Intrinsic channel noise is fundamental to neural processing, yet its state-dependent nature, when constrained by strict Feller boundary conditions, is often overlooked. Here, we demonstrate that this bounded multiplicative noise is not merely a source of jitter but an active dynamical force that fundamentally reshapes neural excitability. Investigating a 5D Hodgkin-Huxley-type cortical pacemaker model, we utilize a full-truncation semi-implicit Euler scheme to ensure rigorous probability conservation and domain-preserving integration. Through comprehensive parameter sweeps, we uncover a rich triphasic landscape of noise-induced transitions dictated by the underlying bifurcation structure. Deep in the subthreshold regime, multiplicative noise acts as a constructive force, triggering stochastic awakening via Kramers escape. Near the subcritical Hopf bifurcation, this evolves into highly robust coherence resonance (CR). Crucially, in the supra-threshold oscillatory regime, our framework reveals a striking dynamical shift: a generalized, noise-accelerated Kramers escape. Under extreme multiplicative noise - characteristic of sparse channel populations - strictly bounded fluctuations actively amplify escape rates from the hyperpolarized slow manifold, transforming regular pacing into high-frequency, irregular bursting. Conductance perturbation experiments confirm the profound biological robustness of this transition. These findings establish a physically rigorous mechanism for how boundary-constrained noise drives high-dimensional oscillators toward states of pathological hyperexcitability.

Specialized accelerators dominate AI workloads, but CPUs remain critical for orchestrating these accelerators and running datacenter services. As a result, CPU performance increasingly shapes end-to-end system efficiency, making it necessary for benchmarks to reflect modern workloads and bottlenecks. However, it remains unclear how emerging CPU benchmark suites reflect these shifts. To address this, we present the first comprehensive characterization of SPEC CPU2026 across nine platforms spanning recent Intel, AMD, Ampere, and Nvidia processors. We find that, compared to SPEC CPU2017, SPEC CPU2026 increases instruction volume and memory footprint, and shifts pressure toward emerging bottlenecks, most notably higher instruction-cache stress. We next examine whether the full suite is necessary for architectural evaluation. Using clustering-based representativeness analysis, we identify that compact subsets of 4-5 workloads per group preserve 96.4-99.9% of full-suite behavior, substantially reducing evaluation costs without sacrificing fidelity. To better position SPEC CPU2026, we compare it against SPEC CPU2017, DCPerf, and MLPerf using cross-suite microarchitectural metrics. SPEC CPU2026 remains a general-purpose suite with complementary characteristics: it is less vector-intensive than MLPerf and has lower frontend pressure than DCPerf, yet moves closer to real-world CPU behavior than prior SPEC CPU generations. Finally, we show that SPEC CPU2026 supports practical architectural studies beyond aggregate scores through case studies on page sizes and allocators, prefetching, compiler optimizations, ISA sensitivity, and many-core scaling. The new round-robin stagger mode generates proxy workloads that approximate DCPerf, reducing the IPC gap to 13.7%. Overall, SPEC CPU2026 sets a new foundation for rigorous and cost-effective CPU evaluation.

Compositional data, which are vectors of proportions constrained to the probability simplex, arise frequently in modern scientific applications, including microbiome relative abundances across body sites and cell-type mixture weights derived from single-cell genomics. While regression methods for compositional data are well developed, no existing graphical model framework addresses the problem of learning conditional dependence structures among multiple compositional vectors. This paper introduces a novel framework for directed tree structure learning over compositional nodes. We employ the Kullback-Leibler divergence as the scoring function and model the conditional expectation of each child composition as a mixture of a baseline composition and a parent-driven component parameterized by a column-stochastic transition matrix. This formulation respects the simplex geometry, handles zero-inflated compositions gracefully, and, combined with a non-degeneracy condition on the transition matrix, ensures identifiability of edge directions from observational data. We prove consistency of structure recovery and derive finite-sample guarantees that characterize the required sample size in terms of the signal gap, node dimension, and penalty level. The efficacy of our approach is demonstrated through simulations and applications to multi-site microbiome data and single-cell data, yielding interpretable directed structures that align with known biological mechanisms.

