December 2025 arXiv papers — page 27

Jianxiang Xie, Yuncheng Hua, Mingyue Cheng, Flora Salim

While modern multivariate forecasters such as Transformers and GNNs achieve strong benchmark performance, they often suffer from systematic errors at specific variables or horizons and, critically, lack guarantees against performance degradation in deployment. Existing post-hoc residual correction methods attempt to fix these errors, but are inherently greed

Amy K. Lin, Natan A. Spear, Geoffrey A. Blake, Scott K. Cushing

Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate superionic conducti

Y. Sungtaek Ju

Reconstructing time-resolved flow fields from temporally sparse velocimetry measurements is critical for characterizing many complex thermal-fluid systems. We introduce a machine learning framework for uncertainty-aware flow reconstruction using sparse variational Gaussian processes in the Kolmogorov-Arnold network topology (SVGP-KAN). This approach extends

Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim, Kundan Thind

Fluence map prediction is central to automated radiotherapy planning but remains an ill-posed inverse problem due to the complex relationship between volumetric anatomy and beam-intensity modulation. Convolutional methods in prior work often struggle to capture long-range dependencies, which can lead to structurally inconsistent or physically unrealizable pl

Caitlin LaNeve, Edward J Oughton, Noah Rivera, Lucy Wilkerson

The Gannon Storm in May 2024 was the largest space weather event experienced in 20 years, generating auroras latitudes as low as 35{\deg}. Such activity can pose significant operational challenges for critical infrastructure operators, particularly those managing electricity transmission networks, satellite constellations, and aviation systems. Substantial p

Amil Khan, Matheus Palhares Viana, Suraj Mishra, B. S. Manjunath

Label-free 3D brightfield microscopy offers a fast and noninvasive way to visualize cellular morphology, yet robust volumetric segmentation still typically depends on fluorescence or heavy post-processing. We address this gap by introducing Bright-4B, a 4 billion parameter foundation model that learns on the unit hypersphere to segment subcellular structures

Jonathan Lam, Roberto Manduchi

This contribution describes S-BLE, a data set created for supporting the design of robust and reliable Be-In-Be-Out systems in public transit. S-BLE was recorded by the smartphones of 28 participants during their daily transit routines in a university campus setting. 20 shuttle bus vehicles in the campus fleet were equipped with two Bluetooth low energy (BLE

Zihan Lin, QiZhi He

We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknown heterogeneous parameter fields in a low-dimensional nonlin

Mohannad Alkhraijah, Devon Sigler, Daniel K. Molzahn

Efficiently solving large-scale optimal power flow (OPF) problems is challenging due to the high dimensionality and interconnectivity of modern power systems. Decomposition methods offer a promising solution via partitioning large problems into smaller subproblems that can be solved in parallel, often with local information. These approaches reduce computati

Yi-Hung Chou, Boyuan Jiang, Yi Wen Chen, Mingyue Weng

Large language models (LLMs) are reshaping software engineering by enabling "vibe coding," in which developers build software primarily through prompts rather than writing code. Although widely publicized as a productivity breakthrough, little is known about how practitioners actually define and engage in these practices. To shed light on this emerging pheno

Vasileios Koutavas, Yu-Yang Lin, Nikos Tzevelekos

We present a game semantics framework for open-world safety analysis of Ethereum smart contracts. We model the interaction between a contract and its environment as a two-player game between the contract and the environment, and prove up to gas model approximations soundness: every assertion violation found corresponds to a real execution; and completeness:

Chenggong Zhang, Haopeng Wang, Hexi Meng

Hallucinations in Large Language Models (LLMs) pose a significant challenge, generating misleading or unverifiable content that undermines trust and reliability. Existing evaluation methods, such as KnowHalu, employ multi-stage verification but suffer from high computational costs. To address this, we integrate the Hughes Hallucination Evaluation Model (HHEM

Eugenio Simao

Biological systems organize into hierarchies to manage complexity, yet the mechanisms governing hierarchical control remain incompletely understood. Using information theory and the Lambda phage lysis-lysogeny decision as a model system, we discover that hierarchical control operates through hierarchical preemption - higher layers collapse decision space rat

