April 2026 arXiv papers — page 117

Xuanyi Liu, Chunan Yu, Deyi Ji, Qi Zhu

Reconstructing dense 3D geometry from continuous video streams requires stable inference under a constant memory budget. Existing $O(1)$ frameworks primarily rely on a ``pure eviction'' paradigm, which suffers from significant information destruction due to binary token deletion and evaluation noise from localized, single-layer scoring. To address these bott

Federico Pierucci, Matteo Prandi, Marcantonio Bracale Syrnikov, Marcello Galisai

This paper advances a methodological proposal for safety research in agentic AI. As systems acquire planning, memory, tool use, persistent identity, and sustained interaction, safety can no longer be analysed primarily at the level of the isolated model. Population-level risks arise from structured interaction among agents, through processes of communication

Guangzhuo Peng, Laurentiu Rodina, Anna Tokareva, Yongjun Xu

We develop a primal bootstrap framework for effective field theories in the presence of a graviton pole, based on finite-resolution sampling rather than smearing, while also allowing direct control over the number of subtractions. We show that this approach reproduces the known projective bounds obtained from smearing in $D{\ge}6$, while yielding slightly st

Radu Andrei, Ivan Morera, Jonathan B. Curtis, Immanuel Bloch

Magnetic correlations of doped Mott insulators hold the key to the unusual characteristics of many quantum materials. Recent experiments with ultracold atoms in optical lattices have provided new information about the magnetic properties of the Fermi-Hubbard model on a square lattice. We demonstrate that recent measurements indicate that a single doping-depe

Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani, Jean-Flavien Bussotti

NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users express queries iteratively (2) questions often span multiple data sources beyond the closed-world assumption of a single database, and (3) queries frequently rely on commonsense or ext

Xiaochen Zhang, Haitao Du, Yanyu Cheng, Yushen Lin

Pinching antenna (PA) systems have recently gained significant attention. While their physical-layer security (PLS) is being explored, most studies rely on idealized lossless models, ignoring practical waveguide attenuation. In this paper, we investigate the PLS performance of PA systems under a more realistic attenuation-incorporated waveguide model. Specif

Tristan Gamot, Nils Thibeau--Sutre, Tom J. M. Van Dooren

Non-parametric approaches to test for trends in time series make use of the Mann-Kendall statistic. Based on asymptotic arguments, these tests assume that its distribution follows a Gaussian distribution, even for autocorrelated time series. Recent results on the lack of validity of this assumption urge a robustness analysis of these approaches. While the is

Manit Paul, Arun Kumar Kuchibhotla

``Behind every limit theorem, there is an inequality'' said Kolmogorov. We say ``for every inequality, there is an approximate inequality under approximate regularity conditions.'' Suppose $X, X'$ are independent and identically distributed random variables. Then $X \le X'$ with a probability of at least $1/2$, irrespective of the underlying (common) distrib

Xing-Chen Guo, Benchi Zhao, Xin Wang

Pure quantum states are a fundamental resource for quantum information processing, but unavoidable noise degrades their purity and limits the performance of quantum tasks. Quantum state purification attempts to restore purity by jointly processing several noisy copies of an unknown state, typically in a probabilistic way. Whether purification is possible, an

Nicklas Neu, Thomas Ebner, Jasmin Primus, Bernhard Schenkenfelder

Embryo selection is one of multiple crucial steps in in-vitro fertilization, commonly based on morphological assessment by clinical embryologists. Although artificial intelligence methods have demonstrated their potential to support embryo selection by automated embryo ranking or grading methods, the overall impact of AI-based solutions is still limited. Thi

Nanfei Jiang, Yi Zhou, Josh A. Taylor, Mahnoosh Alizadeh

Optimized charging of electric vehicles (EVs) at public locations consists of two decisions: how much energy to deliver at what times, which is continuous, and where to plug in, which is binary. This makes optimizing EV charging a mixed-integer linear program (MILP). This discreteness undermines traditional marginal pricing methods. In this paper, we develop

Antoine Mouzard, Immanuel Zachhuber

In this paper, we prove existence and uniqueness of energy solutions for nonlinear Schr\"odinger equations with a multiplicative white noise on $R^d$ with $d\le3$. We rely on an exponential trans-form and conserved quantities for existence of energy solutions. Using paracontrolled calculus, we prove Strichartz inequalities which encode the dispersive propert

