December 2025 arXiv papers — page 92

Ehsan Roohi

The high computational cost of kinetic solvers such as DSMC remains a major challenge in rarefied flow simulations. This work presents a unified framework combining deep neural networks and neural operators to accelerate kinetic and hybrid solvers while preserving physical fidelity. GPU-native DNN surrogates eliminate costly moment-closure operations in Fokk

Tushar Singh, Shiv Datt Kumar

Let $R$ be a commutative ring with identity, $S \subseteq R$ be a multiplicative set. In this paper, we establish that the intersection of all $S$-prime ideals in an $S$-reduced ring is $S$-zero. Also, we show that an $S$-Artinian reduced ring is isomorphic to the finite direct product of fields. Furthermore, we provide an example of an $S$-reduced ring whic

Yifei Li, Haixu Wu, Zeyi Xu, Tuur Stuyck

Learning-based methods have made significant progress in physics simulation, typically approximating dynamics with a monolithic end-to-end optimized neural network. Although these models offer an effective way to simulation, they may lose essential features compared to traditional numerical simulators, such as physical interpretability and reliability. Drawi

Wanfu Gao, Zebin He, Jun Gao

Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapted to the characteristics of multi-label tasks. To address the above issues, we propose Feature Engineering Automation for Multi-Label Learn

Richard Helder Moulton, Austin O'Brien, John D. Hastings

Although large language models (LLMs) are increasingly used in security-critical workflows, practitioners lack quantitative guidance on which safeguards are worth deploying. This paper introduces a decision-oriented framework and reproducible methodology that together quantify residual risk, convert adversarial probe outcomes into financial risk estimates an

Gaurav Bansal

Accurate localization is essential for autonomous vehicles, yet sensor noise and drift over time can lead to significant pose estimation errors, particularly in long-horizon environments. A common strategy for correcting accumulated error is visual loop closure in SLAM, which adjusts the pose graph when the agent revisits previously mapped locations. These t

Hanwen Liu

A Riemannian metric is termed a Hessian metric if in some coordinate system it can be locally represented as the Hessian quadratic form of some locally defined smooth potential function. Under very mild extra technical conditions, we first theoretically describe the potentials of flat Hessian metrics on surfaces, and then construct these potentials explicitl

Chaeyoung Jeong, Kwangsu Kim

3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for 3D reconstruction, providing explicit, point-based representations and enabling high-quality real time rendering. However, when trained with sparse input views, 3DGS suffers from overfitting and structural degradation, leading to poor generalization on novel views. This limitation

Gavin R. Putland

A class of numerical quadrature rules is derived, with equally-spaced nodes, and unit weights except at a few points at each end of the series, for which "corrections" (not using any further information about the integrand) are added to the unit weights. If the correction sequences overlap, the effects are additive. A fundamental parameter ("alpha") in the d

Tushar Singh, Ajim Uddin Ansari, Shiv Datt Kumar

Let $R$ be a commutative ring with identity, $S\subseteq R$ be a multiplicative set and $J$ be an ideal of $R$. In this paper, we introduce the concept of $S$-$J$-Noetherian rings, which generalizes both $J$-Noetherian rings and $S$-Noetherian rings. We study several properties and charaterizations of this new class of rings. For instance, we prove Cohen's-t

Julian Sahasrabudhe

In many applications of the probabilistic method, one looks to study phenomena that occur ``with high probability''. More recently however, in an attempt to understand some of the most fundamental problems in combinatorics, researchers have been diving deeper into these probability spaces and understanding phenomena that occur at much smaller probability sca

Shlok Tomar, Aryan Deshwal, Ethan Villalovoz, Mattia Fazzini

We consider the task of generating functionally correct code using large language models (LLMs). The correctness of generated code is influenced by the prompt used to query the given base LLM. We formulate the problem of finding the appropriate prompt as combinatorial search process and propose a Bayesian optimization (BO) approach referred to as {\em BO for

Yukihiro Oda

Dynamic logic is a modal logic for reasoning about programs. A cyclic proof system is a proof system that allows proofs containing cycles and is an alternative to a proof system containing (co-)induction. This paper introduces a sequent calculus and a non-labelled cyclic proof system for an extension of propositional dynamic logic obtained by adding backward

