October 2025 arXiv papers — page 121

Hwiyeol Jo, Joosung Lee, Jaehone Lee, Sang-Woo Lee

Evaluating generative models, such as large language models (LLMs), commonly involves question-answering tasks where the final answer is selected based on probability of answer choices. On the other hand, for models requiring reasoning, the method of answer extraction plays a critical role. Our research reveals that the performance of reasoning models and th

Xu Chi, Chao Zhang, Yang Su, Lingfeng Dou

Accurate and high-fidelity demonstration data acquisition is a critical bottleneck for deploying robot Imitation Learning (IL) systems, particularly when dealing with heterogeneous robotic platforms. Existing teleoperation systems often fail to guarantee high-precision data collection across diverse types of teleoperation devices. To address this, we develop

Zhang Nengbo, Hann Woei Ho, Ye Zhou

Reliable communication in Micro Air Vehicle (MAV) swarms is challenging in environments, where conventional radio-based methods suffer from spectrum congestion, jamming, and high power consumption. Inspired by the waggle dance of honeybees, which efficiently communicate the location of food sources without sound or contact, we propose a novel visual communic

M. Siddikov, I. Zemlyakov, M. Roa

In this manuscript we analyze the exclusive photoproduction of the $\chi_{c}\gamma$ pairs. We focus on the small-$x$ kinematics and evaluate the cross-sections in the Color Glass Condensate framework. We found that in the leading order in the strong coupling $\alpha_{s}$, this process is sensitive only to the forward color dipole scattering amplitude. We est

Andreas Filipp, Tri Nguyen, Laurence Perreault-Levasseur, Jonah Rose

Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differenc

Giuseppe Lorenzo Catalano, Agata Marta Soccini

Space exploration increasingly relies on Virtual Reality for several tasks, such as mission planning, multidisciplinary scientific analysis, and astronaut training. A key factor for the reliability of the simulations is having accurate 3D representations of planetary terrains. Extraterrestrial heightmaps derived from satellite imagery often contain missing v

Parameshwar R. Pasnoori

In this paper we consider the problem of solving quantum field theories with time dependent interaction strengths. We show that the recently formulated framework [P. R. Pasnoori, Phys. Rev. B 112, L060409 (2025)], which is a generalization of the regular Bethe ansatz technique, provides the exact many-body wavefunction. In this framework, the time-dependent

Yunwen Li, Shuangshuang Ying, Xingwei Qu, Xin Li

Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. We present COIG-Writer, a novel Chinese creative writing dataset that captures both diverse outputs and their underlying thought processes through systematic reverse-engineering of

Boštjan Brešar, Tanja Dravec, Michael A. Henning

A set $S$ of vertices in a graph $G$ is a dominating set of $G$ if every vertex not in $S$ is adjacent to a vertex in~$S$. An independent dominating set in $G$ is a dominating set of $G$ with the additional property that it is an independent set. The domination number, $\gamma(G)$, and the independent domination number, $i(G)$, are the minimum cardinalities

Eliseo Curcio

Artificial intelligence and machine learning are increasingly used for forecasting, optimization, and policy design in the energy sector, yet no standardized framework exists to evaluate whether these systems reason correctly. Current validation practices focus on predictive accuracy or computational efficiency, leaving the logical integrity of analytical co

Binhao Wang, Fan Yang, Chen Xu, Peng Zhang

The coherent-state initial-value representation (IVR) for the semi-classical real-time propagator of a quantum system, developed by Herman and Kluk (HK), is widely used in computational studies of chemical dynamics. On the other hand, the Boltzmann operator $e^{-\hat{H}/(k_B T)}$, with $\hat{H}$,$k_B$, and $T$ representing the Hamiltonian, Boltzmann constant

Scott Carter, Benjamin Cooper, Mikhail Khovanov, Vyacheslav Krushkal

We show that an oriented surface in $\mathbb{R}^4$ containing double point singularities induces a map between the Khovanov homology groups of its boundary links in a functorial way. As part of this work, the movie moves of Carter and Saito are extended to surfaces with double points.

