October 2025 arXiv papers — page 67

Prakrut Kotecha, Ganga Nair B, Shishir Kolathaya

Model-free reinforcement learning (RL) has enabled adaptable and agile quadruped locomotion; however, policies often converge to a single gait, leading to suboptimal performance. Traditionally, Model Predictive Control (MPC) has been extensively used to obtain task-specific optimal policies but lacks the ability to adapt to varying environments. To address t

Jessica Santiago, Kerkyra Asvesta, Maria Giovanna Dainotti, Pisin Chen

We present a new analysis of cosmic dipole anisotropy using gamma-ray bursts (GRBs) as high-redshift standardizable candles. GRBs are ideal probes for testing the cosmological principle thanks to their high luminosity, wide redshift range, and nearly isotropic sky coverage. For the first time, we employ the luminosity-time (L-T) relation, known in the litera

Dennis Barak, Beau F. Harrison, Adam Watts

This book begins with the basic accelerator knowledge required to understand the latter chapters. This is followed by topics from Fermilab accelerator specifics to general accelerator physics concepts. These chapters are accompanied by descriptions of the instrumentation and utilities required to operate a high-energy physics laboratory. Last, we discuss the

Gabriele Parisi, Vincenzo Nugara, Salvatore Plumari, Vincenzo Greco

The determination of the shear viscosity is a central topic in various areas of modern physics. In particular, it is often necessary to evaluate the shear viscosity $\eta$ of fluids made up of more than one species, all interacting with different cross sections. Since it may be difficult to extract information on the interaction among different species, vari

Felipe Avencourt Soares, Muriel F. Franco, Eder J. Scheid, Lisandro Z. Granville

Generative Artificial Intelligence (AI) tools have been used to generate human-like content across multiple domains (e.g., sound, image, text, and programming). However, their reliability in terms of correctness and functionality in novel contexts such as programmable networks remains unclear. Hence, this paper presents an empirical evaluation of the source

Marco Cappiello, Eliakim Cleyton Machado

We study the Cauchy problem for a class of linear evolution equations of arbitrary order with coefficients depending both on time and space variables. Under suitable decay assumptions on the coefficients of the lower order terms for $|x|$ large, we prove a well-posedness result in Gelfand-Shilov spaces.

Marco Hoffmann, Shaohai Chen, Gunasheel Kauwtilyaa Krishnaswamy, Hang Khume Tan

Voltage control of magnetic anisotropy (VCMA) induced by charge accumulation is typically considered as an ultrafast process, enabling energy-efficient and high-speed magnetization switching in spintronic devices. In this work, we investigate the real-time dynamics of VCMA-assisted switching of magnetic tunnel junctions via relaxation in a magnetic field. We

Bryan Eikema, Anna Rutkiewicz, Mario Giulianelli

Minimum Bayes Risk (MBR) decoding has seen renewed interest as an alternative to traditional generation strategies. While MBR has proven effective in machine translation, where the variability of a language model's outcome space is naturally constrained, it may face challenges in more open-ended tasks such as dialogue or instruction-following. We hypothesise

Yaxuan Kong, Yoontae Hwang, Marcus Kaiser, Chris Vryonides

We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain: (i) aligning and fusing heterogeneous data modalities, nume

Salima Jaoua, Nicolò Pagan, Anikó Hannák, Stefania Ionescu

Social media platforms provide millions of professional content creators with sustainable incomes. Their income is largely influenced by their number of views and followers, which in turn depends on the platform's recommender system (RS). So, as with regular jobs, it is important to ensure that RSs distribute revenue in a fair way. For example, prior wor

W. J. Pearson, L. Wang, V. Rodriguez-Gomez, B. Margalef-Bentabol

Galaxy mergers can change the rate at which stars are formed. We can trace when these changes occur in simulations of galaxy mergers. However, for observed galaxies we do not know how the star formation rate (SFR) evolves along the merger sequence as it is difficult to probe the time before or after coalescence. We aim to derive how SFR changes in observed m

Jing Bi, Guangyu Sun, Ali Vosoughi, Chen Chen

Multimodal large language models (MLLMs) that integrate visual and textual reasoning leverage chain-of-thought (CoT) prompting to tackle complex visual tasks, yet continue to exhibit visual hallucinations and an over-reliance on textual priors. We present a systematic diagnosis of state-of-the-art vision-language models using a three-stage evaluation framewo

