Papers

Short-range correlations (SRCs) dominate the nuclear force at short distances, including the repulsive core and intermediate-range attraction, and are one of the most fascinating aspects of the nucleon-nucleon (NN) interaction. In this work, we investigate the effects of SRCs on the final-state observables in $^{16}$O+$^{16}$O collisions at $\sqrt{s_{NN}}=5.36$~TeV, using the iEBE-VISHNU hybrid model with {T\raisebox{-0.5ex}{R}ENTo} initial conditions. We embed the nucleon configurations of $^{16}$O generated from variational Monte Carlo (VMC) simulations with realistic SRCs in the initial stage, along with uncorrelated VMC samples and Woods–Saxon distributions implementing a hard repulsive core or short-range attraction as comparison runs. We find that SRCs reduce both $v_2\{2\}$ and $v_2\{4\}$ in the most central collisions, with the decrease of $v_2\{4\}$ being more pronounced due to the event-by-event flow fluctuations. Meanwhile, SRCs noticeably enhance the correlation between the elliptic flow and the mean transverse momentum, $\rho_2(v_2^2,[p_T])$. These effects are mainly attributed to the strong repulsive core of the SRCs, which suppresses the probability of finding nucleon pairs at short relative distances and renders the initial geometry more uniform. This interpretation is supported by the calculations based on Woods–Saxon distributions with a hard repulsive core, which reproduce similar results, while the contribution from a pure intermediate-range attraction is negligible. Our results demonstrate that relativistic nuclear collisions provide a complementary probe of the short-range structure of the nuclear force, beyond the traditional nuclear experiments at low energies.

Highly entangled and highly non-stabilizer quantum states need not be hard to learn. We give efficient algorithms for testing and recovering hidden tensor-product structure in unknown pure state vectors of the form $\lvert\psi\rangle = U_C \bigotimes_i \lvert\psi_i\rangle$, where $U_C$ is an arbitrary unknown Clifford unitary. Although the Clifford can thoroughly scramble the visible product structure, we prove that the Bell distribution retains a characteristic family of quadratic symmetries. By simultaneously block-diagonalising these symmetries, our algorithms linearize the problem and manage to recover both a disentangling Clifford and the hidden partitions with polynomial sample and computational complexity. This may be viewed as an extension of the abelian StateHSP paradigm in which classical post-processing exposes genuinely quadratic structure. Applied to Choi states, the method yields efficient proper learning algorithms for typical $t$-doped Clifford unitaries in regimes containing exponentially more $T$ gates than previously accessible: the required condition fails only for an exponentially small fraction of circuits when $t\sim n$, and continues to hold for a constant fraction even when $t=2n$. Our framework also provides tools for compressing structured many-body Hamiltonians and suggests benchmarking protocols for encoded logical product states in the early fault-tolerant regime.

The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customized neurons, as task-based neurons capture useful prior knowledge from task-related data. To facilitate the use of task-based neurons in scientific research and industrial applications, we introduce TNLearn, an open-source Python package that provides automated construction of task-based neurons and networks, enabling smooth training of task-based networks. Comprehensive documentation, including technical exposition, API reference, and representative examples, is available online. TNLearn is open-sourced at https://github.com/NewT123-WM/tnlearn and has become a PyTorch ecosystem project.

In this paper, we investigate the growth, distortion, pre-Schwarzian and Schwarzian norms of functions in the exponentially convex class \(\mathcal C_{e^\lambda}\), \(0<\lambda\le\pi/2\), defined by \(1+zf''(z)/f'(z)\prec e^{\lambda z}\). By representing the associated Schwarz function explicitly, we derive parameter-dependent estimates for \(f\), \(f'\), and the pre-Schwarzian derivative. We further obtain Schwarzian norm estimates under both the general normalization and the additional condition \(f''(0)=0\). The corresponding extremal problems are analyzed through suitable Schwarz functions, and the dependence of the resulting bounds on the exponential parameter is made explicit.