Simar Kareer, Karl Pertsch, James Darpinian, Judy Hoffman

Vision-language-action (VLA) models can enable broad open world generalization, but require large and diverse datasets. It is appealing to consider whether some of this data can come from human videos, which cover diverse real-world situations and are easy to obtain. However, it is difficult to train VLAs with human videos alone, and establishing a mapping b

Mert Ikinci, Luna Toma, Karin U. Loeffler, Leticia Ussem

Accurate grading of Conjunctival Melanocytic Intraepithelial Lesions (CMIL) is essential for treatment and melanoma prediction but remains difficult due to subtle morphological cues and interrelated diagnostic criteria. We introduce INTERACT-CMIL, a multi-head deep learning framework that jointly predicts five histopathological axes; WHO4, WHO5, horizontal s

Tatsuhiro Hattori, Kazuyuki Sekizawa

In the outer core of neutron stars, $^3$P$_2$ superfluid neutrons and $^1$S$_0$ superconducting protons are deemed to exist, forming quantum vortices and magnetic fluxtubes, respectively. Those quantum vortices and fluxtubes play an important role in explaining observed sudden changes of rotational frequency, known as pulsar ``glitches.'' While the m

Nikolay V. Golubev, Mohammed Th. Hassan

How quantum electron and nuclei motions affect biomolecular chemical reactions remains a central challengeable question at the interface of quantum chemistry and biology. Ultrafast charge migration in deoxyribonucleic acid (DNA) has long been hypothesized to play a critical role in photochemistry, genome stability, and long-range biomolecular signaling, howe

Xin Guan

We present a numerical study of three-dimensional gravity-capillary standing waves by using cubic and quintic truncated Hamiltonian formulations and the Craig-Sulem expansion of the Dirichlet-Neumann operator (DNO). The resulting models are treated as triply periodic boundary-value problems and solved via a spatio-temporal collocation method without executin

J. Bezerra-Sobrinho, L. G. Medeiros

We analyze the Starobinsky inflation model and the impact of curvature corrections, particularly a cubic $R^3$ term, to assess their behavior in light of the latest observational results from the Atacama Cosmology Telescope (ACT). With the recent sixth data release (DR6), the scalar spectral index was measured to be $n_s=0.9743 \pm 0.0034$, which appears to

Jean C. Pereira, Valter J. S. Leite, Guilherme V. Raffo

This paper addresses the motion control problem for underactuated mechanical systems with full attitude control and one translational force input to manage the six degrees of freedom involved in the three-dimensional Euclidean space. These systems are often classified as second-order nonholonomic due to their completely nonintegrable acceleration constraints

Marco Giunti

This paper critically examines the recent publication "ChatGPT-4 in the Turing Test" by Restrepo Echavarría (2025), challenging its central claims regarding the absence of minimally serious test implementations and the conclusion that ChatGPT-4 fails the Turing Test. The analysis reveals that the criticisms based on rigid criteria and limited experim

Hongying Huang, Huili Zhang, Xiangcheng Zheng

We develop a numerical scheme for subdiffusion of variable exponent by combining the $L2-1_σ$ temporal discretization with finite element spatial approximation. In existing works, determining the superconvergence points requires solving a nonlinear equation related to the variable exponent at each time step. This work relaxes the selection criterion of super

Feiyi Liu, Min Guo, Mingyang Liu, Ruanjing Zhang

In this paper, we study excitations and dissipation in two infinite parallel metallic plates undergoing relative motion. The degrees of freedom of the electrons in both plates are modeled using the 1+2 dimensional Dirac field, and a nonlocal potential is selected to describe the interaction between the two plates. The internal relative motion is introduced v

Y. Belousov

In this paper, we introduce a prime factorization of open meanders, articulated through the framework of 2-colored operads. We demonstrate that each open meander can be canonically constructed from building blocks of two types: iterated snakes and irreducible meanders. We find out that iterated snakes allow efficient enumeration, and thus the problem of enum

Yunhong Lyu, Bouchra R. Nasri, Bruno N. Rémillard

In this article, we study sequential change-point methods for discretely observed generalized Ornstein-Uhlenbeck processes with periodic drift. Two detection methods are proposed, and their respective performance is studied through numerical experiments for several choices of parameters.