Emmanuel Pio Pastore, Francesco De Rango

We introduce a validity-filtered, certificate-based view of reactions grounded in Algorithmic Information Theory. A fixed, total, input-blind executor maps a self-delimiting advice string to a candidate response, accepted only if a decidable or semi-decidable validity predicate V(x, r) holds. The minimum feasible realizer complexity M(x) = min_{r: V(x,r)=1}

Galina Weinstein

This paper completes a three-part study of Einstein's 1905 special relativity by reconstructing the experimental pressures that shaped his thinking from 1895 to June 1905. Following Stachel's historiographical line, I trace Einstein's path under the cumulative weight of a series of recalcitrant experiments: stellar aberration, Arago's prism test, Fresnel's p

Julio Parra-Martinez, Alessandro Podo

We provide a symmetry argument for the vanishing and non-renormalization of static Love numbers for spherically symmetric black holes at full nonlinear order in four-dimensional General Relativity. The symmetry is realized both in full GR and in the worldline EFT, allowing for a unified treatment and proving both vanishing and non-renormalization to all orde

Gustavo Madeira, Bruno Morgado, Chrystian Pereira, Giovana Ramon

A recent stellar occultation revealed that the Centaur (2060) Chiron hosts a broad disk extending beyond ~200 km from its centre, embedding three ring-like structures (Chi1R, Chi2R, and Chi3R), while a tenuous outer ring (Chi4R) lies beyond the Roche limit. Here, we present a first dynamical assessment of the system's stability through numerical simulations

Yanlin Song, Ben Liu, Víctor Gutiérrez-Basulto, Zhiwei Hu

Knowledge Graph Question Answering aims to answer natural language questions by reasoning over structured knowledge graphs. While large language models have advanced KGQA through their strong reasoning capabilities, existing methods continue to struggle to fully exploit both the rich knowledge encoded in KGs and the reasoning capabilities of LLMs, particular

Darij Grinberg

In this expository paper, various properties of matrix traces, determinants and adjugate matrices are proved, including the *trace Cayley-Hamilton theorem*, which says that \[ kc_k + \sum_{i=1}^k \operatorname{Tr} (A^i) c_{k-i} = 0 \qquad \text{for every } k\in\mathbb{N} \] whenever $A$ is an $n\times n$-matrix with characteristic polynomial $\det (tI_n - A)

Oliver Braunsdorf, Tim Lange, Konrad Hohentanner, Julian Horsch

Unsafe Rust code is necessary for interoperability with C/C++ libraries and implementing low-level data structures, but it can cause memory safety violations in otherwise memory-safe Rust programs. Sanitizers can catch such memory errors at runtime, but introduce many unnecessary checks even for memory accesses guaranteed safe by the Rust type system. We int

Rong-Lan Li, Hao-Ning He, Da-Ming Wei

The Large High Altitude Air Shower Observatory (LHAASO) has detected ultra-high-energy (UHE; E>100 TeV) gamma-ray emission from five microquasars, suggesting their potential as Galactic PeV cosmic-ray accelerators. At these energies, the Klein-Nishima effect strongly suppresses leptonic processes, making neutrinos observation a crucial test for hadronic acce

Manuel Zechmann, Helmut Hlavacs

This paper presents the development of two distinct real-time procedural planet generators within the Godot engine: one employing Fractal Brownian Motion (FBM) with Perlin Noise, and another adapting Minecraft-inspired layered noise techniques. We detail their implementation, including a quadtree-based Level of Detail (LOD) system and solutions for planetary

Ming-Ming Yu, Fei Zhu, Wenzhuo Liu, Yirong Yang

Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of object categories during training, overlooking the real-world requirement for continual adaptation to evolving scenarios. To facilitate related studies, we introduce the continual

Funani Sinethemba, Ndiweni Odilo, Nkonkobe Sithembele

Given an ordered set partition, when one insert a number of bars in-between the blocks of the ordered set partition the result is a barred preferential arrangement. In this study, using the notion of barred preferential arrangements we propose a combinatorial interpretation of a type of generalized Bell polynomials. We also define a new higher order generali

Georgios Mentzelopoulos, Ioannis Asmanis, Konrad P. Kording, Eva L. Dyer

Brain-computer interfaces (BCIs) promise to enable vital functions, such as speech and prosthetic control, for individuals with neuromotor impairments. Central to their success are neural decoders, models that map neural activity to intended behavior. Current learning-based decoding approaches fall into two classes: simple, causal models that lack generaliza

