October 2025 arXiv papers — page 118

Alexandros Vasilopoulos, Michail Akritidis, Nikolaos G. Fytas, Martin Weigel

We investigate the percolation behavior of Fortuin-Kasteleyn--type clusters in the spin-$1/2$ Baxter--Wu model with three-spin interactions on a triangular lattice. The considered clusters are constructed by randomly freezing one of the three sublattices, resulting in effective pairwise interactions among the remaining spins. Using Monte Carlo simulations co

Douglas S. Bridges

In constructive mathematics the metric complement of a subset S of a metric space X is the set -S of points in X that are bounded away from S. In this note we discuss, within Bishop's constructive mathematics, the connection between the metric double complement, -(-K), and the logical double complement, not not K, where K is a convex subset of a normed linea

François Ezard, Can Umut Ileri, Jérémie Decouchant

Following the design of more efficient blockchain consensus algorithms, the execution layer has emerged as the new performance bottleneck of blockchains, especially under high contention. Current parallel execution frameworks either rely on optimistic concurrency control (OCC) or on pessimistic concurrency control (PCC), both of which see their performance d

Heather Liddell, Beth Kelley, Liz Wachs, Alberta Carpenter

A physical assessment of material flows in an economy (e.g., material flow quantification) can support the development of sustainable decarbonization and circularity strategies by providing the tangible physical context of industrial production quantities and supply chain relationships. However, completing a physical assessment is challenging due to the scar

Miraç Buğra Özkan

Procedural content generation (PCG) has become an increasingly popular technique in game development, allowing developers to generate dynamic, replayable, and scalable environments with reduced manual effort. In this study, a novel method for procedural level design using Deep Reinforcement Learning (DRL) within a Unity-based 3D environment is proposed. The

Ana Lawry Aguila, Dina Zemlyanker, You Cheng, Sudeshna Das

Diffusion models have recently emerged as powerful generative models in medical imaging. However, it remains a major challenge to combine these data-driven models with domain knowledge to guide brain imaging problems. In neuroimaging, Bayesian inverse problems have long provided a successful framework for inference tasks, where incorporating domain knowledge

Minh-Quy Pham

We study the distance set problem for pairs of compact sets $A, B\subset \mathbb{R}^n$, $n\geq 2$. We show that if $B$ is contained in a hyperplane and \begin{align*} \dim_{H} A+\dim_{H} B>n, \end{align*} then the distance set $ \Delta(A,B):=\left\{ \vert x-y\vert: x\in A, y\in B\right\}$ has positive Lebesgue measure, and the dimensional threshold is sharp.

Stepan Vakhrushev

In this note we generalize the results of the recent work by Tom Bohman and Jacob Hofstad on the independence number in G(n, p) to the case of the random k-uniform hypergraph. Concentration in two values occurs in the regime $p>n^{-(k-1)k/(k+1)+\varepsilon}$.

Sarod Yatawatta

Direction of arrival (DOA) estimation is mostly performed using specialized arrays that have carefully designed receiver spacing and layouts to match the operating frequency range. In contrast, radio interferometric arrays are designed to optimally sample the Fourier space data for making high quality images of the sky. Therefore, using existing radio interf

Kirill Semenov, Rico Sennrich

For multilingual factual knowledge assessment of LLMs, benchmarks such as MLAMA use template translations that do not take into account the grammatical and semantic information of the named entities inserted in the sentence. This leads to numerous instances of ungrammaticality or wrong wording of the final prompts, which complicates the interpretation of sco

Marios-Nektarios Stamatopoulos, Elias Small, Shridhar Velhal, Avijit Banerjee

This article presents a fully autonomous aerial masonry construction framework using heterogeneous unmanned aerial vehicles (UAVs), supported by experimental validation. Two specialized UAVs were developed for the task: (i) a brick-carrier UAV equipped with a ball-joint actuation mechanism for precise brick manipulation, and (ii) an adhesion UAV integrating

Anutam Srinivasan, Aaron Nielsen

Slow-moving vehicles relying on crustal magnetic anomaly navigation (MagNav) or vehicles revisiting the same location in a short time - such as those used for surveys in magnetic anomaly mapping - require fixed ground stations within 100 km of the vehicle's trajectory to measure and remove the geomagnetic disturbance field from magnetic readings. This approa