Cryovolcanism is a fundamental geodynamical process that has been observed on several icy moons. Europa stands out as a primary target for investigating potential cryovolcanic activity and its role in shaping icy moon surfaces. This work characterizes the physical state of ascending cryomagma and evaluates the relevance of terrestrial analogues to explain Europa's surface features. After investigating cryomagma depressurization, we developed CryoPy, a numerical model based on Smoothed-Particle Hydrodynamics (SPH) for computational fluid dynamics and heat transfer. We simulated flow propagation and surface cooling using this model. Our simulations show that snow is a relevant physical analogue for cryolava, as the resulting flows closely match the smooth plains observed by Galileo in both extent and morphology. Additionally, we established the temporal thermal signatures of these events. Such active cryovolcanic events are within the detection limits of, primarily, the E-THEMIS infrared imager, providing a predictive framework for the Europa Clipper mission.

This reply addresses a recent comment concerning the proof of the Nernst theorem. I clarify how a Carnot engine can consistently operate at $T=0$ through a continuous deformation of a cycle operating at $T>0$. By examining the limit where heat exchange with the cold reservoir vanishes, I show that the Nernst theorem ensures that the concept of temperature remains physically consistent at the absolute zero limit.

Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a proxy for communicative success. We test this assumption through a controlled behavioral study of rendered 3D avatars across a pristine condition and fourteen geometric, photometric, temporal, and combined distortions. Fifty-nine participants contributed 2,688 judgments of perceived action, response confidence, and visual quality. We identify Misleading Quality in this dataset as distorted renderings that retain above-average perceived quality but yield below-average action-recognition accuracy. We also derive an Intent Quality Score (IQS) combining recognition correctness and confidence as the behavioral target for objective metrics. Among 126 distorted content–condition cells, 31 (24.6%) exhibited Misleading Quality; temporal and geometric distortions showed the highest rates, at 50.0% and 31.1%, respectively. The results reveal a quality–accuracy dissociation where distortion families affect appearance and communication differently. Across 24 direct-scoring IQA/VQA metrics and three supervised feature-regression baselines, alignment with IQS remained limited; at $\lambda=0.5$, the best leave-one-content-out baseline reached PLCC $=0.4435$. Under this controlled protocol, visual fidelity alone is insufficient for avatar communication, motivating intent-aware quality assessment and streaming objectives.

We investigate stellar oscillations driven by the time-dependent gravitational potential of ultralight dark matter (ULDM) in the de Broglie regime. The response is controlled by the ratio of the ULDM coherence time to the stellar mode damping time. When the field decoheres faster than the damping time, ULDM acts as a stochastic source that excites modes below a characteristic de Broglie frequency, while higher-frequency modes are suppressed. In the opposite, long-coherence regime, individual modes can be resonantly driven. Focusing on solar oscillations, we find a signal far below current sensitivity, and estimate that other stars are similarly unlikely to provide observable signals.

Studying syntactic patterns in naturally occurring language requires a large parsed corpus, but manual annotation is costly and difficult to scale. Thai has a manually annotated dependency treebank for training and evaluating parsers, but lacks a large automatically parsed corpus for quantitative syntactic research. We present ThaiTrees, a 342M-token corpus drawn from news, Wikipedia, spoken transcripts, and social media. We develop a reproducible pipeline for cleaning, processing, and parsing Thai text under the Universal Dependencies framework. The resulting corpus makes grammatical relations searchable and supports the study of syntactic distributions. We release a frequency lexicon and CoNLL-U parses in machine-readable formats suitable for both AI-assisted and conventional programmatic analysis.

The thermal continuum of Cygnus X-1 in the soft state is often modelled with a multicolour disc in a Newtonian spacetime approximation. That choice is convenient for estimating the inner radius, but it neglects relativistic effects near the black hole; these are usually restored through colour-correction and boundary-condition factors. Using Suzaku data from one exemplary observation, we compare this Newtonian description with some fully relativistic disc models, keeping the same treatment of absorption, ionised winds, Comptonization and reflection. With the mass, distance and inclination fixed to the best current dynamical values, the Newtonian inner radius is consistent with a high-spin Kerr innermost stable circular orbit. The relativistic models do not provide a uniformly better description of the spectrum: some worsen the fit or leave spin, inclination and hardening unconstrained, while the formally best model requires an inclination much larger than the dynamical one or a smaller spin. Apparent tensions with the models arise from data degeneracies and from the limited energy band, not from a failure of general relativity.

