May 2025 arXiv papers — page 32

Miha E. Habič, Charles Weng, Cathy Zhang

We extend the results of arXiv:1808.01509 on nonamalgamable forcing extensions to families of posets with wide projections. We also use a different coding method to obtain nonamalgamable extensions by filter-based Mathias forcing.

Zaixi Zhang, Zhenghong Zhou, Ruofan Jin, Le Cong

DNA, encoding genetic instructions for almost all living organisms, fuels groundbreaking advances in genomics and synthetic biology. Recently, DNA Foundation Models have achieved success in designing synthetic functional DNA sequences, even whole genomes, but their susceptibility to jailbreaking remains underexplored, leading to potential concern of generati

Johannes Lederer, Anne Sabourin, Mahsa Taheri

A notoriously difficult challenge in extreme value theory is the choice of the number $k\ll n$, where $n$ is the total sample size, of extreme data points to consider for inference of tail quantities. Existing theoretical guarantees for adaptive methods typically require second-order assumptions or von Mises assumptions that are difficult to verify and often

Haomiao Qiu, Miao Zhang, Ziyue Qiao, Weili Guan

Continual Learning requires a model to learn multiple tasks in sequence while maintaining both stability:preserving knowledge from previously learned tasks, and plasticity:effectively learning new tasks. Gradient projection has emerged as an effective and popular paradigm in CL, where it partitions the gradient space of previously learned tasks into two orth

Jia-Le Ling, Guo-Hong Du, Tian-Nuo Li, Jing-Fei Zhang

Recently, the baryon acoustic oscillations (BAO) measurements from the DESI survey have suggested hints of dynamical dark energy, challenging the standard $\Lambda $CDM model. In this work, we adopt an improved inverse distance ladder approach based on the latest cosmological data to provide a model-independent perspective, employing a global parametrization

Enfang Cui, Yujun Cheng, Rui She, Dan Liu

The rapid evolution of Large Language Model (LLM) agents has highlighted critical challenges in cross-vendor service discovery, interoperability, and communication. Existing protocols like model context protocol and agent-to-agent protocol have made significant strides in standardizing interoperability between agents and tools, as well as communication among

Yu Xia, Alex McAvoy, Qi Su

The orderly behaviors observed in large-scale groups, such as fish schooling and the organized movement of crowds, are both ubiquitous and essential for the survival and stability of these systems. Understanding how such complex collective behaviors emerge from simple local interactions and behavioral adjustments is a significant scientific challenge. Histor

Filippo Rinaldi, Giacomo Capitani, Lorenzo Bonicelli, Donato Crisostomi

Foundation models serve as the backbone for numerous specialized models developed through fine-tuning. However, when the underlying pretrained model is updated or retrained (e.g., on larger and more curated datasets), the fine-tuned model becomes obsolete, losing its utility and requiring retraining. This raises the question: is it possible to transfer fine-

Aswathy Menon Kavumpadikkal Radhakrishnan, Suraj Prasad, Neelkamal Mallick, Raghunath Sahoo

RHIC and LHC have injected $^{16}\rm O$ nuclei in their accelerator complexes with a focus on investigating collectivity and the origin of quark-gluon plasma signatures in small collision systems. The $^{16}\rm O$ nuclei are known to possess clusters of $\alpha$-particles ($^{4}\rm He$) inside the nucleus. This paper attempts to study the clustered-nuclear-g

Lukas Liedtke, Per Gunnar Kjeldsberg, Frank Alexander Kraemer, Magnus Jahre

The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT systems require extensive evaluation to demonstrate that they are sufficiently performant across the full range of expect

Lorenzo Lamberti, Antoine Lemenant

In this paper we slightly improve the regularity theory for the so called optimal design problem. We first establish the uniform rectifiability of the boundary of the optimal set, for a larger class of minimizers, in any dimension. As an application, we improve the bound obtained by Larsen in dimension~2 about the mutual distance between two connected compon

Yunhak Oh, Junseok Lee, Yeongmin Kim, Sangwoo Seo

Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recent graph-based methods leverage both gene expression and spatial information to identify relevant spatial domains. However, these approaches fall short in obtaining meaningful s

