October 2025 arXiv papers — page 195

Eliška Krátká, Aurél Gábor Gábris

In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum Support Vector Machine (QSVM) algorithm compared to SVM. A publicly available dataset containing raw binaries of Portable Executable (PE) fil

Islomjon Shukhratov, Sergey Gorinsky

Capturing and rendering three-dimensional (3D) objects in real time remain a significant challenge, yet hold substantial potential for applications in augmented reality, digital twin systems, remote collaboration and prototyping. We present an end-to-end pipeline that leverages 3D Gaussian Splatting (3D GS) to enable rapid acquisition and interactive renderi

Gennaro Ciampa, Renato Lucà

We consider the phenomenon of magnetic reconnection, namely a change in the topology of magnetic lines, for sufficiently regular solutions of the three-dimensional periodic magnetohydrodynamic (MHD) equations. We provide examples where magnetic reconnection occurs on time scales shorter than the resistive one, due to enhanced dissipation emerging from advect

Haotian Wu, Shufan Jiang, Chios Chen, Yiyang Feng

As large language models (LLMs) advance in role-playing (RP) tasks, existing benchmarks quickly become obsolete due to their narrow scope, outdated interaction paradigms, and limited adaptability across diverse application scenarios. To address this gap, we introduce FURINA-Builder, a novel multi-agent collaboration pipeline that automatically constructs ful

Somasekhara Goud Sontti, Arnab Atta

A three-dimensional, volume-of-fluid (VOF) based CFD model is presented to investigate droplet formation in a microfluidic T-junction. Genesis of Newtonian droplets in non-Newtonian liquid is numerically studied and characterized in three different regimes, viz., squeezing, dripping and jetting. Various influencing factors such as, continuous and dispersed p

Louis Golowich, Venkatesan Guruswami

It is a major challenge to construct good quantum codes supporting fault-tolerant (e.g. transversal) non-Clifford gates with low-weight parity-check measurements. In this paper, we construct the first known quantum codes with linear dimension and distance supporting transversal non-Clifford gates that have sublinear locality (i.e. parity-check weight). Speci

Hayato Shinto, Yu Ohki, Kenji Mizumoto, Kei Saito

In conjunction with a social gathering held on a university campus, the movement of attendees were tracked within the venue for approximately two hours using a UWB indoor positioning system, in order to visualize their interpersonal communication. Network and community analyses were performed on attendee interaction data, and the evolution of communities ove

Thomas J. G. Smits, Jannis Teunissen, Ute Ebert

CO$_2$ with an admixture of C$_4$F$_7$N could serve as an eco-friendly alternative to the extreme greenhouse gas SF$_6$ in high-voltage insulation. Streamer discharges in such gases are different from those in air due to the rapid conductivity decay in the streamer channels. Furthermore, since no effective photoionisation mechanism is known, we expect discha

Augustin Muster, Diego Romero Abujetas, Frank Scheffold, Luis S. Froufe-Pérez

We propose a method to tune interactions between absorptionless colloidal particle pairs. This is achieved via optimization of the spectral energy density of a homogeneous random optical field. Several standard and more exotic interaction potentials, as well as their negative counterparts, are shown to be successfully tuned. We show that the effective dimens

Nhan Nguyen, Maria Ruas, Saurabh Trivedi

In this paper, we introduce the notion of Lipschitz modality for isolated singularities $ f: (\mathbb{C}^n, 0) \to (\mathbb{C}, 0)$ and provide a complete classification of Lipschitz unimodal singularities of corank~2 with non-zero $4$-jets. As a consequence, such singularities are Lipschitz unimodal if they deform to $J_{10}$ but not to $J_{3,0}$. Furthermo

Changlin Song, Yunzhong Hou, Michael Randall Barnes, Rahul Shome

Extreme amodal detection is the task of inferring the 2D location of objects that are not fully visible in the input image but are visible within an expanded field-of-view. This differs from amodal detection, where the object is partially visible within the input image, but is occluded. In this paper, we consider the sub-problem of face detection, since this