Song Yu, Ke Zhang, Zhengyu Zong

For a toric Calabi-Yau 3-orbifold relative to s Aganagic-Vafa outer branes, we prove a correspondence among the genus-zero open Gromov-Witten invariants with maximal winding at each brane and: (i) closed invariants of a toric Calabi-Yau (3+s)-orbifold; (ii) formal relative invariants of a formal toric Calabi-Yau (FTCY) 3-orbifold with maximal tangency to s d

Meng Yang, Ling Zhu, Niankun Yu, Yu Lei

This paper aims to investigate the galaxy-halo connection using a large sample of individual galaxies with $\mathrm{H\,I}$ integrated spectra. We determine their dark matter content by applying a dynamical method based on $\mathrm{H\,I}$ line widths measured with the curve-of-growth technique, together with inclination corrections inferred from optical image

Hamza Virk, Yihren Wu, Majnu John

Standard jump-diffusion models assume independence between jumps and diffusion components. We develop a multi-type jump-diffusion model where jump occurrence and magnitude depend on contemporaneous diffusion movements. Unlike previous one-sided models that create arbitrage opportunities, our framework includes upward and downward jumps triggered by both larg

Alexander While, Chen Chen

Formulation symmetry in mixed-integer programming (MIP) can hinder solver performance by inducing redundant search, but detecting such symmetries is also a significant computational challenge. This paper explores the potential for quantum computing to handle symmetry detection. Quantum is a promising alternative to classical compute, but this emerging techno

Ziyu Shang, Haoran Liu, Rongchao Zhang, Zhiqian Wei

Generating consistent human images with controllable pose and appearance is essential for applications in virtual try on, image editing, and digital human creation. Current methods often suffer from occlusions, garment style drift, and pose misalignment. We propose Pose-guided Multi-view Multimodal Diffusion (PMMD), a diffusion framework that synthesizes pho

Debu Sinha

Retrieval-Augmented Generation (RAG) systems remain susceptible to hallucinations despite grounding in retrieved evidence. While current detection methods leverage embedding similarity and natural language inference (NLI), their reliability in safety-critical settings remains unproven. We apply conformal prediction to RAG hallucination detection, transformin

Chenxiao Zhang, Runshi Zhang, Junchen Wang

Medical ultrasound videos are widely used for medical inspections, disease diagnosis and surgical planning. High-fidelity lesion area and target organ segmentation constitutes a key component of the computer-assisted surgery workflow. The low contrast levels and noisy backgrounds of ultrasound videos cause missegmentation of organ boundary, which may lead to

Constantinos Kalapotharakos, Zorawar Wadiasingh, Alice K. Harding, Demosthenes Kazanas

We present a framework that links equatorial-current-sheet (ECS) physics to catalog-level, phase-averaged gamma-ray pulsar properties. Guided by analytic scalings and particle-in-cell (PIC) simulations, we show that the pulsar ``Fundamental Plane'' (relating gamma-ray luminosity, spectral cutoff energy, spin-down power $\dot{\cal{E}}$, and surface magnetic f

Timo Hillmann

(Abridged.) This thesis investigates scalable fault-tolerant quantum computation through the development of bosonic quantum codes, quantum LDPC codes, and decoding protocols that connect continuous-variable and discrete-variable error correction. We investigate superconducting microwave implementations of continuous-variable quantum computing, including the

Nadia Abdolkhani, Nada Abdel Khalek, Walaa Hamouda, Iyad Dayoub

This letter presents a novel deep reinforcement learning (DRL) approach for joint time allocation and power control in a cognitive Internet of Things (CIoT) system with simultaneous wireless information and power transfer (SWIPT). The CIoT transmitter autonomously manages energy harvesting (EH) and transmissions using a learnable time switching factor while

Pandega Abyan Zumarsyah, Igi Ardiyanto, Hanung Adi Nugroho

This study develops meta-learners for few-shot weakly-supervised segmentation (FWS) to address the challenge of optic disc (OD) and optic cup (OC) segmentation for glaucoma diagnosis with limited labeled fundus images. We significantly improve existing meta-learners by introducing Omni meta-training which balances data usage and diversifies the number of sho

Hanyu Li, Xiaotie Deng

Classic no-trade theorems attribute trade to heterogeneous beliefs. We re-examine this conclusion for AI agents, asking if trade can arise from computational limitations, under common beliefs. We model agents' bounded computational rationality within an unfolding game framework, where computational power determines the complexity of its strategy. Our central