Two-dimensional (2D) van-der-Waals materials are a promising platform for exciton state engineering. In this paper, we study the properties of excitons in 2D group VI transition-metal dichalcogenide (TMD) semiconductors that are modified by a periodic electrostatic potential through the quadratic Stark effect. Using a model that retains only center-of-mass and valley degrees-of-freedom, we find that electrostatic potentials can drive optical valley splitting up to 10meVs and induce valley selective exciton dispersion. We explain why both properties are sensitive to the rotational symmetry of the electrostatic trapping potential using a combination of numerical results and analytical approximations. An important consequence of valley-splitting is that the lowest exciton band is non-degenerate and has a linear dispersion around $γ$ that is expected to suppress thermal excitations, allowing true Bose condensation and superfluidity of excitons in two space dimensions.

By confining a Bose-Einstein condensate in a vertical lattice subjected to a gravitational potential, we analyze the quantum Fisher information to determine its scaling with respect to time, system size and particle number. Our results reveal that in the localized phase, on-site interactions $U$ amplify the quantum Fisher information by a factor with respect to resonance condition $U=mh$ where $U$ is factor of gradient field amplitude $h$. This precision enhancement can be employed in gravitational acceleration measurements with a finite number of particles trapped in optical lattices.

Reporting systems in multiplayer video games allow players to express their dissatisfaction with others and combat in-game toxicity. In this work, we examined the act of reporting through the lens of expectancy-value theory. Using a distributed survey (n = 98) and follow-up interviews (n = 19), we explored the value players place on reporting, their desired outcomes, and their expectations that these outcomes will be achieved. Our findings revealed that reporting is motivated by both altruistic and retributive factors, with players seeking short-term revenge while also looking to foster an improved long-term community. Yet, players felt that reporting may not always meet these goals, with belief in the system being mediated by factors such as developer reputation, reporting transparency, and alignment with the community. By understanding the value and expectancy of reporting systems, we discuss their implications on broader digital moderation and consider current and potential future designs of reporting systems.

Open-weight large language models (LLMs) are usually named as model artifacts, but production users often consume them as hosted API services. This paper argues that the operational unit is a service object: a provider-specific, time-varying endpoint defined by model variant, protocol behavior, context capacity, listed price, latency and throughput distribution, reliability, and task feasibility. Using sampled request logs, provider metadata, compatibility probes, pricing snapshots, and continuous latency measurements collected by AI Ping during Q4 2025, we study how this service layer changes the meaning of "the same model." Three empirical patterns emerge. First, observed demand is concentrated but persistent across versions: in the displayed family aggregate, the largest family carries 32.0% of relative demand and the top five carry 87.4%, with a Gini coefficient of 0.693, while older variants remain active after newer releases. Second, supply and use separate: provider listing breadth does not imply realized adoption, and listed prices are more anchored than latency, throughput, context length, protocol support, and error semantics. Third, task mix matters: applications induce different token-length regimes, so provider choice is a constrained decision over provider-model-task-time tuples rather than a lookup by model name. In two representative counterfactuals under observed feasibility constraints, routing lowers Qwen3-32B cost by 37.8% and raises DeepSeek-V3.2 average throughput by about 90% relative to direct official access. The results support a measurement view of hosted open-weight LLMs as heterogeneous services, not static catalog entries. We open-source the measurement methodology and reproduction artifacts at https://github.com/haoruilee/llm_api_measurement_study to support result reproduction.

This paper studies how the possibility of strategic misreporting shapes endogenous communication networks. Agents observe noisy private signals about a common state, form costly communication links, exchange private messages with their neighbors, and then choose actions. Payoffs reward both accuracy and coordination with linked agents. A link is valuable because it gives access to information, but it is useful only if the induced local information structure makes truthful transmission incentive compatible. We show that clique components support truthful communication: within a clique, all members observe the same profile of local signals, choose the same posterior action, and therefore have no incentive to distort reports. With heterogeneous signal precisions and convex linking costs, the core selects assortative information clubs ordered by signal precision. These stable truthful networks need not be socially efficient. Because the informational value of precision is decreasing, concentrating high-precision agents in the same club may be privately stable but socially dominated by more mixed partitions.