Gabriele Visentin, Patrick Cheridito

We present a novel method for efficiently computing optimal transport maps and Wasserstein barycenters in high-dimensional spaces. Our approach uses conditional normalizing flows to approximate the input distributions as invertible pushforward transformations from a common latent space. This makes it possible to directly solve the primal problem using gradie

Amir Shahhosseini, Thomas Chaffey, Rodolphe Sepulchre

A novel splitting algorithm is proposed for the numerical simulation of neuromorphic circuits. The algorithm is grounded in the operator-theoretic concept of monotonicity, which bears both physical and algorithmic significance. The splitting exploits this correspondence to translate the circuit architecture into the algorithmic architecture. The paper illust

Aihu Zhang, Jiaxing Xu, Mengcheng Lan, Shili Xiang

Graph Neural Networks (GNNs) have achieved significant success in various learning tasks on graph-structured data. Nevertheless, most GNNs struggle to generalize to heterophilic neighborhoods. Additionally, many GNNs ignore the directional nature of real-world graphs, resulting in suboptimal performance on directed graphs with asymmetric structures. In this

Xiangyu Chang, Xi Chen, Yining Wang, Zhiyi Zeng

This paper studies a bandit optimization problem where the goal is to maximize a function $f(x)$ over $T$ periods for some unknown strongly concave function $f$. We consider a new pairwise comparison oracle, where the decision-maker chooses a pair of actions $(x, x')$ for a consecutive number of periods and then obtains an estimate of $f(x)-f(x')$. We show t

Kewen Chen, Xiaobin Hu, Wenqi Ren

Recent advances in large-scale text-to-image generation models have led to a surge in subject-driven text-to-image generation, which aims to produce customized images that align with textual descriptions while preserving the identity of specific subjects. Despite significant progress, current methods struggle to disentangle identity-relevant information from

Matan Schliserman, Tomer Koren

We study the generalization performance of unregularized gradient methods for separable linear classification. While previous work mostly deal with the binary case, we focus on the multiclass setting with $k$ classes and establish novel population risk bounds for Gradient Descent for loss functions that decay to zero. In this setting, we show risk bounds tha

Zhiyi Wan, Wanrou Du, Liang Li, Miao Pan

Large language models (LLMs) often suffer from catastrophic forgetting in continual learning (CL) scenarios, where performance on previously learned tasks degrades severely while training on sequentially arriving tasks. Although pioneering CL approaches using orthogonal subspaces can mitigate task interference, they typically employ fixed budget allocation,

David C. Luo, Shaun Stevens

In this paper, we use type theory to construct a family of depth $\frac{1}{N}$ minimax supercuspidal representations of $\text{GL}(2N, F)$ which we call middle supercuspidal representations. These supercuspidals may be viewed as a natural generalization of simple supercuspidal representations, i.e. those supercuspidals of minimal positive depth. Via explicit

Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot

Deploying machine learning models in safety-critical domains poses a key challenge: ensuring reliable model performance on downstream user data without access to ground truth labels for direct validation. We propose the suitability filter, a novel framework designed to detect performance deterioration by utilizing suitability signals -- model output features

Yongkang Liu, Xingle Xu, Ercong Nie, Zijing Wang

Parameter-Efficient Fine-Tuning (PEFT) methods achieve performance comparable to Full Fine-Tuning (FFT) while requiring significantly fewer computing resources, making it the go-to choice for researchers. We find that although PEFT can achieve competitive results on some benchmarks, its performance falls short of FFT in complex tasks, such as reasoning and i

Judith Sieker, Clara Lachenmaier, Sina Zarrieß

This paper examines how LLMs handle false presuppositions and whether certain linguistic factors influence their responses to falsely presupposed content. Presuppositions subtly introduce information as given, making them highly effective at embedding disputable or false information. This raises concerns about whether LLMs, like humans, may fail to detect an

Noora Al-Emadi, Ingmar Weber, Yin Yang, Ferda Ofli

Detecting vehicles in satellite images is crucial for traffic management, urban planning, and disaster response. However, current models struggle with real-world diversity, particularly across different regions. This challenge is amplified by geographic bias in existing datasets, which often focus on specific areas and overlook regions like the Middle East.

