April 2026 arXiv papers — page 17

Risto Raitio

We show that in a self-consistent preon model, where Standard Model quarks and leptons are three-body composites confined at a metacolor scale Lambda_cr ~ 10^14 GeV, both leptoquarks and the Standard Model gauge group SU(3)_c x SU(2)_L x U(1)_Y emerge as structural predictions rather than inputs. Combining the preon content of a quark with that of a lepton g

Johann Gabriel Meyer, Felix Ritzkowsky, Fatemehsadat Ghaffari, Kevin Schwarz

At laser wavelengths longer than the prominent 1 {\mu}m range of high-power ytterbium-doped lasers, nonlinear phase shifts produced in nonlinear media for spectral broadening and subsequent pulse compression decrease drastically. Consequently, at the 2 {\mu}m wavelength range, the threshold of the applicable peak power for pulse compression in gas-filled mul

Shotaro Kato, Jiryo Komeda, Takeshi Takahashi

Let $C \subset \mathbb{P}^3$ be a canonical curve of genus $4$ over an algebraically closed field $k$ of characteristic zero. For a line $l \subset \mathbb{P}^3$, we consider the projection $\pi_l: C \to \mathbb{P}^1$ from $l$ and the induced extension of function fields $\pi_l^*: k(\mathbb{P}^1)\hookrightarrow k(C)$. A line $l$ is called an \emph{$S_3$-line

Giorgi Tigilauri

For a finite group $G$, we construct a simplified model for the $G$-symmetric monoidal $G$-$\infty$-category of rational $G$-spectra. Using this model, we classify $\mathcal{I}$-normed algebras in rational $G$-spectra for a given indexing system $\mathcal{I}$. We show that such an algebra is equivalently described as a collection $\{\mathcal{X}(G/H)\}_{(H\le

May Hammad, Menatallh Hammad

Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional "Lost-in-Space" (LIS) algorithms often suffer from high computational overhead and sensitivity to sensor-induced noise. While deep learning has emerged as a promising alternative, standard regression models are often confounded by

Sam Insley, Michael J. Williams, Rahul Dhurkunde, Ian Harry

Identifying compact binary coalescences buried within the non-Gaussian and non-stationary data taken by gravitational-wave interferometers requires sophisticated search pipelines, such as the PyCBC analysis. A critical task for these pipelines is determining the statistical significance of candidate events by comparing a "ranking statistic" against a

I. V. Iukhnovets, M. Y. Goloshchapov, A. P. Gordeev, O. V. Bychkova

We present a method for generating a laser beam with flat intensity and phase profiles in the focal region where the beam interacts with neutral $^{87}$Rb atoms in an array of optical dipole traps. We synthesize the beam as a superposition of Hermite--Gaussian or Laguerre--Gaussian modes. Then we give analytical expressions for the coefficients of such a sup

Firuz Rakhmonov, Parviz Rakhmonov

For $H \ge N^{1-\frac{1}{2c}} \ln^2 N$, where $c$ is a fixed non-integer number satisfying $$ \|c\| \ge 3c\left(2^{[c]+1}-1\right)\frac{\ln \ln N}{\ln N}, \qquad c > \frac{4}{3}\left(1 + \frac{52\ln \ln N}{\ln N}\right), $$ we obtain an asymptotic formula for the number of representations of a sufficiently large integer $N$ in the form $$ p_{1} + p_{2} + [n^

S. Iserte, M. Madon, G. Da, J. Pierson

Dynamic Resource Management (DRM) techniques can be leveraged to maximize throughput and resource utilization in computational clusters. Although DRM has been extensively studied through analytical workloads and simulations, skepticism persists among end administrators and users regarding their feasibility under real-world conditions. To address this problem

Z. Y. Chen, Y. X. Zhao

Momentum-space nonsymmorphic symmetries have recently attracted significant interest in both artificial and condensed-matter crystals, whereas real-space nonsymmorphic symmetries have long played an important role in the study of crystalline topological phases. Here, we establish a general theory of momentum-space crystallographic groups that emerge from pro

X. D. Jia, D. H. Gao, J. H. Chen, Q. Wu

Fast radio bursts (FRBs) are enigmatic millisecond-duration radio transients whose physical origins remain debated. To shed light on this, we analyze the CHIME/FRB Catalog 2. By using the probability distribution of dispersion measured (DM) derived from the IllustrisTGN simulation, we compute the pseudo-redshift with $1\sigma$ error for each FRB. To derive t