Bangti Jin, Zehui Zhou

Stochastic variance reduced gradient (SVRG) is an accelerated version of stochastic gradient descent based on variance reduction, and is promising for solving large-scale inverse problems. In this work, we analyze SVRG and a regularized version that incorporates a priori knowledge of the problem, for solving linear inverse problems in Hilbert spaces. We prov

Paolo Martini, Kostas Kanellopulos, Silvan Schmid

Thermomechanical infrared (IR) detectors have emerged as promising alternatives to traditional photon and thermoelectric sensors, offering broadband sensitivity and low noise without the need for cryogenic cooling. Despite recent advances, the field still lacks a unified framework to guide the design of these nanomechanical systems. This work addresses that

Eva Sextl, Rolf-Peter Kudritzki

A VLT/MUSE population synthesis study of metallicities in the nuclear star-forming rings of four disk galaxies (NGC 613, NGC 1097, NGC 3351, NGC 7552) is presented. Disentangling the spectral contributions of young and old stellar populations, we find a large spread of ages and metallicities of the old stars in the nuclear rings. This indicates a persistent

Manar Abdelatty, Maryam Nouh, Jacob K. Rosenstein, Sherief Reda

Large Language Models (LLMs) are increasingly used to automate hardware design tasks, including the generation of Verilog code. While early benchmarks focus primarily on functional correctness, efficient hardware design demands additional optimization for synthesis metrics such as area, delay, and power. Existing benchmarks fall short in evaluating these asp

Noémie Marquet, Yannick Bidel, Malo Cadoret, Alexis Bonnin

Rotations play a detrimental role in achieving ultra-high-performance inertial measurements with an atom interferometer, leading potentially to a total loss of interference contrast and the emergence of dominant phase shift biases. This becomes particularly significant when considering operation in dynamic conditions such as those encountered in Earth orbiti

Rubén A. Hidalgo, Sebastián Reyes-Carocca

An action of a finite group $G$ is a pair $(S,\hat{G})$, where $S$ is a compact Riemann surface of genus $g \geqslant 2$ and $\hat{G} \leqslant {\rm Aut}(S)$ is isomorphic to $G$. To each action $(S,\hat{G})$ there is associated a signature $(\gamma;k_{1},\ldots,k_{r})$ that codifies the orbifold structure of $S/\hat{G}$. Two actions of $G$, say $(S_{1},G_{1

Xu Wu, Zhihui Lai, Xianxu Hou, Jie Zhou

Low-light image enhancement (LLIE) aims to improve illumination while preserving high-quality color and texture. However, existing methods often fail to extract reliable feature representations due to severely degraded pixel-level information under low-light conditions, resulting in poor texture restoration, color inconsistency, and artifact. To address thes

Javier Cembrano, Jose Correa, Svenja M. Griesbach, Victor Verdugo

Traditionally, the problem of apportioning the seats of a legislative body has been viewed as a one-shot process with no dynamic considerations. While this approach is reasonable for some settings, dynamic aspects play an important role in many others. We initiate the study of apportionment problems in an online setting. Specifically, we introduce a framewor

Divyat Mahajan, Sachin Goyal, Badr Youbi Idrissi, Mohammad Pezeshki

Next-token prediction (NTP) has driven the success of large language models (LLMs), but it struggles with long-horizon reasoning, planning, and creative writing, with these limitations largely attributed to teacher-forced training. Multi-token prediction (MTP) partially mitigates these issues by predicting several future tokens at once, but it mostly capture

İsmail Emir Yüksel, Ataberk Olgun, F. Nisa Bostancı, Haocong Luo

We experimentally demonstrate a new widespread read disturbance phenomenon, ColumnDisturb, in real commodity DRAM chips. By repeatedly opening or keeping a DRAM row (aggressor row) open, we show that it is possible to disturb DRAM cells through a DRAM column (i.e., bitline) and induce bitflips in DRAM cells sharing the same columns as the aggressor row (acro

Kenji Saotome, Koji Nakazawa

This paper investigates the admissibility of the substitution rule in cyclic-proof systems. The substitution rule complicates theoretical case analysis and increases computational cost in proof search since every sequent can be a conclusion of an instance of the substitution rule; hence, admissibility is desirable on both fronts. While admissibility is often

Jialiang Hu, Xiaozhou Zhao, Guiping Zhou, Yuhao Chen

Through three-dimensional MHD simulations, we have uncovered a kind of fast coronal wave originating from both ends of a current sheet (CS) during a solar eruption. These waves are observed to appear near the top and bottom ends of the reconnection-related CS. The simulations demonstrate the presence of termination shock regions above the two ends of the CS.

E. S. Medvedev, V. G. Ushakov

The ability of our semi-empirical irregular dipole-moment functions (2022) and (2025) to predict the intensities of the yet unobserved lines, as well as to describe the observed ones not used in the fitting, is demonstrated by comparison with recent measurements in the 0-0, 1-0, 3-0, and 7-0 bands.