David Garofalo, Zhiyuan Liu, Atticus V. Magerko

Double-double radio galaxies (DDRGs) display inner and outer jets or lobes thought to result from intermittent accretion. Due to randomly triggered accretion events, the lifetime of the retriggered jet is not expected to have any connection to the time of quiescence between jets, yet we show that a correlation between the two quantities may exist, which we i

Hangyang Meng, Yuting Yang

Let $G$ be a finite group. For a prime $p$ and an integer $e \geq 0$, we denote by $\Gamma_{p,e}(G)$ the set of all pairs $(H, \varphi)$, where $H$ is a $p$-subgroup of $G$ of order greater than $p^e$ and $\varphi$ is a complex irreducible character of $H$. In this paper, we investigate the connected components of the poset $\Gamma_{p,e}(G)$. For the case $e

Amro Gamar, Ahmed Abduljalil, Alargam Mohammed, Ali Elhenidy

This paper presents the development of a fully autonomous delivery robot integrating mechanical engineering, embedded systems, and artificial intelligence. The platform employs a heterogeneous computing architecture, with RPi 5 and ROS 2 handling AI-based perception and path planning, while ESP32 running FreeRTOS ensures real-time motor control. The mechanic

Carl Christopher Haynes-Magyar

Lowering the barriers to computer programming requires understanding how to scaffold learning. Parsons problems, which require learners to drag-and-drop blocks of code into the correct order and indentation, are proving to be beneficial for scaffolding learning how to write code from scratch. But little is known about the ability of other problem types to do

Hansang Lee, Chaelin Lee, Nieun Seo, Joon Seok Lim

We propose DeFloMat (Detection with Flow Matching), a novel generative object detection framework that addresses the critical latency bottleneck of diffusion-based detectors, such as DiffusionDet, by integrating Conditional Flow Matching (CFM). Diffusion models achieve high accuracy by formulating detection as a multi-step stochastic denoising process, but t

Tahereh Azizi, Mojtaba Alimoradi

We study a thick brane scenario within the Palatini formulation of gravity, where the metric and affine connection are treated as independent variables. By introducing a non-minimal coupling between a bulk scalar field and the Ricci scalar, we obtain analytic solutions under a flat, four-dimensional Poincar\'e-invariant metric with a kink-like scalar configu

Quanzhi Fu, Qiyu Wu, Dan Williams

With the significant increase in enrollment in computing-related programs over the past 20 years, lecture sizes have grown correspondingly. In large lectures, instructors face challenges on identifying students' knowledge gaps timely, which is critical for effective teaching. Existing classroom response systems rely on instructor-initiated interactions, whic

Chia-Yu Hsu, Shubhanshu Shekhar

We study the problem of active nonparametric sequential two-sample testing over multiple heterogeneous data sources. In each time slot, a decision-maker adaptively selects one of $K$ data sources and receives a paired sample generated from that source for testing. The goal is to decide as quickly as possible whether the pairs are generated from the same dist

Bhanu Prakash Vangala, Tanu Malik

Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framework that makes self-hosted LLM orchestration scalable and ec

Micah Chrisman, Killian Davis, Anup Poudel

For links $L \subset \Sigma \times [0,1]$, where $\Sigma$ is a closed orientable surface, we define a $U_q(\mathfrak{gl}(1|1))$ Reshetikhin-Turaev invariant with coefficients in $\mathbb{Z}[H_1(\Sigma)]$. This invariant turns out to be equivalent to an infinite cyclic version of the Carter-Silver-Williams (CSW) polynomial. The importance of the CSW polynomia

Alberto Escalante, Edmundo Suárez-Polo, Luis A. Huerta-del Campo

A detailed canonical analysis for Pontryagin and Euler classes with a Barbero-Immirzi [BI] parameter is developed. We rewrite the topological invariants by introducing a set of Holst-like variables, and then study the set of all constraints. We report the complete canonical structure and the symmetries of the theory; we count the physical degrees of freedom

Ziyang Wang

Artificial Intelligence (AI) is transforming domains from healthcare and agriculture to finance and industry. As progress on Earth meets growing constraints, the next frontier is outer space, where AI can enable autonomous, resilient operations under extreme uncertainty and limited human oversight. This paper introduces Space AI as a unified interdisciplinar