Zhiquan Tan, Yinrong Hong

Improving large language model (LLM) reasoning requires supervision that is both aligned with the model's own test-time states and informative at the token level. Reinforcement learning with verifiable rewards provides on-policy exploration but offers sparse, high-variance credit; supervised fine-tuning and distillation provide dense targets but often train

María Belén Rodríguez, Petra van den Bos

This paper combines methods from the fields of Model-Based Testing (MBT) and Behaviour-Driven Development (BDD) to define a testing approach with human-readable specifications and test cases, as in BDD, while using the modelling techniques and automatic test generation algorithms from MBT. We introduce PICKLES, a Precise Input and Control-flow Keyword-based

Jialiang He, Jintao Luo, Shuguo Zhang

We show that under $\mathsf{ZF} + \mathsf{CC}_{\mathbb R}$, if the Ramsey property holds for all sets in a good pointclass $\Gamma$, then there is no MAD family in $\Gamma$, proving a long-standing conjecture made by A.R.D.\ Mathias in 1977. This also holds for $\mathcal I$-MAD families with respect to analytic ideals $\mathcal I$ including $\mathcal{ED}$, $

Seongmin Kim, Daegyu Lee

Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementary rules, and hundreds of training problems must be solved to supervise a Graph Neural Network (GNN) object scorer. We propose LLM-Flax, a three-stage framework that eliminates all three sources of manual eff

Darren Fürst, Sebastian Steindl, Ulrich Schäfer

Speech Sound Disorders (SSD) affect roughly five percent of children, yet speech-language pathologists face severe staffing shortages and unmanageable caseloads. We test a hierarchical approach to SSD classification on the granular multi-task SLPHelmUltraSuitePlus benchmark. We propose a cascading approach from binary classification to type, and symptom clas

Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang, Nikolay Aristov

Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastructure. These features make electric truck routing a coupled logistics and energy problem, limiting the practicality of heuristics-based methods and rendering the

Mingji Ge, Qirui Chen, Zeqian Li, Weidi Xie

Long-term video understanding requires interpreting complex temporal events and reasoning over procedural activities. While instructional video corpora, like HowTo100M, offer rich resources for model training, they present significant challenges, including noisy ASR transcripts and inconsistent temporal alignments between narration and visual content. In thi

Cédric Pilatte

Let $\lambda$ denote the Liouville function. We prove that $$\sum_{X \leq x < 2X} \sup_{\alpha \in \mathbb{R}/\mathbb{Z}} \bigg\lvert\!\sum_{x \leq n < x+H} \lambda(n) e(n\alpha)\bigg\rvert = o(HX)$$ as $X\to \infty$, in the regime $H = H(X) \geq \exp((\log X)^{2/5+\varepsilon})$. This improves upon a result of Walsh towards the Fourier uniformity conjecture

Mihir Bhattacharya, Anup Pramanik

This paper characterizes the single-peaked domain on a tree via the strategy-proofness of extreme rules defined on that tree. For any tree, these rules are unanimous and anonymous on any preference domain. In particular, we show that they are strategy-proof only on the single-peaked domain associated with that tree.

L. A. Williamson, W. McEniery, F. Cerisola, J. Anders

Two qubits strongly coupled to a common bosonic reservoir can become entangled with each other, despite having no direct interaction. In equilibrium, such coupling-induced coherences can be described by the mean-force Gibbs state. Here we derive approximate, analytic expressions for the two-qubit mean-force Gibbs state, and use these to characterize equilibr

Ariel Sela

Multi-agent deliberation systems using large language models (LLMs) are increasingly proposed for policy simulation, yet they suffer from artificial consensus: evaluator agents converge on the same option regardless of their assigned value perspectives. We present the AI Council, a three-phase deliberation framework, and conduct 120 deliberations across two

Vitalii Shtender, Chin Shen Ong, Pedro Berastegui, Olivier Donzel-Gargand

The high-temperature ceramic compound Al5C3N with promising application usage belongs to the scarcely studied Al-C-N system. It was originally reported as an ordered compound in the non-centrosymmetric space group P63mc and described as a nanolaminate with an -Al2C-AlN-Al2C2- stacking sequence. The recently reported structural disorder in the related compoun

Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun Zou

Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis testing: if a neural network can distinguish, for example, an image of a handwritten digit from another, can it also distinguish an "image of a sample" (such as a scatter plot) gener

