October 2025 arXiv papers — page 215

X. Tao, P. Chen, M. Tsami, F. Khayati

This paper outlines the design vision and planned evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate multimodal sensing, real-time reasoning, and intelligent control. The envisioned system will include

Huaian Diao, Xieling Fan, Hongyu Liu

This paper investigates a novel mechanism for quasi-singularity formation in both linear and nonlinear hyperbolic wave equations in two and three dimensions. We prove that over any finite time interval, there exist inputs such that the H\"older norm of the resulting wave field exceeds any prescribed bound. Conversely, the set of such almost-blowup points has

Simon Boche, Jaehyung Jung, Sebastián Barbas Laina, Stefan Leutenegger

To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localization and Mapping (SLAM) system building dense volumetric occupancy maps, while scalable to large environments and operating in realtime. Our unified SLAM framework seamlessly integrat

Pawel Weichbroth, Maciej Lotysz, Michal Wrobel

The time pressure associated with software development, among other factors, often leads to a diminished emotional state among developers. However, whether emotions affect perceived productivity remains an open question. This study aims to determine the strength and direction of the relationship between emotional state and perceived productivity among softwa

XinXin Qi, Hao Sun

We consider the mixed WIMP-FIMP scenario in a two-component dark matter model with $Z_2 \times Z_4$ symmetry, where a singlet scalar $S$ and a Majarano fermion $\chi$ are introduced as dark matter candidates. We also introduce another singlet scalar $S_0$ with a non-zero vacuum expectation value to the SM so that the fermion dark matter can obtain mass after

Shreya Chappidi, Jennifer Cobbe, Chris Norval, Anjali Mazumder

Accountability regimes typically encourage record-keeping to enable the transparency that supports oversight, investigation, contestation, and redress. However, implementing such record-keeping can introduce considerations, risks, and consequences, which so far remain under-explored. This paper examines how record-keeping practices bring algorithmic systems

G. A. Grigorian

Extencion of Krein's special method for solving of integral equation to that method for solving of systems of integral equations is established. Generalizations of formulae for solution of integral equations are obtained. The result obtained is demonstrated by examples.

Yuan Wang, Mingyu Li, Haibo Chen

Computer-use agents (CUAs) powered by large language models (LLMs) have emerged as a promising approach to automating computer tasks, yet they struggle with the existing human-oriented OS interfaces - graphical user interfaces (GUIs). GUIs force LLMs to decompose high-level goals into lengthy, error-prone sequences of fine-grained actions, resulting in low s

Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig

Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the linear last layer weights is known in closed-form. We propose to leverage this during optimization, treating the last layer as a function of the backbone parameters, and optimizing solely for these parameters. We sh

Jingyao Zhang, Tianlin Li, Xiaoyu Zhang, Qiang Hu

Autoregressive Large Language Models (AR-LLMs) are widely used in software engineering (SE) but face limitations in processing code structure information and suffer from high inference latency. Diffusion LLMs (DLLMs) offer a promising alternative with global bidirectional encoding and decoupled generation steps. This work presents the first comprehensive eva

Milan Hladík

Absolute value linear programming problems is quite a new area of optimization problems, involving linear functions and absolute values in the description of the model. In this paper, we consider interval uncertainty of the input coefficients. Our goal is to determine the best and the worst case optimal values. For the former, we derive an explicit formula,

Guochen Yan, Luyuan Xie, Qingni Shen, Yuejian Fang

The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaustion. Federated Learning (FL) emerges as a practical solution for the next generation of AI on a decentralized Web, enabling privacy-preserving collaborative fine-tuning on decentr

Marta Volonteri

These notes review theoretical models of massive black hole formation, growth and observables. They start with a brief summary of basic properties of massive black hole properties. The current view on massive black holes and active galactic nuclei at high redshift is then summarized, highlighting the JWST ``revolution'' and the questions raised by the recent

Pierre-Louis Giscard, Omid Faizy, Christian Bonhomme

We present novel, exotic types of frame changes for the calculation of quantum evolution operators. We detail in particular the biframe, in which a physical system's evolution is seen in an equal mixture of two different standard frames at once. We prove that, in the biframe, convergence of all series expansions of the solution is quadratically faster than i