Ulysse Remond, Pierre-Emmanuel Emeriau, Liam Lysaght, Jean Ruel

We present a variational quantum algorithm for structural mechanical problems, specifically addressing crack opening simulations that traditionally require extensive computational resources. Our approach provides an alternative solution for a relevant 2D case by implementing a parametrized quantum circuit that stores nodal displacements as quantum amplitudes

Alejandro Alés, Juan Ignacio Cerato, Leandro Marchioni, Miguel Hoyuelos

In dilute gases, transport properties such as the thermal conductivity, self-diffusion, and viscosity are significantly affected by interatomic collisions, which are determined by the potential form. This study explores these transport properties in the presence of a Langevin thermostat in systems where particles interact through various potentials, includin

Stefano Scali, Josh Kirsopp, Antonio Márquez Romero, Michał Krompiec

Quantum phase estimation (QPE) is a cornerstone algorithm for extracting Hamiltonian eigenvalues, but its standard, eigenstate-centric form relies on carefully prepared coherent inputs that are costly or impractical for many strongly correlated systems. We overcome this bottleneck via DOS-QPE, an incoherent, purification-based variant of QPE that works direc

Dávid Puskás, Sandro Tacchella, Charlotte Simmonds, Gareth C. Jones

Galaxy mergers and interactions are often invoked to explain enhanced star formation, black hole growth, and mass build-up of galaxies at later cosmic times, but their effect is poorly understood at high redshift ($z>2$). We use JADES data to analyse a mass-complete sample of 2095 galaxies at $z=3-9$ with ${\rm log}(M_\star/{\rm M_\odot}) = [8, 10]$, identif

Mehran Khosrojerdi, Alessandro Cuccoli, Paola Verrucchi, Leonardo Banchi

Drawing the quantum phase diagram of a many-body system in the parameter space of its Hamiltonian can be seen as a learning problem, which implies labelling the corresponding ground states according to some classification criterium that defines the phases. In this work we adopt unsupervised learning, where the algorithm has no access to any priorly labeled s

Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe, Giovanni Bellitto

Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large language models to generate global, textual explanations of visual classifiers. DEXTER operates by optimizing text prompts to synthesize class

Alexander Okupnik, Johannes Schneider, Kyriakos Flouris

Recent success with large language models has sparked a new wave of verbal human-AI interaction. While such models support users in a variety of creative tasks, they lack the embodied nature of human interaction. Dance, as a primal form of human expression, is predestined to complement this experience. To explore creative human-AI interaction exemplified by

Mohsen Alishahiha, Mohammad Javad Vasli

We investigate Krylov state complexity as a probe of the quantum Mpemba effect in quantum spin chains. For models without global $U(1)$ symmetry, Krylov complexity exhibits clear Mpemba-like crossings, consistent with conventional diagnostics such as the trace distance, while offering a complementary interpretation in terms of Hilbert-space exploration and d

Giorgio Minati, Enrico Urbani, Nicolò Spagnolo, Valeria Cimini

Squeezed light enables quantum-enhanced phase estimation, with crucial applications in both fundamental physics and emerging technologies. To fully exploit the advantage provided by this approach, estimation protocols must remain optimal across the entire parameter range and resilient to instabilities in the probe state. In this context, strategies that rely

Mengzhao Jia, Zhihan Zhang, Ignacio Cases, Zheyuan Liu

Multimodal large language models (MLLMs) have rapidly advanced from perception tasks to complex multi-step reasoning, yet reinforcement learning with verifiable rewards (RLVR) often leads to spurious reasoning since only the final-answer correctness is rewarded. To address this limitation, we propose AutoRubric, a framework that integrates RLVR with process-

Shijian Wu

In this paper, we study delay differential equations involving the Schwarzian derivative $S(f,z)$, expressed in the form \begin{equation*} f(z+1)f(z-1) + a(z)S(f,z) =R(z,f(z))= \frac{P(z,f(z))}{Q(z,f(z))} \end{equation*} where $a(z)$ is rational, $P(z,f)$ and $Q(z,f)$ are coprime polynomials in $f$ with rational coefficients. Our main result shows that if a