Tavish McDonald, Bo Lei, Stanislav Fort, Bhavya Kailkhura

Test-time reasoning has raised benchmark performances and even shown promise in addressing the historically intractable problem of making models robust to adversarially out-of-distribution (OOD) data. Indeed, recent work used reasoning to aid satisfaction of model specifications designed to thwart attacks, finding a striking correlation between LLM reasoning

Zheng Wei, Yunqi Li, Yucheng He, Yuelu Li

Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan, a region famous for its extr

Paolo Onorati, Sofia Ruiz-Suarez, Radu Craiu

Population dynamics models play an important role in a number of fields, such as actuarial science, demography, and ecology, as they help explain past fluctuations and predict future population. The accuracy of these models is often influenced by the uncertainty introduced by sampling error. Statistical inference for these models can be difficult when, in ad

Wen-Yu Chang, Tzu-Hung Huang, Chih-Ho Chen, Yun-Nung Chen

Amid the rapid rise of agentic dialogue models, realistic user-simulator studies are essential for tuning effective conversation strategies. This work investigates a sales-oriented agent that adapts its dialogue based on user profiles spanning age, gender, and occupation. While age and gender influence overall performance, occupation produces the most pronou

Zhen Cao, Felix Aharonian, Yunxiang Bai, Yiwei Bao

Ultra-high-energy (UHE), exceeding 100 TeV (10^12 electronvolts), {\gamma}-rays manifests extreme particle acceleration in astrophysical sources. Recent observations by {\gamma}-ray telescopes, particularly by the Large High Altitude Air Shower Observatory (LHAASO), have revealed a few tens of UHE sources, indicating numerous Galactic sources capable of acce

Yun-Ning, Hung, Igor Pereira, Filip Korzeniowski

In recent years, significant advances have been made in music source separation, with model architectures such as dual-path modeling, band-split modules, or transformer layers achieving comparably good results. However, these models often contain a significant number of parameters, posing challenges to devices with limited computational resources in terms of

Dmytro Zakharov, Oleksandr Kurbatov, Artem Sdobnov, Lev Soukhanov

In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-s

Akshit Singh, Shyam Marjit, Wei Lin, Paul Gavrikov

Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn directly from their environment. In this work, we propose TTRV to enhance vision language understanding by adapting the model on the fly at inference time, without the need for any

Kaichun Yang, Jian Chen

We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visualization questions to compare inference accuracies between the agentic GPT-5 and multimodal GPT-4V, for difficult image instances, where GPT-4V failed to produce correct answers. Ou

Yu-Li He, Youshin Loh

The emerging field of epigenetics has recently unveiled a dynamic landscape in which gene expression is not determined solely by genetic sequences but also by intricate regulatory mechanisms. This review examines the interactions between these regulatory mechanisms, including DNA methylation and non-coding RNAs (ncRNAs), that orchestrate gene expression fine

Luca Giordano, Simon Razniewski

Large Language Models (LLMs) encode substantial factual knowledge, yet measuring and systematizing this knowledge remains challenging. Converting it into structured format, for example through recursive extraction approaches such as the GPTKB methodology (Hu et al., 2025b), is still underexplored. Key open questions include whether such extraction can termin

Biqiang Zhao

In this paper, we study the CR Yamabe type equation \begin{align} \Delta_b u+F(u)=0 \nonumber \end{align} on complete noncompact $(2n+1)$-dimensional Sasakian manifolds with nonnegative curvature. Under some assumptions, we prove a rigidity result, that is, the manifold is CR isometric to Heisenberg group $\mathbb{H}^n$. The proofs are based on the Jerison-L

Attapol T. Rutherford, Jullajak Karnjanaekarin, Narongkorn Panitsrisit, Pontakorn Trakuekul

This technical report introduces JAI-1, a Thai-centric language model with 75B parameters. Recent Thai models have primarily relied on existing open-source models, applying additional training without structural modifications to specialize in Thai. However, this approach risks eroding pre-existing knowledge in the model's parameter space during the injection