D. Piatti, LUNA Collaboration

A precise determination of the proton capture rates on oxygen is mandatory to predict the abundance ratios of oxygen isotopes in a stellar environment where the hydrogen burning is active. The 17O(p,{\gamma})18F reaction, in particular, plays a crucial role in AGB nucleosynthesis as well as in explosive hydrogen burning occurring in novae and type I supernov

Tian-Ren Jin, Yu-Ran Zhang, Heng Fan

Multipartite entanglement has a much more complex structure than bipartite entanglement. A state that lacks generic multipartite entanglement is 2-producible, i.e. it can be written as a tensor product of at most 2-partite entangled states. Recently, it has been proved that a tripartite pure state is 2-producible if and only if the gap between the entangleme

Yulong Huang, Jianxiong Tang, Chao Wang, Ziyi Wang

Large Language Models (LLMs) have achieved remarkable performance across tasks but remain energy-intensive due to dense matrix operations. Spiking neural networks (SNNs) improve energy efficiency by replacing dense matrix multiplications with sparse accumulations. Their sparse spike activity enables efficient LLMs deployment on edge devices. However, prior S

Seon-Geun Jeong, Mai Dinh Cong, Dae-Il Noh, Quoc-Viet Pham

Quantum annealing provides a practical realization of adiabatic quantum computation and has emerged as a promising approach for solving large-scale combinatorial optimization problems. However, current devices remain constrained by sparse hardware connectivity, which requires embedding logical variables into chains of physical qubits. This embedding overhead

Yilin Mei, Peng Qiu, Wei Zhang, WenChao Zhang

Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dimensional, dynamic, and partially observa

Junsei Ito, Yasuaki Wasa

This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply mo

Torbjörn Sjöstrand

The observation of an excess of ttbar production in the threshold region, by CMS and ATLAS, has been interpreted as a toponium contribution, i.e. from below-threshold ttbar virtual states. The news here is the nontrivial experimental extraction of such a signal, not its existence as such. Indeed, already 35+ years ago an NRQCD Green's function approach was u

Sai Anandhi Seetharaman, Soumyadipta Maiti, Ambesh Gupta, Beena Rai

The yield strength plateau of two BCC refractory high entropy alloys (RHEAs) - MoNbTaVW and MoNbTaW was examined through hybrid Monte Carlo and molecular dynamics (MC/MD) simulations. By analyzing atomic diffusivities derived from vacancy formation and migration energies around the edge dislocation cores, the number of critical atomic swaps were calculated a

Shurui Li

Rapid advances in artificial intelligence necessitate a re-examination of the epistemological foundations upon which we attribute consciousness. As AI systems increasingly mimic human behavior and interaction with high fidelity, the concept of a "perfect mimic"-an entity empirically indistinguishable from a human through observation and interaction-shifts fr

Sheng Wang, Ruiming Wu, Charles Herndon, Yihang Liu

Diagnosing a whole-slide image is an interactive, multi-stage process of changing magnification and moving between fields. Although recent pathology foundation models demonstrated superior performances, practical agentic systems that decide what field to examine next, adjust magnification, and deliver explainable diagnoses are still lacking. Such limitation

Anshu Kumari, Nat Gopalswamy

Type II solar radio bursts are commonly associated with shocks generated by coronal mass ejections (CMEs), where plasma waves are excited by magnetohydrodynamic (MHD) processes and converted into radio waves at the local plasma frequency or its harmonics. However, there are instances where type II bursts occur in the absence of whitelight CMEs. We analysed o

Jianshu Zhou, Jing Shu, Tianle Pan, Puchen Zhu

Grasping objects across vastly different sizes and physical states-including both solids and liquids-with a single robotic gripper remains a fundamental challenge in soft robotics. We present the Everything-Grasping (EG) Gripper, a soft end-effector that synergistically integrates distributed surface suction with internal granular jamming, enabling cross-sca

Carlo Kneissl, Christopher Bülte, Philipp Scholl, Gitta Kutyniok

Probabilistic regression models the entire predictive distribution of a response variable, offering richer insights than classical point estimates and directly allowing for uncertainty quantification. While diffusion-based generative models have shown remarkable success in generating complex, high-dimensional data, their usage in general regression tasks oft

