December 2025 arXiv papers — page 8

Bo Jiang, Taolue Yang, Youyuan Liu, Xubin He

Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, which can scale to several gigabytes as sequence length and batch size increase. In this pa

M. Rizki Oktavian

Large language models (LLMs) have recently shown strong reasoning capabilities beyond traditional language tasks, motivating their use for numerical optimization. This paper presents LLMize, an open-source Python framework that enables LLM-driven optimization through iterative prompting and in-context learning. LLMize formulates optimization as a black-box p

Ghazi Khan, Thomas E. Roth

Superconducting qubits have emerged as a leading platform for realizing quantum computers. Accurate modeling of these devices is essential for predicting performance, improving design, and optimizing control. Many modeling approaches currently rely on lumped circuit approximations or other simplified treatments that can be limited in resolving the interplay

Michael Tsironis

This thesis studies skein relations in cluster algebras arising from punctured surfaces. We introduce skein-type identities expressing cluster variables associated with incompatible curves on a surface in terms of cluster variables corresponding to compatible arcs. Incompatibility arises from phenomena such as intersections, self-intersections, and opposite

Patrick Wyrod, Ashesh Chattopadhyay, Daniele Venturi

Chaotic dynamical systems exhibit strong sensitivity to initial conditions and often contain unresolved multiscale processes, making deterministic forecasting fundamentally limited. Generative models offer an appealing alternative by learning distributions over plausible system evolutions; yet, most existing approaches focus on next-step conditional predicti

Akash Samanta, Sheldon Williamson

Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt to gradient statistics, they largely ignore the temporal structure of the error signal it

Ralf Spatzier

In honor of Zhiren Wang on the occasion of being awarded the Brin Prize, we report on his exciting and deep work on rigidity of higher rank abelian groups and lattices in higher rank semisimple groups.

The Tien Mai, Mai Anh Nguyen, Trung Nghia Nguyen

High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While established for fully supervised data, these techniques remain underexplored in weak-supervision settings such as Positive-Confidence (Pconf) classification. Pconf learning utiliz

Lindsey E. Turner, Carolyn T. Bramante, Thomas A. Murray

In randomized controlled trials, ordinal outcomes typically improve statistical efficiency over binary outcomes. The treatment effect on an ordinal outcome is usually described by the odds ratio from a proportional odds model, but this summary measure lacks transparency with respect to its emphasis on the components of the ordinal outcome when proportional o

Alexander I. Bufetov

Simple sufficient conditions are given that ensure the uniform continuity in distribution for Borel transformations of random fields.

Theodore MacMillan, Nicholas T. Ouellette

Large data-driven physics models like DeepMind's weather model GraphCast have empirically succeeded in parameterizing time operators for complex dynamical systems with an accuracy reaching or in some cases exceeding that of traditional physics-based solvers. Unfortunately, how these data-driven models perform computations is largely unknown and whether their

Nimrod Sherf, Si Tang, Dylan Hafner, Jonathan D. Touboul

Neural circuits exhibit structured connectivity, including an overrepresentation of reciprocal connections between neuron pairs. Despite important advances, a full understanding of how such partial symmetry in connectivity shapes neural dynamics remains elusive. Here we ask how correlations between reciprocal connections in a random, recurrent neural network

Akshad Shyam Purushottamdas, Pranav K Nayak, Divya Mehul Rajparia, Deekshith Patel

While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by the Vision Transformer (ViT) encoder through the lens of compositionality. We introduce a framework, analogous to prior work on measuring compositionality in representation learnin

Yanet Alvarez, Mariela Portesi, Romina Ramirez, Marta Reboiro

We investigate uncertainty relations for quantum observables evolving under non-Hermitian Hamiltonians, with particular emphasis on the role of metric operators. By constructing appropriate metrics in each dynamical regime, namely the unbroken-symmetry phase, the spontaneously broken-symmetry phase, and at exceptional points, we provide a consistent definiti