Dan Wang, Yutung Yau

On a prequantizable K\"ahler manifold $(M, \omega, L)$, Chan-Leung-Li constructed a genuine (non-asymptotic) action of a subalgebra of the Berezin-Toeplitz star product on $H^0(M, L^{\otimes k})$ for each level $k$ [14]. We extend their framework to any non-singular polarization $P$ by developing a theory of transverse differential operators associated to $P

Abuobaida M. khair, Wenjing Jiang, Moritz Wildgruber, Wenjun Xi

Magnetic Particle Imaging (MPI) is a promising imaging modality that tracks magnetic nanoparticles (MNPs) to generate real time, high-resolution images. However, achieving an optimal balance between strong signal strength and sharp image clarity remains challenging. Higher drive field frequencies improve the signal-to-noise ratio (SNR), but also risk image b

Wei Liang, Han Hu, Lijie Sun, Pingxing Chen

The principle of least action, a fundamental principle in variational mechanics with broad applicability to classical physical systems, is employed to formulate a novel attrition model for combat dynamics. This formulation extends the Lanchester's square law through second-order temporal derivatives by requiring the resultant Euler-Lagrange equation to coinc

Ehsan Mohammadi, Fanghua Chen, Yizhou Cai, Yun Yang

The Stratified Bootstrap Test (SBT) provides a nonparametric, resampling-based framework for assessing the stability of group-specific ranking patterns in multivariate survey or rating data. By repeatedly resampling observations and examining whether a group's top-ranked items remain among the highest-scoring categories across bootstrap samples, SBT quantifi

Bingjie Li, Jiadai Xu, Yiqing Sun, Feiyue Pan

Multiple myeloma (MM) is preceded by a long preclinical phase spanning decades, yet scalable, non-specialist tools to identify individuals at elevated risk before end-organ damage are lacking. In a prospective analysis of 299,035 cancer-free UK Biobank participants followed for a median of 12.4 years, during which 768 developed incident MM, we conducted a bi

Yifei Bian, Banglei Guan, Zibin Liu, Ang Su

Large-scale structures suffer high-frequency deformations due to complex loads. However, harsh lighting conditions and high equipment costs limit measurement methods based on traditional high-speed cameras. This paper proposes a method to measure high-frequency deformations by exploiting an event camera and LED markers. Firstly, observation noise is filtered

Y. Gong, W. Reich, M. R. Rugel, K. M. Menten

Extended radio continuum emission and its linear polarization play a key role in probing large-scale structures of synchrotron and free-free emission in the Milky Way. Despite the existence of many radio continuum surveys, sensitive and high-angular-resolution single-dish surveys of extended radio continuum emission remain scarce. As part of the GLObal view

Reza Chandra, Adang Suhendra, Lintang Yuniar Banowosari, Prihandoko

In 2020, a total of 59,741 websites were blocked by the Indonesian government due to containing negative content, including pornography, with 14,266 websites falling into this category. However, these blocked websites could still be accessed by the public using virtual private networks (VPNs). This prompted the research idea to quickly identify pornographic

Fanzhe Fu

The transition of Large Language Models (LLMs) from stochastic chat interfaces to reliable software components necessitates a fundamental re-engineering of interaction paradigms. Current methodologies, predominantly heuristic-based "prompt engineering," fail to provide the deterministic guarantees required for mission-critical applications. We introduce the

Hongbo Wang, AprilPyone MaungMaung, Isao Echizen

Disclaimer: Samples in this paper may be harmful and cause discomfort. Multimodal large language models (MLLMs) enable multimodal understanding but inherit toxic signals from weakly curated pretraining corpora, leading to explicitly toxic outputs, especially under adversarial triggers that late, opaque training-free detoxification methods struggle to handle.