Thomas Bernard, François Grondin, Jean-Michel Lavoie

This paper presents the design and deployment of a process-aware cyber-physical system that automates plant-level logistics, traceability, and sanitation in a centralized maple-syrup boiling center. The system replaces ad-hoc, manual operations with event-driven orchestration on a local server, employing reusable device abstractions and a centralized interlo

Xinhe Mu, Zhaoqi Zhou, Zaijiu Shang, Chuan Zhou

Learned cardinality estimation requires accurate model designs to capture the local characteristics of probability distributions. However, existing models may fail to accurately capture complex, multilateral dependencies between attributes. Diffusion models, meanwhile, can succeed in estimating image distributions with thousands of dimensions, making them pr

Chintan Patel, Sayantan Bhattacharya, Karan Akbari, Sudip Bhattacharyya

We present a timing analysis of the black hole X-ray binary (BHXRB) 4U 1630-47 using AstroSat observations from 10-19 March 2023, for the first time capturing a rare and rapid transition in variability properties. Within less than a day, the source evolved from a type-C quasi-periodic oscillation (QPO) state, with centroid frequencies between 3-5 Hz, to the

Kuntal Samanta, Sphinx J. Svensson, Sonja Franke-Arnold, Niclas Westerberg

Features of complex vector light become important in any interference effects, including scattering, diffraction, and non-linear processes. Here we are investigating the role of polarization-structured light in atomic state interferometers. Unlike optical or atomic path interferometers, these facilitate local interference between atomic transition amplitudes

Jonas Klauke, Tom Ohlmer, Stefan Schott, Serena Elisa Ponta

Modern software development reuses code by importing libraries as dependencies. Software projects typically include an average of 36 dependencies, with 80% being transitive, meaning they are dependencies of dependencies. Recent research indicates that only 24.9% of these dependencies are required at runtime, and even within those, many program constructs rem

Junjie Zheng, Gongyu Chen, Chaofan Ding, Zihao Chen

In real-world singing voice conversion (SVC) applications, environmental noise and the demand for expressive output pose significant challenges. Conventional methods, however, are typically designed without accounting for real deployment scenarios, as both training and inference usually rely on clean data. This mismatch hinders practical use, given the inevi

Franziska Zeuner, Luca Belluzzi, Ernest Alsina Ballester, Roberto Casini

Scattering polarization signals offer a unique diagnostics of the physical conditions in the solar atmosphere, in particular magnetic fields via the Hanle effect. However, their spatial structure remains poorly constrained due to the difficulty of achieving high spatial resolution and polarimetric sensitivity simultaneously. We present the first direct obser

Tommaso Grigoletto

This paper introduces a novel method for approximating the dynamics of a large autonomous system projected onto a fixed subspace. The core contribution is a novel recursive algorithm to construct an effective time-dependent generator that is polynomial in the time variable, ensuring accuracy for short time scales. The derivation is based on the Taylor expans

Rakshith R, Shubham Sharma, Mohammed Sameer Khan, Ankush Chopra

This study presents the multilingual e-commerce search system developed by the Tredence_AICOE team. The competition features two multilingual relevance tasks: Query-Category (QC) Relevance, which evaluates how well a user's search query aligns with a product category, and Query-Item (QI) Relevance, which measures the match between a multilingual search query

Jinhee Kim, Jae Jun An, Kang Eun Jeon, Jong Hwan Ko

Multi-bit quantization networks enable flexible deployment of deep neural networks by supporting multiple precision levels within a single model. However, existing approaches suffer from significant training overhead as full-dataset updates are repeated for each supported bit-width, resulting in a cost that scales linearly with the number of precisions. Addi

Rundong Fang, Ji-Heng Guo, Jia Liu, Xiao-Ping Wang

We investigate long-range, purely leptophilic axial-vector interactions mediated by a light gauge boson $A'$ that couples to charged leptons and, by weak symmetry, to left-handed neutrinos. We analyze two realizations, a minimal effective model with muon-only couplings and an anomaly-free axial $U(1)'$ with inter-generation cancellations. In both cases, the

Subham Kumar, Lekhansh Shukla, Animesh Mukherjee, Koustav Rudra

Determining the appropriate locus of care for addiction patients is one of the most critical clinical decisions that affects patient treatment outcomes and effective use of resources. With a lack of sufficient specialized treatment resources, such as inpatient beds or staff, there is an unmet need to develop an automated framework for the same. Current decis

