Papers

This study investigates storm-to-storm variability and hemispheric differences in magnetosphere-ionosphere (MI) coupling during the extreme (G5) geomagnetic storm of May 10-11 and the severe (G4) storm of October 10-11, 2024. Global field-aligned current (FAC) patterns derived from the Active Magnetosphere and Planetary Dynamics Response Experiment (AMPERE), together with conjugate observations from ground-based magnetometers within the SuperMAG network and Global Positioning System (GPS)-derived total electron content (TEC), are analyzed to examine high-latitude electrodynamic and ionospheric responses in both hemispheres. The May event exhibits broad and relatively organized Region 1/Region 2 FAC systems encircling the polar caps across multiple local time sectors, accompanied by intervals of correspondence in conjugate magnetic perturbations and structured TEC enhancements with temporal offsets between hemispheres. In contrast, the October event shows more localized, asymmetric, and uneven FAC morphology with pronounced hemispheric and dawn-dusk asymmetries, together with greater divergence in conjugate magnetic responses and spatially heterogeneous TEC variability. These differences are consistent with enhanced mesoscale variability and asymmetric current closure under storm-time conditions. Overall, the results highlight that even under similarly strong solar wind driving, the coupled MI system can exhibit substantially different spatial organization and interhemispheric coupling, reflecting the combined influence of FAC morphology, ionospheric conductance, and local electrodynamic conditions.

The unitary group of a von Neumann algebra on an $\aleph_0$-dimensional Hilbert space is polynomially weak$^*$-dense (equivalently, $G_{delta}$ dense or residual) in the normal unit ball if and only if the canonical finite atomic quotient of the von Neumann algebra is trivial. The analogous result holds for the usual weak$^*$ topology, in which case one can drop the normality constraint. In the latter form, this answers a question of T. Eisner and generalizes the counterpart for full bounded-operator algebras.

This paper develops a droop-aware extension of the GridFM power systems foundation model, embedding droop gains and frequency/voltage deadband parameters as per-bus node features to enable control-aware AC power-flow analysis. Existing power-flow datasets encode only static electrical features, conflating operating points from qualitatively different control regimes; this work resolves that gap by exposing droop and deadband parameters as structured node features, with deadband discontinuities handled through a smooth tanh approximation that preserves solver differentiability. A transformer-based graph neural network is pre-trained on masked reconstruction and fine-tuned on the resulting control-aware datasets. The framework is validated against PSCAD electromagnetic-transient simulations on a two-bus system (0.11% maximum steady-state error) and cross-validated against an independent PyPower droop solver on the IEEE 24-bus RTS. On the 24-bus system the surrogate attains R2 = 0.9996 for active generation and 0.0015 p.u. voltage-magnitude RMSE; scalability is confirmed on the IEEE 300-bus system (0.0036 p.u. RMSE, R2 = 0.9841 for voltage magnitude across 299,700 predictions). A three-mode control study further shows that the deadband widens the control-error distribution while leaving total droop compensation unchanged, establishing deadband width as an actionable node-level design feature.

We study the relationship between the integrals of Yuan's concave transforms and the characteristic volumes of adelic line bundles on projective varieties. We establish an identity in dimension one and an inequality in higher dimensions. These results yield counterexamples to an identity conjectured by Yuan.

We investigate non-Hermitian quantum phase transitions driven by staggered imaginary Dzyaloshinskii–Moriya (DM) interactions in a transverse-field $XY$ chain. By virtue of a staggered nonunitary transformation, we exactly diagonalize the Hamiltonian. For $D<1$, this procedure maps the non-Hermitian model onto a standard Hermitian $XY$ chain, allowing analytical derivation of the full phase boundaries. The parameter line $D=1$ forms an exceptional boundary with coalesced quasiparticle eigenvalues and eigenvectors, while the entire $D>1$ regime falls into the $\mathcal{RT}$-symmetry-broken phase containing two distinct $z$-ferromagnetic phases. We examine common quantum-information probes and show that conventional measures, such as entanglement entropy, quantum discord, and quantum coherence, fail to capture the $\mathcal{RT}$ symmetry-breaking transition. To address this deficiency, we propose a novel coherence measure $\widetilde{\mathrm{QC}}_{\max}^{\rm LR}$ based on $\mathcal{RT}$ symmetry and complex-conjugate eigenpair correlations. Embedding intrinsic non-Hermitian information of left and right eigenstates, $\widetilde{\mathrm{QC}}_{\max}^{\rm LR}$ reliably identifies the $\mathcal{RT}$ symmetry-breaking transition and accurately resolves all magnetic phase boundaries.