Ozan Oguztuzun, Cerag Oguztuzun

Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that adapts frozen KG embeddings to individual us

Behzad Parvaresh, Rahmat K. Adesunkanmi, Adel Alaeddini

Continuous fiber-reinforced composite manufactured by additive manufacturing (CFRC-AM) offers opportunities for printing lightweight materials with high specific strength. However, their performance is sensitive to the interaction of process and material parameters, making exhaustive experimental testing impractical. In this study, we introduce a data-effici

Bhanu Prakash Vangala, Sajid Mahmud, Pawan Neupane, Joel Selvaraj

Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMa

Brendan J. Mahoney, Craig S. Lent

The transverse-field Ising model is useful for studying interacting qubit arrays. The Lieb--Robinson correlation function can be used to characterize the propagation of quantum information in Ising chains. Considerable work has been done to establish bounds on this correlation function in various circumstances. To actually calculate the value of the correlat

Jean-Pierre Magnot

We study orthogonal polynomial systems arising from general pre-Hilbert inner products on polynomial spaces, beyond the classical framework of measures. To each such inner product we associate a canonical Laplacian defined from an abstract derivation, and we investigate the operator-theoretic structures induced by this construction. Our main contribution is

Megh Bahadur KC, Ziqi Song

The move towards electric school buses (ESBs) marks a critical step in creating a healthier and more sustainable future for students. To meet the ambitious goal of zero-emission school buses by 2035,this review focuses on the need assessment, practices, gaps, challenges, and way forward. We conducted a comprehensive assessment of more than 100 relevant sourc

Alexander Venus, Erik Leitinger, Klaus Witrisal

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach for future 6G networks to jointly estimate the positions of transmitters and receivers together with the propagation environment. In cooperative MP-SLAM, information collected by multiple mobile-terminals (MTs) is fused to enhance accuracy and robustness. Existing methods

Himanshu Naidu, Yuxiang Zhang, Sachin Mehta, Anat Caspi

Accurate, up-to-date sidewalk data is essential for building accessible and inclusive pedestrian infrastructure, yet current approaches to data collection are often costly, fragmented, and difficult to scale. We introduce iOSPointMapper, a mobile application that enables real-time, privacy-conscious sidewalk mapping on the ground, using recent-generation iPh

Chandrasekhar Gokavarapu

Let \(\T\) be a commutative ternary \(\Gm\)-semiring in the sense of the triadic, \(\Gm\)-parametrized multiplication \(\{a,b,c\}_{\gamma}\). Building on the affine \(\Gm\)-spectrum \(\SpecG(\T)\), the structure sheaf, and the equivalence between \(\Gm\)-modules and quasi-coherent \(\Gm\)-sheaves on affine \(\Gm\)-schemes, we construct and organize the deriv

Yuze Li, Srinivasan Ramachandra Sharma, Charitha Saumya, Ali R. Butt

Branch mispredictions cause catastrophic performance penalties in modern processors, leading to performance loss. While hardware predictors and profile-guided techniques exist, data-dependent branches with irregular patterns remain challenging. Traditional if-conversion eliminates branches via software predication but faces limitations on architectures like

Jesús Algaba, Samuel Blazquez, Cristóbal Romero-Guzmán, Carlos Vega

Carbon dioxide (CO2) hydrates hold promising applications in capturing and separating CO2 for climate change mitigation. Understanding their behavior at the molecular level is therefore essential, and computer simulations have become powerful tools for exploring their formation and stability, providing valuable insights into their underlying mechanisms. In t

Omar Alsaqa, Linh Thi Hoang, Muhammed Fatih Balin

Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their application to large graphs is hindered by computational costs. The need to process every neighbor for each node creates memory and computational bottlenecks. To address this, we introduce BLISS, a Bandit Layer Importance Sampling Strategy. It uses multi-armed

Athithan Elamaran, Christian Ferko, Sterling Scarlett

We propose a data-driven approach to identifying the functionally independent invariants that can be constructed from a tensor with a given symmetry structure. Our algorithm proceeds by first enumerating graphs, or tensor networks, that represent inequivalent contractions of a product of tensors, computing instances of these scalars using randomly generated