Joel Perca, Luis Sante, Juanpablo Heredia, Joao Rulff

Extracting actionable insights from long-duration urban videos is often labor-intensive: analysts must manually sift through raw footage to pinpoint target events or uncover broader behavioral trends. In this work, we present URBANCLIPATLAS, a visual analytics system for exploring long urban videos recorded at street intersections. URBANCLIPATLAS combines re

Manan Gupta, Inderjeet Nair, Lu Wang, Dhruv Kumar

The $\textit{LLM-as-a-judge}$ paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: that judges evaluate text strictly on its semantic content, impervious to surrounding contextual framing. We investigate $\textit{stakes signaling}$, a previously unmeasured vulnerability where informing a ju

Oz Levy, Ilya Dikman, Natan Levy, Michael Winokur

Artificial Intelligence is increasingly introduced into systems engineering activities, particularly within requirements engineering, where quality assessment and validation remain heavily dependent on expert judgment. While recent AI tools demonstrate promising capabilities in analyzing and generating requirements, their role within formal systems engineeri

Vladimir Igoshin, Daniil Klimov, Yuri Utkin, Sergey Ermakov

The optical theorem is a powerful tool of scattering theory that directly relates the extinction cross section of a scatterer to its forward scattering amplitude. While widely used in electromagnetism and optics, its application in acoustics has remained limited, primarily due to experimental challenges. These include the finite size of practical sound sourc

Mario Cavani

We present a mathematical model of a market with $m$ shares traded across $n$ investor groups, each one with similar motivations and trading strategies. The market of each asset consists of a fixed amount of cash and shares (no additions are allowed over time, so the system is closed), and the trading groups are influenced by trend and valuation motivations

Jacob Hafjall, Thomas A. Ryttov

We study conformal quantum mechanics by first considering the perturbative $S$-matrix in various dimensions. The model has two couplings and we study perturbatively the degree of ultraviolet divergences arising in the interplay between the two couplings. We then focus on the inverse square potential in one spatial dimension and compute the beta function to a

Xiao-Quan Ye, Shao-Jiang Wang

Inflationary models that contain a transient ultra-slow-roll phase can exhibit strong non-perturbative dynamics, making the usual perturbative treatment of cosmological fluctuations incomplete. In such regimes, quantum diffusion and the nonlinear gravitational response of the background can both play important roles, motivating a framework that treats them s

Rohan Goyal, Venkatesan Guruswami, Jun-Ting Hsieh

The subspace design property for additive codes is a higher-dimensional generalization of the minimum distance property. As shown recently by Brakensiek, Chen, Dhar and Zhang, it implies that the code has similar performance as random linear codes with respect to all "local properties". Explicit algebraic codes, such as folded Reed-Solomon and multiplicity c

Zewei Kong, Paul A. Parker, Jonathan R. Bradley, Scott H. Holan

The American Community Survey (ACS) Public Use Microdata Sample (PUMS) provides access to a wide range of unit-level survey data consisting of correlated Gaussian and binomial distributed survey responses along with associated survey weights. As such, we propose a Bayesian hierarchical framework for jointly modeling unit-level Gaussian and binomial survey da

Oscar Romero, Aika Silveira Miura, Lorena Parra, Jaime Lloret

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an

Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi, Jens Eisert

Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained model on new data requires estimating a weighted sum $\sum_{i=1}^N \alpha_i k(x,x_i)$ of $N$ kernel values to additive precision $\varepsilon$, where $\alpha$ is the vector of trained

Quentin Schaeverbeke, Viktor Radović, Jean-Marc Divanon, Bing Hong Teh

We explore the expected performance of a semiconducting spin cQED quantum processor for Multiple Hypothesis Tracking (MHT) algorithm via a quantum annealing procedure. From two different benchmarking scenarios we evaluate this type of quantum annealer on a quantum emulator in which we incorporated both dynamical coherent errors and incoherent errors. From es

James R. N. Tett, Finlay Johnston, Brennan Sprinkle, Alice L. Thorneywork

We study the orientational dynamics of superparamagnetic colloidal dimers that carry both an induced magnetic moment, proportional to the applied field, and an effective permanent moment. In a static, uniform magnetic field, dimers that are permanently fixed together hop between two preferred in-plane angles, developing a bimodal steady-state orientation dis