Bodon Jeong, Hongsu Byun, Youngjae Kim, Weikuan Yu

The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key challenge arises from Key-Value (KV) caches, which often exceed available device memory. Although NVMe-based offloading offers scalable capacity, existing file-based designs rely heavily on the kernel page cache,

Tony Xu, Sarah Klamt, Katherine Turner, Anne Brustle

GPU-accelerated Self-Organizing Map (SOM) implementations are among the most competitive options for large-scale SOM analysis, but growing dataset sizes increasingly challenge their practical use because workloads no longer fit cleanly within device-memory limits. We introduce FloatSOM, a SOM framework for scalable training and deployment that supports multi

Daniil D. Reshetnikov, Anna A. Kretova, Anastasia A. Fominova, Evgenii A. Vashukevich

Random numbers sequences (RNSs) play a vital role in various scientific and engineering applications. They are critical to the integrity of classical and quantum cryptography, the accuracy of mathematical modeling and Monte Carlo simulations, and the core mechanics of applications in fields as diverse as gambling and statistical sampling. While the primary c

Jinho Choo, JunSeung Lee, Jimyeong Kim, Yeeho Song

Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known as language confusion. Prior mitigation approaches based on sequence-level fine-tuning, such as DPO, ORPO, and GRPO, operate at the level of entire responses and can lead to unint

Shoushuo Zhang, Rang Liu, Qian Liu, Ming Li

Cooperative integrated sensing and communication (ISAC) based on orthogonal frequency-division multiplexing (OFDM) enables network-wide sensing by exploiting the spatial diversity of multi-base-station (BS). This paper studies performance analysis and time-frequency resource allocation for a multi-BS cooperative OFDM-ISAC network with fine-grained resource-e

Guillermo Iglesias, Gema Bello-Orgaz, María Navas-Loro, Cristian Ramirez-Atencia

The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning models. Privacy regulations restrict data sharing, making synthetic data generation a promising alternative. The use of Large Language Models (LLMs) in a data augmentation pipeline could be leveraged as an al

Daniele Calandriello, Alessandro Lazaric, Michal Valko

We introduce Sparse-HFS, a scalable algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.

Ashish Bawalia, Zdzisław Brzeźniak, Manil T. Mohan

In this work, we investigate the well-posedness of a stochastic heat equation with an arbitrary (but polynomial) nonlinearity in any dimension $d\geq 1$ perturbed by a multiplicative white noise in the Stratonovich form, subject to an $L^2-$norm constraint on the solution. In bounded smooth domains, we establish the existence of a martingale solution taking

Aada Hakula, Pauliina Hirvi, Nuutti Hyvönen, Altti Jääskeläinen

This paper presents a projection-based technique to mitigate the impact of modeling errors related to domain truncation, changes in the optode coupling coefficients, and misspecified optical parameters of different tissue types in diffuse optical tomography. The approach considers the primary Jacobian matrix of the forward map in the image reconstruction sch

Ashish Bawalia, Zdzisław Brzeźniak, Manil T. Mohan

In this work we investigate the phenomenon of pathwise non-uniqueness for the stochastic incompressible Euler equations with a passive tracer on the whole Euclidean space. The stochastic perturbations are interpreted as a transport noise and a linear multiplicative noise in the Stratonovich sense. In both cases, via classical transformations, we convert the

Avishek Bhandari, Ipsita Parida, Hitesh Kumar Sahu

We address the joint detection-and-attribution problem in cross-border financial contagion through a two-stage framework. The first stage applies wavelet-quantile transfer entropy across time-scales and lower, median, and upper-tail quantiles. The second stage attributes each significant link to one of five channels comprising of i) Trade, ii) Financial, iii

Micha C. J. Philipp, Lukas Köbbing, Alexander Karger, Andreas Jossen

Higher energy density and longer lifetime are the requirements for next-generation lithium-ion batteries. A promising anode material is silicon, which offers high specific capacity, but its significant volume change during lithiation and delithiation enormously reduces battery lifetime. A physical understanding of the processes degrading the battery is key t

Sungsoo Kim, Sangmin Lee

In the background of a Kerr-Newman black hole, the motion of a scalar particle is integrable by virtue of an extra conserved charge known as Carter charge. When the particle is endowed with spin, it is known that another conserved charge, the R\"udiger charge, maintains the integrability at least at low orders in the spin magnitude. We explore the extent of