The local–global principle, which concerns the relationship between local structure and global parameters, has attracted considerable attention in extremal combinatorics over the past few decades. In this paper, we study how global density forces small dense subhypergraphs in uniform hypergraphs. For fixed $r\ge3$ and $s>1$, let $t_r(n,d,s)$ be the smallest integer $t$ such that every $n$-vertex $r$-graph of average degree at least $d$ contains a nonempty subhypergraph on at most $t$ vertices with average degree at least $s$. We show that the behavior of $t_r(n,d,s)$ undergoes a phase transition at $s=mainr/(r-1)$. We determine $t_r(n,d,s)$ and obtain asymptotically sharp bounds in several parameter regimes. This answers, up to polylogarithmic factors, a question of Feige and Wagner that was later restated as Problem~3.3 by Janzer, Sudakov and Tomon. In particular, when $r=3$ and $s=2$, our result implies a conjecture of Feige.

In this paper, we investigate the dynamics and long-time stability of hierarchical configurations in an $N$-point vortex system on $\mathbb{R}^2$ and in simply connected domains, without the Cantor-set restriction inherent in KAM theory. Since the Hamiltonian perturbation lacks a standard power-series expansion in the usual normalized variables, we develop a modified Birkhoff normal form based on successive ratios of adjacent variables, tailored to the hierarchical structure. A key feature is the preservation of the resulting natural exponent structure throughout the normal form procedure. Consequently, for $n$ sufficiently large, the hierarchical configuration persists for times of order at least $\varepsilon^{-\left(n-2(N-1)\right)}$.

Industrial mobile feed systems rely on a retrieval-ranking pipeline to serve large-scale, heterogeneous, and fast-changing content under strict latency constraints. However, existing pipelines still suffer from two critical issues: hierarchical quantization instability in candidate retrieval and information loss between separated retrieval and ranking stages. These issues hurt long-tail and cold-start recommendation and complicate efficient serving. To address them, we present UNIQUE, a unified retrieval and ranking recommendation framework with single-layer flat quantization. UNIQUE integrates generative code-based retrieval and target-aware ranking into one early-fusion architecture, enabling end-to-end training under a shared representation while preserving efficient candidate generation. A balanced quantization mechanism is further introduced to mitigate codebook imbalance and improve long-tail representation. Offline experiments evaluate UNIQUE from both retrieval and ranking perspectives, while codebook analysis shows more balanced resource allocation than hierarchical quantization. We deploy UNIQUE in the homepage feed, discovery-page, and short-video recommendation scenarios of Mobile Baidu, serving large-scale real-world traffic. Online A/B tests achieve a 0.96% gain in total watch duration and a 1.08% gain in total distribution volume, with notable improvements for new users and highly active users. Serving measurements show 89 ms P99 latency and 44.23% online inference MFU. These results show that UNIQUE provides a stable, efficient, and production-ready framework for unified retrieval and ranking in industrial recommendation.

Global vision–language similarities compress an image and a caption into one vector, preserving semantics but not which word corresponds to which region or how those regions are arranged; a model can recognize every word and object yet prefer a compositionally incorrect caption. We argue that a frozen encoder retains this association structure, so the problem is to read it rather than to rebuild it beside the pretrained similarity. We introduce BindCLIP, a pairwise scorer built on one latent object: a balanced token–patch–depth optimal-transport coupling that places both candidate captions and several visual depths in a single plan. Semantic, entity, order, and spatial evidence are read as energies of this state, and exchanging the candidates permutes the plan, making the score exactly antisymmetric. A geometric refinement inside the coupling contracts moves that the candidates and the visual depths do not support. No task label, parser, relation inventory, or detector is used. One checkpoint and one inference path improve the official What'sUp, ARO, and SugarCrepe benchmarks over frozen global CLIP, with the strongest transfer on the relation splits. Controls rule out patch access and caption-length shortcuts, and an inference-time lesion localizes spatial arrangement to the coupling.