Jacek Miȩkisz, Siamak Taati

We construct a four-dimensional lattice-gas model with finite-range interactions that has non-periodic, "quasicrystalline" Gibbs states at low temperatures. Such Gibbs states are probability measures which are small perturbations of non-periodic ground-state configurations corresponding to stacked tilings of the plane with Ammann's aperiodic tile

Alexandre Rodrigues Mesquita

This report investigates the extension of the Bayesian Subspace System Identification method proposed in our previous work to the Multiple-Input Multiple-Output (MIMO) case. We derive new equivariant priors and posterior distributions specifically suited for the MIMO framework. Numerical results utilizing the DAISY dataset are reported to validate the approa

Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri

This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation of virtual assets in a digital twin. Data-driven ROMs are explainable, computationally efficient scientific machine learning models that aim to preserve the underlying physics of co

Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus X. V. Ferreira

We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with line

Eduardo Barrera, Bheeshmon Thanabalasingam, Rafik Addou, Damian Allis

Scanning Tunneling Microscopy (STM) enables fabrication of atomically precise structures with unique properties and growing technological potential. However, reproducible manipulation of covalently bonded atoms requires control over the atomic configuration of both sample and probe - a longstanding challenge in STM. Here, we introduce inverted-mode STM, an a

Ashley Dickson, Niccolò Di Eugenio, Federico Ledda, Daniele Torsello

Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which

Reza Javadinezhad, Ali Seraj

We analyze the influence of gravitational waves on a Sagnac interferometer formed by the interference of two counter-propagating beams traversing a closed spatial loop. In addition to the well-known Sagnac phase shift, we identify an additional contribution originating from a relative rotation in the polarization vectors. We formulate this effect as a gravit

Qian Wang, Omar Abdellall, Tony Gao, Xiatao Sun

3D meshes are a fundamental representation widely used in computer science and engineering. In robotics, they are particularly valuable because they capture objects in a form that aligns directly with how robots interact with the physical world, enabling core capabilities such as predicting stable grasps, detecting collisions, and simulating dynamics. Althou

Sascha H. Hauck, Sandip Saha, Narsis A. Kiani, Jesper N. Tegner

Understanding how gene regulatory networks (GRNs) give rise to stable and dynamic cellular states remains a central challenge in theoretical biology, particularly when slow epigenetic feedback reshapes the underlying regulatory landscape. While experimental approaches such as single-cell transcriptomics reveal rich dynamical behaviour, a tractable theoretica

Zhenghao "Mark" Peng, Wenhao Ding, Yurong You, Yuxiao Chen

Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet these models primarily describe what they perceive and intend to do, rarely questioning whether their planned actions are safe or appropriate. This work introduces Counterfactual V

Immanuel Christopher Jebaraj, Mikhail Malkov, Nicolas Wijsen, Jens Pomoell

Collisionless fast-magnetosonic shocks are often treated as smooth, planar boundaries, yet observations point to organized corrugation of the shock surface. A plausible driver is upstream turbulence. Broadband fluctuations arriving at the front can continually wrinkle it, changing the local shock geometry and, in turn, conditions for particle injection and r

Vinicius Mikuni, Ibrahim Elsharkawy, Benjamin Nachman

Foundation models build an effective representations of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearned foundation model for collider physics and showed that it could significantly advance discovery potential across collider experiments. In this paper we go beyond collider physics and show that Foundation Mo

Zhi-Chao Li, H. Khodabakhshi, H. Lu

We prove that, in the framework of the Oppenheimer-Snyder collapse, the Schwarzschild exterior maximizes the event horizon formation time $\Delta T_{\text{eh}}=\frac{19}{6}m$ among all asymptotically flat, static, spherically-symmetric black holes with the same ADM mass $m$ that satisfy the weak energy condition. This bound extends the typical black hole ine

Brandon Robinson

Neural Network Field Theories (NN-FTs) typically describe Generalized Free Fields that lack a local stress-energy tensor in two dimensions, obstructing the realization of Virasoro symmetry. We present the ``Log-Kernel'' (LK) architecture, which enforces local conformal symmetry via a specific rotation-invariant spectral prior $p(k) \propto |k|^{-2}$. We anal