The increasing prevalence of software vulnerabilities highlights the need for effective Automatic Vulnerability Repair (AVR) tools. While LLM-based approaches are promising, they struggle to incorporate structured security knowledge from sources like CWE and NVD. Current methods either use this information superficially by concatenating the CWE-ID into the input prompt, yielding negligible benefits, or rely on few-shot learning with rigid, non-generalizable examples, which limits their effectiveness in real-world scenarios. To address this gap, we propose VulKey, an LLM-based AVR framework that leverages a hierarchical abstraction of expert knowledge to guide patch generation. Our novel three-level abstraction formulates repair strategies in terms of CWE type, syntactic actions, and semantic key elements. This approach captures the essence of a security fix with greater generality than concrete examples and more semantic richness than traditional syntax-based templates, overcoming the coverage limitations of prior methods. VulKey is implemented as a two-stage pipeline: first, expert knowledge matching predicts an appropriate repair pattern for the vulnerability; second, repair code generation uses a pattern-guided, fine-tuned LLM to produce secure patches. On the real-world C/C++ dataset PrimeVul, VulKey achieves 31.5% repair accuracy, surpassing the best baseline by 7.6% and outperforming leading tools such as VulMaster and GPT-5. Moreover, VulKey demonstrates cross-language and cross-model generalizability, with state-of-the-art performance on the Java benchmark Vul4J. These results underscore the importance of structured expert knowledge in advancing AVR effectiveness. Our work demonstrates that explicitly modeling and integrating expert security knowledge through hierarchical patterns is a crucial step toward building more effective and reliable AVR tools.

Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP) brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic quantification. Extending this approach to 3D transcranial ULM imaging in NHPs, we show efficient correction of skull induced aberrations and removal of artifacts, such as vessel duplications. By providing a fully automated and generalizable solution for aberration correction, this work lowers a major technical barrier to transcranial ultrasound imaging, enabling broader adoption of non-invasive, super-resolution and functional neuroimaging across laboratories and across species.

Red-teaming Vision-Language Models is essential for identifying vulnerabilities where adversarial image-text inputs trigger toxic outputs. Existing approaches treat image generation as a black box, returning only terminal toxicity scores and leaving open the question of when and how toxic semantics emerge during multi-step synthesis. We introduce STARE, a hierarchical reinforcement learning framework that treats the denoising trajectory itself as the attack surface, under a direct white-box T2I and query-only black-box VLM setting. By coupling a high-level prompt editor with low-level T2I fine-tuning via Group Relative Policy Optimization (GRPO), STARE attains a 68% improvement in Attack Success Rate over state-of-the-art black-box and white-box baselines. More importantly, this trajectory-level view surfaces the Optimization-Induced Phase Alignment phenomenon: vanilla models exhibit diffuse toxicity, whereas adversarial optimization concentrates conceptual harms into early semantic phases and detail-oriented harms into late refinement. Targeted perturbations of either window selectively suppress different toxicity categories, indicating that this temporal structure is a genuine causal handle rather than a side effect of the hierarchical design. The phenomenon turns toxicity formation from a chaotic process into a small set of predictable vulnerability windows, providing both a potent attack engine and a basis for phase-aware safety mechanisms. Content warning: This paper contains examples of toxic content that may be offensive or disturbing.

This paper provides the spectral decomposition of $(\star,ε)$-palindromic quadratic matrix polynomial $P(λ)$ by a standard pair and a parameter matrix. When $J$ is assumed to be a block diagonal matrix, the parameter matrix $Γ$ has a special structure. And then the spectral decomposition is applied to solve the inverse eigenvalue problem and the eigenvalue embedding problem with no spill-over.

We develop a novel strategy for accessing the transversity parton distribution function (PDF) of the nucleon within collinear factorization using near-side energy-energy correlators in the dihadron fragmentation framework. We show how this removes the complications of previous approaches that must model either intrinsic parton transverse momentum or resonances in the invariant mass distribution of a final-state dihadron. We present leading-order analytical results for transverse-spin observables in semi-inclusive deep-inelastic scattering and electron-positron annihilation, highlighting their close similarity to the expressions one uses in extracting (un)polarized PDFs and (single-hadron) fragmentation functions in collinear factorization. We make predictions for kinematics relevant for existing and future facilities that demonstrate the feasibility of an energy-energy correlator program in extracting the transversity PDF.