Lukas Handl, Max Kaiser, Miro Duhovic, Martin Gurka

Shape adaptive shape memory alloy hybrid composites (SMAHCs) are composites that incorporate shape memory alloys (SMAs) to realize shape transformation. Despite the availability of numerous analytical and finite element models for predicting the transient response of SMAHCs, many approaches exhibit limitations with respect to the thermomechanical coupling an

Hatice Merve Vural, Doga Kukul, Ege Erdem Ozlu, Demir Ekin Arikan

Humor is one of the few cognitive tasks where getting the reasoning right matters as much as getting the answer right. While recent work evaluates humor understanding on benchmarks such as the New Yorker Cartoon Caption Contest (NYCC), it largely treats it as black-box prediction, overlooking the structured reasoning processes underlying humor comprehension.

D Renu, Smitha Subramanian, Koshy George

Bars play an integral role in regulating star formation (SF) in spiral galaxies, from triggering central starbursts to driving quenching. The diverse SF morphologies observed in local barred galaxies reflect different evolutionary stages of the bar, motivating studies across these stages. Here we study 12 nearby barred galaxies (z=0.01-0.06) identified as ce

Jun Tian, Bin Li, Y. J. Liu, Z. J. Xu

We present comprehensive photometric characterisation of 2025 FA$_{22}$, a Potentially Hazardous Asteroid (PHA) discovered on 29 March 2025 and observed during the seventh International Asteroid Warning Network (IAWN) coordinated campaign. The asteroid's close approach at 2 lunar distances on 18 September 2025 provided an opportunity for rapid physical chara

Annalisa Baldi, Alessandro Rosa

The study of Sobolev and Poincar\'e inequalities for differential forms in Carnot groups and in the more general sub-Riemannian setting is still an open problem in its full generality. One may conjecture that, for general Carnot groups, these inequalities are expressed in terms of suitable graded Lebesgue norms. In recent years, many results have been obtain

Wenzhi Liu, Wei Wang, Liping Yuan, Tudor Zamfirescu

Let $S\subset \mathbb{R}^d$ $(d\geq 2)$. A set $S$ is said to be $m$-point convex, if for every $m$ distinct points in $S$, at least one of the line-segments determined by them lies in $S$. We also say that $S$ has property $P_m$. Let ${x,y,z}\in \mathbb{R}^{d}$. If $\mathrm{conv}\{x,y,z\}$ is a right triangle, then $\{x,y,z\}$ is called a {\it right triple}

A. A. Mazanik, F. S. Bergeret

We study the penetration of an external magnetic field into a superconductor with collinear $d$-wave altermagnetic order. We demonstrate that instead of circular Abrikosov vortices, the magnetic field generates elliptical vortices with their major axis oriented along one of the crystallographic axis, along which the altermagnetic spin splitting is maximal. U

Raunak Agarwal, Markus Wenzel, Simon Baur, Jonas Zimmer

Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human oversight. Multi-label text classification (MLTC) is a central task in this domain, yet remains challenging due to label imbalances, dependencies, and combinatorial complexity. Existing

Carlos S. Sepúlveda, Gonzalo A. Ruz

Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical methods are often compared on small ad hoc examples or on rectangular grids. This paper presents a reproducible benchmark of deterministic single-vehicle coverage path planning heuristics on irregular hexagon

Steven A. Senczyszyn, Timothy C. Havens, Nathaniel Rice, Jason E. Summers

As reinforcement learning (RL) deployments expand into safety-critical domains, existing evaluation methods fail to systematically identify hazards arising from the black-box nature of neural network enabled policies and distributional shift between training and deployment. This paper introduces Reinforcement Learning System-Theoretic Process Analysis (RL-ST

Alex Waldron, Hao Yin

We study the question of whether a sequence of non-instanton Yang-Mills connections can limit to a bubbling configuration composed only of instantons. In the case that the Uhlenbeck limit and the bubbles are of opposite charge, we determine an obstruction coming from deformations of the Uhlenbeck limit. As an application, we prove that instantons are the onl

Hanae Tilquin

Rare and very rare decays of third-generation particles, including $b$-hadrons and $\tau$ leptons, provide sensitive probes of physics beyond the Standard Model (SM). Unlike direct searches limited by collider energies, they probe new physics at much higher energy scales. Many of these decays have SM-predicted branching fractions below the sensitivity of cur