Textual-gradient methods automate prompt optimization through natural-language feedback, but their iterative updates can be unstable. We identify two sources of this instability: noisy gradients produced from already-correct examples and over-specialization to hard cases that degrades performance on simpler inputs. We introduce STEVE, a stabilization framework with two coupled mechanisms. Error-Driven Refinement generates gradients only from incorrectly handled examples, concentrating updates on informative failures. Regularized Verification treats every update as provisional and accepts it only when improvement on hard cases does not cause unacceptable regression on a preservation set. Across ten reasoning benchmarks, three evaluator/optimizer models, and established prompt-optimization baselines, STEVE reduces degradation and produces more robust prompts. Additional evaluations with gpt-5.4-mini/gpt-5.4 on symbolic reasoning, GSM8K-Platinum, and DS-1000 show that these gains persist with newer models and larger test sets. STEVE therefore provides a practical way to improve the stability and effectiveness of textual-gradient prompt optimization.

Multimodal large language models (MLLMs) incur substantial computational overhead due to the reliance on hundreds of visual tokens to represent images. While token pruning has emerged as a promising approach to reduce the inference cost of MLLMs, existing methods typically reassign position embeddings to the retained tokens using either sparse or continuous position embeddings, each introducing distinct limitations. Sparse position embeddings tend to decrease the attention value allocated to visual tokens, thereby degrading the perception capability of MLLMs, whereas continuous position embeddings disrupt the original spatial correspondence of visual tokens, leading to weakened grounding capability. To mitigate this issue, we perform layer-wise analysis of the language decoder and observe that intermediate layers play a critical role for maintaining the grounding capability of MLLMs under token pruning. Based on this observation, we propose a layer-aware position embedding strategy, which switches to sparse position embeddings at grounding-sensitive layers while maintaining continuous position embeddings elsewhere. Extensive experiments across representative pruning methods and diverse benchmarks demonstrate that our approach improves the comprehensive multimodal performance of pruned MLLMs compared with standard sparse and continuous position embeddings.

Infectious bovine pinkeye is a contagious ocular disease that adversely affects cattle health, welfare, and agricultural productivity. Conventional diagnosis relies primarily on clinical observation, which can be subjective, time-consuming, and difficult to implement efficiently in large herds or remote settings. This study evaluated and compared You Only Look Once (YOLO) v11 and YOLOv26 for automated bovine pinkeye classification and investigated the effects of class-balancing strategies on model performance. Five variants (n, s, m, l, and x) of each architecture were trained and evaluated using the original imbalanced dataset, Random Minority Oversampling (RMO), and an adapted Synthetic Minority Oversampling Technique (SMOTE). Both YOLOv11 and YOLOv26 demonstrated strong classification performance, although the effects of class balancing varied across model variants. For YOLOv11, RMO-s achieved an accuracy of 0.99, a macro F1-score of 0.98, and a true positive rate (TPR) of 1.00, with no false-negative classifications. RMO-m also achieved a TPR of 1.00 with no false negatives. For YOLOv26, the original l, RMO-m, and RMO-l variants each achieved an accuracy of 0.99 and a macro F1-score of 0.98, with RMO-l attaining a TPR of 1.00 and no false negatives. Overall, RMO generally provided greater improvements in minority-class detection than adapted SMOTE, whereas the strong performance of the original YOLOv26-l demonstrates that oversampling was not necessary for all model variants. These findings demonstrate the potential of YOLOv11 and YOLOv26 for automated detection of bovine pinkeye and support further evaluation for livestock health monitoring.