Junghyun Min, York Hay Ng, Sophia Chan, Helena Shunhua Zhao

Cantonese, although spoken by millions, remains under-resourced due to policy and diglossia. To address this scarcity of evaluation frameworks for Cantonese, we introduce \textsc{\textbf{CantoNLU}}, a benchmark for Cantonese natural language understanding (NLU). This novel benchmark spans seven tasks covering syntax and semantics, including word sense disamb

Debojyoti Ghosh, Adrijit Goswami

Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid de

Jinbin Bai, Yu Lei, Hecong Wu, Yuchen Zhu

This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model". Instead, we follow one clear road: from early masked models that unified representation learning across modalities, to unified architectures that share a single paradigm, then to intera

Maria Sanz-Paz, Nicole Siegel, Guillermo Serrera, Javier Gonzalez-Colsa

Single-photon emitters radiate as electric dipoles, which limits light collection efficiency and complicates integration into flat photonic devices. Developing nanophotonic structures capable of directing photon emission with tunable angular distributions in the visible spectrum has been pursued for applications ranging from integrated optical systems to dis

Mariona Jaramillo-Civill, Luis González-Gudiño, Tales Imbiriba, Pau Closas

Global Navigation Satellite System (GNSS) signals are vulnerable to jamming, particularly in urban areas where multipath and shadowing distort received power. Previous data-driven approaches achieved reasonable localization but poorly reconstructed the received signal strength (RSS) field due to limited spatial context. We propose a hybrid Bayesian mixture-o

Sara Porras-Bedmar, Manuel Meyer

The flux of extragalactic gamma rays is attenuated through interactions with optical and infrared photons of the extragalactic background light (EBL). The EBL is an isotropic, diffuse photon field that is difficult to measure directly at these wavelengths due to strong foreground emission. We present niebla, the first open-source code to compute the EBL from

Yu-tin Huang, Chia-Kai Kuo, Chi Zhang

In this work, we analyze the infrared divergence of two-loop amplitudes at arbitrary multiplicity in three-dimensional $\mathcal{N}=6$ Chern-Simons matter theory. We introduce the Bern-Dixon-Smirnov (BDS) integrand, which captures the full infrared structure while remaining free of unphysical cuts. We show that these local integrands, together with their kin

Zhengwei Liu, Zishuo Zhao

We establish a framework with reflection positivity as the first principle for establishing the boundary theory of topologically ordered quantum spin systems. For any reflection positive frustration-free Hamiltonian, We proved that the local topological quantum order (LTQO) condition of ground states on a disk holds if and only if the ground state on the sph

Chen Zhao, En Ci, Yunzhe Xu, Tiehan Fan

Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain : 1) the absence of a large-scale high-quality UHR T2I dataset, and (2) the neglect of tailored training strategies for fine-grained detail synthesis in UHR scenarios. To tackle the first challenge, we introduce \textbf{UltraHR-100K}, a hi

Shiqiu Zheng

In this paper, we prove that under the domination condition: \begin{equation*} {\cal{E}}^{-\mu,-\nu}[-\xi|{\cal{F}}_t]\leq\rho_t(\xi)\leq{\cal{E}}^{\mu,\nu}[-\xi|{\cal{F}}_t],\quad \forall\xi\in \mathcal{L}^{\exp}_T\ (\text{resp.}\ L^2(\mathcal{F}_T)),\ \forall t\in[0,T], \end{equation*} where ${\cal{E}}^{\mu,\nu}$ is the $g$-expectation with generator $\mu|

Ho Jang, Yang Yang, Gia-Wei Chern

We present a machine learning (ML) force-field framework for simulating the non-equilibrium dynamics of charge-density-wave (CDW) order driven by the Peierls instability. Since the Peierls distortion arises from the coupling between lattice displacements and itinerant electrons, evaluating the adiabatic forces during time evolution is computationally intensi

Guangze Gu, Michael Ruzhansky, Guoyan Wei, Zhipeng Yang

In this paper, we investigate the nonrelativistic limit and qualitative properties of bound-state solutions for the nonlinear Dirac equation (NLDE) defined on noncompact quantum graphs: \[ -i c \frac{d}{d x} \sigma_1 \psi+m c^2 \sigma_3 \psi-\omega \psi=g(|\psi|) \psi, \quad \text { in } \mathcal{G} \] where \( g : \mathbb{R}\rightarrow\mathbb{R} \) is a con