Krishnendu Gongopadhyay, Sagar B. Kalane

Let $\PSp(n,1)$ denote the isometry group of the quaternionic hyperbolic space $\mathbb{H}^n$. A pair $(g_1,g_2)$ $\PSp(n,1)$ is \emph{strongly doubly reversible} if $(g_1,g_2)$ and $(g_1^{-1},g_2^{-1})$ are simultaneously conjugate in $\PSp(n,1)$ by an involution. Equivalently, there exist involutions $i_1,i_2,i_3 \in \PSp(n,1)$ such that $g_1 = i_1 i_2$, $

Yijie Bi, Zhenhao Cai, Xinyi Li, Balázs Ráth

We establish sharp asymptotic bounds for the critical intensity of the Finitary Random Interlacements (FRI) model in four and higher dimensions with general trajectory length distributions. Our proof reveals that the construction of near-critical FRI clusters in four and higher dimensions is essentially analogous to a Galton-Watson process, whose expected nu

Ishani Cheshire, Joseph J. Armstrong, Jonathan C. Tan

Aims: Studying the dynamical evolution of young clusters is crucial for a more general understanding of the star formation process. Methods: We took spectra of >600 candidate pre-main sequence (PMS) stars in several nearby young clusters (NGC 2264 N & S, Collinder 95, and Collinder 359) using MMT/Hectospec. These spectra were analyzed for H{\alpha} emission

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A measurement of $C\!P$ asymmetry in $D^0 \to K^0_{\rm S} K^0_{\rm S}$ decays is reported, based on a data sample of proton-proton collisions collected with the LHCb detector in 2024 at a centre-of-mass energy of $13.6\,$TeV, corresponding to an integrated luminosity of $6.2\,\mathrm{fb}^{-1}$. The $D^0 \to K^0_{\rm S} \pi^+ \pi^-$ decay is used as calibrati

Narsimha Reddy Rapakaa, Mohamed Kamel Riahi

This article introduces the Generalized Fourier Series (GFS), a novel spectral method that extends the clas- sical Fourier series to non-periodic functions. GFS addresses key challenges such as the Gibbs phenomenon and poor convergence in non-periodic settings by decomposing functions into periodic and aperiodic com- ponents. The periodic part is represented

Alejandro Cano, Cristóbal Camarero, Carmen Martínez, Ramón Beivide

High-radix, low-diameter networks like HyperX and Dragonfly use a Full-mesh core, and rely on multiple virtual channels (VCs) to avoid packet deadlocks in adaptive routing. However, VCs introduce significant overhead in the switch in terms of area, power, and design complexity, limiting the switch scalability. This paper starts by revisiting VC-less routing

Francesco Rinaldo Talenti, Luca Lovisolo, Zijun Xiao, Zeina Saleh

Technological advances in the fabrication of nanophotonic circuits have driven the scientific community to increasingly focus on the precise tailoring of their key optical properties, over a broadband spectral domain. In this context, the modulation of the local refractive index can be exploited to customize an effective reflectivity by the use of distribute

Gnanasekaran Shanmugasundaram, Jitraj Saha, Rafael Díaz Fuentes

This study examines a fully parabolic predator-prey chemo-alarm-taxis system under homogeneous Neumann boundary conditions in a bounded domain $\Omega \subset \mathbb{R}^n$ with a smooth boundary $\partial\Omega$. Under specific parameter conditions, it is shown that the system admits a unique, globally bounded classical solution. The convergence of the solu

Antony Bartlett, Cynthia Liem, Annibale Panichella

Testing deep reinforcement learning (DRL) agents in safety-critical domains requires discovering diverse failure scenarios. Existing tools such as INDAGO rely on single-objective optimization focused solely on maximizing failure counts, but this does not ensure discovered scenarios are diverse or reveal distinct error types. We introduce INDAGO-Nexus, a mult

Dingzhou Xie, Rushi Lan, Cheng Pang, Enhao Ning

Recent object detection methods have made remarkable progress by leveraging attention mechanisms to improve feature discriminability. However, most existing approaches are confined to refining single-layer or fusing dual-layer features, overlooking the rich inter-layer dependencies across multi-scale representations. This limits their ability to capture comp

Sami C. Al-Izzi, Yao Du, Jonas Veenstra, Richard G. Morris

Active filaments are a workhorse for propulsion and actuation across biology, soft robotics and mechanical metamaterials. However, artificial active rods suffer from limited robustness and adaptivity because they rely on external control, or are tethered to a substrate. Here we bypass these constraints by demonstrating that non-reciprocal interactions lead t