Poulomee Ghosh, Shubhendu Bhasin

This paper proposes an adaptive tracking controller for uncertain Euler-Lagrange (E-L) systems with user-defined state and input constraints in presence of bounded external disturbances. A barrier Lyapunov function (BLF) is employed for state constraint satisfaction, integrated with a saturated controller that ensures the control input remains within pre-spe

Konrad K. Dabrowski, Tala Eagling-Vose, Matthew Johnson, Giacomo Paesani

For an integer $d\geq 1$, the $d$-Cut problem is that of deciding whether a graph has an edge cut in which each vertex is adjacent to at most $d$ vertices on the opposite side of the cut. The $1$-Cut problem is the well-known Matching Cut problem. The $d$-Cut problem has been extensively studied for $H$-free graphs. We extend these results to the probe graph

Michele Stecconi, Anna Paola Todino

We investigate the random variable defined by the volume of the zero set of a smooth Gaussian field, on a general Riemannian manifold possibly with boundary, a fundamental object in probability and geometry. We prove a new explicit formula for its Wiener-It\^o chaos decomposition that is notably simpler than existing alternatives and which holds in greater g

Aditya Kanade, Tanuja Ganu

Multimodal Large Language Models (MLLMs) show reasoning promise, yet their visual perception is a critical bottleneck. Strikingly, MLLMs can produce correct answers even while misinterpreting crucial visual elements, masking these underlying failures. Our preliminary study on a joint perception-reasoning dataset revealed that for one leading MLLM, 29% of its

Anjie Xu, Ruiqing Ding, Leye Wang

Scientific research heavily depends on suitable datasets for method validation, but existing academic platforms with dataset management like PapersWithCode suffer from inefficiencies in their manual workflow. To overcome this bottleneck, we present a system, called ChatPD, that utilizes Large Language Models (LLMs) to automate dataset information extraction

Zhijian Shi, Shengjie Xu, Jianfeng Wang, Yi Du

As an ideal platform for studying interplays between symmetry, topology and magnetism, the magnetic topological insulator (MTI) MnBi2Te4 has attracted extensive attentions. However, its strong n-type intrinsic defects hinder the realizations of exotic phenomena. Stimulated by recent discoveries that Ge doping can efficiently tune the position of Fermi level,

Poulomee Ghosh, Shubhendu Bhasin

This paper proposes a robust model reference adaptive controller (MRAC) for uncertain multi-input multi-output (MIMO) linear time-invariant (LTI) plants with user-defined constraints on the plant states, input amplitude, and input rate. The proposed two-layer barrier Lyapunov function (BLF)-based control design considers the input and the input rate as state

Alexandre Benatti, Roberto M. Cesar, Luciano da F. Costa

Different types of graphs and complex networks have been characterized, analyzed, and modeled based on measurements of their respective topology. However, the available networks may constitute approximations of the original structure as a consequence of sampling incompleteness, noise, and/or error in the representation of that structure. Therefore, it become

Nikhil Behari, Aaron Young, Tzofi Klinghoffer, Akshat Dave

Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary co

Zhonghao Lyu, Yulan Gao, Junting Chen, Hongyang Du

Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer transformative potential to further enhance the intelligence of LAE services. However, deploying LAIMs in LAE poses several challenges, including the significant gap between their compu

Shriram M Sathiyanarayanan, Xinyue Hao, Shihao Hou, Yang Lu

The success of the machine learning field has reliably depended on training on large datasets. While effective, this trend comes at an extraordinary cost. This is due to two deeply intertwined factors: the size of models and the size of datasets. While promising research efforts focus on reducing the size of models, the other half of the equation remains fai

Pınar Kirezli, Doğukan Taşer

In this study, the source of the black bounce is discussed in the context of $f(Q)$ theory. A body of research has been dedicated to the study of symmetric black bounce solutions that are generated by a combination of a scalar field with a non-zero potential and a magnetic charge within the framework of non-linear electrodynamics. As exact solutions are not