Continuous-variable quantum key distribution (CVQKD) using passive state preparation (PSP) offers low-cost, high-rate secure communication. However, the existing PSP-CVQKD scheme with a transmitted local oscillator has high photon leakage noise and poor stability, making it unsuitable for high-loss transmission. In this work, for the first time, we propose and implement a local local oscillator (LLO) CVQKD system using a self-referenced (SR) PSP scheme, and give a theoretical proof of the equivalence of the PSP and GMCS protocol using temporal-mode theory. By employing the novel self-referenced pilot scheme to achieve high-precision time-varying frequency and phase compensation algorithms, we significantly improve the system' s signal-to-noise ratio and stability. The system achieves a record-high asymptotic secret key rate of 10.34 Mbps over a free-space channel with up to 23.5 dB loss, while maintaining low excess noise and robust performance under turbulent conditions. This work establishes the feasibility of SR-LLO CVQKD, providing a practical pathway toward secure, high-rate quantum communication in realistic environments.

We study coordination under restricted information, where classical local models fail to implement certain correlated distributions because agents cannot condition on past history. We show that quantum systems overcome this limitation even when using only separable states. Both classically diagonal encodings (shared latent variables) and separable states with noncommuting local structure (quantum discord) enable the implementation of joint distributions that are unattainable by any classical local rules under the same information constraints. The quantum advantage arises from enabling latent-variable coordination without requiring agents to condition on the latent variable itself -- a construction that succeeds where no classical local model can. Separable states with nonzero quantum discord provide an alternative mechanism for realizing such coordination. At the same time, quantum models remain strictly limited by the information structure: unlike perfect recall, they cannot reproduce fully adaptive dependence on realized past outcomes that are observationally indistinguishable. Thus, quantum correlations serve as a partial substitute for perfect recall.

Category-based coordination mechanisms allocate resources by mapping a declared service category to a fixed resource profile, without observing individual demand types. We establish three results for this class of mechanisms. First, the relative welfare gap Delta satisfies a tight two-sided bound in terms of the aggregate within-category allocation variance epsilon: (alpha/2W*)epsilon <= Delta <= (beta/2W*)epsilon. Second, the expected misreporting gain is bounded by the same epsilon without assumptions on agent strategy; demand-derived categories minimise both welfare loss and misreporting incentive simultaneously. Third, aggregate outcome metrics strictly dominate per-agent metrics for service-level detection under a homogeneity condition, for all parameter values, with a finite-sample power gap of O(1/m). At any fixed K, the demand-derived category label is the sufficient statistic for coordination: collecting per-agent data beyond the category label adds noise to the detection problem without reducing the welfare gap. However, welfare and detection impose structurally opposed demands on K: welfare improves with finer categories, detection worsens. The designer faces a feasibility band [Kmin, Kmax] and must choose K within it as a value judgement. We claim that any protocol achieving welfare gap Delta <= epsilon* and missed-detection rate <= beta* requires at least Hlb(epsilon*, beta*) bits of category entropy. We illustrate the mechanism on a synthetic population of 50,000 demand vectors and five weeks of production performance-management data from four anonymised operator networks (28,249 cells).

We conjecture that any scalar-flat Kähler surface in which the Weyl tensor acting on 2-forms annihilates the Ricci form must be either Ricci-flat or locally isometric to a Riemannian product of two real surfaces with mutually opposite nonzero constant Gaussian curvatures. This amounts to the nonexistence of proper weakly Einstein anti-self-dual Kähler surfaces. We prove the above conjecture in three special cases: when the manifold is compact, when one of the Ricci eigendistributions is integrable, and when the norms of the Ricci and Weyl tensors are functionally dependent