Clay Cordova, Diego García-Sepúlveda, Jeffrey A. Harvey

We study the interplay between holomorphic conformal field theory and dualities of 3D topological quantum field theories generalizing the paradigm of level-rank duality. A holomorphic conformal field theory with a Kac-Moody subalgebra implies a topological interface between Chern-Simons gauge theories. Upon condensing a suitable set of anyons, such an interf

Yevgeny Bar Lev, Jad C. Halimeh, Achilleas Lazarides

With the advent of quantum simulation experiments of lattice gauge theories (LGTs), an open question is the effect of non-Hermiticity on their rich physics. The well-known PXP model, a U$(1)$ LGT with a two-level electric field in one spatial dimension, has become a paradigm of exotic physics in and out of equilibrium. Here, we introduce a non-Hermitian vers

Sean Moss, Sam Staton

In Fritz & Rischel, Infinite products and zero-one laws in categorical probability, the problem was posed of finding an interesting Markov category which is causal and has all (small) Kolmogorov products (there Problem 6.7). Here we give an example where the deterministic subcategory is the category of Stone spaces (i.e. the dual of the category of Boolean a

Mohamad Akkad, Hüseyin Bodur

Recording entry and exit records for a country, with properties such as confidentiality, integrity, and auditability, is increasingly important due to rising international mobility and security requirements. Traditional border control systems, which rely on centralised databases, are vulnerable to data manipulation and have limited interoperability between i

Jingyu Zhang

Customer-service LLM agents increasingly make policy-bound decisions (refunds, rebooking, billing disputes), but the same ``helpful'' interaction style can be exploited: a small fraction of users can induce unauthorized concessions, shifting costs to others and eroding trust in agentic workflows. We present a cross-domain benchmark of profit-seeking direct p

Ramsés H. Mena, Christos Merkatas, Theodoros Nicoleris, Carlos E. Rodríguez

Discrete random probability measures are central to Bayesian inference, particularly as priors for mixture modeling and clustering. A broad and unifying class is that of proper species sampling processes (SSPs), encompassing many Bayesian nonparametric priors. We show that any proper SSP admits an exact two-stage finite-mixture representation built from a la

Grace V. Ringlein, Trang Quynh Nguyen, Peter P. Zandi, Elizabeth A. Stuart

Proximal causal inference (PCI) has emerged as a promising framework for identifying and estimating causal effects in the presence of unobserved confounders. While many traditional causal inference methods rely on the assumption of no unobserved confounding, this assumption is likely often violated. PCI addresses this challenge by relying on an alternative s

Javier Giménez, José A. Cortés, Luis Díez

Orthogonal frequency division multiplexing (OFDM) signals with rectangular pulses exhibit low spectral confinement. Shaping their power spectral density (PSD) is imperative in the increasingly overcrowded spectrum to benefit from the cognitive radio (CR) paradigm. However, since the available spectrum is non-contiguous and its occupancy changes with time, th

Yan Meng, Daniel A. Donoho, Marcelle Altshuler, Omar Arnaout

The development of effective training and evaluation strategies is critical. Conventional methods for assessing surgical proficiency typically rely on expert supervision, either through onsite observation or retrospective analysis of recorded procedures. However, these approaches are inherently subjective, susceptible to inter-rater variability, and require

Chester Palen-Michel, Constantine Lignos

Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of different approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with an

Zhang Hangyue

We construct a smooth nontrivial mixed partially hyperbolic system and explicitly identify its skeleton. This example shares characteristics with the classical examples. Moreover, the support of each physical measure contains three fixed points with mutually distinct unstable indices. By appropriately perturbing the skeleton, we provide an example where the

Bohong Chen, Haiyang Liu

Generating realistic, dyadic talking head video requires ultra-low latency. Existing chunk-based methods require full non-causal context windows, introducing significant delays. This high latency critically prevents the immediate, non-verbal feedback required for a realistic listener. To address this, we present DyStream, a flow matching-based autoregressive