In this paper, we prove that every rationally connected projective manifold admits a smooth uniformly RC-positive Hermitian metric on its tangent bundle, answering Yang's question and giving a characterization of rational connectedness by uniform RC-positivity. We find an example to show that RC-positivity alone does not characterize rational connectedness. We also prove that uniform RC-positivity of the tangent bundle is preserved under blow-ups along connected smooth centers on compact complex manifolds. In the non-Kähler setting, we construct such metrics on all Hopf and Kato surfaces and obtain classification results for compact complex surfaces.

We investigate the semi-discrete Active Flux numerical method on Cartesian grids applied to two-dimensional linear and nonlinear shallow water equations with Coriolis source terms. These hyperbolic systems admit non-trivial stationary solutions governed by a geostrophic equilibrium. In this setting, we analyze the stationarity-preserving properties of our semi-discrete Active Flux formulation. To do so for the linear system, we apply a discrete spatial Fourier transform to the semi-discrete method and analyze the resulting evolution matrix. We show that its discrete kernel is a non-trivial discretization of the geostrophic equilibrium, which implies that the method preserves a discrete geostrophic equilibrium state. This is an inherent feature of Active Flux and is achieved without any additional modifications. Numerical experiments confirm the method's ability to maintain geostrophic stationary states for the linear system. Additionally, we numerically investigate the performance of the Active Flux method for the nonlinear shallow water system.

Multimodal learning is emerging as a powerful paradigm for AI for Physics (AI4Physics), where predicting physical systems requires the joint interpretation of heterogeneous observations, measurements, and domain knowledge. However, existing approaches typically represent physical quantities and governing equations as generic numerical or textual tokens, overlooking the physical constraints that determine their spatiotemporal interactions. To address this limitation, we introduce the physical-field modality and propose PhyMo, a physics-grounded multimodal framework that organizes heterogeneous measurements through PDE-associated operators. PhyMo follows a three-stage learning procedure: the physical-field encoder is first pretrained through field reconstruction under PDE residual supervision, its representations are subsequently aligned with visual embeddings in a shared latent space, and the fused multimodal representations are finally processed by corresponding downstream prediction heads. Experiments on five datasets spanning diverse physical environments show that PhyMo achieves state-of-the-art performance, compared to the strongest baseline on each dataset, demonstrating the superiority of PhyMo on multimodal representation learning in AI4Physics.

Magnetic navigation has emerged as a promising alternative for navigation in Global Positioning System (GPS)-denied environments, leveraging geomagnetic field maps in conjunction with onboard magnetometer measurements. However, its performance is highly sensitive to trajectory-dependent observability, which limits its practical effectiveness under conventional flight paths. This paper proposes an optimal trajectory design framework for magnetic navigation that maximizes information content along the flight path. The trajectory generation problem is formulated as an optimal control problem that minimizes the posterior Cramér–Rao lower bound on the position estimation error, subject to a penalty on path length. The resulting trajectories are non-intuitive and significantly enhance the observability of the navigation system. Simulation results demonstrate that the proposed optimal trajectories yield substantial reductions in estimation error compared to conventional straight-line trajectories, highlighting the critical role of trajectory design in enabling high-accuracy magnetic navigation. These findings suggest that trajectory optimization can substantially improve the viability of magnetic navigation as a robust alternative for aerospace applications in GPS-denied environments.

This paper proposes a robust high-dimensional minimum variance distortionless response (MVDR) beamforming method for array observations corrupted by heavy-tailed noise. The proposed approach constructs an MVDR-oriented precision matrix estimator by combining Maronna's robust scatter estimator with spiked covariance modeling. Using tools from random matrix theory, we derive a deterministic equivalent of the MVDR output power and obtain asymptotically optimal shrinkage weights for the dominant signal subspace. A fully sample-based implementation is then developed for practical beamformer design. Numerical simulations under elliptically distributed noise demonstrate that the proposed beamformer achieves stronger interference suppression and higher output SINR than competing methods in high-dimensional and implusive noise regimes.