James Chok, Domenic Petzinna

We propose a reflection-free Langevin framework for sampling and optimization on compact polyhedra. The method is based on the inverse Hessian of the logarithmic barrier, which defines a Dikin--Langevin diffusion whose drift and noise adapt to the local interior-point geometry. We show that trajectories started in the interior remain feasible for all finite

Omer Eldadi, Yarin Dekimhi, Gershon Tenenbaum

The study explores the effects of motivational climate on communication features, emotional states, collective efficacy, and performance in collaborative gaming environments. Forty participants with no prior gaming experience were randomly assigned to 20 gender-matched teams of three (including one confederate) across two motivational climates: positive-supp

Dang Anh, Rick Nouwen, Massimo Poesio

Our goal is to study how LLMs represent and interpret plural reference in ambiguous and unambiguous contexts. We ask the following research questions: (1) Do LLMs exhibit human-like preferences in representing plural reference? (2) Are LLMs able to detect ambiguity in plural anaphoric expressions and identify possible referents? To address these questions, w

Clément Bonet, Elsa Cazelles, Lucas Drumetz, Nicolas Courty

The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian manifolds and generalizes the notion of hyperplanes. As several sources of data can be conveniently modeled as probability distributions, it is natural to study this function in the W

Richard Varro

In this paper, we introduce gonosomic algebras to algebraically translate the phenomenon of genetic sterility. Gonosomic algebras extend the concept of gonosomal algebras used as algebraic model of genetic phenomena related to sex-determination and sex-linked gene transmission by allowing genetic sterility to be taken into account. Conditions under which gon

Juncheng Wang, Chao Xu, Cheng Yu, Zhe Hu

While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation

Yuhta Takida, Satoshi Hayakawa, Takashi Shibuya, Masaaki Imaizumi

Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adversarial networks often struggle to balance the dual objectives of assessing authenticity and conditional alignment of input samples within their conditional discriminators. To add

Juliane U. Klamser, Ludovic Berthier

We numerically study the collective dynamics of dense particle assemblies driven by non-reciprocal pairwise forces of amplitude $\kappa$. At a critical value $\kappa_{\rm c}$, the system undergoes a dynamical phase transition from an absorbing state ($\kappa < \kappa_{\rm c}$) to a chaotic steady state ($\kappa > \kappa_{\rm c}$). The chaotic phase is marked

Wenchao He, Tao Jia

Large-scale outbreaks of epidemics, misinformation, or other harmful contagions pose significant threats to human society, yet the fundamental question of whether an emerging outbreak will escalate into a major epidemic or naturally die out remains largely unaddressed. This problem is challenging, partially due to inadequate data during the early stages of o

Haoqiang Kang, Yizhe Zhang, Nikki Lijing Kuang, Nicklas Majamaki

Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM's autoregressive decoding may limit the ability to revisit and refine earlier tokens in a holistic manner, which can also lead to inefficient exploration for diverse solutions. In this paper, we propose LaDiR} (Latent Diffusion Reasoner),

Gerhard Knieper, JeongHyeong Park, Norbert Peyerimhoff

We consider simply connected Riemannian manifolds without conjugate points for which the horospherical mean curvature function is continuous, reversible and invariant under the geodesic flow. We show under mild additional curvature tensor conditions that rank one manifolds in this family are automatically asymptotically harmonic. In particular, compact rank

Péter Jeszenszki, Edit Mátyus

The simplest, algebraic quantum-electrodynamical corrections, due to the double-negative energy subspace and instantaneous interactions, are computed to the no-pair energy of two-spin-1/2-fermion systems. Numerical results are reported for two-electron atoms with a clamped nucleus and positronium-like genuine two-particle systems. The Bethe-Salpeter equation

B. Longo, M. Perucho, V. Bosch-Ramon, J. M. Martí

Jets in active galactic nuclei have to cross significant distances within their host galaxies, meeting large numbers of stars of different masses and evolution stages in their paths. Given enough time, supernova explosions within the jet will eventually happen, and may have a strong impact on its dynamics, potentially triggering powerful non-thermal activity

Jian'an Zhang

We formulate option market making as a constrained, risk-sensitive control problem that unifies execution, hedging, and arbitrage-free implied-volatility surfaces inside a single learning loop. A fully differentiable eSSVI layer enforces static no-arbitrage conditions (butterfly and calendar) while the policy controls half-spreads, hedge intensity, and struc