Rubens Kim, Stephan Carney, Yvonne Fonken, Soham Hans

We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cybersecurity professionals who conducted red-team operations against a simulated enterprise network. We employed multilevel mixed-effects Poisson regression with technique counts nested

Tobias Hartnick, Filippo Sarti

We establish an induction isomorphism in the context of measurable bounded cohomology of discrete measured groupoid, which generalizes the Eckmann-Shapiro isomorphism in bounded cohomology of lattices due to Burger and Monod. In our wider setting, the role of lattices is taken by the class of transverse measured groupoids $(\mathcal{G}, \nu)$ associated with

Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez, Galen T. Craven

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic

Ross G. Pinsky, Dominic T. Schickentanz

In the first part of the paper, we study the inversion statistic of random permutations under the family $(\mathbb{P}_\theta^{(n)})_{\theta \ge 0}$ of Ewens sampling distributions on $S_n$. We obtain a rather simple exact formula for the expected number of inversions under $\mathbb{P}_\theta^{(n)}$. In particular, we show that this expected number of inversi

Jack Butler, Nikita Kozodoi, Zainab Afolabi, Brian Tyacke

As Large Language Models (LLMs) continue to evolve, practitioners face increasing options for enhancing inference-time performance without model retraining, including budget tuning and multi-step techniques like self-reflection. While these methods improve output quality, they create complex trade-offs among accuracy, cost, and latency that remain poorly und

Rolf-Peter Kudritzki, Fabio Bresolin, Miguel A. Urbaneka, Eva Sextl

Blue supergiant distances of nearby galaxies obtained with the flux-weighted gravity-luminosity relationship are used for a measurement of the zero points of Tully-Fisher relationships at different photometric passbands. The Cousins I-band and the infrared WISE bands W1 and W2 are investigated. The results are compared with previous work using Cepheid and Ti

Quan Li, Wenchao Yu, Suhang Wang, Minhua Lin

Extreme events frequently occur in real-world time series and often carry significant practical implications. In domains such as climate and healthcare, these events, such as floods, heatwaves, or acute medical episodes, can lead to serious consequences. Accurate forecasting of such events is therefore of substantial importance. Most existing time series for

Santanu S. Dey, Dahye Han, Yang Wang

For mixed-integer programs (MIPs), strong branching is a highly effective variable selection method to reduce the number of nodes in the branch-and-bound algorithm. Extending it to nonlinear problems is conceptually simple but practically limited. Branching on a binary variable fixes the variable to 0 or 1, whereas branching on a continuous variable requires

Francesco Di Filippo

It is common knowledge that black holes necessarily contain a region where general relativity breaks down, due to the inevitable formation of either a curvature singularity or a Cauchy horizon. In this work we challenge this view by analyzing a charged spherically symmetric black hole formed through gravitational collapse and evaporating via Hawking radiatio

Payman Eskandari, Kumar Murty, Yusuke Nemoto

We first give a geometric construction of a 2-dimensional mixed motive over $\mathbb{Q}$ with the Catalan constant $\mathbf{G}=1-1/3^2+1/5^2-1/7^2+\cdots$ as a period. We then use this motive to obtain a supply of linear forms in 1 and $\mathbf{G}$. We also explicitly compute the coefficients of 1 and $\mathbf{G}$ in these linear forms.

Alan Saji, Raj Dabre, Anoop Kunchukuttan, Ratish Puduppully

Large Reasoning Models (LRMs) achieve strong performance on mathematical, scientific, and other question-answering tasks, but their multilingual reasoning abilities remain underexplored. When presented with non-English questions, LRMs often default to reasoning in English, raising concerns about interpretability and the handling of linguistic and cultural nu

Andrea Bianchi, Kaif Hilman, Dominik Kirstein, Christian Kremer

We introduce a notion of Poincar\'e duality for pairs of $\infty$-categories, extending Poincar\'e-Lefschetz duality for pairs of spaces. This categorical extension yields an efficient book-keeping device that affords, among other things, a uniform treatment of Wall's Poincar\'e ads of spaces, iterated Poincar\'e cobordisms, and in general, diagrams of space

Nitin Awathare

Despite the popularity of Hashed Time-Locked Contracts (HTLCs) because of their use in wide areas of applications such as payment channels, atomic swaps, etc, their use in exchange is still questionable. This is because of its incentive incompatibility and susceptibility to bribery attacks. State-of-the-art solutions such as MAD-HTLC (Oakland'21) and He-HTLC