The increasing adoption of software defined networking and network function virtualization, combined with rapid advances in Machine Learning (ML), is driving the evolution toward intelligent network softwarization across cloud, edge, and distributed environments. However, deploying complex learning models in such heterogeneous environments introduces challenges in latency, computation, and energy consumption. Knowledge Distillation (KD) has emerged as a promising approach to enable lightweight and efficient intelligence by transferring knowledge from high-capacity teacher models to compact student models. Despite its extensive study in general ML domains, the integration of KD into intelligent softwarized networks remains fragmented and underexplored. In this paper, we review and classify recent efforts that incorporate KD within softwarized networks, analyze current trends and limitations, and outline open challenges and future directions toward scalable, adaptive, and energy-aware distillation mechanisms.

We give a unified treatment of definite Albert algebras over $\mathbb{Z}$, with exceptional Siegel–Weil identities as the main arithmetic tool. Starting from the standard octonion order alone, its first scalar theta coefficient saturates the genus average and forces every remaining class to be rootless. A cubic-ring coefficient of the $G_2$ theta lift then gives the mass of the remaining scale-$2$ framed orders, also recovered from shell geometry. A uniform Niemeier–Golay stabilizer bound saturates this mass, proving that there is exactly one further class and determining both automorphism orders, frame transitivity, and the $2457$ frames on $819$ rank-one elements. The associated generalized hexagon identifies the nonstandard group as ${}^3D_4(2):3$ only after its order has been proved. An independent weighted-theta argument gives the trace-zero root-system alternative $\varnothing$ or $A_2E_8^3$. The construction relates positive masses of framed objects to exceptional Eisenstein coefficients.

This article is the second of three articles whose goal is to generalize the Cartier transform of Ogus and Vologodsky to the logarithmic setting. We generalize a topos-theoretic version of this transform, due to Oyama. Let $k$ be a perfect field of positive characteristic $p$ and equip $S=\operatorname{Spec}k$ with the trivial log structure. For a log smooth morphism of logarithmic schemes $X \rightarrow S,$ we construct crystalline-like ringed topoi $\mathcal{E}'$ and $\underline{\mathcal{E}}$ and subcategories of crystals of quasi-coherent modules $\mathcal{C}'$ and $\underline{\mathcal{C}},$ equivalent respectively, under some lifting assumption, to modules with Higgs fields and integrable connections, both satisfying certain nilpotence conditions, and a morphism of topoi $\underline{\mathcal{E}} \rightarrow \mathcal{E}'.$ We then prove that the pullback functor of this morphism of topoi preserves quasi-coherent crystals and hence induces a functor $\mathcal{C}' \rightarrow \underline{\mathcal{C}},$ generalizing the Cartier transform. We finally use a log flat descent theorem for morphisms, that we proved in the first article, to prove that this functor is fully faithful.

We propose a time-stepping minimum-residual (MinRes) framework for transient coercive variational problems, applicable to finite-element and neural-network trial approximations. For the Backward Euler scheme, we measure the residual at each time level in the dual norm induced by the steady test-space norm, scaled by the time step. This choice yields stability estimates controlling a discrete parabolic energy quantity, up to the time-discretization defect. We develop conforming and broken-test formulations and specialize the analysis to diffusion-advection-reaction problems. For finite-dimensional test spaces, we derive fully discrete reliability estimates by supplementing the computable discrete residual with a complementary contribution that accounts for residual components the test space does not resolve. We then extend the framework to neural-network trial classes and obtain computable residual decompositions for conforming and broken polynomial test spaces. Numerical experiments confirm the expected finite-element convergence rates, examine the effect of test-space refinement for a global space-time neural approximation, and demonstrate residual-driven spatial refinement for a transient problem with a moving localized feature.

Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These stages show heterogeneous CPU and GPU demands, making efficient resource utilization difficult. Recent systems overlap rollout (simulation and generation) with training for efficiency, but exclusive GPU allocation and synchronized barrier in rollout still leave substantial hardware resource waste. In this paper, we present EBRL, an asynchronous embodied RL training system with two core techniques. The asynchronous pipelined scheduler overlaps rollout and training, pipelines simulation and generation across environment groups, and carries out each environment independently, eliminating synchronization stalls. The fine-grained resource manager pools CPU cores and GPU streaming multiprocessors, and uses stage profiles and runtime feedback to adjust resource quotas and batch sizes to meet the shifting demands among stages. We implement EBRL on RLinf and evaluate it with four embodied policies and four simulation benchmarks across heterogeneous GPU testbeds. Experiments show that EBRL achieves 1.30-3.47 times the end-to-end rollout throughput and 2.5 times of training convergency compared to the SOTA embodied RL systems.

Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely focus on the no-shift setting. In this work, we study robust score estimation under Wasserstein perturbations of a reference distribution. For the Ornstein–Uhlenbeck diffusion, we show that robust estimation decomposes into two fundamental components: the statistical cost of learning the reference distribution and the intrinsic cost of distribution shift. The latter scales quadratically with the Wasserstein radius, and this dependence is minimax optimal. We construct an explicit finite-sample estimator achieving the resulting robust minimax rate without knowing the shift radius. When the reference distribution lies on an unknown low-dimensional subspace, the statistical term adapts to the intrinsic dimension while the shift cost remains unchanged. Finally, we show that the same decomposition governs positive-time reverse sampling and obtain matching minimax guarantees in KL divergence. Together, these results characterize how finite data, intrinsic dimension, and distribution shift affect the robustness of score-based diffusion models.

Wavelength-division multiplexing (WDM) is pivotal for expanding the capacity and functionality of photonic systems, from communications to computing. However, on-chip implementation for broadband operation is fundamentally limited by the narrow free spectral range (FSR) and inefficient drop transmission of conventional optical microcavities. Here, we transcend this limit with a scalable integrated WDM processing platform based on cascaded dual-sided coupled Fabry-Pérot add-drop resonators. This unit achieves a record-large FSR-free bandwidth (> 300 nm) and low insertion loss (< 0.5 dB), while enabling independent manipulation of monochromatic light in both the wavelength and intensity domains. Leveraging these performances, we architect programmable cores with up to 20 channels in a single bus waveguide to demonstrate application versatility by configuring as: a high-capacity on-chip WDM communication fabric (2.4-Tbps single-link WDM transmission and O-to-C-band multi-subband (de)triplexing), a reconfigurable optical processor (broadband and hitless wavelength-selective switching), and a parallel computing accelerator (theoretically capable of 1.28-TOPS convolution operations, 16-channel modulation). This work establishes a unified and versatile WDM processing platform that bridges high-speed optical communication with in-line computing, charting a scalable and backward-compatible path toward petabit-per-second-class links and hundreds of TOPS of on-chip computation, resolving a critical bottleneck for next-generation photonic systems.

This paper presents a representation-aware framework for geometry-based screening of hull-form producibility at early design stages. The proposed signature combines dimensionless total and signed developability deviation with curvature-class area fractions, distributed fields, metric-specific validity, and representation provenance. These descriptors characterize surface features relevant to plate forming and developability, but are not calibrated predictors of fabrication cost, forming effort, or process feasibility. Native IGES/STEP boundary representations (BReps) are evaluated through direct differential geometry and trimmed-domain quadrature, whereas triangulated surfaces use discrete curvature recovery and area-weighted aggregation. Analytical and semi-analytical controls verify the formulation, while matched-face BRep-to-mesh tests assess discrete curvature recovery. Application to DTMB 5415, KCS, JBC, and KVLCC2M shows that curvature intensity and areal extent provide complementary information and that derivative-based outcomes can be representation sensitive. KCS, for example, exhibits approximately 24% greater developability deviation than DTMB 5415, while double-curved regions occupy 72.9% of its valid surface versus nearly the entire DTMB valid surface. The resulting quantities provide an early geometric screening layer for subsequent use as objectives, constraints, surrogate responses, or design-space features. HullProd, the companion open-source software, implements the signature, distributed fields, validity, and provenance.