Naman Gupta, Shreeyash Gowaikar, Arun Iyer, Kirankumar Shiragur

Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the context window (straining selectivity), or stage multiple agents to read in pieces. In staged pipelines (e.g., Chain of Agents, CoA), free-form summaries passed between agents can

Takashi Kagaya, Masatomo Takahashi

In this paper, we deal with an inverse curvature flow of $\ell$-convex Legendre curves. Since the Legendre curve is a natural generalization of regular curve, the flow is a generalization of the classical inverse curvature flow of regular curves. For the initial value problem, we study on the unique existence of the flow in global time, the monotonicity of t

T. Borkovits, S. A. Rappaport, T. Mitnyan, R. Gagliano

We have identified more than a hundred close triply eclipsing hierarchical triple star systems from data taken with the space telescope TESS. Many of them have outer periods less than or, close to 100 days, hence, we call them `ultracompact hierarchical triples'. These systems are noteworthy in that we can potentially determine their dynamical and astrophysi

Honglin Liu, Chao Sun, Peng Hu, Yunfan Li

Conventional representation learning methods learn a universal representation that primarily captures dominant semantics, which may not always align with customized downstream tasks. For instance, in animal habitat analysis, researchers prioritize scene-related features, whereas universal embeddings emphasize categorical semantics, leading to suboptimal resu

Jinyang Jiang, Bernd Heidergott, Jiaqiao Hu, Yijie Peng

Distortion Risk Measures (DRMs) capture risk preferences in decision-making and serve as general criteria for managing uncertainty. This paper proposes gradient descent algorithms for DRM optimization based on two dual representations: the Distortion-Measure (DM) form and Quantile-Function (QF) form. The DM-form employs a three-timescale algorithm to track q

Pascal Thibaudeau, Mouad Fattouhi, Liliana D. Buda-Prejbeanu

The atomistic Landau-Lifshitz-Gilbert equation is challenged when modeling spintronic devices where Joule heating is significant, due to its core assumption of a constant magnetization magnitude. Based on a statistical framework that treats the magnetization magnitude as a dynamic variable coupled to a thermal bath, we derive a dynamic Landau-Lifshitz-Bloch-

Yash Deepak Kashtikar, Pranay Mathur, Sudharsan Senthil, Avhishek Chatterjee

Fault-tolerance using constant space-overhead against long-range correlated errors is an important practical question. In the pioneering works [Terhal and Burkard, PRA 2005], [Aliferis et al, PRA 2005], [Aharonov et al, PRL 2006], fault-tolerance using poly-logarithmic overhead against long-range correlation modeled by pairwise joint Hamiltonian was proven w

Honghao Fu, Yuan Ouyang, Kai-Wei Chang, Yiwei Wang

Recent advances demonstrate that multimodal large language models (MLLMs) exhibit strong multimodal in-context learning (ICL) capabilities, enabling them to adapt to novel vision-language tasks from a few contextual examples. However, existing ICL approaches face challenges in reconciling scalability with robustness across diverse tasks and noisy contextual

Mohsen Amiri, V Venktesh, Sindri Magnússon

This paper investigates the identification of the top-m user-scheduling sets in multi-user MIMO downlink, which is cast as a combinatorial pure-exploration problem in stochastic linear bandits. Because the action space grows exponentially, exhaustive search is infeasible. We therefore adopt a linear utility model to enable efficient exploration and reliable

Kyoji Saito

We illustrate a rank 1 model of virtual period maps and their associated winding quotient, where the winding quotient is a new phenomenon appeared in a recent study of virtual period maps and it requires a reformulation of the classical Jacobi inversion problem for the period maps due to the appearance of exponents which are imaginary numbers. We answer to t

Huiqiu Lin, Lianping Liu, Zhe You, Da Zhao

Inspired by the Li--Yau eigenvalue-diameter estimates, we investigate lower bounds for the first Dirichlet eigenvalue in terms of the diameter (or inscribed radius) of a graph. Let $G = (V, E)$ be a graph with boundary $B$. Assume that the interior $\Omega = V \setminus B$ is connected. Let $r$ be the inscribed radius of $(G, B)$ and $d$ be the maximum degre