Reuben Dorent, Polina Golland, William Wells

Mutual Information (MI) is a fundamental measure of statistical dependence widely used in representation learning. While direct optimization of MI via its definition as a Kullback-Leibler divergence (KLD) is often intractable, many recent methods have instead maximized alternative dependence measures, most notably, the Jensen-Shannon divergence (JSD) between

Longchen Niu, Gennaro Notomista

In this paper, we introduce a decentralized optimization-based density controller designed to enforce set invariance constraints in multi-robot systems. By designing a decentralized control barrier function, we derived sufficient conditions under which local safety constraints guarantee global safety. We account for localization and motion noise explicitly b

Karel Van Bockstal, Khonatbek Khompysh, Arshyn Altybay

In this paper, we investigate the inverse problem of determining an unknown time-dependent source term in a semilinear pseudo-parabolic equation with variable coefficients and a Dirichlet boundary condition. The unknown source term is recovered from additional measurement data expressed as a weighted spatial average of the solution. By employing Rothe's time

Andrea Agiollo, Andrea Omicini

Thanks to the remarkable human-like capabilities of machine learning (ML) models in perceptual and cognitive tasks, frameworks integrating ML within rational agent architectures are gaining traction. Yet, the landscape remains fragmented and incoherent, often focusing on embedding ML into generic agent containers while overlooking the expressive power of rat

Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit, Hadrien Reynaud

Recent progress in vision-language modeling for 3D medical imaging has been fueled by large-scale computed tomography (CT) corpora with paired free-text reports, stronger architectures, and powerful pretrained models. This has enabled applications such as automated report generation and text-conditioned 3D image synthesis. Yet, current approaches struggle wi

Kseniia Storozheva, Anastasia Markina, Vladik Avetisov

Molecular-scale materials with bistable behavior and tunable properties are increasingly relevant for next-generation nanoscale electronic devices. Helical foldamers are promising candidates, but their structural and mechanical properties are highly sensitive to conformational stability and environmental conditions. A systematic methodology based on quantum-

Hyun Jong Yang, Hyunsoo Kim, Hyeonho Noh, Seungnyun Kim

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this a

Eric Ngoiya, Tianshu Bao

This paper introduces the Fluidity Index (FI) to quantify model adaptability in dynamic, scaling environments. The benchmark evaluates response accuracy based on deviations in initial, current, and future environment states, assessing context switching and continuity. We distinguish between closed-ended and open-ended benchmarks, prioritizing closed-loop ope

Haoyu Wang, Sihang Jiang, Yuyan Chen, Xiaojun Meng

Curiosity serves as a pivotal conduit for human beings to discover and learn new knowledge. Recent advancements of large language models (LLMs) in natural language processing have sparked discussions regarding whether these models possess capability of curiosity-driven learning akin to humans. In this paper, starting from the human curiosity assessment quest

Zhenhuan Zhou, Jingbo Zhu, Yuchen Zhang, Xiaohang Guan

Efficient analysis and processing of dental images are crucial for dentists to achieve accurate diagnosis and optimal treatment planning. However, dental imaging inherently poses several challenges, such as low contrast, metallic artifacts, and variations in projection angles. Combined with the subjectivity arising from differences in clinicians' expertise,

Satoshi Nishimoto

We present a systematic study of multi-magnon bound states (MBSs) in the spin-$\tfrac{1}{2}$ FM-AFM $J_1$-$J_2$ chain under magnetic fields using the density-matrix renormalization group method. As a quantitative measure of stability, we compute the magnon binding energy $E_{\rm b}(M,p)$ for bound clusters of size $p$ over wide ranges of the frustration rati

Shuyi Xie, Ziqin Liew, Hailing Zhang, Haibo Zhang

Large Language Models (LLMs) excel on general-purpose NLP benchmarks, yet their capabilities in specialized domains remain underexplored. In e-commerce, existing evaluations-such as EcomInstruct, ChineseEcomQA, eCeLLM, and Shopping MMLU-suffer from limited task diversity (e.g., lacking product guidance and after-sales issues), limited task modalities (e.g.,

Kuntal Som, Thirumulanathan D, Joydeep Dutta

Bilevel programming is one of the very active areas of research with many real-life applications in economics and engineering. Bilevel problems are hierarchical problems consisting of lower-level and upper-level problems, respectively. The leader or the decision-maker for the upper-level problem decides first, and then the follower or the lower-level decisio

