April 2026 arXiv papers — page 39

Laurenz Reichardt, Nikolas Ebert, Oliver Wasenmüller

3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We introduce PointTransformerX (PTX), a fully PyTorch-native vision transformer backbone for 3D point clouds, removing all custom CUDA operators and external libraries while retainin

Benjamin Keenlyside, Arnon A. B., Dylan Marques, Ivo Vellekoop

Optical scattering presents a major obstacle to high resolution imaging in biological tissue and other turbid media. Conventional photoacoustic imaging can partially overcome this obstacle, enabling imaging of optical absorption in the multiple-scattering regime, but its resolution remains limited by acoustic diffraction. In this work we explore a strategy t

Guillaume Perez, Janarbek Matai, Takahiro Harada

Implicit neural representations (INRs) are increasingly being used as tools to map coordinates to signals, encompassing applications from neural fields to texture compression, shape representations, and beyond. Most INR methods are based on using high-dimensional projections of the initial coordinates through encoders such as grid or positional encoding. Nev

Alexis Langlois-Rémillard, Mateusz Stroiński

The theory of 2-monads entails that, for a strict monoidal category C, there is a strict monoidal category L(C) such that strict monoidal functors from L(C) are precisely the lax monoidal functors from C. We give an elementary, diagrammatic, construction of L(C) and of its variants for oplax and Frobenius lax functors. The diagrams used are analogous to the

Priyanshu Pant, Ranveer Singh

For a simple graph $G$ with adjacency matrix $A(G)$, let $\pi(G,x):=\mathrm{per}(xI-A(G))$ be its permanental polynomial with roots $\mu_1,\ldots,\mu_n \in \mathbb{C}$, and define the permanental energy $E_{\mathrm{per}}(G):=\sum_{i=1}^n |\mu_i|$. We prove a sharp universal lower bound: for every $m$-edge graph $G$, $E_{\mathrm{per}}(G) \ge 2\sqrt{m}$, with

Rhiannon Savage

Proving representability of derived moduli stacks of solutions to non-linear elliptic partial differential equations generally requires significant analytic machinery. In this paper, we instead show that representability naturally follows from an Artin-Lurie style representability theorem. This necessitates the development of a new model for derived differen

Benedikt Hopf, Radu Timofte, Chenfan Qu, Junchi Li

Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight image degradation. In addition to weaker degradations that can accidentally occur in the image processing pipeline, there is another risk of malicious deepfakes that specifically intro

Felix Govaers

We propose a gate-based quantum algorithm for the prediction step of Bayesian state estimation based on the Fokker-Planck equation on a discretized position-velocity state space. The probability density is encoded in the amplitudes of a quantum state, enabling a compact representation of high-dimensional distributions. Exploiting the circulant structure of f

Jan Gogoll

Ethical software development remains stubbornly difficult despite two decades of normative frameworks, professional codes, and participatory methodologies. This paper offers a diagnostic rather than prescriptive contribution: it argues that the persistent gap between ethical intention and ethical implementation is a structural epistemic condition, not primar

Yudong Li, Wenkui Shi, Chunfa Wang, Zhihao Qian

This study proposed the hierarchy of quantum kernel networks by combing multi quantum networks with smoothed particle hydrodynamics (SPH). The Lagrangian quantum network model was further developed based on an improved quantum multilayer perceptron (QMLP). A sequential hybrid quantum-classical framework was constructed to ensure robust particle gradient-base

Ashmi Banerjee, Adithi Satish, Wolfgang Wörndl, Yashar Deldjoo

Evaluating nuanced conversational travel recommendations is challenging when human annotations are costly and standard metrics ignore stakeholder-centric goals. We study LLMs-as-Judges for sustainable city-trip lists across four dimensions -- relevance, diversity, sustainability, and popularity balance, and propose a three-phase calibration framework: (1) ba

Ovidio García-Oliva, Carsten Lemmen

Periods of low dissolved oxygen concentration -- hypoxia and anoxia -- threaten the health of aquatic ecosystems and the services they provide.Hypoxia is strongly influenced by temperature, but the different sensitivities and response functions of oxygen removal and production processes to temperature are not regarded in most models. Here we present OxyPOM -

Ismail Lotfi, Ali Ghrayeb

Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compete for limited radio resources. We investigate the use of Large Language Models (LLMs) as bidding agents in repeated 6G spectrum auctions with budget constraints in vehicular networks. Each user equipment (UE)

Wei Wang, Xiao-Yong Wei, Qing Li

The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximation across layers. We reframe residual networks as a layer-wise approximation process that builds an approximation trajectory from input to target, and prove the existence of progressive trajectories where error

Gadi Lavi

Current AI systems increasingly operate in contexts where their outputs directly trigger real-world actions. Most existing approaches to AI safety, risk management, and governance focus on post-hoc validation, probabilistic risk estimation, or certification of model behavior. However, these approaches implicitly assume that once a decision is produced, it is

Maixent Chenebaux

We present Nautile-370M, a 371-million-parameter small language model designed for efficient reasoning under strict parameter and inference budgets. Nautile-370M uses a hybrid backbone in which two SeqCond Attention (SCA) layers, a linear-time spectral sequence operator inspired by SeqCondenser, alternate with one transformer layer. This design aims to retai

Apranik Fatehi, Reza Jafari Seyedabad, Amir Amiri, Kazem Azizi

We report a detailed simulation study of the search for $s$-channel Higgs boson production in $e^+e^-$ collisions at a center-of-mass (c.m.) energy of $\sqrt{s}=125\,\mathrm{GeV}$ at the CERN Future Circular Collider (FCC-ee), as a means to constrain the electron Yukawa coupling, $y_e$. The process of interest is $e^+e^-\to H\to WW^*\to \ell^\pm\nu + jj$ wit

Yoshitsugu Kitazawa

This paper proposes linear estimation methods for dynamic fixed effects logit models only with time effects (i.e., those only with time dummies and only with time trends). The linear estimators point-identify transformations of parameters of interest for the models if five or more time periods are provided and then point-identify the parameters of interest.

Runci Bai, Kui Jiang, Xiang Chen, Chen Wu

Remote sensing images are frequently degraded by adverse weather conditions, particularly clouds and haze, which severely impair downstream applications. Existing restoration methods typically rely on computationally heavy architectures or sequential pipelines (e.g., detail enhancement followed by color rendition) that suffer from mutual interference and art

Fabio Camilli, Cristian Mendico

We study the semi-discrete approximation of Aubry and Mather sets for Tonelli Lagrangians on the flat torus. Starting from the discrete Lax--Oleinik equation, we introduce natural discrete analogues of these sets and analyze their convergence, as the time step tends to zero, in the sense of Kuratowski. Our results show that the semi-discrete variational fram

Maksym Nechepurenko

Prediction markets are widely treated as forecasting devices that reveal collective expectations about uncertain futures. This article argues that under specifiable conditions they also function as coordination mechanisms: public probabilities that organize the behavior of voters, donors, journalists, traders, and institutions in ways that can be self-fulfil

Xuguang Bai, Mingxuan Liu, Tongxi Song, Yifei Chen

Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imaging increasingly exceed what can be addressed by scan-level prediction alone. Clinically useful AI for CT must not only recognize disease across the whole volume, but also localize abnormalities and provide int

Nihar Ranjan Ghosh, Malay K. Nandy

Asymptotic symmetries are expected to leave subtle but physically meaningful imprints on quantum probes of gravity, yet their manifestation in near-horizon dynamics remains incompletely understood. We examine this question for a closed bosonic string propagating in the near-horizon geometry of a five-dimensional Schwarzschild black hole subjected to a genera

Shouvik Sarkar, Khandakar Md Asif Elahi, Samir Choudhuri, Somnath Bharadwaj

We analyze zenith-pointing $(\delta=-26.7^{\circ})$ Murchison Widefield Array (MWA) $\nu_c=154.2 \,{\rm MHz}$ drift scan observations covering $349.0^{\circ} \le \alpha \le 70.0^{\circ}$ with 163 pointing centers (PCs) spaced by $0.5^{\circ}$. We measure $D_{\ell}$, the mean-squared angular brightness temperature fluctuations, as a function of $\alpha$. A br

Jiayi Tan, Neelabhro Roy, James Gross, Rohit Chandra

Accurate modeling of outdoor-to-indoor (O2I) and indoor-to-indoor (I2I) signal loss is important for improving indoor wireless network performance in dense urban areas. Traditional on-site measurements are expensive, time-consuming, and difficult to conduct across wide regions. Real-world datasets also tend to be noisy and imbalanced, which makes signal loss

Fatih Ucan, Tane Vergili

In this study, Devaney's chaos conditions are revisited within the framework of descriptive proximity. The concepts of descriptive transitivity, the density of descriptive periodic objects, and descriptive sensitivity are defined. The most notable finding of the study is that Banks Theorem, which establishes the hierarchy among these conditions in classical

Bart Van Damme, Alexandre Brun d'Arre, Patrick Danner, Dorina Opris

Among smart materials, piezoelectric materials occupy a very prominent position for sensing and actuation functions. Combined with simple or more advanced shunts, they are also proposed in various vibration mitigation schemes. However, the selection of available piezoelectric materials is mainly limited to ceramics (with an elastic modulus in the order of 10

G. Gatti, C. Berthod, J. Issing, M. Straub

Many-body interactions can couple electronic states in one layer with collective excitations in the adjacent layer, providing a route to tailor properties of heterostructures. However, detecting and quantifying interlayer many-body interactions proved a major challenge. Here, we demonstrate that quasiparticles in monolayer transition metal dichalcogenides (T

Xining Liu, Pu Qiao, Xingzhi Zhan

A longest path in a graph is called a detour. Denote by $a(k,n)$ the minimum number of detours in a connected graph with minimum degree $k$ and order $n,$ and denote by $b(k,n)$ the minimum odd number of detours in such a graph. X. Zhan has posed the problem of determining $a(k,n)$ and $b(k,n).$ It is known that $a(2,n)=4$ for $n\ge 4$ and $b(2,n)=9$ for $n\

Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu, Chia-Hung Yuan

Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distill it into a single step. General one-step methods fine-tune the generative prior into a deterministic mapping, restoring efficiency but discarding its stochastic nature. Conversely

Koichi Miyamoto

This work studies quantum algorithms to solve high-dimensional stochastic differential equations (SDEs) $\mathrm{d} \mathbf{X}_t = A(t) \mathbf{X}_t \mathrm{d} t + B(t) \mathrm{d} \mathbf{W}_t$. Aiming for a speed-up in the dimension $N$ of $\mathbf{X}_t$, we generate quantum states that encode $\mathbf{X}_t$ in the amplitudes, while most of the existing qua

Jean Cardinal, Kevin Mann, Akira Suzuki, Takahiro Suzuki

We initiate the study of the shortest reconfiguration problem for independent sets under the adjacency relation derived from the independent set polytope. Given a graph and two independent sets, the problem asks for a shortest sequence transforming one into the other such that the subgraph induced by the symmetric difference of any two consecutive sets is co

Nanqing Luo, Xusheng Li, Haizhou Wang, Shuangyi Zhu

Spotting encryption loops in binary-only ransomware is a critical reverse engineering task. Since the existence of avalanche effect, an intrinsic characteristic of any secure encryption algorithms, is unavoidable during a victim data encryption attack, it is a very promising direction to spot encryption loops through avalanche effect detection. Unfortunately

Jia-Cheng Zhao

We establish the approximate controllability in $L^2$ for the nonlinear Benjamin-Ono equation on torus via two-dimensional control input. Our proof is based on adaptations of geometric control approach introduced by Agrachev and Sarychev. As an application of this control result, we study long-time dynamics of a randomly forced equation. It is proved that th

Yuhei Kogo

We develop a finite-gap construction for the Pohlmeyer--Lund--Regge (PLR) equation and the associated Lund--Regge curve evolution. From the hyperelliptic spectral data we build a Baker--Akhiezer function and an $\mathrm {SU}(2)$-frame, yielding an explicit theta-quotient formula for the PLR solution. We then derive criteria of the Lund-Regge curve: under nat

Maxence Hussonnois, Thommen George Karimpanal, Santu Rana

Unsupervised skill discovery in reinforcement learning aims to intrinsically motivate agents to discover diverse and useful behaviours. However, unconstrained approaches can produce unsafe, unethical, or misaligned behaviours. To mitigate these risks and improve the practical desireability of discovered skills, recent work grounds the discovery process by le

Rishitej Reddy Vyalla, Kritarth Prasad, Avinash Anand, Erik Cambria

Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or accou

Jinkun Dai, Yuanxin Ye, Peng Tang, Tengfeng Tang

Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. Current multi-modal approaches mainly focus on integrating complementary visual modalities, yet neglect the incorporating of non-visual textual data - a rich source of knowledge tha

Sourav Sarkar, Kalyan Mandal

Negative permittivity metamaterial is a scientifically rich avenue due to its tremendous application in several arena of materials research including novel superlens, band-gap materials, invisibility cloaks, antenna and filter design. Traditionally, epsilon negative (ENG) behaviour is achieved in multi-phase composites with the addition of conducting metal f

Jiayi Wang, Lichun Zhang, Xiaoqi Zhuang, Jiaqi Zhang

Video technology is advancing toward Ultra High Definition (UHD) and High Dynamic Range (HDR), which intensifies the need for higher compression efficiency for these high-specification videos. Beyond advances in traditional codecs, neural video codecs (NVCs) have attracted significant research attention and have evolved rapidly over the past few years. The c

Taihei Takahashi, Kanata Takayasu, Satoshi Suga, Satoshi Kurihara

Accurately extracting patterns that appear frequently only within specific time intervals, together with their dense intervals, is important in many applications such as understanding seasonal demand and detecting anomalous behavior.Frequent itemset mining evaluates support over the entire dataset and therefore cannot detect locally dense patterns. Existing

Y. T. Wang, X. Z. Zhang

Nonreciprocal relaxation matrices can have skin-localized right eigenmodes, but their imprint on a mixed steady state is not fixed by the density profile alone. We develop an exact steady-state theory for number-conserving Gaussian fermion chains and show that the dominant natural orbital of the correlation matrix provides a mode-resolved diagnostic of hidde

Yuda Feng, Weijiang Hu, Shi Li

We propose a new convex programming relaxation for the weighted Nash social welfare (NSW) problem that achieves a matching $(e^{1/e}\approx 1.445)$-approximation via the rounding algorithm of Feng and Li. Unlike the exponential-size configuration LP used in prior work, our formulation can be converted into a compact linear program of polynomial size, incurri

Zonghao Ying, Haozheng Wang, Jiangfan Liu, Quanchen Zou

Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged execution exposes them to severe security risks, particularly direct and indirect prompt injection. Existing defenses face significant challenges in balancing security with utility, often encountering a trade-off wher

Iizalaarab Elhaimeur, Nikos Chrisochoides

Large language model tutors are easy to build in a notebook and hard to run in a real course. We describe ITAS (Intelligent Teaching Assistant System), a multi-agent tutoring system that a graduate quantum computing course used for a semester at Old Dominion University. The system has three layers. The teaching layer is a Spoke-and-Wheel of three parallel sp

Jeffrey Wong, Antoine Creux

Create an idea, prototype it, evaluate if users like it, then learn. It is the circle of business. If AI can operate in all parts of the circle, it will enable rapid iteration and learning speeds for businesses. Experiment platforms that deploy experiments to evaluate return on investment for businesses are abundant, but systems that help businesses learn pe

Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand, Chhavi Kirtani

Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consistent step-by-step reasoning, especially in multilingual and low-resource settings where cross-lingual transfer from English to Indian languages remains limited. We propose IRIS: Interleaved Reinforcement with I

Kai Li, Xiang Gao, Si-Rui Wang, Li-Heng Wang

We present a comprehensive analysis of 146 contact binaries using medium-resolution LAMOST spectra and photometric data from the All-Sky Automated Survey for SuperNovae (ASAS-SN) and the Transiting Exoplanet Survey Satellite (TESS). Radial velocity curves obtained through the cross-correlation function method were modeled simultaneously with the light curves

Kazuki Hasebe

Landau models serve as quantum mechanical systems for generating quantum matrix geometries. In this paper, we demonstrate that Howe duality provides the underlying structure of the super Landau model, reflecting a general feature of coset-type Landau models. The (super) Howe duality relates different Landau levels and accounts for the emergence of a dual fuz

Soumen Kumar Bera, Taotao Fang, Magdalena Kunert-Bajraszewska, Tapan K. Sasmal

We present a multiwavelength follow-up study of 621 winged radio galaxies (WRGs) recently identified from LoTSS DR2, constituting the largest statistically significant samples of X-shaped (XRGs) and Z-shaped (ZRGs) radio galaxies to date. Our results show that WRGs are predominantly strongly radio-dominated, with XRGs on average more radio-luminous than ZRGs

Iizalaarab Elhaimeur, Nikos Chrisochoides

Multi-agent LLM tutoring systems improve response quality through agent specialization, but each student query triggers several concurrent API calls whose latencies compound through a parallel-phase maximum effect that single-agent systems do not face. We instrument ITAS, a four-agent tutoring system built on Gemini 2.5 Flash and Google Vertex AI, across thr

Amareshwara Sainadh Chamarthi

In wall-modelled large-eddy simulations of hypersonic boundary-layer transition, Hoffmann, Chamarthi and Frankel reported that characteristic reconstruction based on conservative-variable eigenvectors produced markedly better results than the corresponding primitive-variable implementation. The observation was empirical. A subsequent wave-appropriate conserv

Iizalaarab Elhaimeur, Nikos Chrisochoides

Quantum computing instructors face a compounding problem: the concepts are counterintuitive, the mathematical formalism is dense, and qualified faculty are scarce outside a small number of well-resourced institutions. Our prior work introduced a knowledge-graph-augmented tutoring prototype with two specialized LLM agents: a Teaching Agent for dynamic interac

Giulia Cavalleri, Pierluigi Colli, Elisabetta Rocca

We study a distributed optimal control problem for a nonisothermal Caginalp-type phase-field model that describes tumour growth under thermal therapy. The PDE system couples a possibly viscous Cahn-Hilliard equation, governing the evolution of the healthy and tumor phases, with an equation for the heat balance, and a reaction-diffusion equation for the nutri

Zuodong Pan, Xu Fang, Wei Ren

This paper addresses the planning and control problem for nonlinear systems under Signal Temporal Logic (STL) specifications. We first decompose an STL task into finite local tasks. A sampling-based method generates sequences of local waypoints to satisfy all local tasks, from which the corresponding satisfaction pair sets are derived. Following a local-to-g

Paolo Cascini, Jihao Liu, Calum Spicer, Roberto Svaldi

We prove that normal projective stable families of maximal variation, of fixed dimension, and with bounded adjoint volume are birationally bounded. This is a consequence of a substantially stronger statement, formulated a priori independently of stable families: algebraically integrable foliations of fixed dimension and bounded adjoint volume are log biratio

Takashi Goda, Yang Liu, Raúl Tempone

This paper proposes a new randomized design of digital nets in which the generating matrices are chosen to be random Hankel matrices. Compared with previous randomized designs of digital nets, this approach simplifies the construction process and reduces the number of random variables required, while still achieving desirable convergence rates when combined

Aditya Hemant Shahane, Anuj Kumar Sirohi, Tanmoy Chakraborty, Prathosh A P

Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issues, we propose a non-autoregressive diffusion framework that generates text by iterative refinement conditioned on an input graph, named as Diffusion Language Model for Graphs (DLM4G). By aligning graph compo

Huaicheng Li, Junhui Zhao, Haoyu Quan, Xiaoming Wang

Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from communication overhead and dynamic environments. Model compression techniques reduce computing and communication burden yet create trade-offs between compression ratios and vehicle participation strategies. I

Jan Křetínský, Tobias Meggendorfer, Maximilian Prokop

Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. These systems are typically represented using either Mealy machines or AIGER circuits. We present the second version of SemML, which outperforms all state-of-the-art tools for finding eith

Johanna Borissova

We present a unified framework for the discussion of black hole thermodynamics of $d$-dimensional static black holes with spherical, toroidal or compact hyperbolic horizon topology satisfying $g_{tt}g_{rr}=-1$ in Schwarzschild gauge. To that end, we consider any such black hole as a solution to an integrable $2$-dimensional effective dilaton theory and there

Ismagil T. Habibullin, Aigul R. Khakimova

In this paper, we study nonlinear integrable equations with three independent variables of the following types: Toda-type lattices, semi-discrete lattices, and fully discrete Hirota-Miwa type models. It is shown that integrable equations of all three types admit reductions in the form of Darboux-integrable hyperbolic systems. It is important that the transit

Gyungmin Cho, Dohun Kim

We study single-copy stabilizer learning, the problem of identifying a stabilizer group of dimension $n-t$ from an $n$-qubit quantum state $\rho$. We obtain two complementary results. First, in the average case, logarithmic-depth local Clifford circuits suffice to efficiently learn almost all stabilizer groups with $t=O(\log n)$, instead of the linear-depth

Yueming Wang, Nan Sun, Chris Dreessen, Gaosheng Huang

Perovskite-silicon tandem technology has exceeded the single junction theoretical efficiency limit. However, there is still distance to the thermodynamic limit mainly caused by the fill factor. This work presents a methodology to illustrate the mechanisms of FF loss in perovskite-Si monolithic tandem solar cells. Apart from the series resistance related loss

Şükran Gül

A Beauville surface is a rigid complex surface of general type, isogenous to a higher product by the free action of a finite group $G$, called a Beauville group. In \cite{GT}, Gonz\'alez-Diez and Torres-Teigell find the number of isomorphism classes of Beauville surfaces for which the group $G$ is $\PSL(2,p)$ with particular types of `Beauville structures'.

Justin williams, Kishor Datta Gupta, Roy George, Mrinmoy Sarkar

Vision-language-action (VLA) models have shown strong semantic grounding and task generalization in manipulation, but aerial deployment remains difficult because drones require low-latency closed-loop guidance under strict onboard compute and communication constraints. We present LiteVLA-H, a compact 256M-parameter VLA system designed for dual-rate operation

June-Woo Kim, Miika Toikkanen, Heejoon Koo, Yoon Tae Kim

Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble methods can improve robustness, but when base models are trained on identical data, models tend to overfit and produce highly correlated predictions, thereby reducing the effectiveness of ensembling. In this work,

Weijun Chen, Yuxi Fu, Yangluo Zheng

Vector addition systems (VAS) constitute an important model of computation and concurrency that is equally expressive as the Petri net model. Recently, a lot of research has been conducted on vector addition systems with states (VASS), which are VASes equipped with a finite state control. Results on VASS naturally carry over to VAS, but no straightforward im

Katya Mikhailova, Lerato Takana, Guanxiong Qu, Juan A. Hofer

The mechanisms underlying magnon damping are of fundamental and technological interest in low-damping materials. We find low-damping ferrimagnetic insulator Li$_{0.5}$Al$_{1.0}$Fe$_{1.5}$O$_4$ (LAFO) thin films to be a promising model system for probing these mechanisms because of its distinct temperature dependent spin diffusion length (SDL) trends for elec

Miao-Miao Yi, Guo-Jian Qiao, Xin Yue, C. P. Sun

The quantization of superconducting transmission-line resonators is usually introduced phenomenologically by modeling the resonator as an effective LC circuit and imposing canonical commutation relations on macroscopic variables such as charge and flux. Although this approach is highly successful, it leaves open why these macroscopic variables should obey qu

Haozhi Han, Ruge Zhang, Haoquan Chen, Yifeng Chen

Lifetime prediction of reactor pressure vessel (RPV) steel requires bridging atomistic degradation mechanisms with service-scale spatial and temporal regimes, from Angstroms and picoseconds to meters and decades. Existing engineering-scale models provide long-range reach but rely on fitted degradation laws, while recent atomistic kinetic Monte Carlo (AKMC) a

Ravi Acharya, Shao Qi Lim, Brett C. Johnson, Nicholas Gillespie

Magnetic clock transitions (CTs), defined by vanishing first-order sensitivity of the transition frequency to magnetic field fluctuations, provide a powerful route to suppress decoherence in donor spin systems. Here, we present the observation of magnetic field CTs from an ensemble of near-surface $^{75}$As ($I = 3/2$) spins in silicon using low-field ($< 10

Aditya Hemant Shahane, Anuj Kumar Sirohi, Devansh Arora, Nitin Kumar

Bridging molecular structures and natural language is essential for controllable design. Autoregressive models struggle with long-range dependencies, while standard diffusion processes apply uniform corruption across positions, which can distort structurally informative tokens. We present BiMol-Diff, a unified diffusion framework for the paired tasks of text

Man Liu, Xingchen Liu, Xingjian Tian, Bing Lu

Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacerbate errors under frequent communication and introduce significant computational overhead during compression. To this end, we propose TACO (Tensor-parallel Adaptive COmmunication c

Martin G. Frasch

Modern machine learning optimizes for accuracy without explicit treatment of internal computational cost, even though physical and biological systems operate under intrinsic energy constraints. We evaluate energy-aware learning across 2,203 experiments spanning vision, text, neuromorphic, and physiological datasets with 10 seeds per configuration and factori

Yuri Nesterenko

The long-standing hypothesis formulated by Goreinov, Tyrtyshnikov and Zamarashkin \cite{GTZ1997} has recently been solved affirmatively in the case of real two-column matrices by Sengupta and Pautov \cite{SP2026}. In this paper, we consider the complex variant of this problem and prove the asymptotically tight upper bound for spectral norms of the best-bound

Kai Yang, Zedong Chu, Yingnan Guo, Zhengbo Wang

While Vision-Language-Action (VLA) models have been demonstrated possessing strong zero-shot generalization for robot control, their massive parameter sizes typically necessitate cloud-based deployment. However, cloud deployment introduces network jitter and inference latency, which can induce severe spatiotemporal misalignment in mobile navigation under con

Vivi Andersson, Martin Monperrus

Cryptographic API misuse represents a critical vulnerability class that undermines the security foundations of modern software. Yet, it remains largely unexplored in Go despite its dominance in security-critical infrastructure. This paper presents the first comprehensive study of cryptographic API misuse detection in Go, identifying and analyzing 4 state-of-

Alister W. Graham

The `Triangal' galaxy evolution schema is used to assess whether the Tuning Fork (bar strength) or the van~den~Bergh Trident and ATLAS$^{3D}$ Comb (spiral strength) offer greater evolutionary insight. A new catalogue of quantitative bar strengths (measured by the bar-to-total luminosity ratio, $P$), refined galaxy morphologies, and dust bin classifications i

Hikmat Karimov, Rahid Zahid Alekberli

This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium thermodynamics to stochastic control for the ethical alignment of autonomous systems. By establishing a formal isomorphism between non-equilibrium thermodynamics and stochastic control, we define systemic ano

Xinhe Wang, Katia Sycara, Yaqi Xie

Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to have the model learn a refusal boundary between safe and unsafe, based on the user's intent. It has been found that this binary training regime often leads to brittleness, since the user intent cannot reliably

Xuanzhe Shen, Xiaohe Ma, Kun Zhou, Hongzhi Wu

Traditional analytical reflectance models, while compact and interpretable, lack the capacity to accurately represent physical measurements. Recent neural models, which closely fit input data, are less generalizable and often more expensive to store and evaluate. To combine the strengths and overcome the limitations of these two classes of models, we present

Lea-Marie Sussek, Stefan Heindorf

Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the information to make predictions is rather unexplored, leading to potentially faulty or biased models. This work introduces two novel mode

Hongzhang Chen, Jianxi Li, Yongtao Li, Lele Liu

A classical result of Cioab\u{a} states that if $G$ is a connected graph with the unit Perron vector $\mathbf{x}$, then any independent set $S$ of $G$ satisfies $\sum_{v\in S} x_v^2 \le \frac{1}{2}$, with equality if and only if $G$ is a bipartite graph and $S$ is one of the partite sets. Let $\chi(G)= k $ be the chromatic number of $G$. A well-known conject

Hikmat Karimov, Rahid Zahid Alekberli

As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accuracy are often insucient to characterize system reliability. This study proposes a thermodynamic inspired modeling framework for analyzing the stability of LLM outputs under conditions of uncertainty and pertu

Changdeuck Bae

All-solid-state batteries fail not only by bulk transport limits, but by a reactive interface that evolves during cycling. We show that degradation is governed by two coupled processes: interfacial breathing, the cycle-scale oscillation of lithium contact, and reactive memory, the slow accumulation of electrolyte decomposition. Four descriptors capture breat

Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade, Yanli Zhao

Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictions based on these derived interests. The inherent heterogeneity in data characteristics frequently result in substantial under-utilization of computational resources during large-s

Sajad Ebrahimi, Soroush Sadeghian, Ali Ghorbanpour, Negar Arabzadeh

The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools to assess review quality. We present PeeriScope, a modular platform that integrates structured features, rubric-guided large language model assessments, and supervised prediction to evaluate peer review qualit

Jon-Paul Cacioli

Small instruct-tuned LLMs produce degenerate verbal confidence under minimal elicitation: ceiling rates above 95%, near-chance Type-2 AUROC, and Invalid validity profiles. We test whether confidence-conditioned supervised fine-tuning (CSFT) with self-consistency-derived targets can close the gap between internal information and verbal readout. A pre-register

Jacek Dobrzyniecki, Thomas Busch

We investigate the competition between attractive spin-spin interactions and spin-separating external forces in the ground state of a one-dimensional Fermi-Hubbard model. We consider a lattice with open boundary conditions, subject to a linear external potential whose gradient is opposite for the two spin components, so that each spin species sees a potentia

Huahang Li, Wentao Hu, Zhuoyue Wan, Chen Jason Zhang

In daily life, there are many scenarios that people need to tackle data-related tasks, such as filling out forms, analyzing Excel files, and visualize data report. However, the tools available for these tasks often fragment, requiring users to switch between multiple applications and manually orchestrate steps like data processing, querying, and visualizatio

Liu Wang, Tianshu Zhou, Haoyu Wang, Yi Wang

Mobile apps frequently request excessive data access, raising significant privacy concerns. While regulations like GDPR emphasize data minimization, they provide limited guidance on concretely defining and enforcing necessary data access. Existing regulatory mechanisms primarily rely on expert-driven audits that face challenges in scalability, neutrality, an

Harbir Antil, Robert J. Baraldi, Rohit Khandelwal, Drew P. Kouri

We present a trust-region-based adaptive finite-element algorithm for numerically solving a class of nonsmooth PDE-constrained optimization problems that includes problems with sparsifying regularizers and convex constraints. In particular, we consider the class of problems whose objective function is the sum of a smooth, possibly nonconvex, function and a n

A. J. Aertssen, R. G. M. Huisman, I. J. M. Besselink, J. Elfring

This paper presents a trajectory planning method for articulated commercial vehicles, specifically tractor-semitrailers, based on Model Predictive Contouring Control (MPCC). Although MPCC has proven effective for passenger cars, it is generally ill-suited for tractor-semitrailers. These vehicles are significantly larger, the semitrailer follows a different p

Shin Kiriki, Xiaolong Li, Yushi Nakano, Teruhiko Soma

Suppose that $M$ is a closed manifold of dimension greater than two and $r\geq 2$. We show that there exists a $C^r$-diffeomorphism $f:M\longrightarrow M$ with a wild affine blender-horseshoe $\Lambda_f$ which is $C^r$-robustly and strongly pluripotent for $\Lambda_f^{(\mathrm{mj})}$ but not for $\Lambda_f$, where $\Lambda_f^{(\mathrm{mj})}$ is the subset of

Liangru Xiang, Yuxi Ma, Zhihao Cao, Yixin Zhu

Extracting abstract causal structures and applying them to novel situations is a hallmark of human intelligence. While Large Language Models (LLMs) and Vision Language Models (VLMs) have shown strong performance on a wide range of reasoning tasks, their capacity for interactive causal learning -- inducing latent structures through sequential exploration and

Spyridon M. Tzaninis, Apostolos Bozikas

In this work, we derive a complete characterization of all ruin-inducing probability measures that preserve the structure of a given compound renewal process in terms of suitable pairs of functions $(\gamma,\delta)$. This result allows us to obtain an explicit representation of the infinite-time ruin probability as an expectation under any ruin-inducing prob

Maximilian Engelhardt, Carsten Andrich, Daniel Stanko, Alexander Ihlow

Precise synchronization is essential in various technical disciplines, being especially challenging in mobile scenarios. Unfortunately, state-of-the-art global navigation satellite system (GNSS) disciplined oscillators (GNSSDOs) are designed and optimized for stationary operation. We present a novel solution that is optimized for mobile use from the ground u

Abdoulaye Thiam

This paper develops a quantitative regularity theory for the Lyapunov exponents of random products of matrices in $\operatorname{GL}(2,\mathbb{R})$, with extensions to $\operatorname{GL}(d,\mathbb{R})$ for all $d \geq 2$. At every compactly supported measure $\nu$ with simple Lyapunov spectrum, we give an explicit closed-form H\"older exponent $\beta_*(\nu,

Yujia Wu, Panxu Yuan, Binyan Jiang

Effectively controlling the false discovery rate (FDR) in high-dimensional variable selection is a fundamental statistical problem that has garnered significant research interest. In this paper, we propose a novel, user-friendly, and computationally efficient method called Bi-Gaussian Mirrors (BGM), which offers a conceptually simple yet powerful approach fo