Maxwell Stolarski, Alex Waldron

We provide a comparatively simple proof of the dynamical stability of Ricci flow near a linearly stable Ricci-flat ALE metric with integrable deformations. Our proof relies on the equivalence between integrability and an "almost-orthogonality" property of the Ricci-DeTurck tensor, allowing us to analyze the latter directly. We obtain our main results in weig

Umer Ahmed, Syed Ahmed Mahmood, Fawad Javed Fateh, M. Shaheer Luqman

We propose a novel hierarchical spatiotemporal vector quantization framework for unsupervised skeleton-based temporal action segmentation. We first introduce a hierarchical approach, which includes two consecutive levels of vector quantization. Specifically, the lower level associates skeletons with fine-grained subactions, while the higher level further agg

Emel Altas, Ercan Kilicarslan, Onur Oktay, Bayram Tekin

We revisit static tidal perturbations of relativistic stars with emphasis on two technical issues in the standard quadrupolar formulation. First, we derive the regular-center Frobenius expansion of the interior even-parity master function and obtain a corrected subleading coefficient, which differs from the expression commonly used in the literature. Second,

Pirzada, Ali Muhammad, Tianjun Li, Imtiaz Khan

We present a solvable same-sector effective theory for anomaly-inspired axion inflation, in which a heavy trace-anomaly mode dynamically backreacts on the axion potential. The tree-level elimination of the radial field resums the backreaction into a closed-form Lambert-$W$ potential, naturally flattening the hilltop potential without external plateau operato

Fiammetta Battaglia, Elisa Prato

Symplectic and complex toric quasifolds are a generalization of toric manifolds and orbifolds to the nonrational case. In this paper, we reframe these notions from the viewpoint of algebraic geometry.

Rentian Yao, Young-Heon Kim, Geoffrey Schiebinger

In this paper, we explore quantitative stability of multi-marginal Schr\"odinger bridges with respect to the marginal constraints. We focus on the case where the number of marginal constraints is large (i.e. ``many-marginals"). When this number increases, we show that the Kullback--Leibler (KL) divergence between two multi-marginal Schr\"odinger bridges, as

Ziyang Chen, Renbing Chen, Daowei Li, Jinzhi Liao

Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, building a trustworthy simulator faces two structural challenges. First, information incompleteness causes reasoning-based simulators to over-rationalize when unobserved factors such as offline context and implici

Tingjia Miao, Wenkai Jin, Muhua Zhang, Jinxin Tan

The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we presen

Gal Binyamini, Noriko Hirata-Kohno, Makoto Kawashima, Yuval Salant

Let X be a set definable in a sharply o-minimal structure. We consider the problem of counting the number of points where X intersects algebraic varieties V over Q of dimension k < codim X, as a function of T := deg(V) + h(V), where h(V) is the log-height of V. In particular, we conjecture that after removing a suitable "algebraic part", this number grows po

Huawei Ji, Yuanhao Sun, Yuan Jin, Cheng Deng

Visual token pruning methods effectively mitigate the quadratic computational growth caused by processing high-resolution images and video frames in vision-language models (VLMs). However, existing approaches rely on predefined pruning configurations without determining whether they achieve computation-performance optimality. In this work, we introduce , a n

Marcel Wagenländer, Otto White, Britannio Jarrett, Pedro Silvestre

Agentic workflows carry out complex tasks by orchestrating multiple large language models (LLMs) and tools. Serving such workflows at a target throughput with low latency is challenging because they can be defined using arbitrary agentic frameworks and exhibit unpredictable execution times: execution may branch, fan-out, or recur in data-dependent ways. Sinc

Mitch Adler, Matthew Russo, Michael Cafarella

In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of system that we call Agent-Aided Design. These systems place an agent in a feedback loop in which it generates an assembly of CAD model(s), visualizes the assembly, and then iteratively refines its assembly based

Mert Baştuğ

We investigate the asymptotic behavior of the solutions to the Neumann sieve problem for the Poisson equation in a thin, randomly perforated domain. The perforations (sieve-holes) are generated by a stationary marked point process. According to the scaling between the domain thickness and the typical hole size, three distinct limiting regimes emerge. We also

