Peer Revenue Play credits. Real papers.

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

All of arXiv

Monday 28 September

Quantum Physics

Experimental quantum-computing-enhanced sensing using Grover's algorithm

Mathieu Ouellet, Purnendu Sen, Xiangqin Wang et al. (+6 more)

The combination of quantum sensing with quantum computing to provide an enhancement over conventional quantum sensing has recently emerged as a promising potential application of quantum computing that could give advantages without needing large-scale or fault-tolerant hardware. In this work, we report an experimental demonstration of a recent theoretical proposal to repurpose Grover's search algorithm to improve the ability to detect signals with unknown frequency within a large detection bandwidth. Our experiments were based on a system comprising a single superconducting qubit coupled to a single superconducting cavity, highlighting the modest hardware requirements for realizing the protocol. We found that Grover-based sensing was able to outperform the natural non-Grover baseline for our experimental platform for detection bandwidths $>$10~MHz, with an advantage that empirically grew superlinearly with the bandwidth beyond that break-even point. The use of the Grover-based protocol reduced the amount of signal that needed to be sensed to make an accurate detection decision by more than 10$\times$ for choices of larger detection bandwidth and higher desired detection accuracy. Our results provide a proof-of-principle validation that Grover-based quantum computational sensing can be realized in near-term hardware and provide a metrological advantage well beyond break-even in spite of the additional protocol complexity.
Read the full abstract

All authors Mathieu Ouellet, Purnendu Sen, Xiangqin Wang, Saswata Roy, Xingrui Song, Vladimir Kremenetski, Sridhar Prabhu, Valla Fatemi, Peter L. McMahon

Mathematical Software

A performance enhancement of the Payne-Hanek range reduction algorithm

Tue Ly

Range reduction plays a crucial role in the accuracy and performance of evaluating trigonometric functions, and is often the primary bottleneck for large floating-point inputs. While fast algorithms such as Cody–Waite work efficiently over narrow intervals, the Payne–Hanek algorithm remains the standard technique for accurate reduction across large floating-point inputs. However, many existing implementations of Payne–Hanek suffer from high latency due to heavy branching, conversion overheads, and the use of multi-word integer arithmetic, which hinders SIMD vectorization. In this paper, we analyze and present a branch-free variation of the Payne–Hanek algorithm using only floating-point arithmetic. Our method operates directly over large double-precision inputs ($|x| \ge 2^{16}$) and is well suited to hardware with FMA instructions. We formulate the precision constraints in terms of a truncation error budget, construct a compact lookup table indexed by the input exponent, and prove that the scaled reduced argument has absolute error below $2^{-110}$ and relative error below $2^{-60}$ for every input, including worst cases. The same routine can serve both as the complete range reduction of a single-stage implementation and as the fast path of a correctly rounded one, achieving higher throughput than existing implementations and lower latency than those returning a double-double reduced argument. The algorithm is currently implemented in the LLVM libc project.
Read the full abstract

Soft Condensed Matter

Ergodicity, ergodicity breaking, and temporal evolution of probability density in non-equilibrium: new perspectives by multispeckle DLS

Hans Joachim Schöpe

Testing the ergodicity hypothesis through experimentation in real systems is of crucial importance as it challenges a fundamental assumption underlying a wide range of theoretical models of physical and complex systems. High-calibre experiments can demonstrate whether, when and how a real system explores all of its possible states, and serve as a crucial conduit between abstract theoretical frameworks and tangible real-world applications. This approach is intended to guarantee that our descriptions of complex systems remain both physically meaningful and technologically relevant, thereby facilitating a more nuanced and comprehensive understanding. The development of a novel light scattering experiment has enabled us the direct measurement of the full distribution of fluctuations in the particle concentration in colloidal model systems and thus the direct determination of the probability distribution. This, in turn, facilitates the experimental verification of the ergodic hypothesis and the quantification of ergodicity breaking as a function of metastability.
Read the full abstract

Artificial Intelligence

Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting

Nasser Alkhulaifi

The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feature Engineering (FE). This thesis addresses that dependence through three contributions. First, it establishes and evaluates a comprehensive FE pipeline for ECF and investigates domain-specific features. Second, it introduces AutoEnergy, a domain-tailored automated FE algorithm that generates interpretable features from timestamps and lagged consumption and integrates with AutoML for end-to-end ECF modelling. Across eighteen real-world energy datasets spanning residential, commercial, industrial, renewable, and grid domains, AutoEnergy reduces forecasting error by 19.52%-84.72% relative to baseline AutoML and established automated FE methods, while running 1.31-4.41 times faster, with gains varying by dataset. Third, AutoEnergy is integrated with Decision-Focused Learning (DFL) for a Battery Energy Storage System problem, jointly forecasting electricity prices and demand while optimising charging and discharging decisions. On a real-world UK property dataset, this approach reduces operating costs by 22.9%-56.5% compared with the same DFL models without automated FE. Overall, the results show that domain-specific automated FE can reduce reliance on manual feature design, improve forecasting accuracy, and translate predictive gains into measurable operational benefits in energy management.
Read the full abstract

Optimization and Control

Amortized Feedback Planning: Turning Model-Based Rollouts into Executable Policies