Cormac MacDermott, Carl J. Scarrott, John Ferguson

Evaluating a country's sporting success provides insight into its decision-making and infrastructure for developing athletic talent. The Olympic Games serve as a global benchmark, yet conventional medal rankings can be unduly influenced by population size. We propose a Bayesian ranking scheme to rank the performance of National Olympic Committees by their "l

Qiang Jia, Ran Luo, Jiahua Tian, Yi-Nan Wang

We show that the 't Hooft anomaly of a quantum field theory with continuous flavor symmetry can be detected from rearrangements of the topological defect webs implementing the global symmetry in general spacetime dimension, which is concretized in 2D by the F-moves of the defect lines. Via dualizing the defects to flat background gauge field configurations,

Lino Reggiani, Federico Intini, Luca Varani

We investigate quantum and quantum-relativistic effects associated with the noise power spectrum and the fluctuation--dissipation relation between current--noise spectra and linear--response conductance at low frequencies of the electromagnetic field. At high frequencies, vacuum catastrophe is shown to be avoided by the presence of Casimir force. At low freq

Moretti Elia, Loreau Michel, Benzaquen Michael

Feeding a larger and wealthier global population without transgressing ecological limits is increasingly challenging, as rising food demand (especially for animal products) intensifies pressure on ecosystems, accelerates deforestation, and erodes biodiversity and soil health. We develop a stylized, spatially explicit global model that links exogenous food-de

Hongzheng Chen, Bin Fan, Alexander Collins, Bastian Hagedorn

Modern GPUs feature specialized hardware units that enable high-performance, asynchronous dataflow execution. However, the conventional SIMT programming model is fundamentally misaligned with this task-parallel hardware, creating a significant programmability gap. While hardware-level warp specialization is the key to unlocking peak performance, it forces de

Xingmeng Zhao, Tongnian Wang, Dan Schumacher, Veronica Rammouz

Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, rapid and low-barrier development can introduce risks of bias, privacy violations, and unequal access, especially when systems ignore real-world contexts and diverse user needs. Many

Hristos Tyralis, Georgia Papacharalampous

We examine the theoretical properties of the index of agreement loss function $L_W$, the negatively oriented counterpart of Willmott's index of agreement, a common metric in environmental sciences and engineering. We prove that $L_W$ is bounded within [0, 1], translation and scale invariant, and estimates the parameter $\Bbb{E}_{F}[\underline{y}] \pm \Bbb{V}

Tingyu Lin, Armin Dadras, Florian Kleber, Robert Sablatnig

Camera movement conveys spatial and narrative information essential for understanding video content. While recent camera movement classification (CMC) methods perform well on modern datasets, their generalization to historical footage remains unexplored. This paper presents the first systematic evaluation of deep video CMC models on archival film material. W

S. A. Hosseini, M. Feinberg, I. V. Karlin

We present a new kinetic model and its lattice Boltzmann realization for the simulation of compressible, non-ideal fluid flows. The method employs first-neighbour lattices and introduces a consistent set of correction terms constructed via quasi-equilibrium attractors, ensuring positive-definite and Galilean-invariant Navier-Stokes dissipation rates. This co

Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with well-defined scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We a

Juni Schindler, Mauricio Barahona

Datasets often possess an intrinsic multiscale structure with meaningful descriptions at different levels of coarseness. Such datasets are naturally described as multi-resolution clusterings, i.e., not necessarily hierarchical sequences of partitions across scales. To analyse and compare such sequences, we use tools from topological data analysis and define

Caleb Robinson, Kimberly T. Goetz, Christin B. Khan, Meredith Sackett

Effective monitoring of whale populations is critical for conservation, but traditional survey methods are expensive and difficult to scale. While prior work has shown that whales can be identified in very high-resolution (VHR) satellite imagery, large-scale automated detection remains challenging due to a lack of annotated imagery, variability in image qual

Ha Xuan Son, Nguyen Quoc Anh, Phat T. Tran-Truong, Le Thanh Tuan

The Internet of Medical Things (IoMT) has revolutionized healthcare by transforming medical operations into standardized, interoperable services. However, this service-oriented model introduces significant security vulnerabilities in device management and communication, which are especially critical given the sensitivity of medical data. To address these ris

Manuel Del Piano, Ciro De Simone, Mattia Damia Paciarini, Vittorio De Falco

A variety of robust and effective descriptions have been devised to extract model-independent information about the fundamental properties of black holes from observational data when searching for deviations from general relativity. In this work, we construct explicit transformation maps establishing the equivalence among three relevant parametrizations for