Bryanne McDonough, Sathvika S. Iyengar, Ansa Brew-Smith, Asa F. L. Bluck

We apply Random Forest and XGBoost machine learning algorithms to determine which galaxy properties most effectively predict star formation and quenching in simulated galaxies. Using spatially-resolved data from approximately 63,000 annular bins across 6,189 TNG100 galaxies, we train classification models to predict quenching states and regression models to

Teng Ma, Luca Rosafalco, Wei Cui, Lin Zhao

Extrapolative prediction of complex nonlinear dynamics remains a central challenge in engineering. This study proposes a one-shot learning method to identify global frequency-response curves from a single excitation time history by learning governing equations. We introduce MEv-SINDy (Multi-frequency Evolutionary Sparse Identification of Nonlinear Dynamics)

Nuno Gonçalves, Hugo Pitorro, Vlad Niculae, Edoardo Ponti

Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of work is $\alpha$-entmax attention, a differentiable sparse alternative to softmax that enables input-dependent sparsity yet has lagged behind softmax due to the computational overhead necessary to com

Yulia Koroleva

We study a model problem on the filtration of a conducting fluid through a porous layer. A porous medium is presented as an assemblage of identical spherical cells. Each cell consists of a porous core and liquid shell. We show the dependence of each flow parameter on the Debye radius which characterizes how far the influence of a charge extends in electrolyt

Miguel Carrasco-Arango, Rosa M. Badia, Artur Garcia-Saez

The Metropolis-Hastings algorithm is a cornerstone of Markov Chain Monte Carlo methods, underpinning a wide range of applications in computational physics, Bayesian inference, and machine learning. Quantum variants of Metropolis-Hastings promise accelerated mixing through quantum walks, but their practical realisation remains challenging. In this work, we co

Ranjith Chodavarapu, Lei Xu

KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation. This assumption fails under standard FP16 precision: cache-ON and cache-OFF execution paths employ different floating-point accumulation orderings which, due to FP16 non-associativity, produce a deterministic

J. B. Pendry, S. A. R. Horsley

In the context of waves in space time modulated materials, we ask two questions how much energy does it cost to break time reversal symmetry and transition to a PT symmetric state. and can a PT symmetric system have a ground state in the sense that no photons are present. Our model system is a periodic metamaterial set in virtual motion with velocity cg to b

Enrico Formenti, Eric Goles, Kévin Perrot, Martín Ríos-Wilson

Fungal automata are a nature-inspired computational model, where a rule is alternatively applied verticaly and horizontaly. In this work we study the computational complexity of predicting the dynamics of all fungal freezing totalistic one-dimentional rules of radius $1$, exhibiting various behaviors. Despite efficiently predictable in most cases (with non-d

Yifei Wang, Xuhui Feng, Shimin Pan, Liangfan Zhu

This paper presents a real-time computational framework for multi-node distributed optimization by extending the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm. Our approach integrates adjoint sequential quadratic programming (SQP) techniques to enable efficient approximation of Jacobian information within the ALADIN embedded qu

Marisol Alcántara Ortigoza, Sergey Stolbov

By applying our methodology, we propose a defect in 4H-SiC which combines a Si vacancy and a C atom substituted with S (VSiSC) to have a spin-triplet ground state with the spin qubit functionality. Our calculations confirm that all configurations of the defect have a dynamically and thermodynamically stable triplet ground state and higher energy singlet stat

Yoo-Min Jung, Leekyung Kim

Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses. T

Qing-Hu Hou, Zhi-Wei Sun

In this paper, we evaluate some series via the WZ method, and confirm several previous conjectures. For example, we prove the following two identities conjectured by the second author: $$\sum_{k=0}^{\infty} \frac{(28k^2 + 10k + 1) \binom{2k}{k}^5}{(6k + 1)(-64)^k \binom{3k}{k} \binom{6k}{3k}} = \frac{3}{\pi}$$ and $$\sum_{k=1}^\infty \frac{d^4}{dk^4}\left(\f

Haohui Mai, Xiaoyan Guo, Xiangyun Ding, Daifeng Li

LLM-based coding agents can generate functionally correct GPU kernels, yet their performance remains far below hand-optimized libraries on critical computations such as matrix multiplication, attention, and Mixture-of-Experts (MoE). Peak GPU performance requires coordinated reasoning over tightly coupled optimizations, including tiling, shared-memory staging