H. M. Florez

Multipartite entanglement is an important resource for quantum information processing. It has been shown that it is possible to employ alkali atoms to implement single device multipartite entanglement by using nonlinear processes with spatial modes. This work presents the first microscopic description of such multi-mode generation with two-pump four wave mix

Qixuan Hu

We obtain the simplicity of the first Neumann eigenvalue of convex thin domain with boundary in $R^n$ and compact thin manifolds with non-negative Ricci curvature. For convex thin domain in $R^2$, we get the simplicity of the first k Neumann eigenvalues. The number k depends on the ratio of the corresponding width over the diameter of the domain. For convex

Kai Huang, Xinyu Xie, Chunpeng Chen, Wenjie Guan

In this paper, we aim to explore the stochastic performance limit of large-field-size Random Linear Streaming Codes (RLSCs) in multi-hop relay networks. In our model, a source transmits a sequence of streaming messages to a destination through multiple relays subject to a delay constraint. Most previous research focused on deterministic adversarial channel w

Kaizhe Zhang, Shinan Chen, Qian Zhao, Weizhan Zhang

Scenes reconstructed by 3D Gaussian Splatting (3DGS) trained on low-resolution (LR) images are unsuitable for high-resolution (HR) rendering. Consequently, a 3DGS super-resolution (SR) method is needed to bridge LR inputs and HR rendering. Early 3DGS SR methods rely on single-image SR networks, which lack cross-view consistency and fail to fuse complementary

Yunheng Wang, Yixiao Feng, Yuetong Fang, Shuning Zhang

3D Scene Graphs (3DSGs) constitute a powerful representation of the physical world, distinguished by their abilities to explicitly model the complex spatial, semantic, and functional relationships between entities, rendering a foundational understanding that enables agents to interact intelligently with their environment and execute versatile behaviors. Embo

Zixuan Li, Chen Sun

The search for exactly solvable models is an evergreen topic in theoretical physics. In the context of multistate Landau-Zener models -- $N$-state quantum systems with linearly time-dependent Hamiltonians -- the theory of integrability provides a framework for identifying new solvable cases. In particular, it was proved that the integrability of a specific c

Aparna Parameswaran, Hoyoung Kim, Sangkil Kim

In this work, a Janus metasurface based tensor impedance holographic antenna (JHA) is proposed that simultaneously radiates linearly polarized (LP) and circularly polarized (CP) beams from a single aperture excited by a single feed. The proposed design introduces modified tensor impedance equations to significantly reduce cross-polarization at higher radiati

Chun Fang, Luowen Liu, Kun Huang, Tao Ruan

Recently, joint advertising has gained significant attention as an effective approach to enhancing the efficiency and revenue of advertising slot allocation. Unlike traditional advertising, which allocates advertising slots exclusively to a single advertiser, joint advertising displays advertisements from brands and stores that have established a joint selli

Sijin Sun, Liangbin Zhao, Ming Deng, Xiuju Fu

Dialogue Topic Segmentation (DTS) is crucial for understanding task-oriented public-channel communications, such as maritime VHF dialogues, which feature informal speech and implicit transitions. To address the limitations of traditional methods, we propose DASH-DTS, a novel LLM-based framework. Its core contributions are: (1) topic shift detection via dialo

Wenqi Yang, Dawei Zhai, Wang Yao

Fractional quantum anomalous Hall (FQAH) effect, a lattice analogue of fractional quantum Hall effect, offers a unique pathway toward fault-tolerant quantum computation and deep insights into the interplay of topology and strong correlations. The exploration has been successfully guided by the paradigm of ideal flat Chern bands, which mimic Landau levels in

Te Li, Ping Zhang, Yibin Zhang

In the high-Reynolds-number regime, this work investigates the long-time dynamics of the three-dimensional incompressible Navier-Stokes equations near the Oseen vortex filament. The flow exhibits a strong interplay between vortex stretching, shearing, and mixing, which generates ever-smaller spatial scales and thereby significantly amplifies viscous effects.

Zhenhao Yin, Hanbing Yan, Huishu Lu, Jing Xiong

Large-scale, standardized datasets for Advanced Persistent Threat (APT) research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases (reconciling approximately 11.22\% of inconsistent names) and applies graph-feature dedu

Xiangyang Wang, Huibo Qi, Biao Xu, Shichao Dai

To delve deeply into the nonlinear large-deformation and fracture behaviors of 2D carbon nanostructures (2D CNs), including bilayer graphene, diamane, and their transitional structures, this paper introduces a multiscale auxiliary nodes (MAN) method rooted in atomic structures and potentials. This approach simulates 2D CNs by constructing two virtual continu

Weitao Pan, Meng Dong, Zhiliang Qiu, Jianlei Yang

Reverse engineering of gate-level netlist is critical for Hardware Trojans detection and Design Piracy counteracting. The primary task of gate-level reverse engineering is to separate the control and data signals from the netlist, which is mainly realized by identifying state registers with topological comparison.However, these methods become inefficient for