Magnus Neuman, Jelena Smiljanić, Martin Rosvall

From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround sparsifies a correlation matrix representat

Seungjoo Shin, Jaesik Park, Sunghyun Cho

Compression techniques for 3D Gaussian Splatting (3DGS) have recently achieved considerable success in minimizing storage overhead for 3D Gaussians while preserving high rendering quality. Despite the impressive storage reduction, the lack of learned priors restricts further advances in the rate-distortion trade-off for 3DGS compression tasks. To address thi

Leonie Winter

Offline evaluations in recommender system research depend heavily on datasets, many of which are pruned, such as the widely used MovieLens collections. This thesis examines the impact of data pruning - specifically, removing users with fewer than a specified number of interactions - on both dataset characteristics and algorithm performance. Five benchmark da

Jianghao Lin, Yuanyuan Shi, Xin Peng, Renjie Ding

Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an inference-scaling framework for structured outputs that combines fine-grained beam search with \textbf{ToolPRM}, a process reward model scoring each intra-call decision (function name and argument filling). We buil

Penglong Zhai, Jie Li, Fanyi Di, Yue Liu

The next point-of-interest (POI) recommendation task aims to predict the users' immediate next destinations based on their preferences and historical check-ins, holding significant value in location-based services. Recently, large language models (LLMs) have shown great potential in recommender systems, which treat the next POI prediction in a generative man

M. V. Voitovych, A. Sarikov, V. O. Yukhymchuk, V. V. Voitovych

Peculiarities of formation of inclusions of amorphous Si (a-Si) phase in Si-rich Si oxynitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are studied by combined Raman scattering and infrared (IR) absorption spectroscopy. The Raman scattering results identify presence of a-Si phase in the studied films at the relative Si content exceedi

Bin Liu, Yanjie Zhao, Guoai Xu, Haoyu Wang

Large language model (LLM) agents have demonstrated remarkable capabilities in software engineering and cybersecurity tasks, including code generation, vulnerability discovery, and automated testing. One critical but underexplored application is automated web vulnerability reproduction, which transforms vulnerability reports into working exploits. Although r

Maulidi Adi Prasetia, Muhamad Risqi U. Saputra, Guntur Dharma Putra

Federated Learning (FL) is designed as a decentralized, privacy-preserving machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data. In real-world scenarios, however, clients often have heterogeneous computational resources and hold non-independent and identically distributed data (non-IID), which po

Bang An, Yibo Yang, Philip Torr, Bernard Ghanem

Model merging aims to integrate task-specific abilities from individually fine-tuned models into a single model without extra training. In recent model merging methods, task vector has become a fundamental building block, as it can encapsulate the residual information from finetuning. However, the merged model often suffers from notable performance degradati

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins

We are grateful to the discussants, Levis and Kennedy [2025], Luo and Geng [2025], Wang and van der Laan [2025], and Yang and Kim [2025], for their thoughtful comments on our paper (Nabi et al., 2025). In this rejoinder, we summarize our main contributions and respond to each discussion in turn.

Simon Malatrait, Alex Sirac

FibRace, jointly developed by KKRT Labs and Hyli, was the first large-scale experiment to test client-side proof generation on smartphones using Cairo M. Presented as a mobile game in which players proved Fibonacci numbers and climbed a leaderboard, FibRace served a dual purpose: to engage the public and to provide empirical benchmarking. Over a three-week c

Caio Nunes, Bosco Borges, Georgia Cruz, Ticianne Darin

Escapism in games can support recovery or lead to harmful avoidance. Self-regulation, understood as combining autonomy with positive outcomes, is key to this distinction. We argue that audio, often overlooked, plays a central role in regulation. It can modulate arousal, mark transitions, and provide closure, yet its contribution to well-being remains underex

Yuetong Fang

Let $(X,\omega)$ be a compact Hermitian manifold of dimension $n$. We derive an $L^\infty$-estimate for bounded solutions to the complex $m$-th Hessian equations on $X$, assuming a positive right-hand side in the Orlicz space $L^{\frac{n}{m}}(\log L)^n(h\circ\log \circ \log L)^n$, where the associated weight satisfies Ko{\l}odziej's Condition. Building upon

Junya Shiraishi, Jiechen Chen, Osvaldo Simeone, Petar Popovski

This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks, encompassing possible use cases such as brain-machine interfaces and remote environmental monitoring. In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame. The neur