Emanuela L. Giacomelli, Christian Hainzl, Phan Thành Nam, Robert Seiringer

Our work establishes a three-term asymptotic expansion of the ground state energy of a dilute gas of spin $1/2$ fermions with repulsive short-range interactions, validating a formula predicted by Huang and Yang in 1957. The formula is universal in the sense that it holds for a large class of interaction potentials and depends on those only via their scatteri

Hanyang Wang, Lu Wang, Chaoyun Zhang, Tianjun Mao

Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent work augments RL with textual critiques through prompting or reflection, improving interpretability but leaving model parameters untouched. We introduce Text2Grad, a reinforcement-lea

Samara Ghrer, Christophe Godin, Stefanie Wuhrer

Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that al

Yiming Huang, Jiyu Guo, Wenxin Mao, Cuiyun Gao

Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to

Robert Connelly, Zhen Zhang

Here we present a rigidity result in a global (semi-global, homotopy) setting for a restrictive class of polytopes, those that can be inscribed in a unit sphere, with some additional conditions. The proof of the rigidity result for cabled frameworks on the surface of the sphere uses classical isoperimetric ideas.

Wancai Zheng, Linlin Ou, Jiajie He, Libo Zhou

Recent 3D Gaussian Splatting (3DGS) techniques for Visual Simultaneous Localization and Mapping (SLAM) have significantly progressed in tracking and high-fidelity mapping. However, their sequential optimization framework and sensitivity to dynamic objects limit real-time performance and robustness in real-world scenarios. We present UP-SLAM, a real-time RGB-

Lai Wei, Yuting Li, Kaipeng Zheng, Chen Wang

Recent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in this progress. While "aha moment" patterns--where models exhibit self-correction through reflection--are often attributed to emergent properties from RL, we first demonstrate that

Klaus Altmann, Andreas Hochenegger, Frederik Witt

Let $\mathcal E$ be a torus-linearised reflexive sheaf over a smooth projective toric variety. Generalising a theorem of Perlman and Smith, we prove an explicit sufficient condition for $\mathcal E$ to be acyclic via Weil decorations.

Timo Löhr, Paul Hofman, Felix Mohr, Eyke Hüllermeier

Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we propose a theoretically grounded approach to credal prediction based on the statistical notion of relative likelihood: The target of prediction is the set of all (conditional) probabil

Hanxu Jiang, Haiyue Yu, Xiaotong Xie, Qi Gao

Adaptive sampling based on Gaussian process regression (GPR) has already been applied with considerable success to generate boundary test scenarios for multi-UAV systems (MUS). One of the key techniques in such researches is leveraging the accurate prediction of the MUS performance through GPR in different test scenarios. Due to the potential correlations am

Ilya Starshynov, Maximilian Weimar, Lukas M. Rachbauer, Günther Hackl

Artificial neural networks have become important tools to harness the complexity of disordered or random photonic systems. Recent applications include the recovery of information from light that has been scrambled during propagation through a complex scattering medium, especially in the challenging case where the deterministic input-output transmission matri

Luca Esposito, Lorenzo Lamberti, Dattatreya N. N., Prosenjit Roy

We study the asymptotic behavior of sequences of solutions, energies functionals, and the first eigenvalues associated with the Finsler $p$-Laplace operator, also known as the anisotropic $p$-Laplace operator on a sequence of bounded cylinders whose length tends to infinity. We prove that the solutions on the bounded cylinders converge to the solution on the

Eleni Nisioti, Joachim Winther Pedersen, Erwan Plantec, Milton L. Montero

The ability to continuously and efficiently transfer skills across tasks is a hallmark of biological intelligence and a long-standing goal in artificial systems. Reinforcement learning (RL), a dominant paradigm for learning in high-dimensional control tasks, is known to suffer from brittleness to task variations and catastrophic forgetting. Neuroevolution (N

O. E. Kvitnitskaya, L. Harnagea, O. D. Feia, D. V. Efremov

We measured the I(V) and dV/dI(V) characteristics of TiTe2-based point contacts (PCs) from room to helium temperatures. Features indicating the emergence of a charge density wave (CDW) were detected. They represent symmetrical relatively V=0 maxima in dV/dI(V) around 150 mV at liquid helium temperatures, which disappear above 150 K, similar to the case of si

Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury

Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social Good" (NLP4SG) across nine domains relevant to global development and risk agendas, summarizing principal tasks and challenges. We analyze ACL Anthology trends, finding that inclusi

James M. Adams, Gesine Reinert, Lukasz Szpruch, Carsten Maple

Counterfactual explanations for black-box models aim to pr ovide insight into an algorithmic decision to its recipient. For a binary classification problem an individual counterfactual details which features might be changed for the model to infer the opposite class. High-dimensional feature spaces that are typical of machine learning classification models a

Antonio Caputo

Classical Graph Signal Processing (GSP) provides a robust framework for analyzing signals on irregular domains, utilizing the graph Fourier transform as a cornerstone for spectral analysis and filtering. However, as data structures grow in complexity, there is an increasing need to handle multi-dimensional information. In this paper, we propose a generalizat

Adam B. Cahaya

Recent experimental observations of exchange bias in the La$_{0.67}$Sr$_{0.33}$MnO$_{3}$/LaAlO$_{3}$/SrTiO$_{3}$ heterostructure, which lacks an intrinsic antiferromagnetic layer, have sparked theoretical investigations into the underlying mechanisms. While traditional theories suggest that exchange bias in spin valve structures is mediated by conduction ele

Hongcan Guo, Haolang Lu, Guoshun Nan, Bolun Chu

Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades overall performance during post-training.

Antonio Arnal, Jussi Behrndt, Markus Holzmann, Petr Siegl

We extend the notion of generalized boundary triples and their Weyl functions from extension theory of symmetric operators to adjoint pairs of operators, and we provide criteria on the boundary parameters to induce closed operators with a nonempty resolvent set. The abstract results are applied to Schr\"odinger operators with complex $L^p$-potentials on boun

Xudong Wang, Jian Zhu, Ruichen Zhang, Lei Feng

Recent advances in large language models (LLMs) have opened new possibilities for automated reasoning and decision-making in wireless networks. However, applying LLMs to wireless communications presents challenges such as limited capability in handling complex logic, generalization, and reasoning. Chain-of-Thought (CoT) prompting, which guides LLMs to genera

Entong Zhao, Yu-Jun Liu, Ka Kwan Pak, Peng Ren

We propose a flexible Raman lattice system for alkaline-earth-like atoms to theoretically investigate localization behaviors in a quasi-periodic lattice with controllable non-Hermiticity. Our analysis demonstrates that critical phases and mobility edges can arise by adjusting spin-dependence of the incommensurate potentials in the Hermitian regime. With non-

Anish R Joishy, Ishwar B Balappanawar, Vamshi Krishna Bonagiri, Manas Gaur

A fundamental challenge in reasoning is navigating hypothetical, counterfactual worlds where logic may conflict with ingrained knowledge. We investigate this frontier for Large Language Models (LLMs) by asking: Can LLMs reason logically when the context contradicts their parametric knowledge? To facilitate a systematic analysis, we first introduce CounterLog

Vladimir A. Zyuzin

In this paper we theoretically discuss thermal Hall effect of magnons in insulating N\'{e}el ordered antiferromagnets at zero external magnetic field. We show that for compensated N\'{e}el order the non-zero thermal Hall effect will occur in the absence of any symmetry between the two magnetic sublattices, thus making the system ferrimagnetic. We then show t

Konrad Özdemir, Lukas Kirchdorfer, Keyvan Amiri Elyasi, Han van der Aa

Business process simulation (BPS) is a key tool for analyzing and optimizing organizational workflows, supporting decision-making by estimating the impact of process changes. The reliability of such estimates depends on the ability of a BPS model to accurately mimic the process under analysis, making rigorous accuracy evaluation essential. However, the state

Adam B. Cahaya

We investigate thermal spin pumping in gadolinium iron garnet (GdIG), focusing on the mode-resolved dynamics of antiferromagnetic magnons and their impact on spin and heat transport. Antiferromagnets support both right-handed and left-handed magnon modes, which we treat as positive and negative frequency branches, analogous to electrons and holes in semicond

Ilane Schroetter, Olivier Berné, Emeric Bron, Felipe Alarcon

The chemical composition of exoplanets is thought to be influenced by the composition of the disks in which they form. JWST observations have unveiled a variety of species in numerous nearby disks, showing significant variations in the C/O abundance ratio. However, little is known about the composition and C/O ratio of disks around young stars in clusters ex