Aizierjiang Aiersilan, Haochen Liu, James Hahn

Rotation representations are foundational in fields such as computer graphics, robotics, and machine learning, where precise and efficient modeling of 3D orientations is critical. This paper comprehensively investigates diverse representations of the special orthogonal group $SO(3)$, such as Euler angles, axis-angle vectors, quaternions, rotation matrices, e

Chenxiao Gao, Haotian Sun, Na Li, Dale Schuurmans

In real-world applications with large state and action spaces, reinforcement learning (RL) typically employs function approximations to represent core components like the policies, value functions, and dynamics models. Although powerful approximations such as neural networks offer great expressiveness, they often present theoretical ambiguities, suffer from

Soong Kyum Lee

Saxl's conjecture (2012) asserts that for the staircase partition $\rho_k = (k, k-1, \ldots, 1)$, the tensor square of the corresponding irreducible representation of the symmetric group $S_{T_k}$ contains every irreducible representation as a constituent, where $T_k = k(k+1)/2$ is the $k$th triangular number. We prove this conjecture unconditionally. Our pr

Nalini M. Singh, Tiffany Chien, Arthur R. C. McCray, Colin Ophus

Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic str

Xidan Song, Weiqi Wang, Ruifeng Cao, Qingya Hu

The evaluation of Large Language Models (LLMs) in complex reasoning domains typically relies on performance alignment with ground-truth oracles. In the domain of chess, this standard manifests as accuracy benchmarks against strong engines like Stockfish. However, high scalar accuracy does not necessarily imply robust conceptual understanding. This paper argu

Chris Connell, Yuping Ruan, Shi Wang

Given an isotopy class between two closed hyperbolic surfaces, the Douady--Earle extension provides a unique analytic diffeomorphism representative. In this paper we investigate the Jacobian of the Douady--Earle extension map $F$. We prove that $|\operatorname{Jac} F| \equiv 1$ precisely when $F$ is an isometry. Moreover, we construct a sequence of hyperboli

Mia Mohammad Imran, Robert Zita, Rahat Rizvi Rahman, Preetha Chatterjee

Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires a clear understanding of how harmful conversations unfold. However, most proactive moderation strategies are manual, requiring significant time and effort from community maintainer

Xiaoqi Li, Wenkai Li, Zhijie Liu, Meikang Qiu

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinc

Xiangdi Huang, Weili Meng, Xueyao Zhang

In 1871, Saint-Venant introduced the renowned shallow water equations. Since then, for the two-dimensional viscous or inviscid shallow water equations, the global existence of smooth solutions with arbitrarily large initial data has remained a challenging and long-standing open problem. In this paper, we provide an affirmative resolution to the viscous probl

Liang Peng, Yixuan Ye, Cheng Liu, Hangjun Che

Recently, neighbor-based contrastive learning has been introduced to effectively exploit neighborhood information for clustering. However, these methods rely on the homophily assumption-that connected nodes share similar class labels and should therefore be close in feature space-which fails to account for the varying homophily levels in real-world graphs. A

Rohit Agarwala, Dipankar Basak, Kalyan Dey

We report the observation of partonic radial collectivity in Au+Au collisions at $\sqrt{s_{\rm NN}} = 200$~GeV via the $p_{\rm T}$-differential flow observable $v_{0}(p_{\rm T})$ using the \texttt{AMPT} String Melting model. For inclusive charged hadrons, we establish three signatures of collectivity: long-range pseudorapidity correlations, the factorization

M. Hasan Barbhuiya, Subash Adhikari

A common occurrence in weakly collisional space plasmas is the unequal electron-ion temperatures. The pressure-strain interaction provides a mechanism-agnostic pathway for increasing plasma internal energy through spatiotemporally local isotropic compression and volume preserving deformation, yet its behavior under thermal disequilibrium is largely unexplore

R. Pablo Arribillaga, Agustin G. Bonifacio

In problems involving the allocation of a single non-disposable commodity, we study rules defined on a general domain of preferences requiring only that each preference exhibit a unique global maximum. Our focus is on rules that satisfy a relaxed form of strategy-proofness, known as non-obvious manipulability. We show that the combination of efficiency and n