V. Gilles, T. Sweetnam, B. Mohammadian, M. A. McCulloch

Superconducting Parametric Amplifiers (SPAs) have seen great interest in recent years due to their high gain and quantum limited noise performance. Among these amplifiers, resonant SPAs have been widely developed for experiments where ultra low-noise narrow-band amplification is of interest, such as the search for Axion dark matter in particle physics and th

Pavlína Rutová, Marek Skarka, Jiří Žák, Pavol Gajdoš

This study explores photometric variability in a sample of hot stars in order to test whether variability arises from rotational modulation due to surface temperature spots. Frequencies determined from the projected rotational velocities were compared with frequencies estimated on the basis of TESS light curves. In all five cases, the spectroscopic frequency

Mathias Garny, Florian Niedermann, Martin S. Sloth

We point out that constraints on $\Delta N_\mathrm{eff}$ reported by the ACT collaboration in their DR6 data release are surprisingly sensitive to the assumptions made about the initial power spectrum from inflation. The ACT collaboration reports no evidence of new light degrees of freedom alongside a low value of the expansion rate, thus confirming the Hubb

Natsumi Shibata, Takeshi Miura

We study bijections between the positive cones of spaces of continuous functions vanishing at infinity that satisfy a norm additive condition. Such maps arise naturally in the study of nonlinear functional equations and norm-preserving structures on function spaces. While in the compact (unital) case these maps can often be analyzed via linear extension tech

Gauthier Roussilhe, Béatrice Dromard, Srinjoy Mitra

The ICT sector has been one of the most successful and fastest-growing industry in history. While the environmental issue in this sector has mainly been addressed by assessing its footprint and, to a lesser extent, its avoided emissions or net impacts, the additional emissions from the digitalization of carbon-intensive activities, such as the Oil and Gas (O

Pankaj Chaturvedi, Bikram Nath

We study the dynamics of circular cosmic string loops in a spatially flat Friedmann Lema\^itre Robertson Walker universe within a fractional Polyakov framework that incorporates nonlocal memory effects. Allowing both the loop radius and polar angle to evolve, we obtain a coupled non-autonomous system governed by string tension, cosmological expansion, and an

Dechao Li, Yuhui Zhu

Given that the restricted equivalence functions (REFs) can serve to measure the similarity of two fuzzy sets, this motivates the integration of REFs with similarity-based approximate reasoning systems to enhance inference capabilities. Therefore, this work primarily constructs hierarchical similarity-based approximate reasoning (SBAR) using REFs. Specificall

Enrique Ruiz Arriola, Wojciech Broniowski

The internal structure of hadrons is characterized by form factors which correspond to matrix elements of currents. Among those, the stress-energy-momentum tensor is a universally conserved quantity providing the gravitational form factors, from which mechanical properties may be derived via the response to the space-time fluctuations. They have received muc

Alkistis Aikaterini Sigourou, Zoya Dyka, Peter Langendoerfer, Ievgen Kabin

Scalar multiplication kP is a critical operation in Elliptic Curve Cryptosystems (ECC), often targeted by Side-Channel Analysis (SCA). Despite strategies based on atomic patterns to enhance security, the binary kP algorithms remain susceptible to simple SCA due to energy consumption variations in field multipliers during passing two different or two identica

Tomasz Śmierzchalski

Quantum annealing targets low-energy solutions of Ising/QUBO problems, but reliable assessment requires more than best-energy comparisons. This dissertation develops a benchmarking framework for D-Wave quantum annealers that combines strong classical baselines, sampling and diversity metrics, and thermodynamic cost. Its first contribution, SpinGlassPEPS$.$jl

Sajjad Ahmed, Alexander Kropotov, Roberto Ignacio Genovese, Bernat Homs

The Barcelona Zetascale Lab (BZL) project aims to strengthening Europe's capacity in the design and manufacture of RISC-V based high-performance computing chips. In this context, we present a holistic pre-silicon verification and validation (V&V) methodology targeting highly robust RISC-V chip designs. This paper provides an overview of BZL's V&V approach, w

Malory Marin, Rémi Watrigant

While most classical NP-hard graph problems cannot be solved in time $2^{o(n)}$ on general graphs under the Exponential Time Hypothesis (ETH), many exhibit the square-root phenomenon and admit optimal algorithms running in time $2^{O(\sqrt{n})}$ on certain geometric intersection graphs, such as planar graphs or unit disk graphs. In 2018, de Berg et al. devel