Seifeldin Abdellatif, Ahmad Almasri

Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet standard variable-length attention APIs -- including FlashAttention-2's varlen and PyTorch's NestedTensor SDPA -- fail to translate these savings into proportional wall-clock gains at the short post-pruning sequence leng

Onno Niemann, Gonzalo Martínez Muñoz, Alberto Suárez Gonzalez

Recent work has shown that diffusion models trained with the denoising score matching (DSM) objective often violate the Fokker--Planck (FP) equation that governs the evolution of the true data density. Directly penalizing these deviations in the objective function reduces their magnitude but introduces a significant computational overhead. It is also observe

Youngjin Oh, Junyoung Park, Junhyeong Kwon, Nam Ik Cho

Adverse lighting conditions, such as cast shadows and irregular illumination, pose significant challenges to computer vision systems by degrading visibility and color fidelity. Consequently, effective shadow removal and ALN are critical for restoring underlying image content, improving perceptual quality, and facilitating robust performance in downstream tas

Aleksander Berezowski, Hassan Hassanzadeh, Gouri Ginde

Oil and gas drilling operations generate extensive time-series data from surface sensors, yet accurate real-time prediction of critical downhole metrics remains challenging due to the scarcity of labelled downhole measurements. This systematic mapping study reviews thirteen papers published between 2015 and 2025 to assess the potential of Masked Autoencoder

David Perez-Saura, Miguel Fernandez-Cortizas, Alvaro J. Gaona, Pascual Campoy

Autonomous drone racing demands robust real-time localization under extreme conditions: high-speed flight, aggressive maneuvers, and payload-constrained platforms that often rely on a single camera for perception. Existing visual SLAM systems, while effective in general scenarios, struggle with motion blur and feature instability inherent to racing dynamics,

Marcus Armstrong

Post-training quantization (PTQ) assumes that a well-converged model is a quantization-ready model. We show this assumption fails in a structured, measurable, and previously uncharacterized way. Using a calibration-free per-group INT4 probe applied to all 154 publicly available Pythia-160m training checkpoints, we identify a three-phase divergence structure:

Lisa Vasileva, Karin Sim

LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overgenerations are different from the neurobabble seen in NMT and range from LLM self-explanations, to risky confabulations, to appropriate explanations, where the LLM is able to act as a human translator would, e

Andrea Dotto, Bao V. Le Hung

Under standard assumptions, we compute the GK-dimension of Hecke eigenspaces in the mod $p$ cohomology of an inner form $D^\times$ of $\mathrm{GL}_2$ over a totally real field unramified at $p$, allowing $D$ to be a division algebra at $p$. Our arguments also apply when $D$ is a matrix algebra at $p$, in which case they give a simplified proof of a theorem o

Boyan Li, Ou Ocean Kun Hei, Yue Yu, Yuyu Luo

While Large Language Models (LLMs) demonstrate impressive proficiency in generating SQL queries, they fundamentally lack the capability to self-evaluate correctness without an execution oracle. This limitation creates a stark Generation-Selection Gap, where high potential accuracy (Pass@K) fails to translate into execution accuracy (Pass@1). Although supervi

Jinhao Jia, Yingru Li, Ran Liang, Mei Zhang

We propose a coherent control scheme for engineering quantum correlations in a cavity optomechanical (COM) system consisting of a driven optical cavity with an embedded nonlinear medium and a membrane, assisted by a coherent feedback loop. The nonlinear medium and the membrane are pumped to implement optical and mechanical parametric amplifications with cont

James Dark, Colin Weaver, Remi Delaporte-Mathurin, Sara Ferry

A gas-driven permeation (GDP) platform, SHIELD (Salt-compatible Hydrogen barrier Investigation and EvaLuation for fusion Devices), has been developed to measure hydrogen transport properties in structural materials under controlled thermal and pressure conditions. The system is designed to minimise experimental uncertainties associated with leaks, temperatur

Sofía Rodríguez-Ballesteros, Javier Alcaraz, Laura Anton-Sanchez, Marc Goerigk

In this work, we study a single-machine scheduling problem that aims at minimizing the total cost of a schedule subject to start-time dependent costs. This framework naturally captures scenarios where costs fluctuate throughout the day, such as time-varying energy or labor prices. To model more realistic scenarios, we assume that these costs lie within a bud