Mst Eshita Khatun, Lamine Noureddine, Zhiyong Sui, Aisha Ali-Gombe

With the exponential growth in mobile applications, protecting user privacy has become even more crucial. Android applications are often known for collecting, storing, and sharing sensitive user information such as contacts, location, camera, and microphone data often without the user's clear consent or awareness raising significant privacy risks and exposur

Yunchao Zhang, T. Senthil

We study a class of quantum phase transitions between featureless bosonic atomic insulators in $(2+1)$ dimensions, where each phase exhibits neither topological order nor protected edge modes. Despite their lack of topology, these insulators may be ``obstructed'' in the sense that their Wannier centers are not pinned to the physical atomic sites. These insul

Shih-Yang Liu, Xin Dong, Ximing Lu, Shizhe Diao

Reasoning language models such as OpenAI-o1, DeepSeek-R1, and Qwen achieve strong performance via extended chains of thought but often generate unnecessarily long outputs. Maximizing intelligence per token--accuracy relative to response length--remains an open problem. We revisit reinforcement learning (RL) with the simplest length penalty--truncation--and s

Utku Demir, Tugba Erpek, Yalin E. Sagduyu, Sastry Kompella

In emerging networked systems, mobile edge devices such as ground vehicles and unmanned aerial system (UAS) swarms collectively aggregate vast amounts of data to make machine learning decisions such as threat detection in remote, dynamic, and infrastructure-constrained environments where power and bandwidth are scarce. Federated learning (FL) addresses these

Nikolaos Verykios, Christos Gogos

This paper investigates the algebraic and graphical structure of the ring $\mathbb{Z}_{sp}$, with a focus on its decomposition into finite fields, kernels, and special subsets. We establish classical isomorphisms between $\mathbb{F}_s$ and $p\mathbb{F}_s$, as well as $p\mathbb{F}_s^{\star}$ and $p\mathbb{F}_s^{+1,\star}$. We introduce the notion of arcs and

Ivan H. C. Shum

An algorithm which encodes the $L\times L$ 2D toric code logical state with a circuit of depth $2L+1$, using only local controlled-NOT($CX$) and Hadamard($H$) gates, is presented.

Issam Seddik, Sami Souihi, Mohamed Tamaazousti, Sara Tucci Piergiovanni

As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern. Backdoor attacks, where malicious actors inject hidden triggers into training data, are particularly insidious and difficult to detect. Existing post-training verification solutions like Proof-of-Learning are i

Mengxiang Zhu, Riccardo Rastelli

Feature engineering plays a critical role in handling hyperspectral data and is essential for identifying key wavelengths in food fraud detection. This study employs Bayesian Additive Regression Trees (BART), a flexible machine learning approach, to discriminate and classify samples of olive oil based on their level of purity. Leveraging its built-in variabl

Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang, Liming Jiang

Text-to-video generation has advanced rapidly in visual fidelity, whereas standard methods still have limited ability to control the subject composition of generated scenes. Prior work shows that adding localized text control signals, such as bounding boxes or segmentation masks, can help. However, these methods struggle in complex scenarios and degrade in m

Jessy Lin, Luke Zettlemoyer, Gargi Ghosh, Wen-Tau Yih

Modern language models are powerful, but typically static after deployment. A major obstacle to building models that continually learn over time is catastrophic forgetting, where updating on new data erases previously acquired capabilities. Motivated by the intuition that mitigating forgetting is challenging because trainable parameters are shared across all

Y. Sultan Abylkairov, Matthew C. Edwards, Artyom Ostrikov, Yersultan Tleukhanov

Gravitational waves from core-collapse supernovae provide a unique probe of the equation of state (EOS) of high density matter. In this work, we focus on the bounce signal from numerical simulations of rotating supernovae and explore its potential for EOS inference. We employ a support vector machine, previously shown to perform best among tested methods, to

Yolanne Yi Ran Lee, Kyriakos Flouris

Forecasting high-dimensional, PDE-governed dynamics remains a core challenge for generative modeling. Existing autoregressive and diffusion-based approaches often suffer cumulative errors and discretisation artifacts that limit long, physically consistent forecasts. Flow matching offers a natural alternative, enabling efficient, deterministic sampling. We pr