We study automorphic transcendence degree for quotients of multivariate skew polynomial rings over a division ring D. We identify this invariant with relative growth dimension, the degree of the relative Hilbert polynomial, and a coordinate dimension determined by leading monomials. We establish invariance under finite module extensions, integral extensions, and investigate the Gelfand-Kirillov dimension under a local finiteness condition. When the coefficient automorphisms are independent modulo inner automorphisms, we prove that a quotient is automorphically normalizable precisely when its automorphic transcendence degree equals the number of nonnilpotent coordinate variables, and classify all such normalization subrings.

Prime and Cohen-Macaulay ideals of 2-minors of matrices of variables with linear resolution are classified.

We give a fixed-parameter tractable (FPT) algorithm with running time $f(k,\bar\Delta)\cdot {|I|}^{O(1)}$ for integer linear programs with 4-block structure, parameterized by the maximum block dimension $k$ and the largest absolute entry $\bar\Delta$ in the local-column blocks. This result resolves a long-standing open question in parameterized complexity, and answers a conjecture by Eisenbrand and Rothvoss (2026) in the positive. This result has several implications for other block-structured integer programming models, including mixed fracture number and special cases of 4-block programs with large entries outside of the diagonal. Important tools for this algorithm are structural properties of generalized $n$-fold integer programs shown by Ligthart~(2026) and an algorithm by Veselov, Gribanov, Zolotykh and Chirkov~(2020) for optimizing discrete convic functions. Independently and shortly after the first version of our paper appeared, Koutecký, Lassota, and Ligthart (2026) obtained an FPT algorithm for 4-block integer programming.

The Sylvester simplex $\mathsf{Sylv}_d^k$ is a $d$-dimensional lattice simplex with exactly $k$ interior lattice points. Sylvester simplices are conjectured to be the volume maximizers among all $d$-dimensional lattice polytopes with exactly $k$ interior lattice points for any $k\geq 1$. Even stronger, it is conjectured that they maximize (entry-wise) the $h^\ast$-vector among all $d$-dimensional lattice polytopes with exactly $k$ interior lattice points. Yet, Sylvester simplices seem to be rarely studied in their own right. In particular, their Ehrhart-theoretic properties are far from being well understood. In the present article, we tackle this problem. We describe flag, regular and unimodular triangulations for the Sylvester simplices, and prove that their $h^\ast$-vectors are unimodal. Moreover, we explicitly determine the values of some entries of their $f^\ast$-vectors, and prove that they are Ehrhart magic positive up to dimension $6$ but not in dimension $7$. We conclude by detailing tables of Ehrhart-theoretic quantities (numbers of lattice points, Ehrhart polynomials, local and boundary $h^\ast$-vectors, $f^\ast$-vectors) for Sylvester simplices of dimensions 7 and lower.

We prove quantitative propagation of chaos in total variation for the unregularized repulsive Vlasov–Riesz–Fokker–Planck system on $\mathbb{T}^d$ and $\mathbb{R}^d$, $d\ge2$. For product initial data and potentials with singularity $|x|^{-s}$, $0<s<d/2$, we obtain an algebraic convergence rate for the $k$-particle marginals on a time interval independent of the particle number. The constants grow at most exponentially in $k$. The three-dimensional Coulomb case gives every rate $N^{-b}$ with $0<b<1/3$. The assumptions on its limiting solution hold for weighted Sobolev data with a Gaussian velocity moment and, on $\mathbb{R}^3$, a polynomial spatial moment. The proof combines conditioning of the initial product law, auxiliary particle equations, and weighted BBGKY estimates. An $L^1$ estimate and pointwise exponential energy bounds control the remainder flux in weighted $L^q$. For correlated initial data satisfying pointwise energy and marginal error bounds, the comparison extends to $0<s<d-1$.