S. Dalla, K. Herbst, R. Muscheler, M. J. Owens

Ions and electrons accelerated to high energies during flares and coronal mass ejections at the Sun may escape the solar atmosphere and, guided by the interplanetary magnetic fields, propagate through space to near-Earth locations. These Solar Energetic Particles (SEPs) can be detected directly by spacecraft instrumentation. The highest energy SEPs may also

R. MacLellan, P. Acharya, B. Aharmim, S. Alcantar Anguiano

Material radioactivity compilations, such as the one presented here, are important enablers of science. They are useful for the selection of radiopure materials used in the design and construction of low-energy rare-event search experiments. They allow researchers developing such experiments to save time on material studies and avoid costly duplication of ef

Hari K. Kunduri, James Lucietti

We establish existence and uniqueness results for asymptotically locally Euclidean (ALE) and asymptotically locally flat (ALF) gravitational instantons. In particular, we prove the existence of a unique, Ricci-flat, toric ALE and ALF gravitational instanton, for every admissible rod structure, that is smooth up to possible conical singularites. We also give

Javier de la Cruz

Let $F=\mathbb{F}_q$ and let $K=\mathbb{F}_{q^m}$ be a finite extension of $F$. An additive left group code is a left $FG$-submodule of the group algebra $KG$. Classical idempotent group codes describe images of $KG$-linear projectors and are necessarily $K$-linear. They therefore do not provide a sufficiently broad framework for additive group codes, which

Alice Bazzanella, Carlo Sanna

Let $N$ be a positive integer and let $S_N$ be the set of polynomials with integer coefficients, degree less than $N$, and minimal positive integral over $[0,1]$. D. Bazzanella initiated the study of $S_N$ because of its relation to the distribution of prime numbers. Indeed, it is possible to prove that $\sum_{p^m \leq N} \log p = -\log \int_0^1 P(x) \mathrm

Jonas Rose, Hai Nguyen, Moritz Meißner, Zbigniew Galazka

In this study, we present a comprehensive analysis of the Raman active phonon modes in orthorhombic LaInO$_3$ based on a combination of polarization-angle resolved Raman spectroscopy and density functional theory calculations. By using backscattering from multiple crystallographic surface orientations and employing a full symmetry analysis, we identify and a

Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver, Yidi Qi

Random matrices provide a well-established statistical model for a range of arithmetic phenomena. In this paper, we investigate the extent to which one- and two-dimensional convolutional neural networks (CNNs) can distinguish between arithmetic data arising from elliptic curves with conductor in a fixed interval and random matrix data drawn from the same Sat

Jintao Deng, Ryo Toyota

Let $X$ and $Y$ be uniformly locally finite metric spaces. In this paper, we prove the quantitative coarse Baum--Connes conjecture for the free product $X*Y$ under the assumption that the conjecture holds for both $X$ and $Y$.

Zihao Xu, John Harvill, Ziwei Fan, Yizhou Sun

Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically with input length. Token compression aims to address this challenge by reducing the number of tokens representing inputs. However, existing prompt-compression approaches primarily operate in token space and o

Serik Sagitov

We revisit the random allocation model in which $n$ balls are independently placed into $N$ boxes with probabilities $q_1,\ldots,q_N$. A classical asymptotic result due to Kolchin, Sevastyanov, and Chistyakov for the expectations, variances, and covariances of the occupancy counts is reformulated in a compact and transparent form in terms of the load of a ra

Alexey Khoroshilov, Alexey Chernysh, Orkhan Ekhtibarov, Nini Kamkia

Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic trading strategies remains underexplored. Unlike standard code benchmarks, trading-strategy generation requires simultaneous mastery of domain-specific financial logic, knowledge of a specialized API, and the a

Lukas Helff, Quentin Delfosse, David Steinmann, Ruben Härle

As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode emerges: LLMs gaming verifiers. We study this phenomenon on inductive reasoning tasks, where models must induce and output logical rules. We find that RLVR-trained models systematically abandon rule inductio

Zihan Liang, Yufei Ma, Ben Chen, Zhipeng Qian

Reinforcement learning has emerged as an effective paradigm for training large language models to perform search-augmented reasoning. However, existing approaches rely on trajectory-level rewards that cannot distinguish precise search queries from vague or redundant ones within a rollout group, and collapse to a near-zero gradient signal whenever every sampl