The gas-kinetic scheme (GKS) provides high computational efficiency and accuracy for continuum flow simulations but is unable to reliably capture rarefaction effects. In contrast, although the discrete velocity method (DVM) is better suited for rarefied flows, it exhibits reduced accuracy and slow convergence when applied to continuum regimes. To overcome these limitations, this work proposes a hybrid GKS-DVM method that integrates the strengths of both approaches. The hybrid approach balances the equilibrium distribution function in GKS with the upwind-reconstructed non-equilibrium distribution function in DVM through a numerical collision time. This balancing strategy ensures to recover Navier-Stokes solutions in the continuum limit (asymptotic preserving), while naturally capturing free molecular flows in the rarefied limit. Moreover, the introduction of a numerical collision time significantly enhances robustness in shock capturing for continuum flow applications. To further reduce computational cost of the hybrid approach, several adaptive strategies based on the local Knudsen number and Mach number have been proposed. The effectiveness and accuracy of the proposed hybrid method are systematically assessed through four representative test cases: a flat-plate boundary layer, a lid-driven cavity flow, shock structures, and flow past a semi-cylinder. The first case is subjected to continuum conditions, while the latter two span a broad range of Knudsen numbers. The results demonstrate that the proposed method achieves high solution accuracy and computational efficiency across both continuum and rarefied flow regimes.

In 1978, A. C. Newell [SIAM J. Appl. Math. 35(4) (1978) 650-664] proposed an exactly solvable model called Newell equation, which simulates the investigation of significant interaction mechanism between long and short waves. Nearly fifty years have passed, yet the long-time asymptotics of the Newell equation remains an open problem to date, with no results reported. In this work, the long-time asymptotic behaviors of the solutions to this model under Schwartz class initial conditions are studied by using the Riemann-Hilbert formulation. Through direct and inverse scattering analysis, the corresponding Riemann-Hilbert problem is formulated, and its relationship with the solution to the initial-value problem of the Newell equation is established. The existence and uniqueness of the solution to the Riemann-Hilbert problem is proved by vanishing lemma. Subsequently, the asymptotic expressions of the solution to the initial-value problem in the dispersive wave region are obtained by using the Deift-Zhou nonlinear steepest descent method. This work extends Newell's original results, providing a rigorous proof for the findings presented in Section 4 of his paper, along with explicit expressions. Furthermore, the comparison between direct numerical simulations and the theoretical results obtained in this paper demonstrates the reliability of the asymptotic expressions.

This article contains the first steps in a general analysis of the problem of Krylov solvability of the inverse linear problem in a Banach space. In contrast to the well-studied Hilbert space setting, the Banach space setting presents particular difficulties in creating the connection between Krylov solvability and structural properties of the Krylov subspace itself. At the centre of this is the fact that the closed Krylov subspace may not always have a topological complement. We also develop spectral tools in order to attack the problem using the resolvent operator and exploiting its holomorphic properties on the resolvent set.

We investigate the femtoscopic correlation functions of systems associated with the $Ω(2012)$, $Ω(2380)$, and $Ω_c(3120)$ resonances, with the aim of elucidating their internal structures. By employing effective potential models that incorporate both $s$-wave and $d$-wave interactions, we calculate the correlation functions for the relevant coupled channels. Our numerical results reveal pronounced enhancement structures in the $Ξ^0K^-$ and $Ξ_c^+K^-$ correlation functions, which provide direct evidences for the dynamically generated $Ω(2012)$ and $Ω_c(3120)$ states. Furthermore, significant low-momentum enhancements are observed in the $Ξ^{*0}K^-$ and $Ξ^{*0}K^{*-}$ channel, which are attributed to the $Ω(2012)$ and $Ω(2380)$ resonances. These theoretical calculations provide crucial insights for future high-precision measurements at the LHC and RHIC, offering a novel and independent approach to determine the dynamically generated hadronic molecular nature of these $Ω$ excited states.

We propose the covariate-balanced-and-adjusted response-adaptive randomization (CBARA) procedure for adaptive design in clinical trials, which integrates the complementary strengths of covariate-adjusted response-adaptive randomization (CARA) and covariate-adaptive randomization (CAR). The CBARA procedure updates the target allocation ratio according to observed responses and patient covariate profiles without requiring a correctly specified model, thereby retaining CARA's ethical and efficiency considerations while improving robustness. In addition, the CBARA procedure extends the CAR principle from fixed target allocation ratios to covariate-adjusted adaptive target allocation ratios, yet still pursues balance in treatment allocation with respect to covariate features. This integration is enabled by a newly defined imbalance vector and three interrelated components: the allocation function, parameter estimation and update mechanism. We establish the asymptotic properties of covariate imbalance and the estimators under the CBARA procedure. The results demonstrate that the CBARA procedure can improve balance for both observed and unobserved covariates while preserving the consistency of the allocation ratio. The theoretical analysis is developed through a pseudo-Markov chain framework, where a new discrepancy measure for transition kernels is introduced to handle the continuity of Poisson equation solutions with respect to parameters.