V. A. Sautenkov, S. A. Saakyan, A. A. Bobrov, B. B. Zelener

We discuss nonlinear spectra of selective reflection from high-density rubidium atomic vapor, where the self-broadening of the resonant transition $5S_{1/2}-5P_{3/2}$ dominates over the Doppler width. In the experiments, the hole-burning technique with probe and pump lasers is used. The reflection of weak probe beam is investigated at four atomic densities i

Zheng Hui, Yijiang River Dong, Sanhanat Sivapiromrat, Ehsan Shareghi

When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM providers to achieving state-of-the-art performance and risk data exposure, or relying on smaller, local models guarantees data privacy but often results in a degradation of task p

Ravin Kumar

This paper introduces the Adaptive Base Representation (ABR) Theorem and proposes a novel number system that offers a structured alternative to the binary number system for digital computers. The ABR number system enables each decimal number to be represented uniquely and using the same number of bits, $n$, as the binary encoding. Theoretical foundations and

Francesca Gladiali, Massimo Grossi, Luigi Provenzano

We prove uniqueness and non-degeneracy of the critical point of positive, semi-stable solutions of $-\Delta u=f(u)$ with Dirichlet boundary conditions for a class of star-shaped domains of the sphere and of the hyperbolic plane satisfying a geometric condition. In the spherical case, this condition is weaker than convexity, while in the hyperbolic case it is

Kazufumi Ito, Tiancheng Xue

In this paper, we propose an acceleration framework for a class of iterative methods using the Reduced Order Method (ROM). Assuming that the underlying iterative scheme generates a rich basis for the solution space, we construct the next iterate by minimizing the equation error over the linear manifold spanned by this basis. The resulting optimal linear comb

Alana Renda, Jillian Ross, Michael Cafarella, Jacob Andreas

Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- require models to grapple with incomplete information and reason under uncertainty. Yet most LM evaluations focus on problems with well-defined answers and success criteria. This gap exists in part because natural prob

Chenyang Yu, Xinpeng Xie, Yan Huang, Chenxi Qiu

Accurate traffic forecasting is a core technology for building Intelligent Transportation Systems (ITS), enabling better urban resource allocation and improved travel experiences. With growing urbanization, traffic congestion has intensified, highlighting the need for reliable and responsive forecasting models. In recent years, deep learning, particularly Gr

Md Sabbir Hossain Polak, David Troendle, Byunghyun Jang

Hash tables are essential building blocks in data-intensive applications, yet existing GPU implementations often struggle with concurrent updates, high load factors, and irregular memory access patterns. We present Hive hash table, a high-performance, warp-cooperative and dynamically resizable GPU hash table that adapts to varying workloads without global re

Yanchang Fu, Qiyue Yin, Shengda Liu, Pei Xu

Hand abstraction is crucial for scaling imperfect-information games (IIGs) such as Texas Hold'em, yet progress is limited by the lack of a formal task model and by evaluations that require resource-intensive strategy solving. We introduce signal observation ordered games (SOOGs), a subclass of IIGs tailored to hold'em-style games that cleanly separates signa

Yongjian Tang, Pratap Pal, Matthew Roddy, Jon Schad

Spin Hall magnetoresistance (SMR) measurements provide a way to probe the surface spin structure of insulating magnetic materials. Such measurements produce resistance signals of the form {$\Delta$}R {$\propto$} cos[2($\alpha$-$\alpha$$_0$)], where $\alpha$ is the angle between the current and the external in-plane magnetic field. Previous experiments on a w

Alice Gao, Victoria Sakhnini

Academic procrastination is prevalent among undergraduate computer science students. Many studies have linked procrastination to poor academic performance and well-being. Procrastination is especially detrimental for advanced students when facing large, complex programming assignments in upper-year courses. We designed an intervention to combat academic proc

E. E. Perepelkin, B. I. Sadovnikov, N. G. Inozemtseva, I. Yu. Baibara

This work derives exact solutions to the problem of interacting particle density evolution in relativistic and quasi-relativistic approximations for electromagnetic and gravitational interactions. Two types of radial symmetry for the initial density distribution are considered: spherical and cylindrical. It is shown that the relativistic effect delays the on

Vasily Ilin

We give a short and elementary proof of stability for strong solutions of the spatially homogeneous Landau equation with Coulomb collisions, measured in relative entropy. The argument yields an explicit differential inequality for relative entropy under natural moment and regularity assumptions. The same computation provides an a posteriori error bound for s