Hanbit Oh, Ya-Hui Zhang

The recent experimental observations of high temperature superconductivity in bilayer nickelate have attracted lots of attentions. Previous studies have assumed a mirror symmetry $\mathcal M$ between the two layers and focused on uniform and clean superconducting states. Here, we show that breaking this mirror symmetry via an applied displacement field can s

Jianhao Xu

Magnet errors in storage rings significantly degrade beam performance, impacting the brightness and stability of the light source. Therefore, beam-based correction is crucial for the safe operation of machines and the stability of radiated photons. Unlike traditional correction methods such as linear optics from closed orbit, this paper proposes a machine le

Lin Xu, Xin Zhong

We consider the Cauchy problem of the non-isentropic compressible magnetohydrodynamic equations in $\mathbb{R}^3$ with far-field vacuum. By deriving delicate energy estimates and exploiting the intrinsic structure of the system, we establish the global existence and uniqueness of strong solutions provided that the scaling-invariant quantity \begin{align*} (1

Wenlong Xia, Jinhao Zhang, Ce Zhang, Yaojia Wang

Vision-based imitation learning has enabled impressive robotic manipulation skills, but its reliance on object appearance while ignoring the underlying 3D scene structure leads to low training efficiency and poor generalization. To address these challenges, we introduce \emph{Implicit Scene Supervision (ISS) Policy}, a 3D visuomotor DiT-based diffusion polic

S. Janssens, H. Sana, T. Shenar, J. Bodensteiner

The majority of massive classical Be stars are thought to be binary interactions products. Their rapid rotation and often strong, variable, and emission-line dominated spectrum, make spectroscopic analysis challenging. Hence, robust binary properties and statistical constraints are still lacking for the Be population. In this study, we use seven Be-X-ray bin

Hui Li, Ting Gao, Fengli Yan

Exploring the shareability and distribution of entanglement possesses fundamental significance in quantum information tasks. In this paper, we demonstrate that the square of bipartite entanglement measures $G_q$-concurrence, which is the generalization of concurrence, follows a set of hierarchical monogamy relations for any $N$-qubit quantum state. On the ba

Dapeng Du, Jingyu Li, Xinyue Shi

We consider the following model equation: \begin{equation} \omega_{t} = Z_{11}\omega\,\omega , \end{equation} where \begin{equation} Z_{11} = \partial_{11}\Delta^{-1} \end{equation} is a Calderon-Zygmond operator. We get the existence of self-similar singular solutions with a special form. The main difficulty is the degeneracy of the operator $Z_{11}$ that i

Kyril Kavetsky, Sabine Hong, Chih-Yuan Lin, Roger Yang

Advanced nanopore measurements allow structural probing of molecules with high spatial and temporal resolution. We report high signal-to-noise, 1-10 MHz bandwidth, translocation measurements of the multi-state folding of heme protein cytochrome c in KCl solution through optimally designed silicon nitride pores of 2.3-3.3 nm diameter and 3.6-3.8 nm effective

Weitao Zhao, Xiangcun Meng, Yingzhen Cui, Yunlang Guo

Supersoft X-ray sources (SSSs) are characterized by persistent thermonuclear burning on the surfaces of white dwarfs (WDs).The standard model requires high mass transfer rates of $\sim 10^{-7}\, {\rm M_{\odot}}\,yr^{-1}$ from massive companions, presenting a theoretical impediment to the observed short-period SSSs, whose orbital periods imply low-mass donors

Han L. Gan

We consider the approximation of the stationary distribution of the finite inclusion process with the Poisson-Dirichlet distribution. Using Stein's method, we derive an explicit bound for the approximation error, which is of order 1/N in the thermodynamic limit. The results are achieved from a minor modification to Stein's method for Poisson-Dirichlet distri

Xinyu Huang, Shyam Karthick V B, Taozhao Chen, Mitch Bryson

The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such paradigm shift introduces critical security vulnerabilities stemming from the ''embodiment gap'', a discord between the LLM's abstract reasoning and the physical, context-dependent n

Shuichi Hayashida

This paper has three main objectives: (i) To establish an isomorphism between Jacobi forms of index $D_{2n+1}$ (lattice index) and elliptic modular forms of level $2$. (ii) To provide an explicit formula for the Fourier coefficients of Jacobi--Eisenstein series of index $D_{2n+1}$. (iii) To construct a holomorphic modular form of weight $3/2$ and level $8$ (