Bhanu Prakash Vangala, Ali Adibifar, Ashish Gehani, Tanu Malik

The rise of Large Language Models (LLMs) as coding agents promises to accelerate software development, but their impact on generated code reproducibility remains largely unexplored. This paper presents an empirical study investigating whether LLM-generated code can be executed successfully in a clean environment with only OS packages and using only the depen

Xuegang Hao, Ming Zhang, Alex Li, Xiangyu Qian

Traditional recommendation systems suffer from inconsistency in multi-stage optimization objectives. Generative Recommendation (GR) mitigates them through an end-to-end framework; however, existing methods still rely on matching mechanisms based on inductive patterns. Although responsive, they lack the ability to uncover complex user intents that require ded

Purushottam Saha, Avirup Chakraborty, Sourish Sarkar, Subhamoy Maitra

The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-evaluation metric termed MinDist. The metric modifies the MinScore metric by quantifying the edit distance betwe

Florian Lindenbauer

This thesis focuses on how jets are modified by the nonequilibrium quark-gluon plasma during the initial stages in heavy-ion collisions. Its influence on their propagation is typically encoded in a single medium function, the dipole cross section. Its small distance behavior is characterized by the jet quenching parameter $\hat q$, and we obtain its numerica

Mingsheng Ying

In quantum information and computation research, symbolic methods have been widely used for human specification and reasoning about quantum states and operations. At the same time, they are essential for ensuring the scalability and efficiency of automated reasoning and verification tools for quantum algorithms and programs. However, a formal theory for symb

Bruno Mlodozeniec, Pierre Ablin, Louis Béthune, Dan Busbridge

Hyperparameter tuning can dramatically impact training stability and final performance of large-scale models. Recent works on neural network parameterisations, such as $\mu$P, have enabled transfer of optimal global hyperparameters across model sizes. These works propose an empirical practice of search for optimal global base hyperparameters at a small model

Mohammad Zakaria Haider, Amit Kumar Podder, Prabin Mali, Aranya Chakrabortty

The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks necessitates intelligent and adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network that is trained and optimized through a multi-agent reinforcement learning model. PHANTOM integrat

Pavel Stránský, Pavel Cejnar

We present a method for preparing various exotic modifications of Schr{ö}dinger cat states by coupling a semiclassical oscillator to a system of qubits. Varying the number of qubits and parameters of the protocol (involving quantum quench of the coupled system and a subsequent spin measurement), we bring the oscillator into a coherent superposition composed

Tanayveer Singh Bhatia, Robert H. Cameron, Sami K. Solanki, Damien F. Przybylski

Stellar convection in the presence of magnetic field affects the emergent intensity, as well as the structure and evolution of cool main-sequence dwarfs. We aim to understand the effect of faculae-like field strengths on near-surface stellar convection using 3D radiative MHD simulations of near-surface magneto-convection. We compare simulations of F, G, K an

Donggyun Bae, Jongil Park

We propose a novel framework, termed Fourier-Activated Adapter (FAA), for parameter-efficient fine-tuning of large pre-trained language models. By incorporating random Fourier features into lightweight adapter modules, FAA decomposes intermediate representations into complementary low- and high-frequency components, enabling frequency-aware modulation of sem

Sajjad Taravati

This paper introduces temporal Bragg gratings as a new class of broadband, reconfigurable parametric amplifiers. We present a comprehensive investigation of power amplification in temporal Bragg gratings, spatially periodic structures with refractive index modulated near the Bragg frequency. Through systematic numerical simulations, we explore the effects of

Zubaida Mohammed Albadani, Mohammed Q. Shormani

This study investigates the syntax of qulk-clauses in Yemeni Ibbi Arabic (YIA) within the Minimalist Program. The construction qulk-clause, a morphologically fused form meaning 'I said,' introduces embedded declarative interrogative, and imperative clauses, often eithout complementizer. The central proposal of this paper is that qulk-clauses are biclausal st

Nick Gorkavyi

The proposed multi-impact model explains the formation of the Moon, Charon, and binary asteroids without invoking catastrophic cosmic events. The main elements of the new model are as follows: a. A primordial, low-mass proto-satellite disk with prograde rotation existed around the proto-Earth. b. Most of the lunar material was ejected from Earth mantle by nu

