measure, $μ$, we study the statistic $T_n=\sqrt{n}\,W_1(\hatμ_n,μ)$ and establish asymptotic level-$α$ validity under the null, together with consistency under fixed alternatives. When the invariant measure is unknown, we derive the asymptotic law of the pairwise statistic $\sqrt{n}\,W_1(\hatμ_n^{(i)},\hatμ_n^{(j)})$ for independent trajectories and obtain a corresponding pairwise test, including Bonferroni control for multiple comparisons. To make this estimation feasible when the long-run covariance is unavailable in closed form, we introduce a finite-grid plug-in estimator and show that Gaussian critical values based on the estimated covariance consistently recover the corresponding oracle fixed-grid estimation. Simulation experiments in both linear and nonlinear dynamical settings illustrate the oracle and plug-in regimes, along with the resulting coverage probability and power.

We study quantitative large-time averages for Hamilton--Jacobi equations in a dynamic random environment that is stationary ergodic and has unit-range dependence in time. Our motivation comes from stochastic growth models related to the tensionless (inviscid) KPZ equation, which can be formulated as Hamilton--Jacobi equations with random forcing. Understanding the large-time behavior of solutions is closely connected to fundamental questions concerning fluctuations and scaling in such growth processes. In this article, we establish, up to slowly varying factors, convergence rates with exponent $1/2$ for the large-time averages of both the solutions and the associated metric problem toward their ergodic limits. Our proof relies crucially on a new almost-Lipschitz regularity theory for the metric problem, which is of independent interest.

We show that the quasinormal modes, in the eikonal limit, for accelerating (non-rotating) black holes, are related to the angular velocity of the circular null geodesics and to the corresponding Lyapunov exponent, in the same way as the ones for spherically symmetric black holes are. We compute those quasinormal modes and greybody factors for neutral and charged accelerating black holes, considering massless test scalar fields, and we show that the results are universal for perturbations of any spin. We also determine the radius of the shadow cast by these black holes. Our results for charged black holes are valid for the Reissner-Nordstrom solution simply by setting the acceleration to zero.

Environment-aware 6G wireless networks demand the deep integration of multimodal and wireless data. However, most existing datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field characteristics crucial for low-altitude extremely large-scale multiple-input multiple-output (XL-MIMO) systems. To bridge this gap, this letter introduces Multimodal-NF, a large-scale dataset and specialized generation framework. Operating in the upper midband, it synchronizes high-fidelity near-field channel state information (CSI) and precise wireless labels (e.g., Top-5 beam indices, LoS/NLoS) with comprehensive sensory modalities (RGB images, LiDAR point clouds, and GPS). Crucially, these multimodal priors provide spatial semantics that help reduce the near-field search space and thereby lower the overhead of wireless sensing and communication tasks. Finally, we validate the dataset through representative case studies, demonstrating its utility and effectiveness. The open-source generator and dataset are available at https://lmyxxn.github.io/6GXLMIMODatasets/.

The many-body perturbation theory within the $GW$ approximation is a widely used method for describing the electronic band structures in real materials. Its application to large-scale systems is, however, impeded by its high computational cost. The rate-limiting steps in a typical $GW$ implementation are the evaluation of the polarization function under the random phase approximation (RPA) and the evaluation of the $GW$ self-energy, both of which have a canonical $O(N^4)$ scaling with $N$ being the system size. The conventional space-time algorithm within the plane-wave basis sets reduces the scaling from $O(N^4)$ to $O(N^3)$, albeit with a large prefactor and increased memory cost. Here, we present a space-time algorithm within the numerical atomic orbital (NAO) basis-set framework, for which the evaluation of the polarization function and self-energy is formally reduced to $O(N^2)$ or better with respect to system size. This is achieved by computing these quantities in real space, where low-scaling algorithms can be formulated by leveraging the localized resolution of identity (LRI) technique. The resulting NAO-based, LRI-enhanced space-time $GW$ algorithm has been implemented in the LibRPA library interfaced with the FHI-aims code package. Benchmark calculations for crystalline solids show that the low-scaling implementation yields quasi-particle energies in close agreement with the conventional $O(N^4)$ k-space formalism previously implemented in FHI-aims. For the systems studied here, the observed overall scaling is substantially reduced relative to the canonical approach, and the low-scaling implementation becomes advantageous already for systems containing fewer than 100 atoms.

We investigate Hindman- and Owings-type Ramsey-theoretic statements in Zermelo-Fraenkel set theory without the Axiom of Choice, with some occasional extra assumptions (such as the Axiom of Dependent Choice and/or the Axiom of Determinacy). We study several variations of Hindman's theorem on $\mathbb Q$-vector spaces; notably, we show that the uncountable analog of Hindman's theorem fails for the additive group of $\mathbb R$ (under ZF), and for $\mathbb Q$-vector spaces of uncountable dimension (under DC if such dimension is not well-orderable), among other results. In contrast, for Owings-type configurations, we obtain several positive results, especially when assuming AD. These results highlight the interaction between determinacy, algebraic structure, and dimension in the study of infinite Ramsey theory without the Axiom of Choice.