Lucy Xing, Sanjay Vishwakarma, David Kremer, Francisco Martin-Fernandez

This paper explores the application of machine learning (ML) techniques in predicting the QPU processing time of quantum jobs. By leveraging ML algorithms, this study introduces predictive models that are designed to enhance operational efficiency in quantum computing systems. Using a dataset of about 150,000 jobs that follow the IBM Quantum schema, we emplo

Mingxuan Liu, Yilin Ning, Haoyuan Wang, Chuan Hong

As machine learning models become increasingly integrated into healthcare, structural inequities and social biases embedded in clinical data can be perpetuated or even amplified by data-driven models. In survival analysis, censoring and time dynamics can further add complexity to fair model development. Additionally, algorithmic fairness approaches often ove

Samuel Sebastian, Luca Comisso

We employ first-principles, fully kinetic particle-in-cell simulations to investigate magnetic field-line curvature in magnetically dominated turbulent plasmas and its role in particle acceleration through curvature-drift motion along the motional electric field. By varying the fluctuation-to-mean magnetic-field ratio $\delta B_0/B_0$, we examine curvature $

Lukas Miklautz, Chengzhi Shi, Andrii Shkabrii, Theodoros Thirimachos Davarakis

We introduce H-SPLID, a novel algorithm for learning salient feature representations through the explicit decomposition of salient and non-salient features into separate spaces. We show that H-SPLID promotes learning low-dimensional, task-relevant features. We prove that the expected prediction deviation under input perturbations is upper-bounded by the dime

Philippe G. LeFloch, Yue Ma, Weidong Zhang

We consider the Einstein-Dirac system for a massive field, which describes the evolution of self-gravitating massive spinor fields, and we investigate the global evolution problem, when the initial data set is sufficiently close to data describing a spacelike, asymptotically Euclidean slice of the Minkowski spacetime. We establish the gauge-invariant nonline

Georgios Vacalis, Atsushi Higuchi, Robert Bingham, Gianluca Gregori

The axion is a hypothetical particle associated with a possible solution to the strong CP problem and is a leading candidate for dark matter. In this paper we investigate the emission of axions by accelerated electrons. We find the emission probability and energy within the WKB approximation for an electron accelerated by an electromagnetic field. As an appl

Emily K. Roberts, Pier-Emmanuel Tremblay, Antoine Bédard

Thanks to Gaia and large-scale spectroscopic follow-up surveys (4MOST, DESI, WEAVE, SDSS-V), it is now possible to build representative and minimally biased samples of the local white dwarf population. Here we analyse several volume-limited 100pc samples of white dwarfs, constructed from different surveys and studies, to evaluate their completeness and resid

Federico Cianci, Bernd Schmidt

This paper aims to study the convergence of solutions in three-dimensional nonlinear elastodynamics for a thin rod as its cross section shrinks to zero for displacements that are comparable to the small radius of the rod. Assuming the existence of solutions and proper control of the torsional velocity, we show how these converge to the solutions of an effect

Yuan Sheng, Yanbin Hao, Chenxu Li, Shuo Wang

Long video understanding remains challenging due to its complex, diverse, and temporally scattered content. Although video large language models (Video-LLMs) can process videos lasting tens of minutes, applying them to truly long sequences is computationally prohibitive and often leads to unfocused or inconsistent reasoning. A promising solution is to select

Riccardo Guidotti, Martina Cinquini, Marta Marchiori Manerba, Mattia Setzu

Interpretable-by-design models are crucial for fostering trust, accountability, and safe adoption of automated decision-making models in real-world applications. In this paper we formalize the ground for the MIMOSA (Mining Interpretable Models explOiting Sophisticated Algorithms) framework, a comprehensive methodology for generating predictive models that ba

Emily L. Tucker, Mohammadhossein Mohammadisiahroudi

Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers na

Shu-Hao Zhang, Wei-Cheng Tang, Chen Wu, Peng Hu

Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, we propose the TernaryCLIP, a lightweight computational framework that converts connection weights of both vision and text encoders of CLIP into the ternary format, instead of full-

Timo Reis, Nathanael Skrepek

Building on the recently published work "Modeling of radiating curved cables via coupled telegrapher's and Maxwell's equations", which introduces a model for the interaction between electromagnetic fields and radiating (possibly curved) cables, we analyze the qualitative properties of the resulting dynamical system. The model features inputs and outputs give

Jesús Fernández, Loïc Vanel, Antoine Bérut

This study demonstrates that the free-surface flow dynamics of dense piles of contactless silica microparticles depend on the resting period prior to flow. Microfluidic rotating drum experiments reveal that longer resting times lead to delayed flow onsets and reduced flow velocities, both evolving logarithmically with the resting time. These aging-like effec