Lars van der Laan, Aurélien Bibaut, Nathan Kallus

In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and compare counterfactual policies. Inverse reinforcement learning (IRL) and dynamic discrete choice (DDC) models address this setting by positing an optimality model that links latent rewards to observed actions. Exis

Uria Mor

A valuable feature of the tubal tensor framework is that many familiar constructions from matrix algebra carry over to tensors, including SVD and notions of rank. Importantly, it has been shown that for a specific family of tubal products, an Eckart-Young type theorem holds, i.e., the best low-rank approximation of a tensor under the Frobenius norm is obtain

Soham Pahari, M. Srinivas

Multimodal intelligence development recently show strong progress in visual understanding and high level reasoning. Though, most reasoning system still reply on textual information as the main medium for inference. This limit their effectiveness in spatial tasks such as visual navigation and geo-localization. This work discuss about the potential scope of th

L. Fekésházy, G. Somogyi, S. Van Thurenhout

We present the analytic integration of the iterated single-unresolved subtraction terms in the extension of the CoLoRFulNNLO subtraction scheme to color-singlet production in hadron collisions. We exploit the fact that, in this scheme, subtraction terms are defined through momentum mappings which lead to exact phase space convolutions for real emissions. Thi

Giovanni Lambertini, Matteo Pini, Eugenio Mascaro, Francesco Moretti

In this paper, we describe the automated simulation and reporting pipeline implemented for our autonomous racing stack, ur.autopilot. The backbone of the simulation is based on a high-fidelity model of the vehicle interfaced as a Functional Mockup Unit (FMU). The pipeline can execute the software stack and the simulation up to three times faster than real-ti

Shani Martinez-Weissberg, Will Pazner, Zohar Yosibash

High-resolution voxel-based micro-finite element ($\mu$FE) models derived from $\mu$CT imaging enable detailed investigation of bone mechanics but remain computationally challenging at anatomically relevant scales. This study presents a comprehensive $\mu$FE framework for large-scale biomechanical analysis of an intact New Zealand White (NZW) rabbit femur, i

Krzysztof Ociepa, Łukasz Flis, Remigiusz Kinas, Krzysztof Wróbel

We present Bielik 11B v3, a state-of-the-art language model highly optimized for the Polish language, while also maintaining strong capabilities in other European languages. This model extends the Mistral 7B v0.2 architecture, scaled to 11B parameters via depth up-scaling. Its development involved a comprehensive four-stage training pipeline: continuous pre-

Biagio Montaruli, Serena Elisa Ponta, Luca Compagna, Davide Balzarotti

SourceRank is a scoring system made of 18 metrics that assess the popularity and quality of open-source packages. Despite being used in several recent studies, none has thoroughly analyzed its reliability against evasion attacks aimed at inflating the score of malicious packages, thereby masquerading them as trustworthy. To fill this gap, we first propose a

Gabrielle N. Jones, Philipp W. A. Schönhöfer, Sharon C. Glotzer

Spherical particles confined to a sphere surface cannot pack densely into a hexagonal lattice without defects. In this study, we use hard particle Monte Carlo simulations to determine the effects of continuously deformable shape anisotropy and underlying crystal lattice preference on inevitable defect structures and their distribution within colloidal assemb

Bach Do, Bardia Nabavi, Sina Jafari Ghalekohneh, Taiwo Adebiyi

Many advanced energy harvesting technologies rely on advanced control of thermal emission. Recently, it has been shown that the emissivity and absorptivity of thermal emitters can be controlled independently in nonreciprocal emitters. While significant progress has been made in engineering these nonreciprocal thermal emitters, realizing a highly efficient, p

Teddy X Cai, Nathan H Williamson, Peter J Basser

The Gaussian phase approximation (GPA) underlies many standard diffusion magnetic resonance (MR) signal models, yet its validity is rarely scrutinized. Here, we assess the validity of the GPA by analytically deriving the excess phase kurtosis $\kappa_4/\kappa_2^2$, where $\kappa_n$ is the $n^{\text{th}}$ cumulant of the accumulated phase distribution due to

Osasumwen Cedric Ogiesoba-Eguakun, Suman Rath

Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantum machine learning approaches for detecting coordinated stealth