D. R. Mizuno, T. A. Kuchar, Kathleen E. Kraemer, G. C. Sloan

We present an atlas of full-scan spectra from the Short-Wavelength Spectrometer (SWS) aboard the Infrared Space Observatory (ISO) after reprocessing and improving an earlier version published 22 years ago. The SWS spectra cover the wavelength range from 2.35 to 45.3 {\mu}m. They include scans in 12 separate bands, and we have updated the methods used to comb

Kai Yin, Xiangjue Dong, Chengkai Liu, Allen Lin

Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing general-domain models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inconsistent and unreliable performance. To this end, we introduce

Sofiya Garkot, Maksym Shamrai, Ivan Synytsia, Mariya Hirna

The performance and generalization of foundation models for interactive systems critically depend on the availability of large-scale, realistic training data. While recent advances in large language models (LLMs) have improved GUI understanding, progress in desktop automation remains constrained by the scarcity of high-quality, publicly available desktop int

Abrar Mahbub, Humira Saria, Md. Foysal Hossain, Nafees Mansoor

Student retention is one of the rising problems seen in educational institutions. With the rising cost of education and issues in the education sector, such as curriculum relevance, student engagement, and rapidly changing technological advancements, ensuring the relevance of academic programs in a fast-evolving job market has created a significant concern f

Jillian Eddy, Ryan Pesak, Daniel Qin, Denae Ventura

Local and global amoebas are families of labeled graphs that satisfy interpolation properties on a fixed vertex set. A labeled graph $G$ on $n$ vertices is a local amoeba (resp. global amoeba) if there exists a sequence of feasible edge-replacements between any two labelled embeddings of $G$ into $K_n$ (resp. $K_{n+1}$). Here, a feasible edge-replacement rem

Jose Guzman

We introduce a logarithmic cobordism $\omega^{\text{Log}}$ ring of pairs $(X,D)$ of varieties equipped with a simple normal crossings divisor $D\subset X$, analogous to the algebraic cobordism ring $\omega^{\text{LP}}$ of Levine-Pandharipande, and we provide an application to logarithmic DT invariants. We also prove prove that if we impose the relation $(X',

F. Murgas, E. Pallé, A. Suárez Mascareño, J. Korth

Super-Earths and mini-Neptunes are missing from our Solar System, yet they appear to be the most abundant planetary types in our Galaxy. A detailed characterization of key planets within this population is important for understanding the formation mechanisms of rocky and gas giant planets and the diversity of planetary interior structures. In 2019, NASA's TE

Georgi Ganev, Reza Nazari, Rees Davison, Amir Dizche

The Synthetic Minority Over-sampling Technique (SMOTE) is one of the most widely used methods for addressing class imbalance and generating synthetic data. Despite its popularity, little attention has been paid to its privacy implications; yet, it is used in the wild in many privacy-sensitive applications. In this work, we conduct the first systematic study

B. Joel Gonzalez, Elio Bourcart, J. Kent Wirant, Michael Juan

Photolithography is a key part of modern semiconductor process flows, and photolithography steppers have been used for decades to achieve precise patterning for device fabrication. However, these tools are often large and expensive, which restricts their use to industry and well-funded university laboratories. In this paper, we propose a $3000 maskless photo

L. M. Abreu, R. Higa, R. O. Magalhães, F. S. Navarra

We investigate the interactions of the deuteron with light mesons during the hadronic phase in heavy-ion collisions. We treat the deuteron as a weakly bound state and employ the quasi-free approximation to describe the $d\pi$ interaction. The underlying elementary $N\pi$ amplitudes are described by a hybrid effective model, combining the non-resonant backgro

Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan

Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been limited by reliance on human annotation, which is costly, inconsistent, difficult to scale. Their associated datasets are often limited to specific topics and strategies, posing chal

Changshu Liu, Yang Chen, Reyhaneh Jabbarvand

This paper proposes CES, a task to evaluate the abilities of LLMs in simulating program execution and using that reasoning in programming tasks. Besides measuring the correctness of variable predictions during execution simulation, CES introduces the notion of coherence to determine whether the simulation complies with commonsense execution logic, even if th

Jinming Yang, Zheting Jin, Siqi Wang, Camilla Moir

The mechanism behind superconductivity suppression induced by Pr substitutions in YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO) has been a mystery since its discovery: in spite of being isovalent to Y$^{3+}$ with a small magnetic moment, it is the only rare-earth element that has a dramatic impact on YBCO's superconducting properties. Using angle-resolved photoemission