We investigate the Higgs sector phenomenology of the minimal low scale quark-lepton unification theory. In addition to the Standard Model-like Higgs boson, the theory predicts a new CP-even neutral Higgs, a CP-odd neutral Higgs, and two charged Higgses, together with heavy right-handed neutrinos and leptoquarks. We analyze the scalar spectrum, the Yukawa structure responsible for charged-fermion masses, and the neutrino sector realized through an inverse-seesaw mechanism, emphasizing that the couplings of the new Higgs bosons are directly tied to the structure of matter unification and therefore lead to characteristic decay patterns dominated by third-generation fermions. We compute the branching ratios and production cross sections of the new Higgs states at the Large Hadron Collider (LHC) and assess their impact on present and future collider measurements. We find that current LHC data probe significant regions of parameter space for the neutral heavy Higgs bosons, with the strongest sensitivity arising from channels involving tau leptons, tops, and related final states, while the charged Higgs remains less constrained because of its electroweak production mechanism. Our results show that Higgs measurements provide a powerful and complementary probe of low-scale matter unification beyond direct leptoquark searches and open a new avenue for testing quark-lepton unification at present and future colliders, with discovery potential in high-luminosity LHC running.

Let $S_k(G)$ denote the sum of the $k$ largest eigenvalues of a graph $G$. Motivated by the classical Hoffman program for the spectral radius of a graph, we investigate an additive Hoffman-type problem for $S_k(G)$. For each fixed $k\geq 2$ and sufficiently large order $n$, we characterize all connected graphs satisfying $S_k(G)<2k$. As a consequence, we prove that the path $P_n$ is the unique minimizer of $S_k(G)$ among all connected graphs of order $n$. \vspace*{2mm} We further investigate the first Hoffman-type range \[ 2k\leq S_k(G)<2k+\sqrt{2+\sqrt5}-2. \] We completely characterize the non-tree graphs in this range and reduce the tree case to several explicit families. The proofs combine Ky Fan's variational principle, spectral estimates from vertex-disjoint subgraphs, structural results for graphs with small spectral radius, and long-path arguments for bounded-degree graphs.

In this paper, we present a data-driven artificial neural network (ANN) model for describing the double differential cross sections (DDCS) of neutron emission in nuclear spallation reactions. The ANN model is found to be precise, flexible, and efficient in predicting differential cross sections of nuclear reactions and in learning the complex dependence of neutron DDCS on the projectile energy ($T_p$), target nucleus ($A$ and $Z$), neutron energy ($T_n$), and neutron emission angle ($\theta_n$). The model is trained on replicas of experimental data that incorporate uncertainties. Several regularization schemes are examined during ANN training. The input variables of the constructed ANN framework are also investigated, and the following six key variables are selected for the input layer of the ANN model: $\theta_{n}$, ${\rm log}(T_n/T_p)$, $T_n/T_p$, ${\rm log}(T_p)$, $A^{2/3}$, and $N/Z$. The ANN predictions are compared with training data provided by various experimental collaborations, showing excellent agreement. The resulting model is further tested on test data with projectile energies, target nuclei, and neutron emission angles different from those in the training data, indicating strong predictive power and generalization capability of the ANN framework. As an illustration, the neutron DDCS as functions of $T_n$, $\theta_n$, and projectile energy $T_p$ are predicted and presented for copper target. The proposed high-precision ANN model is expected to be beneficial for accelerator-driven system (ADS) design and many other applications in nuclear physics, astrophysics, and nuclear technology development.

Accurate risk prediction is crucial for clinical-decision making, intervention planning, treatment and transplant allocation. However, longitudinal clinical data are often irregular and subject to censoring. We propose PAR2COX, a joint framework that integrates PARAFAC2 decomposition with Cox proportional hazards model, using patient-specific latent factors as covariates in the likelihood. The proposed alternating optimization framework jointly estimates phenotypes and survival parameters, enabling survival-guided representation learning. PAR2COX accommodates both historical patients with observed outcomes and current patients whose outcomes remain unknown. Numerical experiments and a case study based on MIMIC-IV data demonstrate improved risk stratification compared with existing approaches, highlighting the value of survival-informed phenotype learning.