We investigate the non-equilibrium topology of a periodically driven, dissipative Su-Schrieffer-Heeger chain using the ensemble geometric phase (EGP) $ϕ_{\mathrm{EGP}}$-a generalisation of the Zak phase to open quantum systems. In contrast to earlier work, we use Floquet-Born-Markov theory to describe the coupling to thermal reservoirs microscopically. We show that the steady state can be characterised by a Hermitian purity spectrum, providing a direct analogue of band topology for mixed states. The periodic drive induces nontrivial winding and a quasienergy spectrum with distinct $0$ and $π$ band gaps, with protected edge modes in each gap. We identify a pair of topological invariants $(ϕ^{0}_{\mathrm{EGP}}, Δϕ^π_{\mathrm{EGP}})$, revealing a structure consistent with a $\mathbb{Z}\times\mathbb{Z}$ classification known from isolated Floquet SSH systems, and show how it extends to a dissipative, finite-temperature setting in regimes where the steady-state structure remains well defined. Our results demonstrate when and how known Floquet topology survives in a driven-dissipative Gaussian steady state and establish Floquet topology as a robust concept beyond isolated zero-temperature systems. The underlying formalism provides a general framework for quadratic fermionic systems with linear bath couplings.

The circumgalactic medium (CGM) is known to contain multiphase gas in various stages of evolution and interaction with the galaxy. In order to characterize its detailed behavior on short timescales, we use a subregion of the TNG100 cosmological simulation to study the evolution of the $z=1$ CGM around six galaxies in $10^{11.5}-10^{12}$ $M_{\odot}$ halos at a high time cadence of $\approx2$ Myr. We use Monte Carlo tracer particles to follow this CGM gas forward in time in a Lagrangian way and determine how its thermodynamic and kinematic properties change. We find that CGM gas mixes between different temperature and density phases quickly and within $\approx500$ Myr evolves into distinct cold ($T\approx10^4$ $\rm{K}$) and warm-hot ($T\approx10^{5.5}$ $\rm{K}$) phases at small and large distances from the galaxy, respectively, regardless of its initial ($z=1$) halo-centric radius. This is largely driven by feedback from the galaxy, which heats and ejects cold gas that had previously cooled and accreted toward and occasionally into the galaxy from the outer CGM. We see signatures of this process in autocorrelations of kinematic quantities, which take $\approx400$ Myr to fully decorrelate from their initial values, suggesting a timescale over which feedback disrupts and reprocesses CGM gas. We also examine gas in narrow temperature and density ranges associated with commonly observed ions and find that gas that is O VI-like stays in its phase for hundreds of Myr longer than gas that is Mg II-like or C IV-like, suggesting that CGM observations of different species could probe gas in different evolutionary states, even if the gas is cospatial.

We study an interval ordering problem introduced by Dürr et al. [Discrete Appl. Math. 2012] which is motivated by applications in bioinformatics. The task is to order a given set of n intervals with the goal of minimizing a certain objective which is defined via a given cost function $f$ which assigns a cost to the exposed part of each interval (that is, the pieces not covered by previous intervals). We develop a dynamic programming approach which solves the problem with $O(2^n\text{poly}(n))$ oracle calls to $f$ and arithmetic operations. Moreover, our approach yields polynomial-time algorithms for all cost functions $f$ such that $f-f(0)$ is subadditive or superadditive. This answers an open question for the function $f(x)=2^x$. We contrast these results by proving a running time lower bound of $2^{n-1}$ for any algorithm that solves the problem for every function $f$ (with oracle access only) and further proving NP-hardness for some classes of simple functions. Thus, we significantly narrow the gap regarding the computational complexity of the problem.