Pierre Dumond, Alain Lecavelier des Etangs, Flavien Kiefer, Guillaume Hébrard

The Kepler mission, despite its conclusion over a decade ago, continues to offer a rich dataset for uncovering new astrophysical objects and phenomena. In this study, we conducted a comprehensive search for exocometary transit signatures within the Kepler light curves, using a machine learning approach based on a neural network trained on a library of theore

Tongxuan Liu, Tao Peng, Peijun Yang, Xiaoyang Zhao

We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intellig

Hongyu Lu, Wang Yao

The rapid advances in the study of fractional Chern insulators (FCIs) raise a fundamental question: while initially discovered in flat Chern bands motivated by their topological equivalence to Landau levels, is single- particle band topology actually a prerequisite for these many-body topological orders emergent at fractional fillings? Here, we numerically d

Karel Devriendt

The main result of this article is a geometric interpretation of magnitude, a real-valued invariant of metric spaces. We introduce a Euclidean embedding of a (suitable) finite metric space $X$ such that the magnitude of $X$ can be expressed in terms of the `circumradius' of its embedding $S$. The circumradius is the radius of the unique sphere that goes thro

Devon Graham, Eros Rojas Velez, Kevin Leyton-Brown

Utilitarian algorithm configuration identifies a parameter setting for a given algorithm that maximizes a user's utility. Utility functions offer a theoretically well-grounded approach to optimizing decision-making under uncertainty and are flexible enough to capture a user's preferences over algorithm runtimes (e.g., they can describe a sharp cutoff after w

V. Moloi, S. B. Potter, Z. N. Khangale, D. A. H. Buckley

We present a comprehensive photometric, spectroscopic, and polarimetric study of the intermediate polar (IP) 1RXS J080114.6-462324, using observations from the South African Astronomical Observatory (SAAO) 1.0-m and 1.9-m telescopes and the Southern African Large Telescope (SALT), complemented by archival TESS photometry. Photometric and photo-polarimetric d

Alessandro Carminati, Mario Beraha, Federico Camerlenghi, Alessandra Guglielmi

Clustering observations across partially exchangeable groups of data is a routine task in Bayesian nonparametrics. Previously proposed models allow for clustering across groups by sharing atoms in the group-specific mixing measures. However, exact atom sharing can be overly rigid when groups differ subtly, introducing a trade-off between clustering and densi

Alireza Ataei, Ask Ellingsen, Filippa Getzner, Théotime Girardot

We consider the quantitative description of a many-particle gas of interacting abelian anyons in the plane, confined in a trapping potential. If the anyons are modeled as bosons with a magnetic flux attachment, and if the total magnetic flux is small compared to the number of particles, then an average-field description becomes appropriate for the low-energy

Steffen Hagedorn, Luka Donkov, Aron Distelzweig, Alexandru P. Condurache

Planner evaluation in closed-loop simulation often uses rule-based traffic agents, whose simplistic and passive behavior can hide planner deficiencies and bias rankings. Widely used IDM agents simply follow a lead vehicle and cannot react to vehicles in adjacent lanes, hindering tests of complex interaction capabilities. We address this issue by integrating

Bianca Maria Lerma, Rafael Peñaloza

We introduce NAEL (Non-Anthropocentric Ethical Logic), a novel ethical framework for artificial agents grounded in active inference and symbolic reasoning. Departing from conventional, human-centred approaches to AI ethics, NAEL formalizes ethical behaviour as an emergent property of intelligent systems minimizing global expected free energy in dynamic, mult

Nayan Kumar Singh

Although Finite Element Analysis (FEA) is an integral part of the product design lifecycle, the analysis is computationally expensive, making it unsuitable for many design optimization problems. The deep learning models can be a great solution. However, selecting the architecture that emulates the FEA with great accuracy is a challenge. This paper presents a

Nicolas Dutly, Friederike Groschupp, Ivan Puddu, Kari Kostiainen

To mitigate interrupt-based stepping attacks (notably using SGX-Step), Intel introduced AEX-Notify, an ISA extension to Intel SGX that aims to prevent deterministic single-stepping. In this work, we introduce AEX-NStep, the first interrupt counting attack on AEX-Notify-enabled Enclaves. We show that deterministic single-stepping is not required for interrupt