Aofan Liu, Shiyuan Song, Haoxuan Li, Cehao Yang

The escalating complexity of modern codebases has intensified the need for retrieval systems capable of interpreting cross-component change intents, a capability fundamentally absent in conventional function-level search paradigms. While recent studies have improved the alignment between natural language queries and code snippets, retrieving contextually rel

Chengzhang Fu, Michael S. Jolly, Anuj Kumar, Vincent R. Martinez

Turbulent behavior of the two-parameter family of generalized surface quasigeostrophic equations is examined both rigorously and numerically. We adapt a cascade mechanism argument to derive an energy spectrum that scales as $\kappa^{2\beta/3-3}$ where $\beta$ controls the regularity of the velocity ($\beta=1$ in the special case of the SQG). Direct numerical

Abir Khan Ratul, Sanjay Acharjee, Somin Park, Md Nazmus Sakib

This study introduces a human-in-the-loop pipeline that converts unscaled, hand-drawn floor plan sketches into semantically consistent 3D BIM models. The workflow leverages multimodal large language models (MLLMs) within a multi-agent framework, combining perceptual extraction, human feedback, schema validation, and automated BIM scripting. Initially, sketch

Sami Davies, Benjamin Moseley, Heather Newman

The $\ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $\ell_1$-norm) and ensuring fairness to individual nodes (the $\ell_\infty$-norm). Surprisingly, in the offline setting it is possible to simultaneously approximate all $\ell_p$-norms with a single clustering. Can this powerful

Sixian Jia, Zhiqiao Dong, Chenhui Shao

Two-photon lithography (TPL) is a sophisticated additive manufacturing technology for creating three-dimensional (3D) micro- and nano-structures. Maintaining the health of TPL systems is critical for ensuring consistent fabrication quality. Current maintenance practices often rely on experience rather than informed monitoring of machine health, resulting in

Maria Chudnovsky, Ajaykrishnan E S, Daniel Lokshtanov

An independent set in a graph $G$ is a set of pairwise non-adjacent vertices. A tree decomposition of $G$ is a pair $(T, \chi)$ where $T$ is a tree and $\chi : V(T) \rightarrow 2^{V(G)}$ is a function satisfying the following two axioms: for every edge $uv \in V(G)$ there is a $x \in V(T)$ such that $\{u,v\} \subseteq \chi(x)$, and for every vertex $u \in V(

Dominik Kwietniak, Filip Wierzbowski

We study the dynamics of continuous maps on compact metric spaces containing a free interval (an open subset homeomorphic to the interval $(0,1)$). We provide a new proof of a result of M. Dirb\'ak, \v{L}. Snoha, V. \v{S}pitalsk\'y [Ergodic Theory Dynam. Systems, vol. 33 (2013), no. 6, pp. 1786--1812] saying that every continuous and transitive, but non-mini

Ahmed Ali, Quentin Sablé, Chiara Gabellieri, Antonio Franchi

We introduce the PenduMAV, an exactly actuated (6-input) omnidirectional multirotor that structurally eliminates internal forces at equilibria. The vehicle features one actively-tilting propeller and three propellers mounted on passive pendulum links via universal joints. This architecture achieves full 6D wrench generation while avoiding the structural and

David Atkinson

GenAI companies are strip-mining the web. Their scraping bots harvest content at an unprecedented scale, circumventing technical barriers to fuel billion-dollar models while creators receive nothing. Courts have enabled this exploitation by misunderstanding what property rights protect online. The prevailing view treats websites as mere repositories of intel

Jimmie Adriazola, Panayotis G. Kevrekidis, Vassilis Koukouloyannis, Wei Zhu

The purpose of this article is to provide a perspective -- admittedly, a rather subjective one -- of recent developments at the interface of machine learning/data-driven methods and nonlinear wave studies. We review some recent pillars of the rapidly evolving landscape of scientific machine learning, including deep learning, data-driven equation discovery, {

Deyue Zhang, Dongdong Yang, Junjie Mu, Quancheng Zou

Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks exploiting cross-modal vulnerabilities. In this work, we introduce a novel method that leverages sequential comic-style visual narratives to circumvent safety alignments in state-of-the-art MLLMs. Our method decomposes malicious queries into v