Yiyang Peng, Zheyu Wu, Bruno Clerckx

Microwave linear analog computing (MiLAC) has recently emerged as a promising architecture for analog-domain beamforming. In particular, a hybrid digital-MiLAC architecture was proposed and was shown to achieve fully-digital beamforming flexibility in narrowband systems when the number of RF chains equals the number of data streams. However, its performance

Tony Zeng

A convex polyhedron is Rupert if a hole can be cut into it (making its genus $1$) such that an identical copy of the polyhedron can pass through the hole. Resolving a conjecture of Jerrard-Wetzel-Yuan, Steininger and Yurkevich recently constructed a convex polyhedron which is not Rupert. We propose a search for the simplest possible non-Rupert polyhedron and

Jiangtao Fan, Anish Jindal, Amir Atapour-Abarghouei

Sensor-based Human Activity Recognition (HAR) has attracted increasing attention in medical and healthcare monitoring, particularly with the growth of Internet of Medical Things (IoMT). However, in real-world wearable sensing scenarios, IoMT signals are often corrupted by missing measurements, sensor failures, and environmental noise, which significantly deg

Adrian Kent

Over the past 25 years, I have been involved in some intriguing developments in the foundations of physics, exploring the quantum reality problem, the relationship between quantum theory and gravity and the interplay between consciousness and physical laws. These investigations make it plausible that we will find physics beyond quantum theory, potentially in

Jingche Chen, Han Hong

In this paper, we prove several rigidity results for complete noncompact manifolds with nonnegative intermediate curvatures. We show that when either $3\leq n\leq 5$, $1\leq m\leq n-1$, or $6\leq n\leq 7$, $m\in \{1,n-1,n-2\}$, any manifold of the topological type $M^{n-m}\times \mathbb{T}^{m-1}\times \mathbb{R}$ with nonnegative $m$-intermediate curvature i

Nils Mandischer, Daria Eckert and, Lars Mikelsons

Human-robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their design is mostly personalized, hindering widespread use beyond the individual user. The universal design paradigm is a central pillar of inclusive design, describing usability of

Ermanno Francesco Sannini, Francesco Salzano, Simone Scalabrino, Rocco Oliveto

Smart Contracts are essential blockchain components, mainly written in Solidity. The high availability of public Solidity code leads to frequent reuse and high clone ratios. Since cloning can propagate vulnerabilities and flaws, effective detection is crucial. Although existing techniques work well in detecting syntactic clones, the identification of semanti

Zhijun Li, Minghui Xu, Huayi Qi, Wenxuan Yu

Retrieval-Augmented Generation (RAG) is essential for enhancing Large Language Models (LLMs) with external knowledge, but its reliance on cloud environments exposes sensitive data to privacy risks. Existing privacy-preserving solutions often sacrifice retrieval quality due to noise injection or only provide partial encryption. We propose PRAG, an end-to-end

Dong Xu, Mingwei Liu, Xiwen Wang, Jianfeng Zhong

Maintaining up-to-date, comprehensive documentation for large codebases is a persistent challenge. Recent progress in automated documentation has moved from template-based rules to large language models (LLMs), yet existing tools still process source code as flat fragments, producing isolated documents that lack semantic structure. This design also leads to

Mahnoor Shahid, Hannes Rothe

Large Language Model (LLM)-based agents exhibit systemic failures in compositional generalization, limiting their robustness in interactive environments. This work introduces AGEL-Comp, a neuro-symbolic AI agent architecture designed to address this challenge by grounding actions of the agent. AGEL-Comp integrates three core innovations: (1) a dynamic Causal

Mahnoor Shahid, Hannes Rothe

Compositional generalization remains a foundational weakness of modern neural networks, limiting their robustness and applicability in domains requiring out-of-distribution reasoning. A central, yet unverified, assumption in neuro-symbolic AI is that compositional reasoning will emerge as a byproduct of successful symbol grounding. This work presents the fir

Shupeng Che, Zhiqing Guo, Changtao Miao, Dan Ma

The rapid evolution of deepfake technology poses an unprecedented threat to the authenticity of Graphics Interchange Format (GIF) imagery, which serves as a representative of short-loop temporal media in social networks. However, existing proactive forensics works are designed for static images, which limits their applicability to animated GIFs. To bridge th