Nikhil Sarin, Andrew Levan, Nial Tanvir, Simone Scaringi

Relativistic transients such as gamma-ray bursts (GRBs), jetted tidal disruption events, luminous fast blue optical transients, and fast X-ray transients, represent the brightest explosions in the Universe and serve dual roles as laboratories for extreme physics and as cosmic lighthouses probing the earliest epochs of the Universe. The 2040s will bring trans

Yunseong Nam, Jiwon Jang, Dongkyu Won, Sang Hyun Park

Preference optimization offers a scalable supervision paradigm based on relative preference signals, yet prior attempts in medical image segmentation remain model-specific and rely on low-diversity prediction sampling. In this paper, we propose MAPO (Model-Agnostic Preference Optimization), a training framework that utilizes Dropout-driven stochastic segment

Sandeep Neela

Understanding how prices evolve over time often requires peeling back the layers of market noise to identify clear, structural behavior. Many of the tools commonly used for this purpose technical indicators, chart heuristics, or even sophisticated predictive models leave important questions unanswered. Technical indicators depend on platform-specific rules,

Kohei Suzuki, Takashi Goda

We study the probabilistic existence of point configurations satisfying the $(0, m, d)$-net property in base $b$ within a randomly generated point set of size $N$ in the $d$-dimensional unit cube. We first derive an upper bound on the number of geometric patterns for $(0, m, d)$-nets in base $b$. By applying the elementary probability bounds together with th

Huaying Zhang, Atsushi Hashimoto, Tosho Hirasawa

Skilled human interviewers can extract valuable information from experts. This raises a fundamental question: what makes some questions more effective than others? To address this, a quantitative evaluation of question-generation models is essential. Video question generation (VQG) is a topic for video question answering (VideoQA), where questions are genera

Amelia R. Klein, Hayley J. Austin, Fumikazu Murakami, Jamie Ford

Europium-doped gallium nitride (GaN:Eu) is a promising platform for classical and quantum optoelectronic applications. When grown using organometallic vapor-phase epitaxy, the dominant red emission from Eu exhibits an inhomogeneous photoluminescence (PL) spectrum due to contributions from several non-equivalent incorporation sites that can be distinguished w

Andrew Davis

Let $M$ be a smooth manifold. We use Chern-Weil theory to study the characteristic classes of principal $G$-bundles built from continuous families of $\pi_{1}(M)$-representations, where $G$ is a compact Lie group. We then relate these families to the functorial map $$\text{Hom}(\pi_{1}(M), G)\rightarrow\text{Map}_{*}(M,BG)$$ and use this relationship to stud

Sogol Masoumzadeh, Yufei Li, Shane McIntosh, Dániel Varró

Monitoring issue tracker submissions is a crucial software maintenance activity. A key goal is the prioritization of high risk, security-related bugs. If such bugs can be recognized early, the risk of propagation to dependent products and endangerment of stakeholder benefits can be mitigated. To assist triage engineers with this task, several automatic detec

Marc Gong Bacvanski, Xincheng You, John Hopfield, Dmitry Krotov

The increasing computational demands of modern AI systems have exposed fundamental limitations of digital hardware, driving interest in alternative paradigms for efficient large-scale inference. Dense Associative Memory (DenseAM) is a family of models that offers a flexible framework for representing many contemporary neural architectures, such as transforme

Xuechen Zhang, Koustava Goswami, Samet Oymak, Jiasi Chen

Retrieval-augmented generation (RAG) has strong potential for producing accurate and factual outputs by combining language models (LMs) with evidence retrieved from large text corpora. However, current pipelines are limited by static chunking and flat retrieval: documents are split into short, predetermined, fixed-size chunks, embeddings are retrieved unifor

Zhao Song, Song Yue, Jiahao Zhang

The rapid growth of AI conference submissions has created an overwhelming reviewing burden. To alleviate this, recent venues such as ICLR 2026 introduced a reviewer nomination policy: each submission must nominate one of its authors as a reviewer, and any paper nominating an irresponsible reviewer is desk-rejected. We study this new policy from the perspecti

Xanthe Croot, Kasra Nowrouzi, Christopher Spitzer, Carmen G. Almudever

Experiments with superconducting quantum processors have successfully demonstrated the basic functions needed for quantum computation and evidence of utility, albeit without a sizable array of error-corrected qubits. The realization of the full potential of quantum computing centers on achieving large scale fault-tolerant quantum computers. Science, engineer