Domain experts possess tacit knowledge that they cannot easily articulate through explicit specifications. When experts modify AI-generated artifacts by correcting terminology, restructuring arguments, and adjusting emphasis, these edits reveal domain understanding that remains latent in traditional prompt-based interactions. Current systems treat such modifications as endpoint corrections rather than as implicit specifications that could reshape subsequent reasoning. We propose context-mediated domain adaptation, a paradigm where user modifications to system-generated artifacts serve as implicit domain specification that reshapes LLM-powered multi-agent reasoning behavior. Through our system Seedentia, a web-based multi-agent framework for sense-making, we demonstrate bidirectional semantic links between generated artifacts and system reasoning. Our approach enables specification bootstrapping where vague initial prompts evolve into precise domain specifications through iterative human-AI collaboration, implicit knowledge transfer through reverse-engineered user edits, and in-context learning where agent behavior adapts based on observed correction patterns. We present results from an evaluation with domain experts who generated and modified research questions from academic papers. Our system extracted 46 domain knowledge entries from user modifications, demonstrating the feasibility of capturing implicit expertise through edit patterns, though the limited sample size constrains conclusions about systematic quality improvements.

Labels on platforms such as GitHub support triage and coordination, yet little is known about how well they align with code modifications or how such alignment affects collaboration across contributor experience levels. We present a case study of the Kubernetes project, introducing label-diff congruence - the alignment between pull request labels and modified files - and examining its prevalence, stability, behavioral validation, and relationship to collaboration outcomes across contributor tiers. We analyse 18,020 pull requests (2014--2025) with area labels and complete file diffs, validate alignment through analysis of over one million review comments and label corrections, and test associations with time-to-merge and discussion characteristics using quantile regression and negative binomial models stratified by contributor experience. Congruence is prevalent (46.6\% perfect alignment), stable over years, and routinely maintained (9.2\% of PRs corrected during review). It does not predict merge speed but shapes discussion: among core developers (81\% of the sample), higher congruence predicts quieter reviews (18\% fewer participants), whereas among one-time contributors it predicts more engagement (28\% more participants). Label-diff congruence influences how collaboration unfolds during review, supporting efficiency for experienced developers and visibility for newcomers. For projects with similar labeling conventions, monitoring alignment can help detect coordination friction and provide guidance when labels and code diverge.

Colloidal dispersions are widely found in systems ranging from natural environments to industrial materials. Their macroscopic properties such as viscosity and light scattering depend on their dispersibility, which is characterized by interparticle interactions. Osmotic pressure is induced in a solution with a concentration gradient, in which dispersity is one of the major factors governing the behavior of solutes. Thus, examining the relationship between the interparticle interactions and osmotic pressure may reveal colloidal dispersive properties. Although measuring the osmotic pressure is useful to understand dispersion systems, osmotic pressure is usually extremely low, and only limited experimental methods are available. In this study, we demonstrate that both osmotic pressure and interparticle interactions can be measured within the same experimental system, an optical tweezer system. The directly measured pressure is consistent with both the Brownian dynamics simulation and theoretical results based on the effective hard-sphere model, both of which were calculated using the interparticle interactions directly measured in the experiment. This agreement demonstrates the applicability of the proposed technique for investigating dispersive properties based on particle-level interactions. The proposed technique enables bottom-up design of colloidal materials through particle-level modifications.

We improve a result of Lau and Zhao on the variance of Fourier coefficients of primitive cuspidal modular forms for SL2(Z) in arithmetic progressions. This is achieved by using bounds on the first moment of Rankin-Selberg L-functions in the height aspect and non-trivial estimates for shifted convolution sums.

We derive general upper bounds to pointwise mutual information in terms of stochastic Fisher information and show these bounds average to known results in the literature for bounds to mutual information in terms of Fisher information. These results deepen the connection between information-theoretical quantities and are shown to hold in general cases. We test the bounds in classical systems and provide a quantum generalization. Our results are useful for stochastic dynamics and quantum sensing, establishing fundamental theoretical limits for information extraction in single experimental realizations.

Quantum materials can harbour hidden phases whose microscopic structures differ from conventional ordered states while reproducing their macroscopic signatures, making them easy to miss. Strontium titanate is a longstanding puzzle of this kind: on cooling it shows every hallmark of an incipient ferroelectric, yet never orders, and is usually described as a quantum paraelectric in which fluctuations suppress ferroelectricity. Here we combine uniaxial strain, single-cycle terahertz excitation and femtosecond x-ray scattering to measure the polar collective modes of strontium titanate as a function of momentum and strain. Under modest tensile strain, we observe a new vibrational mode that emerges not at the Brillouin zone centre, as a ferroelectric transition would require, but at finite wavevector, identifying the ordered state as a polar texture on nanometre length scales rather than a uniform ferroelectric. Unstrained quantum paraelectric strontium titanate is then naturally understood as the disordered precursor of this textured phase, offering a resolution to a decades-old puzzle and illustrating how finite-momentum collective excitations can unmask hidden phases in quantum materials.