Xin Yu, Xiaojuan Qi, Zhengqi Li, Kai Zhang

We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectiv

Ayona James, Maryam Ali, Zekai Ye, Phan Thi Yen Nhi

Anisotropic intensity distributions on nanostructured surfaces and polarization-sensitive spectra have been observed in a number of nanoscale infrared spectroscopic imaging methods, including nano-FTIR [Bakir et al., Molecules, 2020, 25, 4295], photothermal induced resonance (PTIR) [Waeytens et al., Analyst, 2021, 146], tapping AFM-IR [Hondl et al., ACS Meas

Javiera Cabezas-Escares, Andrea Echeverri, Francisco Muñoz, Anastassia N. Alexandrova

This work explores how phonon perturbations can induce the breaking of electronic degeneracies near the Fermi level and how this response can be interpreted from a chemical perspective through the SSAdNDP method. We apply this approach to a family of structurally similar yet electronically distinct hexagonal materials-MgB2, graphene, and hBN-to analyze how a

Rustem Khasanov, Thomas J. Hicken, Hubertus Luetkens, Zurab Guguchia

Understanding the magnetic ground state of Ruddlesden-Popper nickelates is crucial, as these materials exhibit superconductivity under high pressure and host competing electronic orders that may play a key role in the pairing mechanism. In this work, we investigate the magnetic properties of the alternating monolayer-trilayer phase of La$_3$Ni$_2$O$_7$ (1313

Hao Tang, Hoje Chun, Rafael Gomez-Bombarelli, Yuri Mishin

The equilibrium vacancy concentration in multi-principal element alloys remains a controversial and nontrivial subject, primarily because of chemical complexity and chemical short-range order (CSRO). Here we derive an exact expression that is amenable to atomistic calculations, using multiple perspectives. We applied this expression to equiatomic CrCoNi allo

Leah Tom, Zachary J. Krebs, Joel B. Varley, E. S. Joseph

The coherence of quantum dot qubits fabricated in semiconductors is often limited by charge noise from defects in gate dielectrics, which are material- and process-dependent. Characterizing these defects is an important step towards reducing their impact and improving qubit coherence. The identification of individual defects requires atomic-scale spatial res

Raúl A. Briceño

This chapter provides a pedagogical introduction to lattice quantum field theory, with strong emphasis on lattice quantum chromodynamics. The chapter reviews key foundational concepts of lattice quantum chromodynamics, as well as a broad summary of ongoing research in the field.

Ángel Aso-Mollar, Diego Aineto, Enrico Scala, Eva Onaindia

Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during planning. This results in a potentially infinite number of applicable actions in a state. In this setting, off-the-shelf numeric heuristics that leverage the action structure are not

Aziz El Ghazouani, Fouad Ibrahim Abdou Amir, Khoulane Mohamed, M'hamed Elomari

The conformable derivative has been promoted in numerous publications as a new fractional derivative operator. This article provides a critical reassessment of this claim. We demonstrate that the conformable derivative is not a fractional operator but a useful computational tool for systems with power-law time scaling, equivalent to classical differentiation

Toni Scarmato

Interstellar comet 3I/ATLAS (C/2025 N1) exhibits an unusual, tightly collimated dust feature in the sunward hemisphere which has been widely described as an anti-tail. At the same time, precise constraints on the nucleus size have been derived from a combination of high-resolution imaging and non-gravitational dynamics. In this work I present a unified analy

Saurabh Deochake, Debajyoti Mukhopadhyay

While Text-to-SQL systems achieve high accuracy, existing efficiency metrics like the Valid Efficiency Score prioritize execution time, a metric we show is fundamentally decoupled from consumption-based cloud billing. This paper evaluates cloud query execution cost trade-offs between reasoning and non-reasoning Large Language Models by performing 180 Text-to

Alex Krasnok

Environmental 'range boosts' in wireless links are often explained through radiation-pattern intuition, yet the underlying physics is more cleanly captured by two environment-controlled quantities: radiative damping of the radiator and \emph{channel coupling} between transmitter and receiver. Building from a dyadic-Green-function current--field formulation,

Pablo Serra

In this paper we address the well-known problem of counting the number of $3n$-letter words that can be formed from a three-letter alphabet by decomposing it into four possible cases based on its remainder when divided by three. The solution to the problem also gives us some sums of trinomial coefficients.