Yu Xing, Yang Liu, Tianyang Xue, Lin Lu

Lattice metamaterials enable lightweight, multifunctional structures, yet homogenization-based evaluation of their effective properties remains computationally expensive. Neural surrogates offer speed but often lack the accuracy and stability required for engineering-grade simulations. We introduce GMT, a Geometric Multigrid Transformer -- a neural solver wi

Achraf Ait Laydi, Sidi Mohamed Sid'El Moctar, Yousef El Mourabit, Hélène Bouvrais

Accurate quantification of the geometry of curvilinear biological structures is essential for understanding cellular mechanics and disease-related morphological alterations. Microtubule curvature is a key descriptor of filament rigidity and mechanical perturbations. However, reliable curvature extraction from fluorescence microscopy images remains challengin

Seungyub Han, Hyungjin Kim, Jungwoo Lee

Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavior. To address this, we present SAS (Self-Alignment for Safety), a transformer-based framework that enables test-time adaptation in offline safe RL without retraining. In SAS, the main mechanism is self-alignme

Alexander Kropotov, Miquel Moreto, Behzad Salami

FPGA-level emulation is a key step in pre-silicon chip design validation. However, emulating large-scale multi-core systems increasingly exceed the hardware resource capacity of a single FPGA, limiting the feasibility of full-system emulation. To address this challenge, we introduce EMiX, a scalable multi-FPGA framework that enables distributed emulation of

Benjamin Poole, David Lunt, Luke Hewitt, Chris Hardie

Grain boundary sliding is typically associated with high temperature deformation in engineering alloys. Here, we examine grain boundary sliding at room temperature in oxygen-free high-conductivity copper under quasi-static tensile testing. By using high-resolution digital image correlation (HRDIC) conducted in-situ within a scanning electron microscope to pr

Albert Zeyer, Tim Posielek, Ralf Schlüter, Hermann Ney

This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facilitate faster recognition. We provide a comprehensive comparison of techniques to integrate text-only data, including modality matching and dynamic downsampling to reach text-level representations within the enco

Gianmassimo Tasinato

We investigate the consequences of a transient phase of enhanced parity violation during inflation. Modeling this phase through a time-localized Chern--Simons-like coupling, we show that it amplifies primordial gravitational waves at small scales, producing a robust spectral shape with a blue growth of effective slope $n_T \simeq 2$, largely insensitive to m

Pei-Ling Luo, I-Yun Chen

Dual-comb spectroscopy enables broadband, high-resolution measurements with microsecond temporal resolution, but extending this capability to the 10~12.5 $\mu$m molecular fingerprint region remains technically challenging, particularly for transient radical kinetics. Here, we demonstrate microsecond-resolved dual-comb spectroscopy in this spectral range usin

Matteo Leonesi, Francesco Belardinelli, Flavio Corradini, Marco Piangerelli

Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences once monitoring is lifted. Current detection methods focus on conversational settings and rely primarily on Chain-of-Thought (CoT) analysis, which provides a reliable signal when strategic reasoning surfaces, bu

Alexander V. Savin

Numerical simulation of the dynamics of planar two- and three-layer molecular structures formed by $\beta$-sheets of polyglycine peptide chains and systems of parallel Kevlar (para-aramid) molecules placed on a graphene sheet has been performed. It is shown that in these structures the $\beta$-sheets retain their shape, due to the presence of parallel chains

Tianwei Ye, Yifan Mao, Minwen Liao, Jian Liu

Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D generative modeling has advanced rapidly, embodied applications impose requirements far beyond visual realism: generated objects must carry kinematic structure and material properties,

Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni

Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms. Conversely, fully offloading inference to the cloud is often impractical in bandwidth-limited environments, where transmitting raw visual data introduc

Ioannis Konstantoulas, Dimosthenis Tsimas, Pavlos Peppas, Kyriakos Sgarbas

Background & Objectives: In the last decade, Machine learning research has grown rapidly, but large models are reaching their soft limits demonstrating diminishing returns and still lack solid reasoning abilities. These limits could be surpassed through synergistic combination of Machine Learning scalability and rigid reasoning. Methods: In this work, we pro

Eduardo Jiménez-Fernández, Jesús Rodríguez-López, Aurora Sánchez-Martín-Orozco, Enrique A. Sánchez-Pérez

In the paper [E. Jim\'enez-Fern\'andez, J. Rodr\'{\i}guez-L\'opez, E. A. S\'anchez-P\'erez, Fuzzy Sets and Systems 406 (2021),66-81], a McShane-Whitney extension theorem is presented for real-valued fuzzy Lipschitz maps between fuzzy metric spaces. Specifically, the codomain space is considered as a so-called Euclidean fuzzy metric space $(\mathbb{R},M_{\phi