Subin Mali, Yingdong Guan, Lujin Min, David Graf

Valleytronics is a rapidly advancing field that explores the use of the valley degree of freedom in electronic systems to encode and process information. It relies on electronic states with spin valley locking, first predicted and observed in monolayer transition metal dichalcogenides such as MoS2. However, very few bulk materials have been reported to host

Peiyi Chen, Irene M. Gamba, Qin Li, Anjali Nair

The reflection coefficient is an important thermal property of materials, especially at the nanoscale, and determining this property requires solving an inverse problem based on macroscopic temperature measurements. In this manuscript, we investigate the stability of this inverse problem to infer the reflection coefficient in the phonon transport equation. W

Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli, Elisabetta Paladino

We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noise effects and achieves gate fidelities above 90% under Random Telegraph and Ornstein-Uhlenbeck noise. Critica

Ibrahim Al Azher, Zhishuai Guo, Hamed Alhoori

Identifying and articulating limitations is essential for transparent and rigorous scientific research. However, zero-shot large language models (LLMs) approach often produce superficial or general limitation statements (e.g., dataset bias or generalizability). They usually repeat limitations reported by authors without looking at deeper methodological issue

Jeongjin Lee, Jennifer Wadsworth

The framework of geometric extremes is based on the convergence of scaled sample clouds onto a limit set, characterized by a gauge function, with the shape of the limit set determining extremal dependence structures. While it is known that a blunt limit set implies asymptotic independence, the absence of bluntness can be linked to both asymptotic dependence

Devika S, Vishnu Hari, Pratik Narang, Tejasvi Alladi

We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression.

Victor V. Golovko

High-purity germanium (HPGe) gamma-ray detectors are core instruments in nuclear physics and astrophysics experiments, where long-term stability and reliable extraction of decay parameters are essential. However, the standard exponential decay analyses of the detector time-series data are often affected by the strong correlations between the fitted parameter

João Victor Corrêa-Rodrigues, Jane Gregorio-Hetem

Star formation is governed by multiple physical processes, making it inherently complicated. One excellent example is the Canis Major OB1/R1 Association, whose complex history of star formation is related to different episodes. Three supernova (SN) events potentially altered the environment and impacted star formation and stellar evolution. Prior investigati

Lukas Eisemann, Ömer Bayraktar, Stefan Richter, Kevin Jaksch

In continuous-variable quantum key distribution (CV-QKD), the performance of the information reconciliation (IR) step is critical for the achievable secret key rate (SKR) and transmission distance. We show how to improve on the recently introduced implementation of an IR-protocol involving multiple decoding attempts (MDA) and validate the method on simulated

Gur-Eyal Sela, Kumar Krishna Agrawal, Bharathan Balaji, Joseph Gonzalez

Live video analytics (LVA) runs continuously across massive camera fleets, but inference cost with modern vision models remains high. To address this, dynamic model size selection (DMSS) is an attractive approach: it is content-aware but treats models as black boxes, and could potentially reduce cost by up to 10x without model retraining or modification. Wit

Song Wang, Lingdong Kong, Xiaolu Liu, Hao Shi

The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in single-modal contexts, integrating their capabilities across diverse sensors like cameras and LiDAR to create a unified understanding remain

Yanlong Ma, Nakul S. Joshi, Christa S. Robison, Philip R. Osteen

Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for large-scale multi-session point cloud map merging that systematically aligns maps collected across different sessions with overlapping regions. The proposed framework employs a ke

Vinh Nguyen, Roman Shvydkoy, Changhui Tan

We study the mean-field limit for a class of agent-based models describing flocking with nonlinear velocity alignment. Each agent interacts through a communication protocol $\phi$ and a non-linear coupling of velocities given by the power law $A(\bv) = |\bv|^{p-2}\bv$, $p > 2$. The mean-field limit is proved in two settings -- deterministic and stochastic. W