Shinwoo An, Seonghyuk Im, Seokbeom Kim, Myounghwan Lee

Given a family $\mathcal{F}$ of graphs, a graph is \emph{$\mathcal{F}$-subgraph-free} if it has no subgraph isomorphic to a member of $\mathcal{F}$. We present a fixed-parameter linear-time algorithm that decides whether a planar graph can be made $\mathcal{F}$-subgraph-free by deleting at most $k$ vertices or $k$ edges, where the parameters are $k$, $\lvert

Fengxiang Zhao, Jianping Gan, Kun XU

This study presents a high-order, space-time coupled arbitrary Lagrangian Eulerian (ALE) compact gas-kinetic scheme (GKS) for the shallow water equations on moving unstructured meshes. The proposed method preserves both the geometric conservation law (GCL) and the well-balanced property. Mesh motion effects are directly incorporated by formulating numerical

Jinglei Zhang, Yuanfan Guo, Rolandos Alexandros Potamias, Jiankang Deng

In recent years, video question answering based on multimodal large language models (MLLM) has garnered considerable attention, due to the benefits from the substantial advancements in LLMs. However, these models have a notable deficiency in the domains of video temporal grounding and reasoning, posing challenges to the development of effective real-world vi

Neil T. Lewis, Tom Joshi-Hartley, Steven M. Tobias, Laura K. Currie

The bulk properties of convection in stellar and giant planet interiors are often assumed to be independent of the molecular diffusivities, which are very small. By contrast, simulations of this process in rotating, spherical shells, which are typically driven by conductive boundary heat fluxes, generally yield results that depend on the diffusivity. This ma

Marco Simoni, Aleksandar Fontana, Andrea Saracino, Paolo Mori

TITAN (Threat Intelligence Through Automated Navigation) is a framework that connects natural-language cyber threat queries with executable reasoning over a structured knowledge graph. It integrates a path planner model, which predicts logical relation chains from text, and a graph executor that traverses the TITAN Ontology to retrieve factual answers and su

Sara Altamirano, Arjan Vreeken, Sennay Ghebreab

Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to algorithmic bias, ML may inadvertently reinforce existing health disparities. We present a systematic literature review of algorithmic bias identification, discussion, and reportin

Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam, Asif Karim

Knowledge distillation (KD) has traditionally relied on a static teacher-student framework, where a large, well-trained teacher transfers knowledge to a single student model. However, these approaches often suffer from knowledge degradation, inefficient supervision, and reliance on either a very strong teacher model or large labeled datasets. To address thes

Kastytis Zubovas, Matas Tartėnas

Large-scale outflows driven by AGNs are an important element of galaxy evolution. Detailed analysis of their properties allows us to probe the activity history of the galactic nucleus and, potentially, other properties of the host galaxy. A recent paper presents detailed radial velocity profiles of outflows in ten AGN host galaxies and shows a common trend o

Shayan Gharib, Marcelo Hartmann, Arto Klami

We address the problem of distribution shift in unsupervised domain adaptation with a moment-matching approach. Existing methods typically align low-order statistical moments of the source and target distributions in an embedding space using ad-hoc similarity measures. We propose a principled alternative that instead leverages the intrinsic geometry of these

Rikard Rosenbacke, Carl Rosenbacke, Victor Rosenbacke, Martin McKee

As large language models (LLMs) become integrated into everyday and high-stakes decision-making, they inherit the ambiguity and biases of human language. While they produce fluent and coherent outputs, they rely on statistical pattern prediction rather than grounded reasoning, creating a risk of outputs that are plausible but incorrect. This paper argues tha

Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu

Generative speech technologies are progressing rapidly, but evaluating the perceptual quality of synthetic speech remains a core challenge. Existing methods typically rely on scalar scores or binary decisions, which lack interpretability and generalization across tasks and languages. We present SpeechLLM-as-Judges, a new paradigm for enabling large language

Maria Teresa Chiri, Christopher Denaro, Xiaoqian Gong, Benedetto Piccoli

Modeling heterogeneous and multi-lane traffic flow is essential for understanding and controlling complex transportation systems. In this work, we consider three vehicle populations: two classes of human-driven vehicles (cars and trucks) and autonomous vehicles, the latter characterized by controlled acceleration. Compared to single-population models, multi-

Xinyue Ma, Pol Pastells, Mireia Farrús, Mariona Taulé

Semantic prosody is a collocational meaning formed through the co-occurrence of a linguistic unit and a consistent series of collocates, which should be treated separately from semantic meaning. Since words that are literal translations of each other may have different semantic prosody, more attention should be paid to this linguistic property to generate ac