William Munizzi, Howard J. Schnitzer

We develop a topological framework for preparing families of non-stabilizer states, and computing their entanglement entropies, in $SU(2)_1$ Chern-Simons theory. Using the Kac-Moody algebra, we construct Pauli and Clifford operators as path integrals over 3-manifolds with Wilson loop insertions, enabling an explicit topological realization of $W_n$ and Dicke

Megan Fairchild, Matthew Lemoine

Topological Data Analysis is a relatively new field of study that uses topological invariants to study the shape of data. We analyze a dataset provided by the Centers for Disease Control and Prevention (CDC) using persistent homology and MAPPER. This dataset tracks mortality week-to-week from January 2020 to September 2023 in the United States during the COV

Michal Feldman

Contract theory studies how a principal can incentivize agents to exert costly, unobservable effort through performance-based payments. While classical economic models provide elegant characterizations of optimal solutions, modern applications, ranging from online labor markets and healthcare to AI delegation and blockchain protocols, call for an algorithmic

Samuel G. D. Turner, Roman R. Rafikov, Alexander A. Philippov

Disc accretion onto astrophysical objects with a material surface proceeds through the boundary layer (BL) -- a radially narrow region in the inner disc where the incoming gas must slow down its rotation before settling onto the surface of the accretor. Here we numerically study a BL in which the angular momentum transport in the layer is accomplished via th

Wenwen Chen, Bin Han, Yao Zhu, Anke Schmeink

Physical layer deception (PLD) combines physical layer security (PLS) with deception: the transmitter actively misleads the eavesdropper with falsified information. We model the transmitter-eavesdropper interaction as a Stackelberg game in which the transmitter commits to a resource allocation and encryption strategy, and each receiver best-responds by selec

Jesse Griff-McMahon, Xavier Vaisseau, William Fox, Kirill Lezhnin

We systematically characterize the focusing behavior of laser-driven proton beams from hemispherical targets of various diameters using mesh radiography. The proton focal location is inferred to be near the geometrical center for the smallest tested hemisphere ($\Psi=D_{hemi}/D_{Laser}=6.1$). However, larger hemispheres ($\Psi=14.6$) degrade the focusing beh

Samuel Paech, Allen Roush, Judah Goldfeder, Ravid Shwartz-Ziv

Widespread LLM adoption has introduced characteristic repetitive phraseology, termed "slop," which degrades output quality and makes AI-generated text immediately recognizable. We present Antislop, a comprehensive framework providing tools to both detect and eliminate these overused patterns. Our approach combines three innovations: (1) The Antislop Sampler,

David Atkinson

Open-source status should not shield generative artificial intelligence systems from ethical or legal accountability. Through a rigorous analysis of regulatory, legal, and policy frameworks, this Article contends that open-source GenAI must be held to the same standards as proprietary systems. While recognizing the value of openness for scientific advancemen

Dennis Lima, Rakesh Saini, Saif Al-Kuwari

Quantum Genetic Algorithms (QGAs) are an emerging field of multivariate quantum optimization that emulate Darwinian evolution and natural selection, with vast applications in chemistry and engineering. The appropriate application of fitness functions and fitness selection are the problem-encoding step and the slowest step in designing QGAs for specific physi

Jose Cribeiro-Ramallo, Agnideep Aich, Florian Kalinke, Ashit Baran Aich

Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of our knowledge, all existing KSD estimators with known rate achieve $\sqrt n$-convergence. In this work, we present two complementary results (with different proof strategies), estab

Wei Hao Tey, Guillermo Olicón-Méndez, Jeroen S. W. Lamb, Kazuyuki Aihara

We develop an early-warning signal for bifurcations of one-dimensional random difference equations with additive bounded noise, based on the asymptotic behaviour of the stationary density near a boundary of its support. We demonstrate the practical use in numerical examples.