Kaixuan Wei, Hector A. Jimenez-Romero, Hadi Amata, Jipeng Sun

Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made innovative optical designs possible across a broad range of applications. Many of these systems utilize diffractive optical components (DOEs) for holography, PSF engineering, or wavefront shaping. Existing approaches have, however

Jujie He, Jiacai Liu, Chris Yuhao Liu, Rui Yan

The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we present Skywork-OR1, an effective and scalable RL implementation for long Chain-of-Thought (CoT) models. Building on the DeepSeek-R1-Distill model series, our RL approach achieves

Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang

With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (L

Shoaib Ahmed Siddiqui, Adrian Weller, David Krueger, Gintare Karolina Dziugaite

Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a small set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning in vision classifiers. We make the surprising discovery that forget-set accuracy can recover

Xiangling Xu, Marc-Olivier Renou, Igor Klep

Whether an almost-commuting pair of operators must be close to a commuting pair is a central question in operator and matrix theory. We investigate this problem for pairs of $C^*$-subalgebras $\mathcal{A}$ and $\mathcal{B}$ of $M_d(\mathbb{C})$, showing that each operator in $\mathcal{B}$ is $O(d^2\epsilon)$-close in operator norm to an operator in the commu

Zachary Shinnick, Liangze Jiang, Hemanth Saratchandran, Anton van den Hengel

Pretraining on large, semantically rich datasets is key for developing language models. Surprisingly, recent studies have shown that even synthetic data, generated procedurally through simple semantic-free algorithms, can yield some of the same benefits as natural language pretraining. It is unclear what specific capabilities such simple synthetic data insti

Manuel Klädtke, Moritz Schulze Darup

We investigate the data usage and predictive behavior of data-driven predictive control (DPC) with 1-norm regularization. Our analysis enables the offline removal of unused data and facilitates a comparison between the identified symmetric structure and data usage against prior knowledge of the true system. This comparison helps assess the suitability of the

Zehua Chen, Yuyang Miao, Liyuan Wang, Luyun Fan

Cardiovascular signals such as photoplethysmography (PPG), electrocardiography (ECG), and blood pressure (BP) are inherently correlated and complementary, together reflecting the health of cardiovascular system. However, their joint utilization in real-time monitoring is severely limited by diverse acquisition challenges from noisy wearable recordings to bur

Md Touhidul Islam, Imran Kabir, Md Alimoor Reza, Syed Masum Billah

We present IKIWISI ("I Know It When I See It"), an interactive visual pattern generator for assessing vision-language models in video object recognition when ground truth is unavailable. IKIWISI transforms model outputs into a binary heatmap where green cells indicate object presence and red cells indicate object absence. This visualization leverages humans'

Jiali Chen, Xusen Hei, HongFei Liu, Yuancheng Wei

Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and corr

Grzegorz Wolny, Michał Szczerbak

In this study, we propose a solution based on a multi-agent LLM architecture and a voice user interface (VUI) designed to update the knowledge base of a digital assistant. Its usability is evaluated in comparison to a more traditional graphical content management system (CMS), with a focus on understanding the relationship between user preferences and the co

Triantafyllos Kormpakis, Manuel Linares, Jordi José

We present a compilation of observed recurrence times ($t_{\rm rec}$) and infer the corresponding local mass-accretion rates ($\dot m$) for type I X-ray bursts, milliHertz quasi-periodic oscillating sources and recurrent novae eruptions. We construct models of the $t_{\rm rec}-\dot m$ relation for accreting white dwarfs and neutron stars and find that both a

Malte Henkel, Stoimen Stoimenov

For ageing systems, quenched onto a critical temperature $T=T_c$ such that the dominant noise comes from the thermal bath, with a non-conserved order-parameter and in addition with dynamical exponent ${z}=2$, the form of the two-time auto-correlator as well as the time-space form of the single-time correlator are derived from Schr\"odinger-invariance, genera