Jeonggyu Huh

Closed-loop planning accounts for future observation-dependent actions but can be expensive to repeat at deployment. Bellman-gradient (BG) refinement differentiates conditional rollouts through an existing actor, corrects the current action, and stores the result in an executable policy. A backward sweep reuses deployed future feedback; full-horizon rollouts in an identified Gaussian belief model need no learned value critic. A nonlinear error recursion links quadratic continuation error, local correction, and policy storage. A controlled non-LQG example exhibits second-order policy accuracy with consistent storage, while ideal affine LQG admits exact backward recovery. In nonquadratic thrust, BG reduces actor cost by 2.47-3.20% and remains within 0.13-0.53% of the tested feedback MPC; original policies execute in 5-6 microseconds in a seed-0 native audit. With matched storage, BG attains competitive plant costs at about one ninth (arm) and one sixtieth (docking) of feedback-teacher-plus-student construction time on one GPU, excluding shared learning and node preparation. Richer common maps substantially narrow some student-BG gaps. These results expose the roles of learned feedback, local correction, and storage in executable control.
Read the full abstract

Machine Learning

Price Stability in the European Union: A Systemic Approach Using Random Matrix Theory

Sami Diaf

Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions. Mainstream economics tried to leverage price stability using price indices and several metrics to shed light on specific dynamics and optimal macroeconomic levels. The wide availability of data led researchers to consider the study of systems using Random Matrix Theory, based on inner correlation patterns. This aims to enhance the multivariate analysis by removing noisy patterns from the signal and improve data quality for further inferences. This work considers the collection of monthly inflation indices in the Eurozone as a system of prices to analyze its eigenvalues' statistical and asymptotic properties and uncover inner country-level insights. Results confirm the system cannot assumed to be randomly generated, and the data exhibit noise-dominated patterns, due to small and persistent variations at the country-level. The latter make the inter-country correlations more dynamic and the separation of the signal from the noise quiet difficult. Findings identified two countries as distorting inflation dynamics besides three other distinct, regional-based groups of countries. Variability sources might stem from economic episodes fueling inflation spikes in some countries, as well as methodological aspects used to ensure data quality and representativeness in the European Union. Despite being complex, the system demonstrates a certain stability, in terms of self-organization; while large monthly fluctuations cannot be considered as rare events, but part of the data-generating process.
Read the full abstract

Signal Processing

User Association and Transmit Beamforming for Rotatable Antenna-Enabled LAE Networks

Bowen Yao, Chao Zhang and Ying-Chang Liang

This paper investigates rotatable antenna (RA)-enabled low-altitude economy (LAE) networks, where multiple base stations (BSs) are deployed to serve both terrestrial and aerial users. Unlike conventional fixed orientation antennas, RAs can adjust their boresights to serve users at diverse azimuth and elevation angles, thereby providing three-dimensional (3D) coverage. To improve the performance of LAE networks, we formulate a network sum-rate maximization problem that jointly optimizes user association, transmit beamforming, and RA orientations. However, in such LAE networks, aerial users are often visible to multiple BSs through line-of-sight (LoS) links, making user association and interference management particularly challenging. Moreover, user association and RA orientations are closely coupled: user association affects the RA pointing directions, while the RA orientations in turn reshape the channel conditions for user association. To solve this problem, we propose an alternating optimization (AO) algorithm that integrates weighted minimum mean-square error (WMMSE) for transmit beamforming, projected gradient ascent updates for user association, and Frank-Wolfe updates for RA orientations. Furthermore, we develop a discrete candidate-scanning scheme that replaces continuous orientation optimization with a finite search to reduce computational complexity. We also propose an antenna block-based scheme to reduce the number of orientation variables. Simulation results show that the proposed joint design achieves a higher sum-rate than the fixed antenna orientation and nearest BS association benchmarks. Moreover, the candidate-scanning and block-based schemes achieve approximately 89.9% and 94.3% of the performance with high transmit power, respectively, while reducing the complexity of orientation updates.
Read the full abstract

Number Theory

Hausdorff Dimension of Weighted Singular Vectors

Bohan Yang and Tianru Zhu

Let $d\ge2$ and let $\mathbf w=(w_1,\ldots,w_d)$ satisfy $w_1\ge\cdots\ge w_d>0$ and $\sum_i w_i=1$. Set $s_*=d-(1+w_1)^{-1}$. We prove that there exist constants $C_{d,\mathbf w}>0$ and $\varepsilon_0=\varepsilon_0(d,\mathbf w)>0$ such that for all $0<\varepsilon<\varepsilon_0$, $$ \dim_H\operatorname{DI}_{\mathbf w}(\varepsilon)\le s_*+C_{d,\mathbf w}\sqrt\varepsilon. $$ Together with the lower bound of Kim–Park, this gives the exact formula $$ \dim_H\operatorname{Sing}(\mathbf w)=s_*. $$
Read the full abstract