Ziqing Lu, Babak Hassibi, Lifeng Lai, Weiyu Xu

Reinforcement learning usually assumes a given or sometimes even fixed environment in which an agent seeks an optimal policy to maximize its long-term discounted reward. In contrast, we consider agents that are not limited to passive adaptations: they instead have model-changing actions that actively modify the RL model of world dynamics itself. Reconfigurin

Mehrshad Gholami Anjiraki, Mustafa Meriç Aksen, Samin Shapourmiandouab, Jonathan Craig

We present a coupled large-eddy simulation (LES) and bed morphodynamics study to investigate the influence of sediment dynamics on the performance of a utility-scale marine hydrokinetic vertical-axis turbine (VAT) parametrized by an actuator surface model. By resolving the two-way interactions between turbine-induced flow structures and bed evolution, the st

Ícaro. B. S. Cortês, Léo. G. Medeiros, Ronaldo C. Batista

A recent determination of the growth index indicates a value significantly higher than the $\Lambda$CDM prediction, suggesting that alternative scenarios to $\Lambda$CDM may be required. In this work, we investigate whether a time-varying Newton's constant, $G_N$, can account for such a high growth index, $\gamma=0.063\pm0.025$. Adopting a phenomenological a

Guido Caldarelli, Oriol Artime, Giulia Fischetti, Stefano Guarino

The boundaries between physical and social networks have narrowed with the advent of the Internet and its pervasive platforms. This has given rise to a complex adaptive information ecosystem where individuals and machines compete for attention, leading to emergent collective phenomena. The flow of information in this ecosystem is often non-trivial and involv

Vinzenz Zimmermann, Amin Hashemi, Kurt Busch, Andrea Blanco-Redondo

We introduce a non-Hermitian photonic filter that harnesses dissipation to selectively isolate a desired topological state. In science and engineering, dissipation is often used to filter incoherent waves, producing a pure coherent output. Here, we apply this principle to topological states, creating a linear filter that effectively isolates a specific topol

Anupama B

The role of dissipation from the thermal bath of minimal warm inflation (MWI)is examined with the Cosmic Microwave Background (CMB) to assess its ability to mimic a dynamical dark energy component. An increase in the dissipation strength modifies temperature anisotropies and shifts the phase and peak structure of the CMB TT angular power spectrum in a manner

Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun

Multimodal large language models (MLLMs) have made rapid progress, yet their reasoning ability often lags behind strong text-only LLMs. Bridging this gap typically requires large-scale multimodal reasoning data or reinforcement learning, incurring substantial cost. An appealing alternative is parameter-space model merging between reasoning-enhanced LLMs and

Fergus J. E. Baker, Andrew J. Young

We introduce Gradus.jl, an open-source and publicly available general relativistic ray-tracing toolkit for spectral modelling in arbitrary spacetimes. Our software is written in the Julia programming language, making use of forward-mode automatic differentiation for computing the Christoffel symbols during geodesic integration, and for propagating derivative

Ryan Ursell, Charlie Hoy, Ian Harry, Laura K. Nuttall

Our ability to infer the true source properties of colliding black holes from gravitational wave observations requires not only accurate waveform models but also their correct use. A key property when evaluating time-domain models is when to start the waveform: choosing a time that is too late can omit low-frequency power from higher order multipoles. By foc

Shiqi Chen, Tongyao Zhu, Zian Wang, Jinghan Zhang

Large Language Models (LLMs) as agents often struggle in out-of-distribution (OOD) scenarios. Real-world environments are complex and dynamic, governed by task-specific rules and stochasticity, which makes it difficult for LLMs to ground their internal knowledge in those dynamics. Under such OOD conditions, vanilla RL training often fails to scale; we observ

Ziqing Zhu

The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To

Abdul Samad Khan, Nouhaila Innan, Aeysha Khalique, Muhammad Shafique

Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this wor

Mathias Dufresne-Piché, Siva Nadarajah

The energy stable flux reconstruction (ESFR) method encompasses an infinite family of high-order, linearly stable schemes and thus provides a flex- ible and efficient framework for achieving high levels of accuracy on unstruc- tured grids. One remarkable property of ESFR schemes is their ability to be expressed equivalently as linearly filtered discontinuous

Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor

In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports. While the methodology holds promise, three challenges limit the

Yichen Li, Zhiyi Li, Brandon Feng, Dinghuai Zhang

Digital twin worlds with realistic interactive dynamics presents a new opportunity to develop generalist embodied agents in scannable environments with complex physical behaviors. To this end, we present GDGen (Generalized Representation for Generalized Dynamics Generation), a framework that takes a potential energy perspective to seamlessly integrate rigid

Xinyi Gu, Jiayuan Mao, Zhang-Wei Hong, Zhuoran Yu

Pretrained multi-modal large language models (MLLMs) demonstrate strong performance on diverse multimodal tasks, but remain limited in reasoning capabilities for domains where annotations are difficult to collect. In this work, we focus on artificial image domains such as charts, rendered documents, and webpages, which are abundant in practice yet lack large