Radu Curticapean, Simon Döring, Daniel Neuen

We consider the parameterized problem $\#$IndSub$(\Phi)$ for fixed graph properties $\Phi$: Given a graph $G$ and an integer $k$, this problem asks to count the number of induced $k$-vertex subgraphs satisfying $\Phi$. D\"orfler et al. [Algorithmica 2022] and Roth et al. [SICOMP 2024] conjectured that $\#$IndSub$(\Phi)$ is $\#$W[1]-hard for all non-meager pr

Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang

Information retrieval plays a crucial role in resource localization. Current dense retrievers retrieve the relevant documents within a corpus via embedding similarities, which compute similarities between dense vectors mainly depending on word co-occurrence between queries and documents, but overlook the real query intents. Thus, they often retrieve numerous

Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang

Large language models (LLMs) exhibit impressive language capabilities but remain vulnerable to malicious prompts and jailbreaking attacks. Existing knowledge editing methods for LLM detoxification face two major challenges. First, they often rely on entity-specific localization, making them ineffective against adversarial inputs without explicit entities. Se

Jiajun Yang, Xiaofeng Li, Linlin Yuan, Li Lao

During femtosecond laser filamentation in air, the velocity ratio (K) between the terahertz (THz) phase velocity and the laser group velocity plays a crucial role in THz waves generation. However, K is typically assumed to be unity and its impact has been long overlooked due to the more attention paid to the more easily controlled filament length. Here, we i

Haosheng Zou, Xiaowei Lv, Shousheng Jia, Lin Li

Adding sequence parallelism into LLaMA-Factory, we open-sourced 360-LLaMA-Factory at https://github.com/Qihoo360/360-LLaMA-Factory. 360-LLaMA-Factory has received wide recognition and used in models such as Light-R1 arXiv:2503.10460, TinyR1 arXiv:2503.04872, Kaggle AIMO math models and also in large companies' training frameworks. This technical report delve

Lucas E. A. Stehouwer, Merrit P. Losert, Maia Rigot, Davide Degli Esposti

Electron spin qubits in Si/SiGe quantum wells are limited by the small and variable energy separation of the conduction band valleys. While sharp quantum well interfaces are pursued to increase the valley splitting energy deterministically, here we explore an alternative approach to enhance the valley splitting on average. We grow increasingly thinner quantu

Tomasz Sobczak, Tymon Frelik

Our study of Goursat distributions originates new types of $k$-contact distributions and Lie systems with applications. In particular, families of generators for Goursat distributions on $\mathbb{R}^4, \mathbb{R}^5$ and $\mathbb{R}^6$ give rise to Lie systems and we characterise Goursat structures that are $k$-contact distributions. Our results are used to s

Dacao Zhang, Kun Zhang, Shimao Chu, Le Wu

With the rapid development of Large Language Models (LLMs), Parameter-Efficient Fine-Tuning (PEFT) methods have gained significant attention, which aims to achieve efficient fine-tuning of LLMs with fewer parameters. As a representative PEFT method, Low-Rank Adaptation (LoRA) introduces low-rank matrices to approximate the incremental tuning parameters and a

Lin Zhong, Lingzhi Wang, Xu Yang, Qing Liao

Large Language Models (LLMs) offer new opportunities for the next Point-Of-Interest (POI) prediction task, leveraging their capabilities in semantic understanding of POI trajectories. However, previous LLM-based methods, which are superficially adapted to next POI prediction, largely overlook critical challenges associated with applying LLMs to this task. Sp

I. Bitter, V. Konakov

The paper presents a generalization of the local limit theorem on the convergence of inhomogeneous Markov chains to the diffusion limit for the case where the corresponding process coefficients satisfy weak regularity conditions and coincide only asymptotically. In particular, the drift coefficients considered by us can be unbounded with at most linear growt

Samuel Frontull, Thomas Ströhle

Large Language Models (LLMs) have demonstrated strong capabilities in multilingual machine translation, sometimes even outperforming traditional neural systems. However, previous research has highlighted the challenges of using LLMs, particularly with prompt engineering, for low-resource languages. In this work, we introduce Fragment-Shot Prompting, a novel