Achyuta Ranjan Dutta Mohapatra

We are concerned in designing a suitable numerical scheme based on the equal-order hybrid high-order (HHO) method for the linear parabolic integro-differential equations. The spatial discretization is made using the equal-order HHO method and subsequently we perform the stability analysis of the corresponding semi-discrete scheme. The convergence results are

Moritz Hauck, Roland Maier, Timo Sprekeler

We study the finite element approximation of linear second-order elliptic partial differential equations in nondivergence form with highly heterogeneous diffusion and drift coefficients. A generalized Cordes condition is imposed to guarantee that a suitably renormalized version of the nondivergence-form differential operator is near the Laplacian. Based on a

Duan Zhou

This work simulates the developmental process of cortical neurogenesis, initiating from a single stem cell and governed by gene regulatory rules derived from mouse single-cell transcriptomic data. The developmental process spontaneously generates a heterogeneous population of 5,000 cells, yet yields only 85 mature neurons - merely 1.7% of the total populatio

Nick Gurski, Niles Johnson

We introduce a notion of parity for formal morphisms between invertible objects and use it to prove a corresponding coherence theorem. Parity is conceptually similar to the sign of underlying permutations, but not defined as such. To give complete details, this work includes a thorough treatment of the free permutative category on an invertible generator, it

Haoyu Yun, Hamid Krim, Yufang Bao

Higher-order learning is fundamentally rooted in exploiting compositional features. It clearly hinges on enriching the representation by more elaborate interactions of the data which, in turn, tends to increase the model complexity of conventional large-scale deep learning models. In this paper, a kernelized Volterra Neural Network (kVNN) is proposed. The ke

Neha Srikanth, Jordan Boyd-Graber, Rachel Rudinger

We introduce DiscoTrace, a method to identify the rhetorical strategies answerers use when responding to information-seeking questions. DiscoTrace represents answers as a sequence of question-related discourse acts paired with interpretations of the original question, annotated on top of rhetorical structure theory parses. Applying DiscoTrace to answers from

Ata Bilgin, Erkin Yapıcı, Yusuf İslam Tek, Ertuğrul Başar

By exploiting noise as an information-bearing re source, noise-driven communication offers a promising frame work for low-complexity wireless system design. In this letter, the scheme of ternary noise modulation (T-NoiseMod) is proposed for noise-based wireless communication scenarios, where infor mation is encoded into the statistical characteristics of art

Alison Charlesworth, Christopher Ramsey, Nicolae Strungaru

The 1-2-3 conjecture has been solved positively in 2024 for finite graphs and by extension for infinite graphs which are locally finite. The solution is non-constructive, and finding explicit solutions for large (or infinite) graphs is very hard. By exploiting the extra structure present in many non-periodic tilings, we find explicit solutions for the Chair

Taras Vasyliev, Saulius Juodkazis, Valeri Lozovski

This paper is devoted to studies of the mechanical deformation of the S. aureus cell wall. The bacterium is modelled as a thin elastic membrane containing cytoplasm, which is treated as an incompressible fluid. Deformation occurs via Van der Waals interactions between the bacterium and a solid metallic surface, both with and without the influence of surface

XiangRui Zhang, Qiang Li, Haining Wang

Binary analysis increasingly relies on large language models (LLMs) to perform semantic reasoning over complex program behaviors. However, existing approaches largely adopt a one-pass execution paradigm, where reasoning operates over a fixed program representation constructed by static analysis tools. This formulation limits the ability to adapt exploration

Rikhav Shah, John Urschel

The discrete Fourier transform matrix is one of the most important matrices in linear algebra, and submatrices of it arise in a variety of applications. Though the discrete Fourier transform matrix is unitary, its submatrices can be exponentially ill-conditioned, an obstacle to accurate computation. This work resolves the exact rate of the exponential ill-co

Olga Loginova, Frank Keller

Reliable procedural monitoring in video requires exposure to naturally occurring human errors and the recoveries that follow. In egocentric recordings, mistakes are often partially occluded by hands and revealed through subtle object state changes, while existing procedural datasets provide limited and inconsistent mistake and correction traces. We present P