Hanna Foerster, Ilia Shumailov, Cheng Zhang, Yiren Zhao

Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static quantization, which applies quantization offline, dynamic quantization operates on tensors at run-time, adapting its parameters to the actual input data. Today's mainstream machine learning frameworks, including

Jeil Jeong, Minsung Yoon, Seokryun Choi, Heechan Shin

Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping-planning pipeline but suffer from accumulated perception errors a

Haosen Li, Wenshuo Chen, Lei Wang, Shaofeng Liang

Diffusion models have achieved remarkable success in synthesizing complex static and temporal visuals, a breakthrough largely driven by Classifier-Free Guidance (CFG). However, despite its pivotal role in aligning generated content with textual prompts, standard CFG relies on a globally uniform scalar. This homogeneous amplification traps models in a well-do

Yibo Gao, Hanlin Xu

Alternating sign matrices (ASMs) arise as the Dedekind-MacNeille completion of the Bruhat order on the symmetric group. They enjoy fruitful combinatorial and geometric properties, with a particularly rich history on enumerations and bijections. In this paper, we explicitly describe the Dedekind-MacNeille completion of the Bruhat order on any parabolic quotie

Shang-Hsuan Chiang, Tsan-Tsung Yang, An-Zi Yen, Wen-Chih Peng

Generating sports game reports from structured tables is a complex table-to-text task that demands both precise data interpretation and fluent narrative generation. Traditional model-based approaches require large, annotated datasets, while prompt-based methods using large language models (LLMs) often struggle with hallucination due to weak table comprehensi

Indrajit Jana, Sunita Rani

We study the central limit theorem (CLT) for linear eigenvalue statistics of several types of matrix models, whose entries are having exploding moments, i.e., moments of the entries are increasing with the size of the matrix. In particular, we study elliptic, centrosymmetric, circulant, and inter-correlated block matrices. The CLTs are established using asym

Julie Tzu-Yueh Wang, Zheng Xiao

This paper investigates the distribution of integral points on projective varieties via two distinct methods: the Ru-Vojta theorem and our higher-dimensional generalization of the Huang-Levin-Xiao inequalities. These approaches operate under distinct geometric conditions, specifically the transverse and proper intersections of boundary divisors. Applying thi

Yanyun Wang, Qingqing Ye, Li Liu, Zi Liang

Adversarial Training (AT) is one of the most effective methods for developing robust deep neural networks (DNNs). However, AT faces a trade-off problem between clean accuracy and adversarial robustness. In this work, we reveal a surprising phenomenon for the first time: Varying input perturbation intensities for training samples near decision boundaries in A

Oleg Solozobov

Agentic AI systems produce decision evidence at scale through execution telemetry, but property-level reconstruction often fails when an external party asks a specific governance question about a specific decision: the assembled evidence is insufficient to answer it. We name this pattern the container fallacy: the automatic equation of evidence-container pre

Masato Kamba, Hirotake Murakami, Akiyoshi Sannai

Code-driven auditing fails when correctness depends on what the specification requires rather than how the code is written. Production blockchain networks expose this directly: byzantine consensus runs many independent clients of a shared specification, so a specification-divergence defect in one client can fork the network or halt finality. Existing tools r

Aljawharah Alzahrani, Tanusree Sharma

Generative AI tools are widely used by youth and have introduced new privacy and safety challenges. While prior research has explored youths safety in GenAI within a Western context, it often overlooks the cultural, religious, and social dimensions of technology use that strongly shape youths digital experiences in countries like Saudi Arabia. To address thi

Andriy Enttsel, Vincent Corlay

Visual data compression is shifting from human-centered reconstruction to machine-oriented representation coding. In this setting, an image is often mapped to a compact semantic embedding, which is then compressed and transmitted for downstream inference. We propose an adaptive transform-coding method for semantic-feature compression motivated by the conditi

Weijia Sun, Cristina Chiappini, Samir Nepal

Large spectroscopic surveys rely on automated pipelines to deliver homogeneous stellar labels, but a substantial fraction of observations are at low signal-to-noise ratio (S/N), where label estimates become imprecise or are omitted. In APOGEE, these low-S/N spectra visits sample faint and distant populations -- the bulge, outer halo, and satellite systems --