Dana Naderi, Christian P Robert, Kaniav Kamary, Darren Wraith

Efficient Bayesian model selection relies on the model evidence or marginal likelihood, whose computation often requires evaluating an intractable integral. The harmonic mean estimator (HME) has long been a standard method of approximating the evidence. While computationally simple, the version introduced by Newton and Raftery (1994) potentially suffers from

Piotr Nowakowski, Franciszek Prus-Wiśniowski

We prove that the boundary of every multigeometric Cantorval is a null set, and extend this result to a larger class of standard achievable Cantorvals. In addition, we discuss the sets of uniqueness of achievement sets and show that they always belong to the Borel class $\mathcal{G}_\delta$.

Aki Rehn, Linzh Zhao, Mikko A. Heikkilä, Antti Honkela

Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for training large models under privacy constraints. We focus on two key hyperparameters in this setting: the clipping bound $C$ and batch size $B$. We show a clear mismatch between the current theoretical understandin

Yang Han, Pengyu Wang, Kai Yu, Xin Chen

Mass spectrometry (MS) plays a critical role in molecular identification, significantly advancing scientific discovery. However, structure elucidation from MS data remains challenging due to the scarcity of annotated spectra. While large-scale pretraining has proven effective in addressing data scarcity in other domains, applying this paradigm to mass spectr

David Landa-Marbán, Tor Harald Sandve, Jakub Wiktor Both, Jan Martin Nordbotten

We present a history matching (HM) workflow applied to the International FluidFlower benchmark study dataset, which features high-resolution images of CO$_2$ storage in a meter-scale, geologically complex reservoir. The dataset provides dense spatial and temporal observations of fluid displacement, offering a rare opportunity to validate and enhance HM techn

Cameron Howat, Robert Laugwitz, Martin Ray

Generalised Temperley-Lieb categories with regions labelled by elements of a commutative algebra were introduced by M. Khovanov and the second author in [Pure Appl. Math. Q. 19 (2023), no. 5]. We consider the case where the regions are labelled by colours, corresponding to a complete set of orthogonal idempotents of a semisimple commutative algebra. We deter

Wenjun Cao

Large Language Models are increasingly adopted as critical tools for accelerating innovation. This paper identifies and formalizes a systemic risk inherent in this paradigm: \textbf{Black Box Absorption}. We define this as the process by which the opaque internal architectures of LLM platforms, often operated by large-scale service providers, can internalize

Mirza Raquib, Niloy Das, Farida Siddiqi Prity, Arafath Al Fahim

Breast cancer is considered the most critical and frequently diagnosed cancer in women worldwide, leading to an increase in cancer-related mortality. Early and accurate detection is crucial as it can help mitigate possible threats while improving survival rates. In terms of prediction, conventional diagnostic methods are often limited by variability, cost, a

Yang Chen, Junyan Wen, Ze-Xu He, Jing-Wei Fan

Lanthanum hydride has attracted significant attention in recent years due to its signatures of superconductivity at around 250 K (1, 2). However, the megabar pressures required for synthesize and maintain its state present extraordinary challenges for experiments, particularly in characterizing its Meissner effect (3, 4). The nitrogen-vacancy (NV) center in

Ali Zain, Sareem Farooqui, Muhammad Rafi

This paper details our submission to the AraGenEval Shared Task on Arabic AI-generated text detection, where our team, BUSTED, secured 5th place. We investigated the effectiveness of three pre-trained transformer models: AraELECTRA, CAMeLBERT, and XLM-RoBERTa. Our approach involved fine-tuning each model on the provided dataset for a binary classification ta

Timur Galimzyanov, Olga Kolomyttseva, Egor Bogomolov

We study retrieval design for code-focused generation tasks under realistic compute budgets. Using two complementary tasks from Long Code Arena -- code completion and bug localization -- we systematically compare retrieval configurations across various context window sizes along three axes: (i) chunking strategy, (ii) similarity scoring, and (iii) splitting

Yuta Kawamoto, Hideaki Iiduka

Stochastic gradient descent (SGD) is the workhorse of large-scale learning, yet classical analyses rely on assumptions that can be either too strong (bounded variance) or too coarse (uniform noise). The expected smoothness (ES) condition has emerged as a flexible alternative that ties the second moment of stochastic gradients to the objective value and the f