Quantum Physics

Enantiomer-specific pumping of chiral molecules in high-$J$ rotational states

Fen Zou, Yan Gao and Peng Zhang

Enantiomer-specific state transfer (ESST) based on electric-dipole transitions has attracted considerable interest in the studies of manipulating molecules with electromagnetic fields. It also holds promise as a powerful tool for enantiomer detection and separation. To date, experimental demonstrations of ESST have focused mainly on molecules in the lowest rotational angular-momentum states, namely, $J=0$ and $J=1$. Realistic molecular samples, however, may contain appreciable populations in higher-$J$ rotational states, making it desirable to develop ESST schemes applicable to such states. Nevertheless, one cannot derive such schemes via a simple and straightforward extension of existing low-$J$ schemes, because higher-$J$ manifolds contain more magnetic sub-levels and involve more complicated networks of electric-dipole transitions. Here, we propose an ESST scheme for chiral molecules involving the $J=1$ and $J=2$ rotational states. Specifically, our scheme is based on enantiomer-specific pumping driven by three linearly polarized microwave fields and one laser beam. Under this scheme, one enantiomer is pumped into an enantiomer-specific dark state within a sub manifold with $J=1,2$, whereas the other enantiomer is dissipatively pumped out of this manifold. Our strategy can be extended to systems involving rotational states with even higher angular momenta.
Read the full abstract

Differential Geometry

Submanifolds of higher rank with curvature normals of constant length

Santiago Castañeda-Montoya, Guillermo Lobos and Carlos Olmos

We study the local geometry of Euclidean submanifolds of rank at least two whose curvature normals with respect to the flat part of the normal bundle have constant length, equivalently, whose adapted third fundamental form has constant eigenvalues. The complete case was fully settled by Di Scala and the third author, who showed that such a complete submanifold has constant principal curvatures. Around a regular point, we show that $M$ is generated, via parallel manifolds, by a hypersurface $N$ whose extrinsic factors are contained in spheres and, when of dimension at least two, have rank one; conversely, such factors can be assembled to construct examples of $M$. The proof relies on the isoparametric rank theorem to rule out non-umbilical factors. As a further application, when the inner products of the curvature normals are constant, we show that there are exactly two eigendistributions, one of which is one-dimensional and autoparallel, extending, for arbitrary codimension when the normal bundle is flat, curvature-homogeneity results of Tsukada and the recent ones of Bryant, Florit, and Ziller for hypersurfaces.
Read the full abstract

Mathematical Physics

GR-FM: Geometrically Regularized Flow Matching for SDF-Based Medical Image Segmentation

Yuxin Ai, Zhichang Guo, Fanghui Song et al. (+1 more)

Medical image segmentation remains challenging in terms of accurate boundary localization and complex-structure preservation, as target regions often exhibit weak boundaries, fine-grained structures, and irregular shapes, while high-quality images and precise annotations are usually limited. Existing generative segmentation approaches based on Flow Matching mainly learn velocity fields in the state space but lack explicit constraints on the spatial regularity and distance-field properties of the recovered representation. This limitation may lead to spatial oscillations, boundary displacement, and discontinuities in fine structures. To address these issues, we propose an image-conditioned Geometrically Regularized Flow Matching framework, termed GR-FM, which formulates segmentation as a continuous probability transport process from an initial distribution to a target implicit representation distribution. Instead of directly modeling binary masks, GR-FM adopts the SDF to describe the target structure, where each pixel is represented by its signed distance to the object boundary, resulting in a continuous geometric field. Flow Matching and ordinary differential equations are then employed to achieve deterministic and efficient distribution transport. The training objective further incorporates a biharmonic regularization term and an Eikonal constraint to enhance the spatial smoothness, structural consistency, and distance-field characteristics of the recovered representation. Moreover, we analyze the continuous transport process under geometric constraints and investigate the evolution of the zero level set. Experiments conducted on the MoNuSeg, GlaS, and DRIVE datasets demonstrate that GR-FM achieves competitive performance in both region overlap and boundary accuracy, reduces performance variation, and maintains stable segmentation results with only a small number of integration steps.
Read the full abstract

All authors Yuxin Ai, Zhichang Guo, Fanghui Song, Dazhi Zhang

Sound

Sub-Model Short-Term Memory Convolutions for Keyword Spotting Systems on Device

Paweł Warlewski, Artur Czeczko, Artur Szumaczuk et al. (+2 more)

Keyword Spotting (KWS) is becoming increasingly important as voice-controlled devices grow more widespread. While voice interaction with smartphones and smart TVs is already common, deploying KWS on heavily resource-constrained edge devices such as wearables remains challenging. These systems must meet high accuracy requirements while operating under strict constraints on computational power, memory footprint, and real-time latency. In this work, we present an application of the STMC (Short-Term Memory Convolutions) framework to adapt a modular CNN model for online, LSTM-like inference. Our approach reduces power consumption and redundant computations while maintaining the stability and simplicity of training CNNs. We achieve up to 82% and 46% MCPS reduction compared to equivalently frequent standard CNN execution and vanilla STMC, respectively. The best configuration achieves 93.8% accuracy on the 11-class Google Speech Commands task and 97.1% on the same task with zero-padded data.
Read the full abstract

All authors Paweł Warlewski, Artur Czeczko, Artur Szumaczuk, Grzegorz Stefański, Szymon Klimaszewski

Superconductivity

Unconventional superconductivity in locally non-centrosymmetric CeNi$_2$As$_2$

Félix Morineau, Jan Knapp, Javier Landaeta et al. (+12 more)