Dongwook Choa, Jiawei Hu, Siu-Cheong Lau, Yan-Lung Leon Li

We develop an equivariant Lagrangian Floer theory for Liouville sectors that have symmetry of a Lie group $G$. Moreover, for Liouville manifolds with $G$-symmetry, we develop a correspondence theory to relate the equivariant Lagrangian Floer cohomology upstairs and Lagrangian Floer cohomology of its quotient. Furthermore, we study the symplectic quotient in

Rodrigo Pereira David

Laplace approximations are among the simplest and most practical methods for approximate Bayesian inference in neural networks, yet their Euclidean formulation struggles with the highly anisotropic, curved loss surfaces and large symmetry groups that characterize modern deep models. Recent work has proposed Riemannian and geometric Gaussian approximations to

T. R. Hunter, C. L. Brogan, G. C. MacLeod, C. J. Cyganowski

We present multi-epoch, multi-band ALMA imaging of the new Class II millimeter methanol masers excited during the accretion outburst of the massive protostar G358.93-0.03 MM1. The highest angular resolution image (24 mas $\approx$ 160 au) reveals a nearly complete, circular ring of strong maser spots in the 217.2992 GHz ($v_t$=1) maser line that closely circ

Alessandro De Angelis

Causality is one of the most fundamental -- and yet elusive -- concepts in physics. From its intuitive role in everyday experience to its formal and often implicit role in scientific theories, causality has challenged philosophers and physicists alike. In what follows, we take a brief historical and conceptual journey through classical and modern physics, tr

Xin Gao, Xiaoyang Wang, Yun Zhu, Mengzhang Cai

The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often rely on heuristic aggregation without a systematic understanding of how individual samples contribute to model performance. In this report, we propose a paradigm shift from ad-hoc

Eugenio Simao

Biological Petri Nets (Bio-PNs) require extensions beyond classical formalism to capture biochemical reality: multiple reactions simultaneously affect shared metabolites through convergent production or regulatory coupling, while signal places carry hierarchical control information distinct from material flow. We present a unified 13-tuple Extended Bio-PN fo

Chandrasekhar Gokavarapu

Formal verification of deep neural networks is increasingly required in safety-critical domains, yet exact reasoning over piecewise-linear (PWL) activations such as ReLU suffers from a combinatorial explosion of activation patterns. This paper develops a solver-grade methodology centered on \emph{incremental certificate learning}: we maximize the work perfor

Konstantin Yakovlev, Anna Markovich, Nikita Puchkin

We study the problem of estimating the score function using both implicit score matching and denoising score matching. Assuming that the data distribution exhibiting a low-dimensional structure, we prove that implicit score matching is able not only to adapt to the intrinsic dimension, but also to achieve the same rates of convergence as denoising score matc

Brett T. Lopez

We present a new stability proof for cascaded geometric control used by aerial vehicles tracking time-varying position trajectories. Our approach uses sliding variables and a recently proposed quaternion-based sliding controller to demonstrate that exponentially convergent position trajectory tracking is theoretically possible. Notably, our analysis reveals

Yuankai Fan, Qizhen Weng, Xuelong Li

Large-scale artificial intelligence models are transforming industries and redefining human machine collaboration. However, continued scaling exposes critical limitations in hardware, including constraints on computation, bandwidth, and memory. These dimensions are tightly interconnected, so improvements in one often create bottlenecks in others, making isol

Shufang Su, Wei Su, Jin Min Yang, Pengxuan Zhu

In the Minimal Supersymmetric Standard Model (MSSM), compressed Higgsinos spectrum ($\Delta m^0 \lesssim 1$ GeV) occurs when $|\mu| \ll |M_1|, |M_2|$ and ${\rm sign}(M_1\cdot M_2)<0$, which leads to a long-lived next-to-lightest neutralino. Such a long-lived neutralino could be copiously produced at the LHC, however escape the detection at the LHC main detec

Danchen Jia, Dashan Dong, Tongyu Li, Haonan Zong

Three-dimensional molecular imaging of living cells is essential for unraveling cellular metabolism and response to therapies. However, existing volumetric methods, including fluorescence microscopy and quantitative phase imaging, either require fluorescent labels or lack chemical specificity. Mid-infrared (mid-IR) photothermal microscopy provides label-free