Adam Białożyt, Dominik Bysiewicz, Maciej P. Denkowski

The medial axis $M_X$ of a closed set $X\subset \mathbb{R}^n$ is the set of points from the ambient space that admit more than one closest point in $X$. We study the problem of reaching the singularities, i.e. of characterising the points of the set $\overline{M_X}\cap X$. In order to tame the geometry, we assume that $X$ is definable in a polynomially bound

Jiancheng Wang, Mingjia Yin, Hao Wang, Enhong Chen

DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that DNNs possess the ability to implicitly capture high-order feature interactions. Conversely, recent studies have highlighted the limitations of DNNs in effectively learning dot prod

Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki, David Joseph Tan

One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack representations that effectively embed spatio-temporal properties of visual scenes at the pixel level. Existing frameworks either train on image-based pretext tasks, which do not account for dynamic elements,

Anton Badev, Martin Pavlov, Boyan Zlatanov

We analyze a canonical extension of the Stackelberg duopoly to a sequential framework, where each firm strategically anticipates the reactions of all subsequent players. In a triopoly (three-firm) settings, we obtain existence and uniqueness of market equilibrium via a reformulation of the equilibrium conditions that draws on coupled fixed-point theory. Even

T. L. Steyn, A. Panchal, O. Vasilovici, F. M. Herrmann

Achieving high-quality electron beams from laser wakefield accelerators critically relies on density tailoring to control electron dynamics during injection, acceleration, and extraction. We report on the experimental observation of electron beam acceleration and shaping, in transverse momentum and longitudinal phase space, controlled by plasma density tailo

Xue-Ying Han, Hao-Fei Gao, Jun Hua, Xiangdong Ji

Heavy meson HQET light-cone distribution amplitudes (LCDAs) are critical for precision predictions of $B$ meson weak decays, but currently are one of dominant theoretical uncertainties that obscure interpretations of $B$ anomalies and CP-violating measurements. Building on the established HQLaMET framework, supplemented by lattice QCD calculations of the OPE

Lili Du, Xu Tang, Yi Zhou

In the previous work [Interfaces Free Bound., 19, 351--369, 2017], de Queiroz and Shahgholian established the optimal $C^{1,\log}_{\mathrm{loc}}$ regularity of solutions for the obstacle problem with singular logarithmic forcing term $$-\Delta u = \log u\,\chi_{\{u>0\}} \quad \text{in } \Omega,$$ where $\Omega\subset\mathbb{R}^d$ ($d\geq 2$) is a smooth boun

Yongshen Zhang, Xin Liu, Nachuan Xiao, Chunming Tang

In this paper, we consider a class of generalized orthogonal optimization constraint problems (GOOCP) over $\mathbb{R}^{n \times p}$, where the variable $X$ is restricted within the intersection of a certain subspace $\mathcal{F}$ and satisfies the quadratic constraint $\{X \in \mathbb{R}^{n \times p}: X^{\top} \phi(X) = I_p\}$. Such constraints generalize a

Hervé Déjean, Stéphane Clinchant

Reranking, the process of refining the output from a first-stage retriever, is often considered computationally expensive, especially when using Large Language Models (LLMs). A common approach to mitigate this cost involves utilizing smaller LLMs or controlling input length. Inspired by recent advances in document compression for retrieval-augmented generati

David Klotz

Advances in generative artificial intelligence, particularly agentic coding systems capable of autonomous software development, are disrupting the economics of the make-or-buy decision for enterprise applications. The "SaaSocalypse" narrative predicts that AI will render large segments of the Software-as-a-Service market obsolete by enabling firms to build s

Rohan Kaushik, Anna Schwarz, Andrea Beck

Reinforcement learning has by now become well established in finding excellent flow control strategies for a variety of scenarios. Existing literature has focused on using a simple two-jet solution (and variants there-of) or a straightforward mean-centered multi-jet setup. This mean-centering approach is however non-injective in nature, such that distinct ac

Chunxiang Wu, Shuijin Chen, Tingyu Zhou, Le Liu

After growing successfully high quality VP$_2$ single crystals, we studied systematically their longitudinal $\rho_{xx}(T)$ and Hall resistivity $\rho_{yx}(T)$ at various magnetic fields, combining the electronic band and Fermi surface (FS) calculations. Band calculations reveal that VP$_2$ is a type-II nodal-line semimetal, evidenced by the Hall resistivity