Recent years have seen intense research on heavy fermion superconductivity, inspired by the discovery of multiple superconducting phases in UTe$_\text{2}$ and CeRh$_\text{2}$As$_\text{2}$. In the latter material, two superconducting phases observed under applied magnetic field oriented along the crystallographic $\textit{c}$ direction are associated with local inversion symmetry breaking. To advance understanding of these phenomena, it is highly desirable to find further examples of heavy fermion superconductivity in materials with locally non-centrosymmetric structure. We have succeeded in this quest, by growing single crystals of the CaBe$_\text{2}$Ge$_\text{2}$ isomorph of CeNi$_\text{2}$As$_\text{2}$. The resulting superconductivity brings more than we had foreseen. In addition to providing the opportunity to compare and contrast with that of CeRh$_\text{2}$As$_\text{2}$, the condensation occurs from an incoherent normal state quantitatively similar to that of UBe$_\text{13}$. Our findings therefore raise profound questions not just about superconductivity in the locally non-centrosymmetric structures, but about unconventional superconductivity itself.
Read the full abstract

All authors Félix Morineau, Jan Knapp, Javier Landaeta, Thomas Lühmann, Lea Richter, Petra Knappova, Soumen Ash, Sushma Lakshmi Ravi Sankar, Konstantin Semeniuk, Elena Hassinger, Christoph Geibel, Manuel Brando, Daniel F. Agterberg, Andrew P. Mackenzie, Seunghyun Khim

Robotics

Learning to Act under Visual Interruptions with Vision-Language-Action Models

Mingle Jiang, Rui Xu, Yunke Wang et al. (+1 more)

Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with all camera streams available throughout task execution. When a camera stops delivering frames during task execution, the policy must continue acting without access to subsequent observations from the missing view. Despite its practical importance, how such interruptions affect closed-loop manipulation remains insufficiently understood. To investigate this problem, we introduce MAIL-Bench, a benchmark that evaluates visual interruptions with VLA models. By interrupting different cameras at multiple stages of each policy's successful reference trajectory, MAIL-Bench measures how well policies retain their capabilities when visual inputs become unavailable. Building on this benchmark, we propose MINT, which first trains VLA policies to remain functional under missing visual inputs. At inference time, MINT selectively supplements missing observations using optical-flow extrapolation or an action-conditioned world model, and withdraws predicted views when they become unreliable. Experiments on $\pi_{0.5}$ and GR00T N1.5 show that MINT significantly improves task success under camera loss over the original models. Experiments on AgiBot G2 further demonstrate the real-robot deployment under camera loss. The benchmark is available at https://minglejiang.github.io/Mail-Bench/
Read the full abstract

All authors Mingle Jiang, Rui Xu, Yunke Wang, Chang Xu

Computer Vision

Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models

Qiankun Li, Yuechen Zhang, Bowen Chen et al. (+4 more)

Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whether a particular adversarial failure is induced by compression or inherited from the underlying model. We define a compression-specific failure (CSF) as an adversarial input that remains correct under full-token inference but fails after compression, casting compression-induced risk as a paired failure attribution problem. Within a controlled diagnostic cohort, counterfactuals show that retained-set allocation causally changes compressed correctness and reveal a negative association between recovery and representation drift in displaced evidence. Motivated by these findings, we propose CIRA, a Compression-Induced Risk Attack for Large Vision-Language Models. Under a vision-encoder white-box setting, CIRA optimizes image perturbations through encoder-side objectives that manipulate token priorities across candidate compression budgets while preserving displaced evidence. CIRA uses no downstream questions or labels and requires no access to the language model, deployed compressor, or exact compression budget. Across 12 dataset-compressor settings evaluated at four budgets, CIRA achieves a mean CSFR of 20.35% while limiting full-token attack success to 6.92%, with similar behavior on additional LVLM families. A cross-view selection-stabilization defense substantially suppresses CIRA, although Adaptive CIRA partially restores its effectiveness. These results show that compression-specific failures persist under restricted access and support paired evaluation of full-token and compressed inference for attributing risk to visual-token compression.
Read the full abstract

All authors Qiankun Li, Yuechen Zhang, Bowen Chen, Shilinlu Yan, Zhenhong Zhou, Kun Wang, Li Sun

Analysis of PDEs

Well-posedness for time-fractional evolution equations by the Yosida approximation

S. E. Chorfi, F. Gölgeleyen and M. Yamamoto

We consider an initial value problem for a time-fractional evolution equation in a Hilbert space $X$: $$ \partial_t^{\alpha} (u(t)-a) = Au(t) \qquad \mbox{for $0<t<T$}, $$ where $\partial_t^{\alpha}$ denotes a fractional differential operator of Caputo type with order $0<\alpha<1,$ $u\colon (0,T) \to X,$ $a$ describes an initial value, and $A$, with domain $\mathcal{D}(A),$ is the generator of a contraction C$_0$ semigroup in $X$. We prove a fractional Hille-Yosida theorem characterizing the unique existence of a weak solution for every $a\in X$. We also discuss the unique existence of a strong solution for $a \in \mathcal{D}(A),$ as well as the continuity of weak solutions. Our results are applicable, for example, to time-fractional transport equations and Boltzmann equations. The proofs are mainly based on constructing an approximating sequence of solutions using the Yosida approximation.
Read the full abstract

Robotics

Graph-Based Simultaneous Path and Foothold Planning for Multi-Limbed Intra-Vehicular Robots in Space Stations