Aparna Komarla, Max Hill

We present a novel approach to modeling market dynamics using ordinary differential equations that explicitly incorporates product competitiveness and consumer behavior. Our framework treats market segments as interacting populations in a dynamical system analogous to predator-prey models, where competitive advantages drive market share transitions through m

Maciej Piróg, Filip Sieczkowski

We introduce dicodensity monads: a generalisation of pointwise codensity monads generated by functors to monads generated by mixed-variant bifunctors. Our construction is based on the notion of strong dinaturality (also known as Barr dinaturality), and is inspired by denotational models of certain types in polymorphic lambda calculi - in particular, a form o

Phillip Rothenbeck, Sai Karthikeya Vemuri, Niklas Penzel, Joachim Denzler

The COVID-19 pandemic has highlighted the need for quantitative modeling and analysis to understand real-world disease dynamics. In particular, post hoc analyses using compartmental models offer valuable insights into the effectiveness of public health interventions, such as vaccination strategies and containment policies. However, such compartmental models

Bin Cheng, Ziyuan Wang, Ruixuan Deng, Jianxin Chen

Classical simulation of quantum circuits is a critical tool for validating quantum hardware and probing the boundary between classical and quantum computational power. Existing state-of-the-art methods, notably tensor network approaches, have computational costs governed by the treewidth of the underlying circuit graph, making circuits with large treewidth i

Lei Xu, Pierre Beckmann, Marco Valentino, André Freitas

Neuro-symbolic NLP methods aim to leverage the complementary strengths of large language models and formal logical solvers. However, current approaches are mostly static in nature, i.e., the integration of a target solver is predetermined at design time, hindering the ability to employ diverse formal inference strategies. To address this, we introduce an ada

Qness Ndlovu

While global AI development prioritizes model performance and computational scale, meaningful deployment in African markets requires fundamentally different architectural decisions. This paper introduces Contextual and Cultural Intelligence (CCI) -- a systematic framework enabling AI systems to process cultural meaning, not just data patterns, through locall

Wadim Gerner

We investigate analytically two questions: 1) How does the coil geometry influence the effect of electric current noise on the induced magnetic field? 2) How does the coil-plasma distance influence our ability to control the pointwise magnetic field error in terms of the average magnetic field error? Regarding (1), we argue that the main geometric quantities

Chris S. Benson, Peter S. Barry, Patrick Ashworth, Harry Gordon-Moys

Historically employed spectroscopic architectures used for large field of view mapping spectroscopy in millimetere and sub-millimetre astronomy suffer from significant drawbacks. On-chip filterbank spectrometers are a promising technology in this respect; however, they must overcome an orders-of-magnitude increase in detector counts, efficiency loss due to d

Sani Y. Harouna-Mayer, Melike Gumus Akcaalan, Jagadesh Kopula Kesavan, Tjark R. L. Groene

Understanding the chemical processes that occur during the solvothermal synthesis of functional nanomaterials is essential for their rational design and optimization for specific applications. However, these processes remain poorly understood, primarily due to the limitations of conventional ex situ characterization techniques and the technical challenges as

Shreya Gopalan, Sundar Narayanan

Protein structure prediction has advanced significantly with the introduction of AlphaFold3, a diffusion-based model capable of predicting complex biomolecular interactions across proteins, nucleic acids, small molecules, and ions. While AlphaFold3 demonstrates high accuracy in folded proteins, its performance on intrinsically disordered proteins (IDPs), whi

Gianmarco Perantoni, Lorenzo Bruzzone

The quantity and the quality of the training labels are central problems in high-resolution land-cover mapping with machine-learning-based solutions. In this context, weak labels can be gathered in large quantities by leveraging on existing low-resolution or obsolete products. In this paper, we address the problem of training land-cover classifiers using hig

Di Zhang, Yinglei Yang, Zilong Liu, Shaobo Jia

Low complexity error correction code is a key enabler for next generation ultra-reliable low-latency communications (xURLLC) in six generation (6G). Against this background, this paper proposes a decoding scheme for linear block code by leveraging certain interesting properties of dual codewords. It is found that dual codewords with flexible weights can prov