William J. Eckner, Theodor Lukin Yelin, Alec Cao, Aaron W. Young

Quantum simulation relies on the preparation and control of low-entropy many-body systems to reveal the behavior of classically intractable models. The development of new approaches for realizing such systems therefore represents a frontier in quantum science. Here we experimentally demonstrate a new protocol for generating ultracold, itinerant many-body sta

Waheed Ahmed Abro, Zied Bouraoui

Although transformer-based models have shown strong performance in word- and sentence-level tasks, effectively representing long documents, especially in fields like law and medicine, remains difficult. Sparse attention mechanisms can handle longer inputs, but are resource-intensive and often fail to capture full-document context. Hierarchical transformer mo

Chuntian Cao, Hans-Georg Steinrück

Electrochemical systems are important for a sustainable and defossilized energy system of the future. While accurate and precise, the corresponding electrochemical measurements, in which many reactions may occur simultaneously, often do not contain enough information to understand the underlying mechanism and processes. This information, however, is crucial

Alexander M. G. Cox, Daniel Hernandez-Hernandez

We consider an agent who has access to a financial market, including derivative contracts, who looks to maximise her utility. Whilst the agent looks to maximise utility over one probability measure, or class of probability measures, she must also ensure that the mark-to-market value of her portfolio remains above a given threshold. When the mark-to-market va

Mina Ghodsi Yengejeh, András Kovács, István Szapudi, István Csabai

The late-time linear Integrated Sachs-Wolfe (ISW) effect directly probes the dynamics of cosmic acceleration and the nature of dark energy. Detecting these weak, secondary temperature anisotropy signals of the CMB requires accurate theoretical predictions of their amplitude across cosmological models. By extending the pyGenISW package, previously limited to

C. Harshitha, C. G. Venketasubramanian

Let $F$ be a non-archimedean local field or a finite field. In this article, we obtain an explicit and complete set of double coset representatives for $S\backslash GL_{2n}(F)/Q$ where $S$ is the Shalika subgroup and $Q$ a maximal parabolic subgroup of the group $GL_{2n}(F)$ of invertible $2n\times 2n$ matrices. We compute the cardinality of $S\backslash GL_

David Alonso-Gutiérrez, Javier Martín Goñi, Joscha Prochno

In this paper, we prove that the Euclidean distance between two independent random vectors uniformly distributed on $l_p^n$-balls $(1 \leq p \leq \infty)$ or on its boundary satisfies a central limit theorem as $n$ tends to $\infty$. Also, we give a compact proof of the case of the sphere, which was proved by Hammersley. Furthermore, we complement our centra

Dibyendu Das

Let $A$ be a finite-dimensional commutative associative algebra with unity over an algebraically closed field $\mathbb{K}$. The purpose of the paper is to study the solvability of $G_A$, where $G_A$ is the identity component of $\text{Aut}_\mathbb{K}(A)$. Inspired by Pollack's work, Saor\'in and Asensio have started this study for a commutative associative a

Richard M. Talman

A theory of cosmic ray production within the solar system (not extra-galactic) is presented. The sun's time variable magnetic flux linkage makes the sun (as well, perhaps, as Jupiter) a natural, all-purpose, betatron storage ring, with semi-infinite acceptance aperture, capable of storing and accelerating counter-circulating, opposite-sign, colliding beams.

Naiming Liu, Brittany Bradford, Johaun Hatchett, Gabriel Diaz

We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner. This white paper provides a multidisciplinary roadmap for making teaching and learning systems context-aware by encoding cognitive, affective, and sociocultural factors over the sh

Joshua Arroyo, Adam Gregory

Bumpless pipe dreams (BPDs) are combinatorial objects used in the study of Schubert and Grothendieck polynomials. Weigandt recently introduced a co-BPD object associated to each BPD and used them to give an analogue to the change of bases formulas of Lenart and Lascoux between these polynomials. She posed the problem of characterizing the set of permutations

Zichuan Zhou, Amany Kassem, Zun Htay, Izzat Darwazeh

We demonstrated combined free-space optics (FSO) and D-band(110-170GHz) millimeter(mm-wave) transceiver enabled by precisely locked lasers with low phase noise. Combined capacity and tolerance to angle misalignment are studied using 100Gb/s optical and mm-wave signals.