We propose a novel method for constructing ghost-free multi-field higher-derivative scalar-tensor theories. The idea is to start from spatially covariant gravity and couple it to an additional scalar field. The resulting spatially covariant scalar field theory can be understood as a bi-scalar-tensor theory in the unitary gauge of one scalar. Within a broad class in which metric velocities enter through the extrinsic curvature and the additional scalar enters through temporal derivatives up to second order, we perform a Hamiltonian constraint analysis. We show that the theory generally propagates five physical degrees of freedom (DOFs), i.e., two tensor modes and three scalar modes. One of the scalar modes is the unwanted mode associated with the additional scalar. We then derive a degeneracy condition and an additional consistency condition. When both conditions hold, the unwanted mode is eliminated and the theory propagates four degrees of freedom. The resulting theories fall into two cases, depending on whether the preservation of the tertiary constraint closes without a new constraint or generates a quaternary constraint. We also study the two-field disformal transformation, derive its invertibility condition, and express it in the unitary gauge. Our construction provides a systematic approach to constructing bi- and multi-scalar-tensor theories with higher derivatives.

Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated deployment framework for evaluating whether those signals remain useful in financial decision systems. Computer science research has developed strong methods for time-series forecasting, text classification, multimodal stock prediction, graph-based market modeling, and machine-learning operations, yet these streams do not provide a domain-specific protocol that jointly tests financial language-model outputs under event-time observability, probability calibration, execution timing, transaction costs, liquidity constraints, capacity limits, operational diagnostics, and statistical inference. We introduce MFAST, a Market-Friction-Aware Sentiment-to-Trading framework that converts timestamped financial text into auditable, reproducible, and market-feasible trading decisions. The application is news-based trading, where firm-specific text must be linked to securities before portfolio decisions can be evaluated. The framework links Refinitiv News Analytics to Center for Research in Security Prices (CRSP) equity data, restricts the primary out-of-sample evaluation to post-release news outside disclosed foundation-model data-freshness periods, and adds a public replication arm using open financial text and public price data. Results show that decoder-only language models outperform encoder baselines and dictionary sentiment in classification, calibration, return prediction, and net portfolio performance, while operational diagnostics reveal trade-offs among accuracy, latency, memory, throughput, and inference cost. The paper shows that credible evaluation of financial language models requires an end-to-end engineering approach combining language understanding, temporal discipline, market-friction-aware deployment, and reproducible validation.

We prove that every action of a countably infinite discrete amenable group on a nonempty compact Hausdorff zero-dimensional space has dynamical comparison, without assuming minimality, freeness, or metrizability. More precisely, strict inequalities under all invariant probability measures imply comparison in the clopen type semigroup with an order-unit remainder. For minimal Cantor actions, the clopen type semigroup is cancellative and almost unperforated, and its canonical map onto the positive cone of the coinvariant group is an isomorphism of ordered monoids. These results answer Melleray's cancellation question and the amenable case of his comparison question. For minimal Cantor actions, we also prove goodness of the associated affine evaluation map and obtain a minimal homeomorphism of the same Cantor space with the same invariant probability measures. We identify the precise obstruction to recovering clopen equidecomposability from affine evaluation alone. This obstruction is given by the infinitesimal subgroup: for every nonempty proper clopen set $A$, it parametrizes the topological-full-group orbits of clopen sets having the same affine evaluation as $A$. Combining comparison with the theorems of Kerr and Szabó, we prove that every free action of a countably infinite discrete amenable group on a nonempty compact metrizable space with the topological small boundary property is almost finite. This settles Naryshkin's almost-finiteness conjecture for finite-dimensional compact metrizable spaces. If the action is also minimal, its reduced crossed product is $\mathcal Z$-stable, has nuclear dimension at most one, and satisfies all the regularity conditions in the Toms–Winter conjecture.