E. Iñiguez, M. Freire, L. Leal, E. Contreras

We employ the Dirac procedure to quantize the self-dual massive Kalb-Ramond-Klein-Gordon model in $2+1$ dimensional spacetimes. The canonical fields are expressed in terms of $2$-surfaces and signed points, ensuring the automatic realization of the quantum algebra. As the duality rotation preserving the action can be implemented infinitesimally, we derive th

Saptarshi Neil Sinha, P. Julius Kuehn, Johannes Koppe, Arjan Kuijper

The preservation of early visual arts, particularly color photographs, is challenged by deterioration caused by aging and improper storage, leading to issues like blurring, scratches, color bleeding, and fading defects. Despite great advances in image restoration and enhancement in recent years, such systematic defects often cannot be restored by current sta

Fanzeng Xia, Yidong Luo, Tinko Sebastian Bartels, Yaqi Xu

Recent research has highlighted that Large Language Models (LLMs), even when trained to generate extended long reasoning steps, still face significant challenges on hard reasoning problems. However, much of the existing literature relies on direct prompting with simple in-context learning examples for evaluation, which largely overlooks advanced techniques t

Yan Zhang, Jun Liao, Xinyan Fan, Kuangnan Fang

In complex systems, networks represent connectivity relationships between nodes through edges. Latent space models are crucial in analyzing network data for tasks like community detection and link prediction due to their interpretability and visualization capabilities. However, when the network size is relatively small, and the true latent space dimension is

Jan Speller, Malte Luttermann, Marcel Gehrke, Tanya Braun

Probabilistic graphical models that encode indistinguishable objects and relations among them use first-order logic constructs to compress a propositional factorised model for more efficient (lifted) inference. To obtain a lifted representation, the state-of-the-art algorithm Advanced Colour Passing (ACP) groups factors that represent matching distributions.

Daniel McDuff, Tim Korjakow, Kevin Klyman, Danish Contractor

Foundation models have had a transformative impact on AI. A combination of large investments in research and development, growing sources of digital data for training, and architectures that scale with data and compute has led to models with powerful capabilities. Releasing assets is fundamental to scientific advancement and commercial enterprise. However, c

Junmin Gong, Sean Zhao, Sen Wang, Shengyuan Xu

We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic architectural design. Current methods face inherent trade-offs between generation speed, musical coherence, and controllability. For example, LLM-based models (e.g.

Haiquan Lu, Yong Zeng, Shaodan Ma, Bin Li

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as 3D maneuverability, flexible deployment, and LoS air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by

M. Bondi, I. Prandoni, M. Magliocchetti, L. Bisigello

Bright SMGs contribute significantly to the star formation rate (SFR) density (20-50\%) and stellar mass density ($\sim$ 30-50\%) at $z=$ 2-4 with SFRs$\ge 1000$ M$_\odot$,yr$^{-1}$ and stellar masses of $\sim 10^{11}$-$10^{12}$ M$_\odot$. The number of bright SMGs with such high SFRs is hard to reconcile with the standard models of galaxy formation and evol

Junyu Fan, Chuanlin Liao, Yi Lin

As a fundamental imaging task, All-in-One Image Restoration (AiOIR) aims to achieve image restoration caused by multiple degradation patterns via a single model with unified parameters. Although existing AiOIR approaches obtain promising performance in closed and controlled scenarios, they still suffered from considerable performance reduction in real-world

Julia Ruohotie, Simon Good, Christian Möstl, Emilia Kilpua

Intermittency has been studied extensively in the fast and slow solar winds but to a far lesser extent in interplanetary coronal mass ejections (ICMEs). While ICMEs are often characterized by their relatively smooth, large-scale magnetic flux rope structures, a spectrum of fluctuations is nonetheless present at smaller scales. We have examined kurtosis and i

M. Athanasakis-Kaklamanakis, M. Au, R. Berger, S. Degenkolb

Searches for electric dipole moments (EDMs) in fundamental particles and quantum systems with spin are pivotal experiments at the intersection of low-energy and high-precision particle physics. These investigations offer a complementary pathway to uncovering new physics beyond the Standard Model, parallel to high-energy collider searches. EDM experiments are

Zihan Xu, Haotian Ma, Gongbo Zhang, Yihao Ding

Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise