April 2026 arXiv papers — page 73

David Muñoz-Lahoz

We characterize the Archimedean lattice-ordered algebras with identity that admit a polynomial growth continuous function calculus. More precisely, for an $n$-tuple $\mathbf{x}=(x_1,\dots,x_n)$ in an Archimedean lattice-ordered algebra $X$ with identity $1_X$, we prove that the existence of a lattice-algebra homomorphism from the algebra $PG_n$ of continuous

Karim Aly, Alexei Sharpanskykh

The increasing adoption of synthetic data in aviation research offers a promising solution to data scarcity and confidentiality challenges. This study investigates the potential of generative models to produce realistic synthetic flight data and evaluates their quality through a comprehensive four-stage assessment framework. The need for synthetic flight dat

Kensuke Homma, Taishi Katsuragawa, Shinya Matsuzaki

Light scalars generically mediate a fifth force incompatible with local tests of gravity unless their couplings are parametrically suppressed or screening mechanisms are introduced. We demonstrate that such suppression can arise from symmetry. We propose a $Z_2$-symmetric mirror extension of the Standard Model within a bi-conformal gravity construction, wher

Pham Phuong Nam Nguyen, Nam Tien Le, Thi Kim Trang Vo, Nhu Tinh Anh Nguyen

Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment, which is especially challenging for x3 SR. We present a deployment-oriented quantized SISR framework based on an extract-refine-upsample design. The student performs most computation in the low-resolution spac

Bingchen Cheng, Tielin Ma, Jingcheng Fu, Lulu Tao

To enable autonomous wind estimation for energy-efficient flight in small unmanned aerial vehicles (UAVs), this study proposes a method that estimates flight states and wind using only the low-cost essential onboard sensors required for autonomous flight, without relying on additional wind measurement devices. The core of the method includes an Extended Kalm

Yixiao Zeng, Jianlei Zheng, Chaoda Zheng, Shijia Chen

Real-time world simulation is becoming a key infrastructure for scalable evaluation and online reinforcement learning of autonomous driving systems. Recent driving world models built on autoregressive video diffusion achieve high-fidelity, controllable multi-camera generation, but their inference cost remains a bottleneck for interactive deployment. However,

Lucia Scardia, Edoardo Giovanni Tolotti

In this note we consider a two-dimensional semi-discrete dislocation energy and propose a simple and physically motivated construction for the grain boundary between two crystal grains with a small orientation difference. In the case of a general Bravais lattice, the energy of this construction matches the logarithmic scaling predicted by Read and Shockley.

Md Maklachur Rahman, Soon Ki Jung, Tracy Hammond

Recent segmentation models have demonstrated promising efficiency by aggressively reducing parameter counts and computational complexity. However, these models often struggle to accurately delineate fine lesion boundaries and texture patterns essential for early skin cancer diagnosis and treatment planning. In this paper, we propose MambaLiteUNet, a compact

Jonas Bauer, Christiane Fuchs, Tamara Schamberger

Football fans frequently exhibit pronounced emotional and physiological reactions during high-stakes matches. However, the temporal dynamics of this football fever are rarely modeled as a latent process. Using intensive longitudinal data from Arminia Bielefeld supporters who wore smartwatches during the 2025 German Football Association (DFB) Cup final, we in

Kosuke Nakanishi, Hiroshi Yano, Yuki Sato

We present an explicit quantum circuit construction for Hamiltonian simulation of a first-order velocity--stress formulation of the three-dimensional elastic wave equation in homogeneous isotropic media. Previous studies have shown how elastic wave equations can be cast into forms amenable to Hamiltonian simulation, but they typically rely on black box Hamil

Mo Zhou, Jianwei Wang, Kai Wang, Helen Paik

Multimodal Entity Linking (MEL) is a fundamental task in data management that maps ambiguous mentions with diverse modalities to the multimodal entities in a knowledge base. However, most existing MEL approaches primarily focus on optimizing instance-centric features and evidence, leaving broader forms of evidence and their intricate interdependencies insuff

Morten Jakobsen

We develop an operator-theoretic framework for the Convergent Born Series (CBS) method applied to the Lippmann--Schwinger equation for high-frequency Helmholtz problems. In contrast to the Fourier-based analysis of Osnabrugge et al., our approach expresses the preconditioned Lippmann--Schwinger iteration entirely in terms of the resolvent of a self-adjoint b

Minghong Wei, Pu Cao, Zhihao Chen, Zhiyuan Zang

With the rapid advancement of intelligent driving and remote sensing, oriented object detection has gained widespread attention. However, achieving high-precision performance is fundamentally constrained by the Angle Boundary Discontinuity (ABD) and Cyclic Ambiguity (CA) problems, which typically cause significant angle fluctuations near periodic boundaries.

Jeonghyeon Kim, Byeongjun Joung, Junwon Lee, Joohyung Lee

Mobile GUI agents can automate smartphone tasks by interacting directly with app interfaces, but how they should communicate with users during execution remains underexplored. Existing systems rely on two extremes: foreground execution, which maximizes transparency but prevents multitasking, and background execution, which supports multitasking but provides

Serkan Hamdi Güğül, Kemal Levi, Burak Acar

Industrial visual inspection systems often suffer from a severe scarcity of labeled defect data, particularly during the early stages of New Product Introduction (NPI). This limitation hinders the deployment of robust supervised detectors precisely when automated quality control is most needed. We present an end-to-end generative framework for high-fidelity,

Ruixing Ren, Minjie Wei, Junhui Zhao

Image transmission for vehicle-to-vehicle collaborative perception in autonomous driving faces challenges including limited on-board terminal resources, time-varying wireless channel fading, and poor robustness under low signal-to-noise (SNR) ratio. Traditional separate source-channel coding schemes suffer from the cliff effect, while existing semantic commu

Jia-Wen Li, Sheng Meng, Xinghua Shi, Jin Zhang

Sliding ferroelectrics built from stacked nonpolar monolayers enable out-of-plane polarization and unconventional switching via interlayer sliding, yet the microscopic sliding dynamics remain unclear. Using machine-learning molecular dynamics, we reveal spontaneous thermally driven interlayer sliding in ferroelectric MoS2 moir\'e superlattices, with relative

Rickmer Schulte, David Rügamer

The analysis of neural representation has become an integral part of research aiming to better understand the inner workings of neural networks. While there are many different approaches to investigate neural representations, an important line of research has focused on doing so through the lens of intrinsic dimensions (IDs). Although this perspective has pr

Aaryan Chaulagain, Daya Nidhi Chhatkuli, Anju Dhakal

Neutrino oscillation experiments are entering an era of precision, requiring both fast calculations and reliable uncertainty estimates. We present a compact three-flavor oscillation calculator for constant-density matter, built on analytic perturbative formulas and validated against established series expansions. Using the NuFIT 6.0 global-fit covariance mat

Adam Tan, Mohamed Hefny, Keval Vora

Many real-world graphs have degree distributions that are well approximated by a power-law, and the corresponding scaling parameter $\alpha$ provides a compact summary of that structure which is useful for graph analysis and system optimization. When graphs contain sensitive relationship data, $\alpha$ must be estimated without revealing information about in

Jan-Philipp Schmidt

We present ActuBench, a multi-agent LLM pipeline for the automated generation and evaluation of advanced actuarial assessment items aligned with the International Actuarial Association (IAA) Education Syllabus. The pipeline separates four LLM roles by adapter: one agent drafts items, one constructs distractors, a third independently verifies both stages and

Qian Liu, Xiaoshuang Xia, Junjie Wang, Peilong Hong

Disorder in moire superlattices simultaneously degrades flat-band localization and induces Anderson localization, yet how these two regimes interact has remained unclear. Here, we introduce a combined framework linking localization-length scaling with differential probability density analysis to map localization transitions in partially disordered one-dimens

Lei Liu, Xiujuan Zhang, Ming-Hui Lu, Yan-Feng Chen

Skyrmions are particle-like topological textures that hold great promise for low-power electronics and wave-based functionalities. Yet their utility is hindered by the lack of robust and controllable transport. Here, we show that band topology can be harnessed to overcome this limitation. We experimentally realize an acoustic quantum skyrmion--valley Hall ef

Kyotaro Ushida, Takayuki Komatsu, Yoshiyuki Ohmura, Yasuo Kuniyoshi

While current deep learning models achieve high performance by learning statistical correlations from vast datasets,which stands in stark contrast to human learning. They lack the flexibility of humans-particularly preverbal infants-to autonomously acquire the underlying structure of the world from limited experience and adapt to novel situations. In this st

Paul A. Bereuter, Alois Sontacchi

Evaluation of musical source separation (MSS) has traditionally relied on Blind Source Separation Evaluation (BSS-Eval) metrics. However, recent work suggests that BSS-Eval metrics exhibit low correlation between metrics and perceptual audio quality ratings from a listening test, which is considered the gold standard evaluation method. As an alternative appr

Zhaochen Li, Xinghao Yan, Runni Zhou, Xiaoyang Li

Background: Osteoporosis and osteopenia are often undiagnosed until fragility fractures occur. Dual-energy X-ray absorptiometry (DXA) is the reference standard for bone mineral density (BMD) assessment, but access remains limited. Knee radiographs are obtained at high volume for osteoarthritis evaluation and may offer an opportunity for opportunistic bone-lo

Tong Zhao, Chenghao Zhang, Yutao Zhu, Zhicheng Dou

Audio carries richer information than text, including emotion, speaker traits, and environmental context, while also enabling lower-latency processing compared to speech-to-text pipelines. However, recent multimodal information retrieval research has predominantly focused on images, largely overlooking audio, especially in the setting of interleaved audio-te

Fumiya Iwashige

Weighted networks encode not only the presence of interactions but also their strength. Existing methods for weighted network community detection often rely on Poisson models, which can be restrictive for overdispersed data and make efficient posterior computation difficult when covariates are incorporated. We propose Bayesian stochastic block models based o

Boling Guo, Ning Jiang, Hui Liu, Yi-Long Luo

In this paper, we first derive the compressible Navier-Stokes/Landau-Lifshitz-Gilbert (NS-LLG) model for magnetoelastic materials via the energetic variational approach (EnVarA). It is important to emphasize that the manner in which the evolution of magnetoelastic materials is influenced by the fluid motion--specifically through the deformation gradient--det

Luiz Lara, Henrique N. Sá Earp

We study the existence of asymptotically $Z$-stable (a.Z stable) bundles over polycyclic surfaces. Our choice of polynomial central charge is related to the existence of solutions of the deformed Hermitian--Yang--Mills equations, with vanishing $B$-field, in the large-volume limit. The main result is a technique to construct rank $3$, strictly a.Z-stable bun

Zhenyu Wang, Geyan Ye, Wei Liu, Man Tat Alexander Ng

Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. However, existing approaches suffer from unconstrained reasoning, uninterpretable predictions, and retrieval signals that are weakly aligned with regulatory topology. To address these limitations, we propose AROMA

Yunbo Geng, Furen Deng, Jixia Li, Shifan Zuo

We present the beam pattern measurement of the Tianlai Cylinder Pathfinder Array. As it is a pure drift-scan instrument, we exploit the North-South motion of the Sun to demonstrate that the primary beam is factorizable. Leveraging this property, we decompose the primary beam into independent East-West (E-W) and North-South (N-S) components. Using the Sun as

Fengxian Dong, Zhi Zheng, Xiao Han, Wei Chen

Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. Traditional methods rely on predefined operator libraries and cannot leverage task semantics, limiting their ability to produce diverse, high-value features for complex tasks. Recent Large L

Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforcement learning (RL)-guided hybrid framework that integrates frozen

Weizhi Nie, Haolin Chen

Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle with irregularly sampled data and suffer from the opaque "black-box" nature of sequential architectures, strictly limiting clinical trust. To address these challenges, we propose CT-Former, integrating continuou

Jingxuan He, Xiyu Wang, Mengyu Zheng, Xiangyu Zeng

Instruction-based image editing (IIE) aims to modify images according to textual instructions while preserving irrelevant content. Despite recent advances in diffusion transformers, existing methods often suffer from over-editing, introducing unintended changes to regions unrelated to the desired edit. We identify that this limitation arises from the lack of

Volker Branding, Simona Nistor, Cezar Oniciuc

The identity map of an Einstein manifold is a critical point of both the classical energy functional and the conformal-bienergy functional. In this paper, we investigate the conformal-biharmonic stability of the identity map of compact Einstein manifolds of dimension at least four and with nonnegative scalar curvature, and we compare it with the harmonic sta

Wei Han, David Martinez, Anna Khanina, Lawrence Cavedon

A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rat

Sha Lu, Jixue Liu, Stefan Peters, Thuc Duy Le

Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cues and may miss anomalies caused by violations of complex feature dependencies. Dependency-based anomaly detection provides a principled alternative by identifying anomalies as vio

Wengyu Zhang, Xiao-Yong Wei, Qing Li

Text-guided molecular design is a key capability for AI-driven drug discovery, yet it remains challenging to map sequential natural-language instructions with non-linear molecular structures under strict chemical constraints. Most existing approaches, including RAG, CoT prompting, and fine-tuning or RL, emphasize a small set of ad-hoc reasoning perspectives

Arend Rensink

One of the advantages of LTL over CTL is that the notion of a counterexample is easy to grasp, visualise and process: it is a trace that violates the property at hand. In this paper we propose a notion of evidence for CTL properties on explicit-state models -- which equally serves as witness for satisfied properties and counterexample for violated ones -- an

Chunming Zheng

We study a stochastic model of collective motion in which individuals update their orientation through pairwise aligning or anti-aligning copying interactions. We analyze both annealed dynamics, where interaction types are chosen probabilistically at each update, and quenched dynamics, where individuals are permanently assigned to aligning or anti-aligning s

Rocco Chirivì, Martina Costa Cesari, Xin Fang, Peter Littelmann

We define and study the higher rank GKZ-fans of point configurations, where the rank one cases coincide with the usual GKZ-fans. A point in a higher rank GKZ-fan is then used to construct higher rank quasi-valuations to degenerate the toric variety associated to the point configuration flatly to a reduced union of toric varieties. Such a union encodes the po

Sunit Das, Amit Agarwal

Intrinsic Hall effects, such as the anomalous Hall effect, originate from the orbital quantum geometry of Bloch states. However, in layered materials, the combined action of out-of-plane electric and magnetic fields couples to layer polarization and orbital moment, generating a mixed layer-orbital quantum geometry in field-dressed Bloch states. We show that

Mengrui Wang, Shouwen Ma, Zewei Luo, Wei Shi

Existing super-resolution microscopy is often constrained by inherent trade-offs between resolution, acquisition speed, phototoxicity, and hardware complexity. Computational post-processing approaches offer a promising alternative, but they typically suffer from linearity distortion, high computational cost, reliance on pre-training data, or reconstruction a

Tong Zhao, Yutao Zhu, Yucheng Tian, Zhicheng Dou

Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the ``one-size-fits-all'' retrieval paradigm, as different queries exhibit distinct preferences for different retrievers. While recent routing techniques attempt to select the optimal retriever dynamically, they t

Adriana Aida, Walid Amer, Katarina Bankovic, Dhruv Behl

Industrial robotic manipulation demands reliable long-horizon execution across embodiments, tasks, and changing object distributions. While Vision-Language-Action models have demonstrated strong generalization, they remain fundamentally reactive. By optimizing the next action given the current observation without evaluating potential futures, they are brittl

Gustaf Åhlgren, Onur Günlü

Fundamental rate-distortion-perception (RDP) trade-offs arise in applications requiring maintained perceptual quality of reconstructed data, such as neural image compression. When compressed data is transmitted over public communication channels, security risks emerge. We therefore study secure RDP under negligible information leakage over both noiseless cha

Wenhong Zhu, Ruobing Xie, Rui Wang, Pengfei Liu

Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of divergence direction, optimization strategy, and data regime. We break down the design of existing KD methods and present a unified view that establishes connections between them, reformulating KD as a reweigh

Kai-Fu Yang, Fu-Ya Luo, Yong-Jie Li

Color constancy is a fundamental ability of many biological visual systems and a crucial step in computer imaging systems. Bio-inspired modeling offers a promising way to elucidate the computational principles underlying color constancy and to develop efficient computational methods. However, bio-inspired methods for color constancy remain underexplored and

Aleksandra Lekić, Nikola Petrović, Dušan Stipanović

In this paper we design a switching control law for the \'Cuk converter in the continuous conduction mode using piecewise linear Lyapunov functions. These Lyapunov functions can be constructed using different number of state variables affecting the system's performance. In the paper, some representative simulations covering construction of different piecewis

Luca Foppiano, Sae Dieb, Malik Zain, Kazuki Kasama

Battery research is a rapidly growing and highly interdisciplinary field, making it increasingly difficult to track relevant expertise and identify potential collaborators across institutional boundaries. In this work, we present a pipeline for constructing an author-centric knowledge graph of battery research built on OpenAlex, a large-scale open bibliograp

Aleksandra Lekić, Dušan Stipanović

Circuits' and in particular DC/DC converters' switching behavior is analyzed in this paper using the equivalent control modeling of the dynamic systems' sliding mode regime. As a representative example and also being one of the most complex circuits among DC/DC converters, the \'Cuk converter is chosen. It is shown how the converter's behavior in the steady

Avinanda Chaudhuri

We investigate the interplay between lepton flavor violation (LFV) and leptogenesis in the minimal scotogenic model, comparing high-scale hierarchical leptogenesis and low-scale resonant leptogenesis within a unified Casas--Ibarra framework. Since the same Yukawa couplings simultaneously govern radiative neutrino mass generation, charged LFV processes, and t

Nils Lid Hjort

This invited paper proposes and discusses several Bayesian attempts at nonparametric and semiparametric density estimation. The main categories of these ideas are as follows: 1) Build a nonparametric prior around a given parametric model. We look at cases where the nonparametric part of the construction is a Dirichlet process or relatives thereof. (2) Expres

Xuchun Duan, Ting Li, Yingli Cui, Yijun Hou

Large solar energetic particle (SEP) events are thought to originate from the shocks driven by fast coronal mass ejections (CMEs) and thus generally accompanied by type II radio bursts. However, a significant proportion of type II radio bursts is not accompanied by SEP events. To study the relationship between SEPs and type II radio bursts and the associated

Georg Wieland, Reinhard Alkofer

Based on a suitable basis system for the quark-gluon vertex' transverse tensor structures and on carefully chosen kinematical variables, the transverse part of the quark-gluon vertex in quenched QCD in the Landau gauge is obtained from a system of Dyson-Schwinger equations. We demonstrate by analysing this solution that the angular dependence of these transv

Enshuai Zhou, Yifan Hao, Chao Wang, Rui Zhang

Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentally limited by heuristics: heuristic budgeting relies on statistical priors rather than task objectives, causing resource misallocation, while heuristic selection relies on coupled q

Chayan Kumar Paul, Krishanu Nath, Indra Narayan Kar, Denis Efimov

This article proposes a Model Reference Adaptive Control (MRAC) strategy to achieve fixed-time convergence of parameter estimation and tracking errors for unknown linear time-invariant systems, without relying on the persistence of excitation condition. Instead, it employs a less restrictive initial/interval excitation condition on the regressor matrix, enha

Lampros Gavalakis, Marcel K. Goh, Ioannis Kontoyiannis

Various lower bounds are established for the entropy of sums, products and their combinations. First, we derive a prime-field analogue of a version of the entropy power inequality established by Tao over torsion-free groups. Next, we prove an entropy sum-product statement: For independent and identically distributed random variables $X,X'$, the maximum o

A. V. Tsiganov

The subject of our discussion is the theory of differential equations as set out in two classical Euler's textbooks "Institutiones Calculi Differentialis" and "Institutiones Calculi Integralis".

Weizhi Nie, Zhen Qu, Weijie Wang, Chunpei Li

Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration. Traditional data-driven models often provide accurate yet opaque predictions, limiting physicians' confidence and clinical applicability. To address this limitation, we propose a Large Language Model (LLM)-g

Shiyu Fang, Xiaocong Zhao, Xuekai Liu, Peng Hang

Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficiency through cooperative decision-making. However, existing methods often overlook the individual needs and heterogeneity of cooperative participants, making it difficult to transfer

Xiaoyang Wang, Yinan Wang, Wenbo Zhao, Hanyu Liu

Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES) remains prohibitive for high-throughput searches. We observe that the self-consistent harmonic approximation (SCHA) FES, as a function of nuclear centroid positions, shares the sa

Magdalena Gołębiowska, Piotr Syga

Speaker verification is a task of confirming an individual's identity through the analysis of their voice. Whispered speech differs from phonated speech in acoustic characteristics, which degrades the performance of speaker verification systems in real-life scenarios, including avoiding fully phonated speech to protect privacy, disrupt others, or when the la

Felix Laga, Teruaki Suyama

We investigate the quantum properties of gravitational waves (GWs) generated by a classical energy-momentum tensor. Treating the GW field as a quantum field coupled to a classical source, we evaluate the expectation value of the GW operator. We demonstrate that this expectation value exactly reproduces the classical retarded solution. Furthermore, we show th

Soumya Sankar, Xingkai Cheng, Junwei Liu, Berthold Jäck

Electric Hall effects generated by an in-plane magnetic field have recently gained attention owing to their intrinsic origin in topological electronic states and potential application in magnetic field sensing. In pratice, the measured transverse electric voltage typically combines contributions from multiple phenomena, such as anisotropy and Berry curvature

Tianshui Chen, Jianman Lin, Zhijing Yang, Chunmei Qing

Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person, where two aligned frames exhibit the same speech content yet differ in emotional expression, limiting the SPFEM

Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao

Evaluating the multilingual and multicultural capabilities of Large Language Models (LLMs) is essential for their global utility. However, current benchmarks face three critical limitations: (1) fragmented evaluation dimensions that often neglect deep cultural nuances; (2) insufficient language coverage in subjective tasks relying on low-quality machine tran

Ming Hsiao

In this paper, we establish a compactness theorem for gradient Ricci solitons with scalar curvature bounds and uniform lower bounds of harmonic coordinates. Our approach is to bootstrap regularity in harmonic coordinates by exploiting the soliton equation. As an application, we show that the regular part of any noncollapsed limit of gradient Ricci solitons w

Kalyan Banerjee

In this paper, we develop the notion of representability of co-dimension three cycles on a fourfold in terms of zero cycles modulo rational equivalence on surfaces.

Aaranay Aadi, Jai Gopal Singla, Amitabh, Nitant Dube

Recent times have seen an increase in demand of high quality Digital Elevation Models (DEMs) for the lunar surface, because they are highly important for studying the moon and planning future missions. However, there is an evident lack of detailed elevation data on the Moon. To overcome this limitation, this study proposes a novel deep learning method that e

Shreel Golwala

Grokking describes a delayed generalization phenomenon in which a neural network achieves perfect training accuracy long before validation accuracy improves, followed by an abrupt transition to strong generalization. Existing detection signals are indirect: weight norm reflects parameter-space regularization and consistently lags the transition, while GrokFa

Angelo Maria Sabatini

This study applies symbolic time series analysis and Markov modeling to explore the phonological structure of Evgenij Onegin-as captured through a graphemic vowel/consonant (V/C) encoding-and one contemporary Italian translation. Using a binary encoding inspired by Markov's original scheme, we construct minimalist probabilistic models that capture both local

Mogens Fosgerau, Jesper R. -V. Sørensen

This paper develops a highly general convex duality framework for the perturbed utility route choice (PURC) model. We show that the traveler's constrained, potentially non-smooth utility maximization problem admits a dual formulation: an unconstrained concave maximization problem with a differentiable objective. The unique optimal flow can be recovered link-

Anand Chitrao, Arindam Jana, Asfak Soneji

Let $F$ be a finite extension of $\mathbb{Q}_p$. The so-called supersingular representations are the basic building blocks in the theory of mod $p$ representations of ${\rm GL}_2(F)$. The space of pro-$p$-Iwahori invariants of a universal module played a crucial role in the construction of the supersingular representations of ${\rm GL}_2(\mathbb{Q}_p)$. In t

Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani

Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributional regression under empirical-quantile supervision, where each input is paired with multiple observed quantile outcomes, and the target distribution is represented by a dense grid of quantiles. We address tw

Dang-Zheng Liu, Guangyi Zou

The first paper in this series introduced a \emph{short-to-long mixing} condition that captures mean-field GOE/GUE edge universality in the supercritical sparsity regime, for symmetric/Hermitian random matrices with independent entries and a Markov variance profile. This condition reduces the universality problem to the mixing properties of the underlying Ma

Tatsuki Tokumura, Ayano Nakai-Kasai, Tadashi Wadayama

This paper provides a sparse signal recovery algorithm, DU-PSISTA (Deep Unfolded-Periodic Sketched Iterative Shrinkage-Thresholding Algorithm), which aims to balance computational efficiency and accuracy for recovering high-dimensional sparse signals, and a convergence analysis under sufficient conditions. DU-PSISTA introduces a random matrix projection know

Juha Park, Jiho Choi, Jong Pil Yun, Yong Chan Park

Accurate segmentation of maxillary sinus in panoramic X-ray images is essential for dental diagnosis and surgical planning; however, this task remains relatively underexplored in dental imaging research. Structural overlap, ambiguous anatomical boundaries inherent to two-dimensional panoramic projections, and the limited availability of large scale clinical

Naihuan Jing, Yinlong Liu, Jian Zhang

In this paper, we study the Littlewood theory associated with the quantum super immanants and supersymmetric polynomials, including both the super case and the quantum generalization. In the setting of quantum super Schur-Weyl duality between the quantum superalgebra $U_q(\mathfrak{gl}_{m|n})$ and the Iwahori-Hecke algebra $\mathcal{H}_r$ of type A, we expli

He Yang Yuan, Xin Wang, Kundi Yao, An Ran Chen

Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practices can inadvertently expose sensitive information or enable attacks such as log injection, posing serious threats to system security and privacy. Prior research has examined general

Rongtao Zhang, Xin Zhu, Masoume Pourebadi Khotbehsara, Warren Dao

Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference

Luke Bailey, Kaiyue Wen, Kefan Dong, Tatsunori Hashimoto

LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together. However, in practice, existing LLM self-play methods do not scale well with large amounts of compute, instead hitting learning plateaus. We argue this is because over long training runs, the

Rayan Mazouz, Luca Laurenti, Morteza Lahijanian

Safety of stochastic dynamic systems in environments with dynamic obstacles is studied in this paper through the lens of stochastic barrier functions. We introduce both time-invariant and time-varying barrier certificates for discrete-time, continuous-space systems subject to uncertainty, which provide certified lower bounds on the probability of remaining w

Yonglei Liu, Shiqi Chen, Zhenyu Guo, Kaiqi Zhu

Optical skyrmions have recently unlocked topological quasiparticle textures of light, rising in prominence for next-generation ultra-robust information processing. However, to date, their study has been mainly confined to coherent laser fields. Here we extend skyrmions to more general light sources of partially coherent, stochastic optical fields. We define

Pagkratis Tagkopoulos, Dimitris Sfondilis, Ilias Tagkopoulos, Tarek Zohdi

The prediction of sensory attributes from ingredient-level formulations is an emerging challenge at the intersection of food science and artificial intelligence. We address the fundamental question of whether the taste of a food can be predicted from its ingredients by treating recipes as composite materials. We apply Hashin--Shtrikman (HS) and Reuss--Voigt

Rui Chen, Bobo Hua

In this paper, we first prove that the following generalized conservation principle holds on complete Riemannian manifolds: for every \(0<s<1\) and \(t>0\), \[ T_t^{(s)}\mathbf 1+\int_0^t T_\tau^{(s)}\mathcal R_s\,d\tau=1 \qquad\text{on }M, \] where \(\mathcal R_s\) is the intrinsic killing term measuring the loss of mass of the subordinate semigroup, and th

Juntao Li, Liang Zhang

Cross-sectional stock ranking is a fundamental task in quantitative investment, relying on both temporal modeling of individual stocks and the capture of inter-stock dependencies. While existing deep learning models leverage graph-based approaches to enhance ranking accuracy by propagating information over relational graphs, they suffer from a key challenge:

Di-Chang Chen, Fei Dai, Bo Ma, Shang-Fei Liu

Correlations between the inner small planets and cold giants encodes the formation and evolution of planetary systems. It remains unclear if the correlation differs on the two sides of the radius valley. In this work, we compute the conditional frequency of cold Jupiters in systems with only inner sub-Neptunes $P(\rm CJ|SN)$ and those with only inner super-E

Marcos Tileria, Santanu Kumar Dash, Profir-Petru Pârţachi, Earl T. Barr

Large Language Models (LLMs) are increasingly deployed in automated software engineering for tasks such as API migration. While LLMs are able to identify migration patterns, they often make mistakes and fail to produce correct glue code to invoke the new API in place of the old one. We call this issue Scaffolding Hallucination, a failure mode where models ge

Soumil Roychowdhury, Chenjie Wang

An anyon-chain-like lattice model with symmetry described by the Ising fusion category is studied. Combining numerical and analytical studies, we uncover a rich phase diagram that contains three phases: a symmetric critical phase and two categorical symmetry breaking phases. The symmetric phase lies in the same universality class as the usual critical Ising

Hardy Chen, Nancy Lau, Haoqin Tu, Shuo Yan

Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a public evaluation file with labels in the workspace, rather than through direct inspection of the agent's intermediate outputs. We study whether multi-round user pressure to improve

Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang

Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However, we show that current mRAG systems suffer from a language bias during reranking, systematically favoring English and the query's native language. By introducing an estimated oracle evidence analysis, we quant

Keita Funayama, Kenichi Yatsugi, Hideo Iizuka

Multi-band topological states enable robust and versatile wave manipulation across a variety of physical platforms. However, the emergence of multi-band topological states has relied on higher-frequency modes with complex spatial profiles, which constrains the realization of robust topological states due to fragile symmetry and pseudospin hybridization in th

Heng Liu, Bingxin Yang, Yiming Lu, Yuan Wang

Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to

Alejandro Ayala, Bruno El-Bennich, Ricardo L. S. Farias, Luis A. Hernández

We use the two-flavor Linear Sigma Model with quarks as an effective description of QCD to investigate the nature of the chiral phase transition at finite baryon chemical potential and zero temperature. We work at one-loop order to set up and solve the system of self-consistent coupled equations for the particle pole masses. The chemical potential-dependent

Samuel Kahn, Ryan Hausen, Hubert Bretonnière, Nicole Drakos

The measurement of galaxy morphological parameters from astronomical images features in a wide range of modern analyses, including galaxy evolution and cosmological weak lensing studies. The precision and accuracy of morphological parameter estimation can be influenced by several key factors. The effective seeing of the image, summarized by the point spread

Zhenyu Jiang, Congcong Wang, Jingxuan He, Yi Zhan

A graphene-optimized silicon carbide PIN detector was fabricated and its radiation tolerance under X-ray irradiation of 160 keV was evaluated. Its electrical properties, charge collection performance and time resolution of beta-particles (90Sr) are reported. After 1 MGy irradiation, the detector maintains an ultralow leakage current of approximately 2.2e-10

Xu Huang, Ruofan Zhang, Lu Cheng, Yuefeng Song

Ensuring functional safety in human-robot interaction is challenging because AI perception is inherently probabilistic, whereas industrial standards require deterministic behavior. We present an LLM-guided safety agent for edge robotics, built on an ISO-compliant low-latency perception-compute-control architecture. Our method translates natural-language safe