Probabilistic forecasting requires accurate central predictions and calibrated uncertainty estimates. This paper presents TEMPER, the Temporal Encoder-Masked Probabilistic Ensemble Regressor, a univariate time-series forecasting algorithm that combines a temporal autoencoder, a differentiable masked neural decision forest, continuous ranked probability score (CRPS) training, and Gaussian-mixture post-processing. The R implementation is built on torch for R and returns horizon-wise density, distribution, quantile, and sampler functions. We evaluate TEMPER on three deterministic synthetic level series with trend, periodic, regime-switching, nonlinear-threshold, and heteroskedastic components. Across 96 rolling-origin forecasts at horizons t + 1, t + 5, t + 20, and t + 60, TEMPER obtains 2.824% mean CRPS normalized by origin level, 3.635% median absolute error, and 68.8% empirical 90% interval coverage after training with a 300-epoch cap and early-stopping patience of 100. A naive persistence bootstrap has the best aggregate CRPS, 2.763%, while TEMPER has the best median absolute error and the best CRPS at t+1 and t+5. The ablation study uses matched series-origin-horizon cells, horizon-wise CRPS deltas, endpoint sensitivity summaries, and a calibration-specific interval study. Relaxing the learned mask improves average CRPS by 0.472 percentage points on the ablation subset, mainly through long-horizon gains. A twofold interval inflation improves held-out coverage from 54.2% to 91.7% and gives the best 90% interval score among tested calibration rules. The results identify calibration, horizon-specific tuning, and component selection as the central research priorities.

Large language model agents are now privileged principals that take consequential actions: editing code repositories, operating inboxes, completing purchases. Their authority is kernel-grade, but it comes without what classical systems security requires: a trusted mediator interposed on every access. Operating-system vendors are now rebuilding the platform around this de-facto agent kernel, inheriting complete mediation as a design problem. We systematize the security of such systems around a single distinction: a crossing mediated over provenance admits a deterministic check, while one over content semantics does not. A trust-boundary taxonomy locates where mediation must occur and isolates the central mediation gap at two kinds of semantic judgment: distinguishing data from instruction in untrusted input, and an authorized action from an unauthorized one. We argue that this gap leaves an irreducible residual of undetected attacks wherever inputs and actions are not restricted in advance to an enumerated set. The same distinction makes attack-success statistics actionable, placing each number on a spectrum from deployment debt (a sound deterministic mediator left unused) to a structural gap (no such mediator known). We systematize defenses across runtime monitoring, architectural separation, and authorization, and show that current evaluations tend to overstate deployed security through evaluation-validity failures. Finally, we carry that analysis forward beyond the de-facto kernel, to an architecture in which the model itself becomes the arbitration core, and derive the design constraints, open challenges, and research agenda for a security-first AI-native OS.

We report the magnetic, magnetotransport, and electronic properties of the kagome ferromag net MgMn6Sn6 using magnetization, angle dependent magnetoresistance, x-ray magnetic circular dichroism (XMCD), angle resolved photoemission spectroscopy (ARPES), and first-principles cal culations. MgMn6Sn6 exhibits ferromagnetic ordering near TC=295 K with pronounced easy plane magnetic anisotropy. At low temperatures and low magnetic fields, the magnetoresistance (MR) is strongly anisotropic with respect to the magnetic field orientation, evolving from a predominantly negative MR for in-plane fields to a more quadratic behavior for out-of-plane fields. Angle depen dent measurements further reveal a pronounced twofold MR anisotropy with additional higher order contributions. The finite orbital-to-spin moment ratio revealed by XMCD suggests a significant role of spin-orbit coupling (SOC) in MgMn6Sn6. The first-principles calculations show a Dirac-like band crossing at the K point and a van Hove singularity (VHS) at the M point, as expected for kagome materials. ARPES measurements resolve a sixfold symmetric Fermi surface and its systematic evo lution with binding energy, in overall agreement with first-principles calculations. The measured band dispersions are also broadly consistent with the calculated multiband electronic structure. These results establish the connection between magnetic anisotropy, anisotropic magnetotransport, and the kagome derived electronic structure of MgMn6Sn6.