Alberto Guilabert, Ernesto Fuenmayor, Pedro Bargueño, Ernesto Contreras

Ghost stars are compact configurations characterized by an arbitrarily small total mass. Such objects require regions of negative energy density -a condition typically regarded as unphysical within the context of conventional stellar models. Nevertheless, negative energy densities arise naturally in traversable wormhole geometries, where the violation of the

Ivan Karpov, Miguel Moreira

Given an abelian category and a stability condition satisfying appropriate conditions, we define generalized $K$-theoretic invariants and prove that they satisfy wall-crossing formulas. For this, we introduce a new associative algebra structure on the $K$-homology of the stack of objects of an abelian category, which we call the $K$-Hall algebra. We first de

Y. -Y. Xiao, Z. -N. Xu, Z. -Q. Yao, C. D. Roberts

Orbital angular momentum (OAM) is not a Poincar\'e invariant quantity; so, its value is observer dependent. Notwithstanding that, in quantum chromodynamics, a Poincar\'e-invariant theory, OAM is part of every hadron wave function. Using continuum Schwinger function methods, we elucidate both the subjective character of in-hadron OAM and expose some of its im

Bithika Karmakar, Dusan Zigic, Igor Salom, Jussi Auvinen

Understanding the temperature dependence of the specific shear viscosity $(\eta/s)$ is crucial for characterizing the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions. Since, low-$p_\perp$ theory and data are only weakly sensitive to the typical forms of $\eta/s(T)$, especially at high temperatures, we use high-$p_\perp$ data a

A. Savchenko, A. Zabrodin

We consider the dispersionless limit of the recently introduced multi-component Pfaff-Toda hierarchy. Its dispersionless version is a set of nonlinear differential equations for the dispersionless limit of logarithm of the tau-function (the F-function). They are obtained as limiting cases of bilinear equations of the Hirota-Miwa type. The analysis of the Pfa

Ella M. Mansfield, Delaney A. Dunne, Dongwoo T. Chung

Line-intensity mapping (LIM) is an emerging observational technique that is used to observe the universe on large scales at low resolution through spectral line emission. Stacking analyses coadd cutouts of LIM data on positions of known signal emitters, robustly detecting signal otherwise hidden in a noisy map. In this article, we present two augmentations o

Adelle M. Wright, Benjamin J. Faber

Maximising particle and energy confinement is crucial for achieving the sustained burning plasma conditions necessary to realise fusion energy. For stellarator reactors, one proposed strategy for avoiding destructive instabilities is to operate at high-field but low(er) plasma pressure. In this work, we investigate the accessibility of such a reactor-relevan

Amin Sadri, M Maruf Hossain

Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. Contribution: In this paper, we present the Coordinate Matrix Machine (CM$^

M. R. Combi, T. Mâkinen, J. -L. Bertaux, E. Quemerais

The Solar Wind ANisotropies (SWAN) all-sky hydrogen Lyman-alpha camera on the Solar and Heliosphere Observatory (SOHO) observed the hydrogen coma of interstellar comet 3I/ATLAS, also called C/2025 N1 (ATLAS), beginning on November 6, 2025, 9 days after perihelion. Water production rates were calculated from each image of 3I/ATLAS using the methodology of Mak

Anna-Maria Alexandrova, Jesus Valdiviezo

A theoretical analysis of the Casimir interaction between an arc and plate is conducted, which remains unexplored despite its relevance to Micro-Electro-Mechanical Systems (MEMS) fabrication. The configuration consists of a rigid finite plate and a flexible curved nanomembrane, with radius 100 micrometers, initially concave toward the rigid plate. The maximu

Adel Daoud

When sociologists and other social scientist ask whether the return to college differs by race and gender, they face a choice between two fundamentally different modes of inquiry. Traditional interaction models follow deductive logic: the researcher specifies which variables moderate effects and tests these hypotheses. Machine learning methods follow inducti

Zhengfei Kuang, Rui Lin, Long Zhao, Gordon Wetzstein

Despite the remarkable progress of Multimodal Large Language Models (MLLMs) in 2D vision-language tasks, their application to complex 3D scene manipulation remains underexplored. In this paper, we bridge this critical gap by tackling three key challenges in 3D object arrangement task using MLLMs. First, to address the weak visual grounding of MLLMs, which st