Column generation is a widely used decomposition technique for large-scale linear programs, but it often suffers from slow convergence due to poor initial dual estimates and dual oscillations. Stabilization techniques such as smoothing and penalization can mitigate these issues, but their effectiveness depends heavily on parameter selection, which requires careful tuning to avoid degrading performance. This paper presents a common framework for smoothing and penalization, showing that despite their different mechanisms, both are governed by two design choices: a reference point in the dual space and stabilization parameters that regulate how strongly that reference influences pricing. Within this framework, we derive parameter bounds that ensure progress, analyze predicted duals as reference points, and establish convergence guarantees for both methods. These results motivate and guide the design of RLSCG, a reinforcement learning-guided framework that adaptively selects stabilization parameters at each iteration. Computational experiments on the Cutting Stock Problem show that RLSCG substantially reduces iteration count and computation time on most synthetic and benchmark instances relative to traditional column generation, rule-based adaptive stabilization, and learning-based column selection, with the largest gains on large-scale instances.

We develop a black hole response formulation of worldline quantum field theory (WQFT) adapted to the gravitational self-force expansion. Integrating out the worldline and graviton fluctuations yields connected graviton response functions: the "zeroth response" one-point function encodes the exact background metric, the "first response" two-point function describes the scattering of a gravitational wave off the black hole, and higher-point functions provide effective vertices for a systematic gravitational self-force (SF) expansion. As a first application, we consider a massless primary source, corresponding to the Aichelburg-Sexl shockwave or an ultra-boosted black hole. We show that the one-point response resums to the exact shockwave metric and that the resummed probe vertices reproduce the exact geodesics in this background. We then compute the off-shell graviton two-point response exactly in the gravitational coupling $G$ by resumming the full post-Minkowskian series. For on-shell external gravitons this yields the exact transfer matrix for gravitational-wave scattering off the shockwave, including recoil. The PM expansion exponentiates to a compact form in which the Born term is dressed by an overall phase which includes the Weinberg phase. Our results provide the basic WQFT ingredients for future 1SF computations of observables such as the impulse and waveform in the ultra high-energy regime.

Incident monitoring can drive safety improvements in high-reliability industries and population-scale technologies, but remains underdeveloped in AI governance. Public databases catalog thousands of AI incidents, but simple incident counts conflate media reporting propensity, system deployment ("exposure"), and harm frequency per unit exposure. We propose a methodological framework that accounts for these factors and calibrates confidence to available evidence in analyzing how AI incidents change over time. The framework comprises three components: a structured monitoring question that defines the scope of the analysis; a tiered estimation process that separately derives harm and exposure trends, including through LLM-assisted filtering of public incident databases; and a classification scheme that maps the resulting trend estimates onto actionable governance categories (Escalating, Mitigating, Concentrating, Receding or Unclassifiable). Through case studies, we examine the framework's clarifying power and limitations, demonstrate governance insight despite real-world data constraints, and provide a proof of concept for AI incident monitoring as a practical governance tool.

In this paper, we fill some gap in the existing literature on higher spins by presenting an explicit solution to the on-shell constraints for a frame-like, gauge invariant description of massive, higher spin fields in d=4. We begin with the massive spin 2 and massive spin 5/2 as simple illustrations, and then consider arbitrary integer and half-integer spin. We also show that our results allow us to find explicit solutions to the so-called unfolded equations that determine all higher-order derivatives of the physical field that are non-zero on-shell.

We propose a method that employs static and dynamic analysis for augmenting a test suite with automatically generated unit tests. The method is most suitable for test suites where the stratification of unit, integration and system tests does not conform to the recommended test pyramid structure: numerous unit tests providing high code coverage and forming the base, fewer integration tests in the middle that verify component collaboration, and far fewer system or UI tests at the top that exercise acceptance or other scenarios of use. Instead, integration and system tests represent the majority of test cases, resulting in coarse-grained tests with limited fault localization and longer execution times. The method leverages integration tests, exercising a component and its dependencies, to generate unit tests that verify component dependencies in isolation. We showcase and empirically evaluate the proposed method in the Node.js platform, although it can be ported and adapted to other languages and platforms. The evaluation is based on a research prototype implemented as a Node.js tool and is conducted in the context of twelve open source JS applications (benchmark projects). Evaluation results support the effectiveness and practicality of our approach.