Debraj Das, Luca Giuggioli

Diffusion in a confining potential offers a minimal setting to understand the interplay between random motion and deterministic forces driving a particle towards a focal point or potential minimum. In continuous space and time, two extensively studied examples are Brownian motion in a linear (V-shaped) or a quadratic (U-shaped) potential. The deterministic b

Roberta Spartà, Rosario Gianluca Pizzone, Livio Lamia, Alessandro Alberto Oliva

This work presents the Trojan Horse Method (THM) as a powerful technique for measuring nuclear reaction cross sections at astrophysical energies. We then explore the impact of THM-derived reaction rates on the predictions of Standard Big Bang Nucleosynthesis (SBBN) using the PRIMAT code. Primordial abundances are shown for the single rate impact and, for the

Dibyendu Das

Let $A$ be a finite-dimensional associative $k$-algebra with identity. The primary aim of this paper is to study the rationality properties of the group of all $k$-algebra automorphisms of $A$, as an affine algebraic group over an arbitrary field $k$. We investigate mainly the $R$-equivalence property of the identity component of $\mathrm{Aut}_{k}(A)$ over a

Max Thannheimer, Marco Oesting

In order to describe the extremal behaviour of some stochastic process $X$, approaches from univariate extreme value theory are typically generalized to the spatial domain. In particular, generalized peaks-over-threshold approaches allow for the consideration of single extreme events. These can be flexibly defined as exceedances of a risk functional $r$, suc

Julia Chuzhoy, Ron Mosenzon, Ohad Trabelsi

We study the directed global minimum vertex-cut problem: given a directed vertex-weighted graph $G$, compute a vertex-cut $(L,S,R)$ in $G$ of minimum value, which is defined to be the total weight of all vertices in $S$. The problem, together with its edge-based variant, is one of the most basic in graph theory and algorithms, and has been studied extensivel

Yi Zhou, Chan Lu, Yiming Ma, Wei Qu

Highly accurate biomolecular structure prediction is a key component of developing biomolecular foundation models, and one of the most critical aspects of building foundation models is identifying the recipes for scaling the model. In this work, we present SeedFold, a folding model that successfully scales up the model capacity. Our contributions are threefo

Shubhankar Mandal, Avijit Pal, Bhaskar Paul

This article develops several functional models for a given $\Gamma_n$-contraction. The first model is motivated by the Douglas functional model for a contraction. We then establish factorization results that clarify the relationship between a minimal isometric dilation and an arbitrary isometric dilation of a contraction. Using these factorization results,

José M. Zapata

We establish sharp large deviation asymptotics for the maximum order statistic of independent and identically distributed heavy-tailed random variables, valid for all Borel subsets of the right tail. This result yields exact decay rates for exceedance probabilities at thresholds that grow faster than the natural extreme-value scaling. As an application, we d

Asja Attanasio, Francesco Corso, Gianmarco De Francisci Morales, Francesco Pierri

Following the death of Jeffrey Epstein, the subreddit r/conspiracy experienced a significant visibility shock that brought mainstream users into direct contact with established conspiracy narratives. In this work, we explore how large-scale surges in public attention reshape participation and discourse within online conspiracy communities. We ask whether a s

Bihag Dave

In this thesis, we investigate the possibility that dark matter consists of ultra light spin-zero particles with mass $m \sim 10^{-22}\ \text{eV}$. We focus on the role of self-interactions, assuming all other non-gravitational couplings to Standard Model particles are negligible. Such ultra light dark matter (ULDM) is expected to form stable self-gravitatin

Henryk Fukś

In three articles published in CNJ in 2012 and 2016 , we discussed some links between mathematical sciences, coin minting and numismatics. This article is a continuation of this cycle. It tells the story of selected important developments in the history of geometry using modern commemorative coins as a background and illustration.