Alexej Brauer, Paul Menzel, Mario V. Wüthrich

Maintaining the predictive performance of pricing models is challenging when insurance portfolios and data-generating mechanisms evolve over time. Focusing on non-life insurance, we adopt the concept-drift terminology from machine learning and distinguish virtual drift from real concept drift in an actuarial setting. Methodologically, we (i) formalize devian

Jian'an Zhang

We introduce Tail-Safe, a deployability-oriented framework for derivatives hedging that unifies distributional, risk-sensitive reinforcement learning with a white-box control-barrier-function (CBF) quadratic-program (QP) safety layer tailored to financial constraints. The learning component combines an IQN-based distributional critic with a CVaR objective (I

Oleh Fedkevych, Jonathan R. Gaunt, Seonagh Smith

Multi-parton distribution functions (mPDFs) are non-perturbative objects that are important in the prediction of multiple scattering rates at hadron colliders. In the case where the scales associated with all partons in the mPDF are the same, we have two theoretical constraints on the mPDF. These are symmetry in exchange of the parton indices, and the number

Vladislav Zemlyanoy

Fine shape, as defined by Melikhov, is an extension of the strong shape category of compacta (compact metrizable topological spaces) to all metrizable spaces, notable for being compatible with both \v{C}ech cohomology and Steenrod-Sitnikov homology. In this work we study fine shape of local compacta (locally compact separable metrizable spaces), and construc

Jorge Leonardo Ruiz Williams

We introduce a scalable witness-based persistent homology pipeline for full-brain MRI volumes that couples density-aware landmark selection with a GPU-ready witness filtration. Candidates are scored by a hybrid metric that balances geometric coverage against inverse kernel density, yielding landmark sets that shrink mean pairwise distances by 30-60% over ran

Gilad Gour

We revisit the quantum reverse Shannon theorem, a central result in quantum information theory that characterizes the resources needed to simulate quantum channels when entanglement is freely available. We derive a universal additive upper bound on the smoothed max-information in terms of the sandwiched R\'enyi mutual information. This bound yields tighter s

Mario Almagro, Diego Ortego, David Jimenez

Product recommendation is the task of recovering the closest items to a given query within a large product corpora. Generally, one can determine if top-ranked products are related to the query by applying a similarity threshold; exceeding it deems the product relevant, otherwise manual revision is required. Despite being a well-known problem, the integration

Pengfei He, Zhenwei Dai, Bing He, Hui Liu

Large language model (LLM)-based agents increasingly rely on tool use to complete real-world tasks. While existing works evaluate the LLMs' tool use capability, they largely focus on the final answers yet overlook the detailed tool usage trajectory, i.e., whether tools are selected, parameterized, and ordered correctly. We introduce TRAJECT-Bench, a trajecto

Shohei Okawa, Yuji Omura, Keyun Wu

We study neutrino mass generation and dark matter in a left-right symmetric model. The model is based on an $SU(3)_c\times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$ gauge theory with a softly broken parity symmetry. Masses of the charged leptons and neutrinos are generated radiatively at one-loop and three-loop level respectively, through their interactions

David Rapagnani, Chemseddine Ananna, Antonino Di Leva, Gianluca Imbriani

Neutron capture reactions are the main contributors to the synthesis of the heavy elements through the s-process. Together with $^{13}$C($\alpha$,n)16O, which has recently been measured by the LUNA collaboration in an energy region inside the Gamow peak, 22Ne({\alpha},n)25Mg is the other main neutron source in stars. Its cross section is mostly unknown in th

Kaito Takanami, Takashi Takahashi, Yoshiyuki Kabashima

In-context learning (ICL) is a key building block of modern large language models, yet its theoretical mechanisms remain poorly understood. It is particularly mysterious how ICL operates in real-world applications where tasks have a common structure. In this work, we address this problem by analyzing a linear attention model trained on low-rank regression ta

Ana M. Montero

The statistical-mechanical study of the equilibrium properties of fluids, starting from the knowledge of the interparticle interaction potential, is essential to understand the role that microscopic interaction between individual particles play in the properties of the fluid. The study of these properties from a fundamental point of view is therefore a centr