Alaa Alahmadi, Mohamed Hasan

Machine vision models, particularly deep neural networks, are increasingly applied to physiological signal interpretation, including electrocardiography (ECG), yet they typically require large training datasets and offer limited insight into the causal features underlying their predictions. This lack of data efficiency and interpretability constrains their c

Nidhi Pandya

Automated news verification requires structured claim extraction, but existing approaches either lack schema compliance or generalize poorly across domains. This paper presents NewsScope, a cross-domain dataset, benchmark, and fine-tuned model for schema-grounded news claim extraction. The dataset contains 455 articles across politics, health, science/enviro

Aleksandar Tomašević, Ana Vranić, Aleksandra Alorić, Marija Mitrović Dankulov

Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms. While prior research examines effects on banned communities or source platform health, the impact on alternative platforms that absorb displaced users remains understudied. We analyze four major Reddit ban waves (2015--2020) and their

Austin Cooper, Sean Meyn

The field of quickest change detection (QCD) focuses on the design and analysis of online algorithms that estimate the time at which a significant event occurs. In this paper, design and analysis are cast in a Bayesian framework, where QCD is formulated as an optimal stopping problem with partial observations. An approximately optimal detection algorithm is

Sroyon Sengupta

Extending the works of Alladi and Sweeting and Woo, we state and prove the general higher order duality between prime ideals in number rings. We then use the second order duality to obtain the a new formula for the Chebotarev Density involving sums of the generalized M\"obius function and the prime ideal counting function. We also provide two estimates of su

Peter C. Gibson

The standard text book theory of ODEs lacks a general method to solve linear equations having variable coefficients, providing instead a collection of special techniques for particular classes of equations. The present article addresses this shortcoming in the basic theory. We introduce the multex integral operator, generalizing to several input functions th

Salvador Centelles Chuliá, Ranjeet Kumar

We propose a flavour model based on an $A_4$ symmetry combined with a type-II seesaw mechanism for neutrino mass generation. The resulting neutrino mass matrix obeys a sum rule that, together with the measured mass-squared differences, fully determines the absolute neutrino mass spectrum. The constrained flavour structure yields correlated predictions for le

E. Šipková, M. Skarka, M. Vaňko, V. Chmelař

Stellar variability may originate from various phenomena such as binarity, pulsations, or rotation. These mechanisms can induce flux variations of similar magnitudes, shapes, and periods. We aim to determine mechanisms responsible for the sinusoidal variations in main-sequence stars hotter than 6500 K. We conducted our analysis using TESS long-cadence data c

Xinghai Zhang, Ziyan Zhu, Justin H. Wilson, Matthew S. Foster

Twisted multilayer moir\'e materials are generically quasiperiodic on the moir\'e scale due to the interference of different misaligned moir\'e periodicities. Spatial inhomogeneities such as these can be detrimental to superconductivity; nonetheless, superconductivity has been observed in quasiperiodic twisted trilayer graphene (TTG). Here, we systematically

Prantik Sarmah, Sovan Chakraborty, Xilu Wang

The MAGIC detection of near-TeV gamma rays from the 2021 RS Oph ($2.45$ kpc) outburst has established recurrent novae as TeV particle accelerators. However, the origin of this emission (hadronic vs leptonic) remains unclear due to the lack of coincident neutrinos detected by IceCube. The upcoming outburst of the much closer T Coronae Borealis (T CrB, $\sim0.

Akash Vijay, Jong Yeon Lee

Quantum critical phases are extended regions of phase space characterized by a diverging correlation length. By analogy, we define an \emph{information critical phase} as an extended region of a mixed state phase diagram where the Markov length, the characteristic length scale governing the decay of the conditional mutual information (CMI), diverges. We demo

Shuoshuo Zhang, Yizhen Zhang, Jingjing Fu, Lei Song

Large vision-language models (VLMs) often benefit from intermediate visual cues, either injected via external tools or generated as latent visual tokens during reasoning, but these mechanisms still overlook fine-grained visual evidence (e.g., polylines in charts), generalize poorly across domains, and incur high inference-time cost. In this paper, we propose