Patrick Graf, Aryaman Patel

We prove an equivalence between two approaches to characterizing complex-projective varieties $X$ with klt singularities and ample canonical divisor that are uniformized by bounded symmetric domains. In order to do so, we show how to construct a uniformizing variation of Hodge structure from a slope zero tensor and vice versa. As a consequence, we generalize

Lingkai Kong, Molei Tao, Yang Liu, Bryan Wang

Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training has been shown to improve the straightness of flow trajectories, enabling more effective inference. However, existing OT-based methods estimate the OT plan using (mini-)batches of s

Ethan B. White, Enrico Vesperini, Emanuele Dalessandro, Anna Lisa Varri

Globular clusters (GCs) host multiple stellar populations differing in their chemical and dynamical properties. A number of models for the formation of multiple populations predict that the subsystem of second generation (SG) stars is characterized by a more centrally concentrated spatial distribution and a more rapid rotation than the system of first genera

Walker Melton, Andrew Strominger, Tianli Wang

We consider a free complex massive scalar on the quotient spacetime AdS$_3/\mathbb{Z}$, which has the isometry group SO(2,2) rather than its universal cover. This problem is of interest as a special example of QFT on a spacetime with closed timelike curves (CTCs), as a new context in which to study generalizations of AdS/CFT and for its role in celestial hol

Mrinal Kanti Sarkar, Saranyo Moitra, Rajdeep Sensarma

We show that odd order R\'enyi entropies $S^{(2q+1)}$ of a system of interacting scalar fields can be calculated as the free energy of $2q+1$ replicas of the system with additional quadratic inter-replica couplings in the subsystem at the time of measurement of the entropy. These couplings replace boundary field matching conditions. This formalism works both

Lucas Z. Brito, J. B. Marston

We apply the correlation matrix Hamiltonian reconstruction technique to the two-dimensional Gutzwiller-projected Fermi sea and {\pi}-flux states on finite-sized square and triangular lattices. Our results indicate no spin Hamiltonian with simple local interaction terms stabilizes such states for finite system sizes. We develop a quantitative assessment of th

Pau Amaro Seoane, Alessandra Mastrobuono Battisti, Chingis Omarov, Denis Yurin

We investigate the orbital eccentricity evolution of supermassive black hole binaries within galactic environments. We analyze the dynamics in triaxial merger remnants and subsequent interactions with geometrically thick nuclear discs. We confirm that gravitational torques in triaxial potentials efficiently extract angular momentum, resulting in binary forma

Zeyang Li, Abhishek V. Karve, Xin Wei, Jonathan Simon

Filters with flat-top pass-bands are a key enabling technology for signal processing. From communication to sensing, the ability to choose a pass \emph{band}, rather than a single pass \emph{frequency}, while still efficiently suppressing backgrounds at other frequencies, is a critical capability for ensuring both detection sensitivity and power efficiency.

Matija Medvidović, Alev Orfi, Juan Carrasquilla, Dries Sels

Variational methods have offered controllable and powerful tools for capturing many-body quantum physics for decades. The recent introduction of expressive neural network quantum states has enabled the accurate representation of a broad class of complex wavefunctions for many Hamiltonians of interest. We introduce a first-principles method for building neura

Berihu Teklu, Victor Montenegro

High-precision sensors that exploit uniquely quantum phenomena have been shown to surpass the standard quantum limit of measurement precision. However, in the general scenario where multiple parameters are simultaneously encoded in a quantum probe, while surpassing the standard quantum limit is possible, its practical attainability is severely hindered. This

Yi Qiu, David Radice, Sherwood Richers, Federico Maria Guercilena

We present numerical relativity simulations of binary neutron star mergers incorporating neutrino flavor transformations triggered by fast flavor instability, quantum many-body effects, or potential beyond standard model physics. In both long-lived and short-lived remnant scenarios, neutrino flavor conversions modify species-dependent neutrino luminosities a

Hui Liu, Raul Perea-Causin, Zhao Liu, Emil J. Bergholtz

The discovery of zero-field fractional Chern insulators (FCIs) in moiré materials has attracted intense interest in the interplay between topology and correlations. Here, we demonstrate that fractionalized topological order can emerge under realistic conditions even within a topologically trivial moiré band. By projecting long-range Coulomb interactions into