Guangze Chen, Anton Frisk Kockum

Three-qubit gates are highly beneficial operations in quantum computing, enabling compact implementations of quantum algorithms and efficient generation of multipartite entangled states. However, realizing such gates with high fidelity remains challenging due to crosstalk, complex control requirements, and the overhead of parametric or tunable couplers. In t

Karoly J. Boroczky, Matyas Domokos, Ansgar Freyer, Christoph Haberl

We classify valuations on lattice polygons with values in the ring of formal power series that commute with the action of the affine unimodular group. A typical example of such valuations is induced by the Laplace transform, but as it turns out there are many more. The classification is done in terms of formal power series that satisfy certain functional equ

Elias Dubbeldam, Reza Mohammadi, Marit Schoonhoven, S. Ilker Birbil

Accurate predictions on tabular data rely on capturing complex, dataset-specific feature interactions. Attention-based methods and graph neural networks, referred to as graph-based tabular deep learning (GTDL), aim to improve predictions by modeling these interactions as a graph. In this work, we analyze how these methods model the feature interactions. Curr

Wolfgang Lehrach, Daniel Hennes, Miguel Lazaro-Gredilla, Xinghua Lou

Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach -- involving prompting for direct move generation -- has significant drawbacks. It relies on the model's implicit fragile pattern-matching capabilities, leading to frequent illegal moves and strategically shallow play.

Jeong Ryeol Choi, Salim Medjber, Salah Menouar, Ramazan Sever

In this work, the dynamics of quark-antiquark pair systems is investigated by modelling them as general time-dependent 3D oscillators perturbed by a Coulomb potential. Solving this model enables the prediction of key mesonic properties such as the probability density, energy spectra, and quadrature uncertainties, offering theoretical insights into the confin

Lei Li, Min Tang, Yuqi Yang

The Discrete Ordinates Method (DOM) is widely used for velocity discretization in radiative transport simulations. However, DOM tends to exhibit the ray effect when the velocity discretization is not sufficiently refined, a limitation that is well documented. To counter this, we have developed the Random Ordinates Method (ROM) by integrating randomness into

Zeng Tao, Zheng Ding, Zeyuan Chen, Xiang Zhang

Existing 2D-lifting-based 3D editing methods often encounter challenges related to inconsistency, stemming from the lack of view-consistent 2D editing models and the difficulty of ensuring consistent editing across multiple views. To address these issues, we propose C3Editor, a controllable and consistent 2D-lifting-based 3D editing framework. Given an origi

Ziyad AlSharawi, Jose S. Cánovas

In this paper, we focus on finding one-dimensional maps that detect global stability in multidimensional maps. We consider various local and global stability techniques in discrete-time dynamical systems and discuss their advantages and limitations. Specifically, we navigate through the embedding technique, the expansion strategy, the dominance condition tec

Sharon Mary Tomson, Boris Goncharov, Rutger van Haasteren

Galaxy observations suggest that mergers of supermassive black hole binaries (SMBHBs) are rare events, with rates of order one per decade across the observable Universe. We present a framework to search for merging SMBHBs in pulsar timing array (PTA) data using a physically complete waveform model including inspiral, merger, ringdown, and gravitational-wave

Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Satoshi Ohshima

This paper proposes "3Dify," a procedural 3D computer graphics (3D-CG) generation framework utilizing Large Language Models (LLMs). The framework enables users to generate 3D-CG content solely through natural language instructions. 3Dify is built upon Dify, an open-source platform for AI application development, and incorporates several state-of-the-art LLM-

Moritz Brunion, Navaneeth Kunhi Purayil, Francesco Dell'Atti, Sebastian Lam

We propose to revisit the functional scaling paradigm by capitalizing on two recent developments in advanced chip manufacturing, namely 3D wafer bonding and backside processing. This approach leads to the proposal of the CMOS 2.0 platform. The main idea is to shift the CMOS roadmap from geometric scaling to fine-grain heterogeneous 3D stacking of specialized

Gong Zhang, Chao Wang, Koon Tong Goh, Si Qi Ng

High-speed generation and efficient entanglement detection on a photonic chip are essential for quantum information applications but hard to achieve due to common photonic chips' material properties and limited component performance. In this work, we experimentally demonstrate entanglement witness on a silicon photonic chip, with multi-rail single-photon ent