I summarise the expanding class of white dwarf pulsars for which the prototype, AR Scorpii, was only discovered in 2016. At the time of this meeting, two further systems had been found (eRASSU J191213.9-441044 and SDSS J230641.47+244055.8). These are thought to be detached (or nearly detached) white-dwarf–red-dwarf binaries, where the white dwarf's magnetic field interacts with the M-star's corona, resulting in the capture and acceleration of electrons to relativistic velocities in the magnetosphere. This gives rise to non-thermal synchrotron emission across the electromagnetic spectrum. The first discovered, and brightest, system, AR Sco (which is the focus of this paper), shows a white dwarf spin-down consistent with magnetic dipole emission in a strongly magnetic white dwarf. The polarisation properties of the two systems for which polarimetry has been obtained (AR Sco and eRASSU J191213.9-441044) show spin-modulated polarisation, primarily linear, which remarkably reaches 40% in AR Sco. Furthermore, the Stokes Q and U parameters vary in the same manner as the optical polarisation of the Crab pulsar, consistent with a rotating magnetic dipole. These systems are intriguing in that each one seems to show different behaviours (e.g. pulse profiles and light-curve properties), presumably mainly due to different viewing aspect angles. It has recently been postulated that these systems represent a short-lived phase of binary evolution linking asynchronously rotating intermediate polars and synchronous polars.

The safety of a tool-using language model agent is usually treated as a property of the model alone. We give controlled, full-precision evidence that it is instead a joint property of the model and the software that renders its chat template and parses its tool calls, the decoding harness, and that both halves are attackable from untrusted input. On the released gpt-oss-20b reasoning model under its published tool sandbox, appending a single string of the model's own channel-control tokens to a user message makes the tokenizer render a reasoning turn that is already complete, so the model writes no chain-of-thought and proceeds directly to the tool call. Across forty tasks the model already completes, the reasoning channel falls from a mean of 52.5 tokens to zero on every trial while the http.post still fires on every trial. A rule monitor and a cross-family language-model monitor detect the unsafe request on all plain trials and no forged trials, and on overtly malicious requests the attack converts 39.6% of the model's refusals into completed exfiltrations. Separately, whether an identical tool-call generation fires is decided by the harness parser, not the model: a truncation-tolerant regular expression fires a call whose closing token is missing while a strict one drops it, and two parsers shipped for the Gemma agent give opposite outcomes on identical greedy generations, firing on all twenty-four trials and on none. We show the suppression can be delivered indirectly and characterize its dependence on the chat template across two more reasoning models, and we evaluate input sanitization, parser hardening, and empty-reasoning detection as defenses; flagging an absent trace catches the basic attack but not an adaptive benign decoy. All measurements use greedy decoding on publicly released models. Code and per-trial logs: https://github.com/Usama1002/deleting-the-trace

A geometry-based framework for sampling electromagnetic fields over surfaces is presented. The approach extends the shadow-area formulation of electromagnetic degrees of freedom (DoF) from a global mode count to a spatially resolved DoF density that determines the sampling distribution. The observation surface is partitioned into cells containing approximately one DoF, with the cell area determined by the DoF density and the cell shape determined by the projection of the DoF density vector onto the observation surface. In the far field, the source shadow area defines a directional DoF density over the observation sphere, while in the near field, the mutual-shadow density and a DoF density direction determine the local sampling geometry. The resulting method requires only the source-observation geometry and does not require an explicit singular-value decomposition of the propagation operator. Numerical results for several source and observation geometries demonstrate reconstruction performance close to that of operator-based sampling methods. A beamforming interpretation further shows that the sampling cells correspond to geometry-dependent beam regions, establishing a unified connection between DoF, sampling, and beamforming.