This paper investigates a non-orthogonal multiple-access (NOMA)-assisted multi-user wireless-fed pinching-antenna system (Wi-PASS). A multi-antenna base station (BS) simultaneously serves one direct user and wirelessly feeds a full-duplex amplify-and-forward relay equipped with a directional horn receiver. The relay injects the NOMA waveform into a dielectric waveguide, and one position-adjustable pinching antenna serves two additional users. Under maximum-gain zero-forcing transmission, ideal successive interference cancellation, and an additive residual self-interference model, we minimize the total consumed power by jointly optimizing the BS powers, relay amplification factor, NOMA power coefficients, decoding order, and pinching-antenna position. For a fixed position and decoding order, a variable transformation reduces the resource-allocation problem to a strictly convex scalar problem and yields a closed-form global solution. The position-dependent decoding order partitions the waveguide into finitely many intervals, and the derivative of the optimized power is governed by a quadratic polynomial on each interval. Hence, the globally optimal position is found by evaluating a small finite candidate set. Simulations at 28~GHz over 1000 random user topologies show that the proposed architecture consistently requires the lowest consumed power among direct-transmission, array-fed, no-PASS, and equal-time orthogonal multiple-access (OMA) benchmarks. At target signal-to-interference-plus-noise ratios of 20, 25, and 30~dB, it reduces the average power by 19.2%, 21.1%, and 21.7%, respectively, relative to equal-time OMA, while preserving its advantage as the BS-relay distance and residual SI increase.

Multi-teacher on-policy distillation allows a student to learn from complementary specialists on its own trajectories. Domain-routed approaches, however, select one teacher per example and keep it fixed throughout the response. This design both depends on domain labels that mixed training corpora often lack and cannot adapt teacher selection when the expertise required changes within a trajectory. We observe that each specialist deviates more from a shared reference on in-domain prompts than on out-of-domain prompts, on average. Based on this observation, we propose TrustMOPD, which replaces example-level teacher selection with label-free, token-level supervision allocation. At each student-generated prefix, TrustMOPD measures this displacement in next-token preferences, calibrates its magnitude across teachers, and uses the resulting scores as proxies for local reliability to weight teacher-specific distillation losses. Evaluated across mathematics, code, and instruction following, TrustMOPD closes 91.5% and 98.0% of the overall-score gap between the initial student and oracle-routed teachers when trained on SingleCap and MultiCap, respectively, compared with 54.4% and 54.5% for the strongest label-free baseline in each setting. On SingleCap, it approaches label-based MOPD without using domain labels. Our code is available at https://github.com/sunjie279/TrustMOPD-.

Let \(\{X_n:n\ge0\}\) be a controlled branching process with controls \(\phi_n\), and let \(N_n=\phi_n(X_n)\) be its progenitor count. We study large deviations of \(X_{n+1}/N_n\), \(N_n/X_n\), and \(X_{n+1}/X_n\), on their natural positive-denominator events, in supercritical and critical regimes. We treat generation-wide random slopes and asymptotically deterministic first-order slopes. For asymptotically affine random-slope controls, an affine recursion and a random environmental product yield a sharp Bahadur–Rao–Petrov upper-tail asymptotic. A positive Cramér tilt identifies the prefactor via a nontrivial normalized limit, and conditional on the upper-deviation event the three ratios concentrate around their environmental centers. With individual-sum controls, the process is an exact branching process in a random environment with immigration (BPREI). Motivated by this connection, we establish sharp upper large-deviation asymptotics for BPREI and transfer its positive-transform and harmonic-moment profiles to the controlled process. In the nondecreasing supercritical case, harmonic moments exhibit an exact environmental–boundary–persistence trichotomy. In a centered finite-variance critical class, finite-horizon-minimum methods yield exact \(n^{-1/2}\)-scale limits for all positive harmonic orders and for the three ratio-deviation probabilities. For deterministic first-order slopes, the environmental-product mechanism disappears. Matched lower and upper probability-generating-function envelopes transfer auxiliary exact-recursion profiles to controls without an exact branching recursion, producing distinct supercritical and critical harmonic-moment trichotomies. Thus the location of first-order randomness determines the lower-tail mechanism, while immigration may change a sharp constant, a phase boundary, or the decay exponent.