Xurui Song, Shuo Huai, JingJing Jiang, Jiayi Kong

Vision-Language Model (VLM) driving agents promise explainable end-to-end autonomy by first producing natural-language reasoning and then predicting trajectory planning. However, whether planning is causally driven by this reasoning remains a critical but unverified assumption. To investigate this, we build DriveMind, a large-scale driving Visual Question An

Ravil R. Fatykhov, Sergey V. Sushkov

This paper explores cosmological scenarios in a scalar-tensor theory of gravity, including both a non-minimal coupling with scalar curvature of the form $R\phi^2$ and a non-minimal derivative coupling of the form $G^{\mu\nu}\phi_{,\mu}\phi_{,\nu}$ in the presence of a scalar field potential with the monomial dependence $V(\phi) = V_0\phi^n$. Critical points

Gayathri Shekar, Saman Atapattu, Prathapasinghe Dharmawansa, Kandeepan Sithamparanathan

Holographic MIMO (HMIMO) has emerged as a promising solution for future wireless systems by enabling ultra-dense, spatially continuous antenna deployments. While prior studies have primarily focused on electromagnetic (EM) modeling or simulation-based performance analysis, a rigorous communication-theoretic framework remains largely unexplored. This paper pr

Yuki Takeuchi, Duo Xu

We present the first construction of a computational Certified Deletion Property (CDP) achievable with classical communication, derived from the compilation of the non-local Magic Square Game (MSG). We leverage the KLVY compiler to transform the non-local MSG into a 2-round interactive protocol, rigorously demonstrating that this compilation preserves the ga

Santhosh KumarRavindran

The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception, and biased outputs, threatening security, trust, and fairness. Extending our adversarial activation patching framework (arXiv:2507.09406), which induced deception in toy networks at a 23.9% rate, we introduce the

Peixue Wu, Yunkai Wang

We investigate superadditivity of quantum capacity through private channels whose Choi-Jamiolkowski operators are private states. This perspective links the security structure of private states to quantum capacity and clarifies the role of the shield system: information encoded in the shield system that would otherwise leak to the environment can be recycled

Ryota Nakai, Hayato Goto

Quantum error-correcting codes (QECCs) require high encoding rate in addition to high threshold unless a sufficiently large number of physical qubits are available. The many-hypercube (MHC) codes defined as the concatenation of the [[6,4,2]] quantum error-detecting code have been proposed as high-performance and high-encoding-rate QECCs. However, the concate

Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki

Masked diffusion models have shown promising performance in generating high-quality samples in a wide range of domains, but accelerating their sampling process remains relatively underexplored. To investigate efficient samplers for masked diffusion, this paper theoretically analyzes the MaskGIT sampler for image modeling, revealing its implicit temperature s

Ask Hällström, Felix Agner, Richard Pates

District heating networks are an integral part of the energy system in many countries. In future smart energy systems, they are expected to enhance energy flexibility and support the integration of renewable and waste energy sources. An important aspect of these networks is the control of flow rates, which dictates the heat delivered to consumers. This paper

Kyoko Matsushita, Marie Kondo, Kosuke Sato, Toru Sasaki

We present an analysis of Suzaku observations of 14 nearby galaxy clusters and groups (z < 0.06), extending radial coverage out to the virial radius (approximately r200). The sample spans a wide mass range, from M500 about 2x10^13 to 7x10^14 solar masses, and includes well-studied systems such as Coma, Perseus, and Virgo. We carefully modeled all background

Yisen Gao, Xingcheng Fu, Qingyun Sun, Jianxin Li

Graph diffusion models have made significant progress in learning structured graph data and have demonstrated strong potential for predictive tasks. Existing approaches typically embed node, edge, and graph-level features into a unified latent space, modeling prediction tasks including classification and regression as a form of conditional generation. Howeve

Nouédyn Baspin, Lucas Berent, Lawrence Z. Cohen

Quantum LDPC codes promise significant reductions in physical qubit overhead compared with topological codes. However, many existing constructions for performing logical operations come with distance-dependent temporal overheads. We introduce a scheme for performing generalized surgery on quantum LDPC codes using a constant number of rounds of syndrome measu