Masazumi Imai, Kentaro Uno, Toshinori Kuwahara et al. (+1 more)

Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Multi-limbed intra-vehicular robots (MLIVRs) equipped with grappling end-effectors have emerged as a promising solution, as they can securely grasp pre-existing interfaces, such as handrails and seat tracks, thereby enabling stable locomotion and forceful manipulation in microgravity environments. Since graspable locations on these interfaces are spatially limited and discretely distributed, motion planning for MLIVRs must be addressed jointly with foothold planning. This paper presents a simultaneous path and foothold planning framework based on graph theory for MLIVRs. The proposed method efficiently searches for feasible stance sequences for a multi-limbed robot while satisfying manipulability constraints. The effectiveness of the proposed framework is validated through simulations in a 3D model of the International Space Station (ISS) cabin, demonstrating its capability to generate feasible and efficient locomotion plans in realistic intra-vehicular environments.
Read the full abstract

All authors Masazumi Imai, Kentaro Uno, Toshinori Kuwahara, Kazuya Yoshida

Solar and Stellar Astrophysics

First results from PADRE/MeDDEA: X-rays from a flare and its CME

Muriel Zoë Stiefel, Säm Krucker, Olivier Limousin et al. (+22 more)

The MeDDEA hard X-ray spectrometer onboard NASAs PADRE CubeSat operates four flight-spare detectors from the Solar Orbiter/STIX instrument. This paper aims to evaluate the accuracy of the MeDDEA calibration and to demonstrate that MeDDEA data are ready for scientific exploitation. Using the first joint observations between MeDDEA and STIX, we investigate faint HXR emission associated with an escaping CME relative to the emission from the full flare. The first large flare observed by MeDDEA is analyzed, and the MeDDEA spectrum is compared with simultaneous observations from Fermi/GBM and ASO-S/HXI. From the Solar Orbiter viewpoint, the flare is highly limb-occulted allowing STIX to isolate high coronal emission associated with the CME while MeDDEA observed the full flare. We combined spectral fitting with HXR/EUV imaging from STIX, HXI, and AIA to investigate the locations and energetics of the flare- and CME-associated HXR sources. The MeDDEA spectral observations are in agreement with FERMI/GBM and ASO-S/HXI within 10 percent in the energy range of 18-30 keV. The CME-associated photon flux observed by STIX in the nonthermal range is faint at about 1 percent of the flare photon flux. The energy content of the thermal component associated with the CME, on the other hand, is found to be of the same order of magnitude as the thermal energy of the flare loop, at least for a filling factor of unity. Imaging information from STIX in combination with AIA reveals that the emission measured by STIX is located within the associated CME. We report that MeDDEA provides scientifically reliable spectral observations and is ready for scientific use.
Read the full abstract

All authors Muriel Zoë Stiefel, Säm Krucker, Olivier Limousin, Niharika Godbole, Gwendoline Marc, Amir Caspi, Kyle Gregory, Ace Stratton, Diana Renaud, Aline Meuris, Laura A. Hayes, André Csillaghy, Pascal Saint-Hilaire, Juan Camilo Buitrago Casas, Christopher Smith, Savannah Sky Perez-Piel, Anton Tremsin, Elizabeth Warren, Bradley Pafchek, Eliad Peretz, Carlos Urdiales, Reynaldo Gonzalez, Chris M. Moeckel, Juan Carlos Martinez Oliveros, Steven Christe

Fluid Dynamics

A generalized, quasi-universal subgrid model for macroscopic moving contact line flows

Vyankatesh Manoj Mundhada and Manoj Kumar Tripathi

Predictions of moving contact line flows are challenging due to the stress singularity associated with the no-slip boundary condition on solid surfaces and the inherently multiscale nature of the phenomenon. Resolving the microscopic length scale renders the numerical simulations intractable for macroscopic flows due to extremely high computational costs. Affordable and predictive numerical solutions for macroscopic flows require a subgrid model, free of case-specific calibration, that mitigates the grid-dependence observed otherwise. We demonstrate a coupling between Cox's matched asymptotic relation and the empirical contact angle models to obtain a quasi-universal subgrid model. The coupling requires a one-time knowledge of the observation length scale or resolution of the experiments used to formulate the empirical models. The present model eliminates the need for prescribing phenomenological parameters such as the microscopic slip length and wall contact angle, allowing for the predictive simulations of such flows. We demonstrate the reduction in error due to grid-dependence by conducting axisymmetric simulations of droplet spreading for a wide range of Reynolds and Weber numbers for three different contact angle models in the advancing and receding contact line motion scenarios.
Read the full abstract

Software Engineering

Making the Invisible Visible: A Framework for Reflective AI Use in Software Engineering Education