Earth matter effects are a leading systematic in long-baseline (LBL) determinations of the CP-violating phase delta13, and eV scale sterile neutrinos remain a phenomenologically open extension of the three-flavor paradigm motivated by short-baseline anomalies. We extend the constant density versus realistic Earth profile analysis of Ref. 1 to a four-flavor (3+1) framework, in which a fourth, mostly sterile mass eigenstate with deltam2 41 = 0.30 eV2 and active-sterile mixing angles theta14 = 8.13degree, theta24 = 5.40degree couples to the active sector both through its mixing and through an additional neutral current (NC) matter potential that acts on the active flavors but not on the sterile state. We derive an analytical, leading-order account of how the NC potential ANC enters the reconstructed delta13 bias through an effective non standard interaction like term generated by adiabatically integrating out the fast deltam241 oscillation; replace the four-shell Earth density model with the continuous, polynomial PREM profile of Dziewonski & Anderson (1981) for the layered true trajectory; and extend the baseline scan to four representative values of the sterile CP phase delta14 is 0, pi/2, pi, 3pi/2 and to a non-zero mixing angle theta34 = 5degree. Within this benchmark, the bias remains below our scan resolution for L <= 5000 km. It grows once the trajectory samples the lower mantle and core, with the sterile sector redistributing rather than uniformly amplifying or suppressing the bias across baseline, in agreement with our original finding and now traced to a specific interference mechanism identified analytically below.

Dynamical black hole scalarization describes the process by which a “no-hair" black hole transitions to a scalarized hairy state. This transition significantly alters the spacetime geometry, including the structure of stable circular orbits of the test particles, which are critical for astrophysical observations and tests of gravity. In this work, we employ numerical relativity simulations to model the time-dependent evolution of a vacuum black hole undergoing scalarization. By evolving timelike geodesic equations within the dynamically changing spacetime background, we investigate how the scalarization process impacts initially stable circular orbits. Our results reveal that the growth of the scalar hair destroys the circular nature of these orbits, forcing them to either become eccentric or plunge into the black hole. This orbital destabilization arises from the rapid change of spacetime curvature induced by the scalar field. Our findings provide novel insights into the interplay between scalar fields and black hole dynamics, with significant implications for gravitational-wave signatures, accretion disk stability, and observational tests of extended theories of gravity.

A major video platform runs on thousands of microservices, each request propagating a context so downstream work can be traced and governed. At handoffs outside instrumented paths, e.g., custom queues and callbacks, the payload continues but the context does not, and the request still succeeds under existing tests. Such breaks are silent and widespread: 673 of 1,133 services carried at least one. Backstitch, a specialized agentic system, repairs them using the surviving execution as its reference: replay determines whether a suspicious call is request-correlated, source analysis reaches the responsible handoff, a bounded change restores its contract, and the same replay validates the fix. Repairs restore the causal chain without disturbing the work it describes: breaks at 240 of the repaired calls fell from 90.46% to 4.69%, and over 112 days the fleet's break rate more than halved.

Universal information extracted from a critical quantum state depends on how the state is probed. Spatial entanglement, participation statistics, and stabilizer Rényi entropy correspond to different replica geometries and need not isolate the same boundary data. We show that the complete distribution of Pauli magnitudes in critical Ising chains carries an exact geometric fingerprint of boundary termination, distinguishing lattice realizations that flow to the same infrared boundary fixed point. This structure is organized by discrete Selberg ensembles associated with the classical root systems $A$, $B$, $C$, and $D$: three open chains sharing the free–free Ising boundary condition retain distinct $B_L$, $C_L$, and $D_L$ trigonometric wall geometries. The standard symmetry-breaking representatives instead produce rectangular, pinned, and character-inserted $C$ ensembles. Their four boundary moments contain exactly two independent multiplicative combinations invariant under local endpoint normalizations. One of these reduces exactly at finite rank to a moment of the fundamental symplectic character and provides a lattice target for a defect–measurement-boundary junction amplitude. A conditional Gaussian-character law predicts its values throughout the unlocked phase, while exact-arithmetic finite-rank results at the $\alpha=4$ transition depart sharply from the Gaussian continuation. These results show that the full Pauli spectrum resolves boundary information invisible to the infrared boundary fixed point alone and provide concrete targets for replicated boundary conformal field theory.