Heiko Koziolek, Thilo Braun, Virendra Ashiwal, Sofia Linsbauer

Distributed control systems (DCS) manage the automation for many industrial production processes (e.g., power plants, chemical refineries, steel mills). Programming the software for such systems remains a largely manual and tedious process, incurring costs of millions of dollars for extensive facilities. Large language models (LLMs) have been found helpful i

Fotis K. Diakonos, P. Schmelcher

Isospectrally patterned lattices (IPL) have recently been shown to exhibit a rich band structure comprising both regimes of localized as well as extended states. The localized states show a single center localization behaviour with a characteristic localization length. We derive a continuum analogue of the IPL which allows us to determine analytically its ei

Ashutosh Tomar, Suvendu Rakshit, Amit Kumar Mandal, Shivangi Pandey

We present measurements of the dusty torus sizes of 51 active galactic nuclei (AGNs) with a redshift of $z<$ 0.8. Our analysis utilizes about 16 years of optical photometric data of 146 AGNs from various time-domain surveys, including ASAS-SN, CRTS, and ZTF, along with 14 years of infrared data in the $W$1 ($\sim$ 3.4 $\mu$m) and $W$2 ($\sim$ 4.6 $\mu$m) ban

Behrooz Farkiani, Fan Liu, Patrick Crowley

HTTP underpins modern Internet services, and providers enforce quotas to regulate HTTP API traffic for scalability and reliability. When requests exceed quotas, clients are throttled and must retry. Server-side enforcement protects the service. However, when independent clients' usage counts toward a shared quota, server-only controls are inefficient; client

Emile Bouaziz

To a smooth variety $X$ with simple normal crossings divisor $D$, we associate a sheaf of vertex algebras on $X$, denoted $\Omega^{ch}_{X}(\operatorname{log}D)$, whose conformal weight $0$ subspace is the algebra $\Omega_{X}(\operatorname{log}D)$ of forms with log poles along $D$. We prove various basic structural results about $\Omega^{ch}_{X}(\operatorname

Parashmani Thakuria, Madhurjya Lalung, Jayanta Kumar Sarma

We present a calculation of the twist-3 generalized parton distributions (GPDs) for gluons in the proton. Our analysis is performed within a light-front constituent model where the proton is treated as a two-body state of a spin-1 gluon and a spin-1/2 spectator system. The requisite light-front wave functions are derived from the soft-wall AdS/QCD correspond

Fumio Nemoto, Nobuyuki Koike, Daichi Sato, Yuuta Kawaai

Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads to bicycle shortages in specific areas and at particular times. To address this challenge, we employ a novel quantum machine learning model that analyzes time series data by fitti

Samantha Tseng, Abhyudaya Chouhan, Dominic Cupidon

As of now, an optimal quantum algorithm solving partial differential equations eludes us. There are several different methods, each with their own strengths and weaknesses. In past years comparisons of these existing methods have been made, but new work has emerged since then. Therefore, we conducted a survey on quantum methods developed post-2020, applying

Achim Eckerle, Martin Spitznagel, Janis Keuper

Accurate and fast urban noise prediction is pivotal for public health and for regulatory workflows in cities, where the Environmental Noise Directive mandates regular strategic noise maps and action plans, often needed in permission workflows, right-of-way allocation, and construction scheduling. Physics-based solvers are too slow for such time-critical, ite

Huanqing Wang, Kaixiang Zhang, Kyungjoon Lee, Yu Mei

Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential in efficient control of soft robots without explicit parametric models. However, in object manipulation tasks, unknown external payloads and disturbances can significantly alter the system dynamics and behavior, leading to offset error and degraded control

Lili Xie, Yi Zhang, Ruihong Qiu, Jiajun Liu

Recommender systems frequently encounter data sparsity issues, particularly when addressing cold-start scenarios involving new users or items. Multi-source cross-domain recommendation (CDR) addresses these challenges by transferring valuable knowledge from multiple source domains to enhance recommendations in a target domain. However, existing reinforcement

Min Wang, Xin Li, Ye He, Yao-Hui Li

The real world is inherently non-stationary, with ever-changing factors, such as weather conditions and traffic flows, making it challenging for agents to adapt to varying environmental dynamics. Non-Stationary Reinforcement Learning (NSRL) addresses this challenge by training agents to adapt rapidly to sequences of distinct Markov Decision Processes (MDPs).