Ali Shakiba and Thomas Chaffey

Generative AI (GenAI) is increasingly embedded in software engineering education, supporting activities such as requirements development, design exploration, documentation, and prototyping. However, educators often have visibility only into final artefacts, with limited insight into how students evaluate, verify, and refine AI-generated outputs during the learning process. This creates challenges for assessing evaluative judgement and responsible AI-assisted practice. This paper introduces the AI Journal, a structured reflection framework designed to make student-GenAI interaction visible in first-year software engineering education. The framework combines execution tracking, which records prompts, outputs, intent, and interaction context, with cognitive auditing, which captures verification strategies, intervention decisions, confidence judgements, critical learning moments, and reflections on AI-supported work. Deployed in a first-semester software engineering course, the AI Journal enabled visibility into aspects of student learning not observable through artefact-based assessments alone. Preliminary observations suggested variation in verification practices, intervention strategies, and perceptions of AI-supported work. Critical learning moments frequently occurred when students evaluated contextual suitability, feasibility, and requirements alignment rather than identifying obvious errors. The AI Journal demonstrates a practical, lightweight, and model-agnostic approach for making AI-assisted learning processes visible. By foregrounding verification, intervention, and reflection, it shifts attention from product-focused assessment toward evaluative judgement and responsible AI-assisted practice.
Read the full abstract

Quantum Physics

Cyclotomic Cosets: Hidden Subgroup and Quantum Sieving Algorithm for Prime-Power Moduli

Mathias Boucher, Pierre-Alain Fouque and Yixin Shen

The Learning With Errors (LWE) problem is a fundamental assumption in post-quantum cryptography. Regev established a quantum reduction from LWE to the Dihedral Coset Problem (DCP). Later, Brakerski et al. introduced the Extrapolated Dihedral Coset Problem (EDCP), proving its equivalence to LWE. However, unlike DCP, EDCP no longer admits a coset structure. This limits the direct application of techniques for hidden subgroup problems. In this work, we introduce the Cyclotomic Coset Problem (CCP), a cyclotomic generalization of DCP that preserves an exact hidden-subgroup structure. Let $\zeta_p$ be a primitive $p$-th root of unity, let $\pi=\zeta_p-1$, and write $q=p^t$ and $L=t(p-1)$. We work over $R_q=\mathbb Z_q[\zeta_p] \cong \mathbb Z[\zeta_p]/(\pi^L)$, where the isomorphism follows from the total ramification identity $(p)=(\pi)^{p-1}$. We exploit the resulting $\pi$-adic ideal chain to construct a quantum sieve that successively reduces phase states modulo $\pi^{L},\pi^{L-1},\ldots,\pi$. For every fixed prime $p$ and modulus $q=p^t$, our algorithm solves the CCP in time and sample complexity $2^{O_p(\log n\log q)}$, using polynomial quantum space. The sieve also applies to uniform EDCP and Gaussian S|LWE>, yielding quasi-polynomial time algorithms for all the above problems when $q=\text{poly}(n)$. This extends the power-of-two EDCP sieve of Bai et al. (CRYPTO 2025) to a cyclotomic setting. However, we emphasize that our result does not, by itself, yield a quasi-polynomial-time algorithm for standard LWE, because the currently known reduction produces only a limited number of approximate CCP states.
Read the full abstract

Quantum Physics

Simulation-Based Quantum System Inference with Neural Posterior Estimation

Hang Zou, Anton Frisk Kockum, Martin Rahm et al. (+1 more)

Models of quantum systems faithfully map system parameters to observations, but the inverse problem of parameter inference from measurement data presents a fundamental challenge: computationally intractable likelihoods due to an exponentially large Hilbert space. Here, we introduce simulation-based quantum system inference, a unified, likelihood-free framework that learns parameter posteriors directly from classical simulation data. The central idea is to pair polynomial-cost classical simulators, such as Pauli propagation and tensor networks, with normalizing flows or other neural density estimators for accurate, reusable inference. A single model, trained once, maps any new measurement record to its posterior in one forward pass—turning per-experiment inference into a fixed, up-front cost. We numerically demonstrate the framework's versatility across Pauli noise learning, quantum error mitigation, quantum state tomography, and Hamiltonian learning, with examples involving 81-qubit shallow circuits and 735-parameter inference. In each case, the approach yields accurate estimates of identifiable parameters, while posterior uncertainty provides additional diagnostics of non-identifiability and indicates where further characterization is needed. Our framework reduces data-acquisition requirements in quantum experiments and accelerates parameter inference, providing a practical route to characterizing and improving large-scale quantum systems.
Read the full abstract

All authors Hang Zou, Anton Frisk Kockum, Martin Rahm, Simon Olsson

Artificial Intelligence

From One-Shot Generation to Incremental Music Composition: Adapting a General-Purpose Instruction LLM for Persistent Symbolic Editing

André Ricardo Ducca Fernandes, Jean-Pierre Briot, Simone Diniz Junqueira Barbosa1 et al. (+1 more)

Most music-generation systems are still framed and evaluated primarily as producers of complete outputs, whereas composition often proceeds through successive revisions to a shared musical artifact. This paper studies a different use of a general-purpose instruction-following large language model: not as a one-shot music generator, but as a reusable operator over an evolving symbolic score. We formulate incremental composition as a sequence of operation-aware state transitions over persistent ABC notation, with explicit requirements on what each operation may change and what it must preserve. The interaction includes two artifact-initialization variants and three editing operations – chord addition, inpainting, and transposition. We instantiate the formulation by adapting Llama 3.1 8B Instruct with Low-Rank Adaptation (LoRA) on 496,038 operation-aware dialogue records derived from Irish traditional music. The comparison with the unadapted model is used to test the feasibility of learning this interaction contract, not to claim novelty for fine-tuning itself. Across 500 dialogues per model (1,750 attempted output states), checker admission rises from 29.37% to 99.37%, while compliance conditional on admission rises from 0.7205 to 0.9798. Strict eligibility for reference-relative musical-feature analysis increases from 14 to 1,548 outputs, and Longest Common Subsequence analysis does not show a systematic increase in high-overlap sequences relative to held-out baselines under the specified protocol. The results support the technical feasibility of persistent, operation-aware symbolic editing with a general-purpose instruction LLM. They do not establish superior musical quality or human-AI co-creativity, which remain questions for musician-centered evaluation.
Read the full abstract

All authors André Ricardo Ducca Fernandes, Jean-Pierre Briot, Simone Diniz Junqueira Barbosa1, Hélio Côrtes Vieira Lopes

Signal Processing

LIVE-RIS: A Comprehensive In-Flight Dataset for UAV-Mounted RIS Channel Measurements

David Müller, Kevin Weinberger, Aydin Sezgin et al. (+1 more)

The dynamic control of the wireless propagation environment enabled by reconfigurable intelligent surfaces (RISs) makes them a promising technology for sixth-generation (6G) wireless networks. Beyond conventional fixed-position deployments, integrating a RIS with an unmanned aerial vehicle (UAV) provides additional spatial flexibility, facilitating favorable Line-of-Sight (LoS) conditions and broadening the range of potential applications. Although UAV-mounted RIS systems have attracted significant research interest, existing studies rely predominantly on numerical simulations and often overlook the impact of practical constraints and real-world disturbances. To address this gap, the authors recently presented the first UAV-mounted RIS prototype and experimentally validated its feasibility through real-world experiments. By using the UAV's extended Kalman Filter (EKF) for real-time RIS reconfiguration, the proposed system effectively mitigates disturbance effects and preserves the performance gains of the RIS-enabled link. To further advance UAV-mounted RIS research and facilitate broader access to real-world experimental insights, this work provides a comprehensive dataset collected from multiple flight experiments. The dataset includes EKF state estimates, ground-truth measurements, corresponding RIS configurations, and S21 channel data.
Read the full abstract

All authors David Müller, Kevin Weinberger, Aydin Sezgin, Martin Mönnigmann

Machine Learning

Composable Decoding on the Probability Simplex: Theory and Implementation

Xiaotong Ji, Ahmed Khaled Khamis, Rasul Tutunov et al. (+2 more)

Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the behaviours they induce and how their underlying objectives relate. We formulate decoding as an optimisation problem over next-token distributions on the probability simplex, balancing expected model score against regularisation under support constraints. This view recovers familiar decoding methods through choices of regularisers and support constraints; more importantly, it enables new decoders to be constructed by composing distributional preferences within a single optimisation problem without external rewards, learned critics, or model parameter updates. We introduce CompoSimplex, a library with configurable support rules, regularisation primitives, and simplex solvers for constructing and evaluating compositional decoders. We evaluate standard samplers, individual regularisers, and compositions across multiple models and reasoning tasks. Our results show that compositions can realise trade-offs between single-sample quality, multi-sample quality, and diversity that are not attained by individual decoding objectives.
Read the full abstract

All authors Xiaotong Ji, Ahmed Khaled Khamis, Rasul Tutunov, Matthieu Zimmer, Haitham Bou-Ammar

Statistics Applications

Artificial Intelligence for early detection of circulatory shock in ICU patients

Jaume Aguiló Piña, Laia Subirats, Aina Frau-Pascual et al. (+3 more)

Circulatory shock is one of the leading causes of mortality in intensive care units (ICUs), and its early detection is critical to enable timely treatment and improve clinical outcomes. This study aimed to develop and evaluate a two-stage cascade machine learning framework for the early detection and etiological classification of circulatory shock in critically ill patients. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, four patient groups were defined: septic shock, cardiogenic shock, hypovolemic shock, and a non?shock control group, comprising a total of 32,907 patients. Vital signs and laboratory data were collected during the first six hours after ICU admission. After data cleaning and missing-value imputation, the mean value of each variable was used for model development. Several machine learning algorithms were compared, including logistic regression, Random Forest, XGBoost, and multilayer perceptron (MLP) networks. Random Forest and XGBoost achieved the highest overall performance, with an AUROC of approximately 0.82-0.83 for shock detection, and a macro-averaged sensitivity of approximately 0.61 and precision of approximately 0.58 across all four classes. Classification performance was highest for the non?shock group, followed by septic and cardiogenic shock, while hypovolemic shock showed the lowest performance. These results indicate that machine learning models can identify early signs of hemodynamic deterioration associated with circulatory shock and may support clinical decision-making in the ICU. However, further improvements are needed for the classification of specific shock subtypes, particularly hypovolemic and cardiogenic shock, as well as for real-time clinical implementation.
Read the full abstract

All authors Jaume Aguiló Piña, Laia Subirats, Aina Frau-Pascual, Alba Gorriz, Rudys Magrans Nicieza, Jordi Morillas Perez

Machine Learning

MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR

Yangyang Ren, Haodong Zhu, Sheng Xu et al. (+4 more)

Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but incurs substantial costs from rollouts and policy updates. Online prompt selection improves efficiency by using per-prompt Bayesian posteriors to predict difficulty and prioritize informative prompts. However, existing methods overlook how reliably learning signals are extracted from sampled responses. In GRPO, a response's advantage depends on both its own outcome and the randomly sampled outcomes of its peers through group normalization. Our theoretical and experimental analyses show that uncertainty in group composition introduces composition noise, a non-vanishing variance component that imposes an irreducible lower bound on gradient estimation error and impairs downstream prompt selection. We propose MaPP (Marginalized Posterior-Predictive), a unified framework for data-efficient RLVR that denoises response-level advantage estimation and improves prompt selection using a shared Beta posterior. For each response, MaPP replaces the standard group-relative advantage with a composition-invariant intrinsic advantage through closed-form Beta-Binomial marginalization. The resulting posterior-predictive estimator has an error that provably diminishes as the posterior concentrates. Using the same posterior, MaPP derives an uncertainty-aware prompt selection score to improve data efficiency without additional rollout cost. Experiments on mathematics, planning, and visual geometry across five model backbones show that MaPP consistently outperforms GRPO and strong selection baselines, achieving up to +2.45 average accuracy improvement over the strongest baseline under the same rollout budget and setting a new state of the art.
Read the full abstract

All authors Yangyang Ren, Haodong Zhu, Sheng Xu, Yanjing Li, Nikolai Yu. Zolotykh, Wentao Zhang, Baochang Zhang

Cosmology and Nongalactic Astrophysics

Localized Enhancements of the Primordial Power Spectrum and the Abundance of Early Bright Galaxies

Shaghayegh Yavari, Hamed Kameli and Shant Baghram

Recent observations of bright galaxies at high redshifts by the James Webb Space Telescope have motivated renewed interest in cosmological mechanisms that can enhance the abundance of early massive dark matter halos. We study a phenomenological modification of the primordial curvature power spectrum in the form of a localized Gaussian enhancement. The modified spectrum is propagated to the linear matter power spectrum, the smoothed density variance, and the Sheth-Tormen halo mass function. We show that a bump centered at small scales can selectively enhance the abundance of rare high-redshift halos and, through a fixed semi-analytic mapping between halo mass and ultraviolet luminosity, shift the bright end of the UV luminosity function toward JWST measurements. The enhancement becomes more pronounced at higher redshift due to the exponential sensitivity of the rare-halo tail to the variance. We emphasize that the effect is limited by small-scale power-spectrum constraints, particularly Lyman-$\alpha$ forest data, and should be interpreted as a partial cosmological contribution rather than a complete solution to the JWST galaxy tension.
Read the full abstract

Computation and Language

The Right Lesson at the Right Step: Deriving Control Updates for Self-Evolving Agents

Yunhe Su, ZiYi Dong, Tong Yu et al. (+4 more)

Self-evolving agents improve future behavior by reusing past experience, typically as global prompts, memories, or reflections. Yet these mechanisms rarely control where experience takes effect. In long tool-use workflows, the same lesson may correct one decision but distract another, making experience reuse a problem of localized control rather than memory alone. We introduce EvoCUE (Evolution through Control Updates from Evidence), a framework for learning reusable control-program updates from completed agent executions. EvoCUE represents the agent as an explicit state-machine controller, whose nodes perform model or tool calls and whose edges define where control passes next. This makes the workflow editable at precise locations, so each learned update can specify what to add, where it acts, and when it applies. From completed trajectories, EvoCUE uses residual goals and observed execution traces to propose localized instruction or skill edits. Each candidate is evaluated at the point where it would act by resuming the parent and edited controllers from the same checkpoint and comparing their final outcomes. Accepted edits are compiled with applicability rules, confirmed on held-out tasks, and inherited by later executions. We evaluate EvoCUE on long tool-use environments where learned conventions must reach the right execution step. From a minimal AppWorld controller without benchmark-specific onboarding instructions, EvoCUE learns the missing task-completion convention and substantially improves success on Test-Normal and Test-Challenge. On PAST-Bench office workflows, EvoCUE transfers organizational requirements from prior episodes to later tasks, improving task-execution quality. These results show that self-evolving agents should place experience inside the control flow, rather than only store it as text.
Read the full abstract

All authors Yunhe Su, ZiYi Dong, Tong Yu, Weijian Deng, Hao Li, Bowen Jiang, Pengxu Wei

Nuclear Experiment

Toward FAIR and AI-Ready Data: An Assessment of the GlueX Experiment's Data Ecosystem at Jefferson Lab

Anil Panta, Brad Sawatzky, Casey Morean et al. (+2 more)

Department of Energy programs increasingly require that experimental data be not only preserved but usable by artificial intelligence (AI) systems, yet no accepted standard defines when a dataset is AI-ready. We argue that data must be FAIR (Findable, Accessible, Interoperable, Reusable) before they can be AI-ready, and that AI readiness is a distinct property requiring its own assessment. Using the GlueX experiment in Hall D at Jefferson Lab as a pilot, we map the data ecosystem, assess it against FAIR, and define an AI-readiness framework whose criteria are backed by deterministic metrics. We also evaluate existing validation software to separate what can be assessed with off-the-shelf tools from what requires domain-specific development. The assessment yields a set of findings, each paired with a recommendation, some of which are already in production or prototyped and others planned as next steps.
Read the full abstract

All authors Anil Panta, Brad Sawatzky, Casey Morean, Dmitry Romanov, Douglas Higinbotham