Ruiyi Zhang, Peijia Qin, Qi Cao, Pengtao Xie

Process Reward Models (PRMs) have become essential for improving Large Language Models (LLMs) via test-time scaling, yet their effectiveness in coding remains limited due to the lack of meaningful step decompositions in code and the noise of Monte-Carlo-generated partial labels. We propose DreamPRM-Code, a coding-focused PRM that treats functions as reasonin

Yiding Wang, Xiaofen Huang, Shao-Ming Fei, Tinggui Zhang

First, we investigate the trade-off relations of quantum battery capacities in two-qubit system. We find that the sum of subsystem battery capacity is governed by the total system capacity, with this trade-off relation persisting for a class of Hamiltonians, including Ising, XX, XXZ and XXX models. Then building on this relation, we define residual battery c

Sibi Parivendan, Kashfia Sailunaz, Suresh Neethirajan

Precision livestock farming requires objective assessment of social behavior to support herd welfare monitoring, yet most existing approaches infer interactions using static proximity thresholds that cannot distinguish affiliative from agonistic behaviors in complex barn environments. This limitation constrains the interpretability of automated social networ

Mohammadhossein Nahavandian, Anter El-Azab, Enrique Martinez

Radiation-induced segregation (RIS) and chemical redistribution in structural alloys can significantly degrade material performance, ultimately leading to failure. In this study, building on previous work by the authors [1], we investigate how the dimensional characteristics of sinks influence solute concentration distributions and segregation behavior. Spec

Junliang Shen

This article surveys some recent developments on the cohomology of the compactified Jacobian associated with a locally planar integral curve. Topics discussed here include the Ng\^o support theorem, the perverse filtration, connections to the Hilbert schemes, and cohomological structures induced by the Arinkin-Fourier-Mukai transform.

Maria Bulychev, Neil G. Marchant, Benjamin I. P. Rubinstein

Recent advances in generative AI have enabled the creation of highly realistic digital content, raising concerns around authenticity, ownership, and misuse. While watermarking has become an increasingly important mechanism to trace and protect digital media, most existing image watermarking schemes operate as black boxes, producing global detection scores wi

Pranav Kakhandiki, Sathya Chitturi, Daniel Ratner, Sean Gasiorowski

The discovery of a minimum energy pathway (MEP) between metastable states is crucial for scientific tasks including catalyst and biomolecular design. However, the standard nudged elastic band (NEB) algorithm requires hundreds to tens of thousands of compute-intensive simulations, making applications to complex systems prohibitively expensive. We introduce Ne

M. Coronado-Vaca

DRL agents circumvent the issue of classic models in the sense that they do not make assumptions like the financial returns being normally distributed and are able to deal with any information like the ESG score if they are configured to gain a reward that makes an objective better. However, the performance of DRL agents has high variability and it is very s

Carlos Purves

Reinforcement learning (RL) has achieved strong results, but deploying visual policies on resource-constrained edge devices remains challenging due to computational cost and communication latency. Many deployments therefore offload policy inference to a remote server, incurring network round trips and requiring transmission of high-dimensional observations.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Despite their wide adoption in various domains (e.g., healthcare, finance, software engineering), Deep Learning (DL)-based applications suffer from many bugs, failures, and vulnerabilities. Reproducing these bugs is essential for their resolution, but it is extremely challenging due to the inherent nondeterminism of DL models and their tight coupling with ha

Yiming Cui, Xin Yao, Yuxuan Qin, Xin Li

Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, molecular structures, and structured visual data. Here, we systematically evaluate 40 proprietary and open-source multimodal LLMs, including GPT-5, o3, Gemini-2.5-Pro, and Qwen2.5-VL,

Chris Kapulkin, Yufeng Li

We develop a general framework for working with structured lifting problems, establishing closure and uniqueness properties of their solutions. In a subsequent paper, we apply these results to axiomatize computation rules of cubical type theory.

Kazuki Sato, Futoshi Takahashi

In this paper, we study the solution structures of Serrin-type overdetermined problems with Kirchhoff-type nonlocal terms. We prove that the exact number of solutions is the same as those of some transcendental equations defined by the nonlocal terms. We also obtain the explicit form of solutions by using the unique solutions of the overdetermined problems w

Jinjian Liu, Yichuan Wang, Xinxi Lyu, Rulin Shao

We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time trade-offs between latency, accur