Bindu G Gowda, Yogesh Goyal, Yash Gupta, Madhav Rao

Single-precision floating point (FP32) data format, defined by the IEEE 754 standard, is widely employed in scientific computing, signal processing, and deep learning training, where precision is critical. However, FP32 multiplication is computationally expensive and requires complex hardware, especially for precisely handling mantissa multiplication. In pra

Tennison Liu, Silas Ruhrberg Estévez, David L. Bentley, Mihaela van der Schaar

Large-scale scientific datasets -- spanning health biobanks, cell atlases, Earth reanalyses, and more -- create opportunities for exploratory discovery unconstrained by specific research questions. We term this process hypothesis hunting: the cumulative search for insight through sustained exploration across vast and complex hypothesis spaces. To support it,

Vivek Maradia, Nick Yue, Adam Molzahn, Jingqian Wang

Proton therapy provides superior dose conformity compared with photon radiotherapy, concentrating radiation within the tumor while sparing adjacent healthy tissue. This advantage has been most effectively realized for static tumors in anatomically stable regions, such as the head and neck. For thoracic and abdominal sites, however, physiological motion remai

Zhu Ye

Let $M$ be an open (i.e. complete and noncompact) manifold with nonnegative Ricci curvature. In this paper, we study whether the volume growth order of $M$ is always greater than or equal to the dimension of some (or every) asymptotic cone of $M$. Our first main result asserts that, under the conic at infinity condition, if the infimum of the volume growth o

Tuyen Nguyen, Mária Kieferová

Machine learning provides a powerful framework for predicting ground-state properties across families of quantum many-body problems, enabling amortized inference and reducing the cost of repeated simulations. Variational quantum algorithms (VQAs) are promising candidates for implementing such learnable solvers on quantum computers, yet rigorous guarantees fo

Shivam Padmani, Akshay Joshi

Function regression/approximation is a fundamental application of machine learning. Neural networks (NNs) can be easily trained for function regression using a sufficient number of neurons and epochs. The forward-forward learning algorithm is a novel approach for training neural networks without backpropagation, and is well suited for implementation in neuro

Zhi Zhang, Yan Liu, Zhejing Hu, Gong Chen

Automating the end-to-end scientific research process poses a fundamental challenge: it requires both evolving high-level plans that are novel and sound, and executing these plans correctly amidst dynamic and uncertain conditions. To address this bilevel challenge, we propose a novel Double-Loop Multi-Agent (DLMA) framework to solve the given research proble

Louis Golowich, Kathleen Chang, Guanyu Zhu

It is a major challenge to perform addressable and parallel logical operations on constant-rate quantum LDPC (qLDPC) codes. Indeed, the overhead of targeting specific logical qubits represents a crucial bottleneck in many quantum fault-tolerance schemes. We introduce fault-tolerant protocols for performing various addressable as well as parallel logical oper

Zhong-Xia Shang, Naixu Guo, Patrick Rebentrost, Alán Aspuru-Guzik

Quantum phase estimation (QPE) and Lindbladian dynamics are both foundational in quantum information science and central to quantum algorithm design. In this work, we bridge these two concepts: certain simple Lindbladian processes can be adapted to perform QPE-type tasks. However, unlike QPE, which achieves Heisenberg-limit scaling, these Lindbladian evoluti

Yue Hang Leung, Yingjie Zhou, Norbert Herrmann

The production of light hypernuclei in relativistic heavy-ion collisions provides a unique opportunity to probe hyperon--nucleon interactions and possible three-body forces, which are central to the resolution of the hyperon puzzle in neutron star matter. In this work, we develop a data-guided coalescence framework in which the source size is extracted from

Sheng Fu, Junchao Zhang, Kailun Yang

Supervised Gaussian denoisers exhibit limited generalization when confronted with out-of-distribution noise, due to the diverse distributional characteristics of different noise types. To bridge this gap, we propose a histogram matching approach that transforms arbitrary noise towards a target Gaussian distribution with known intensity. Moreover, a mutually

Dennis Gross, Helge Spieker, Arnaud Gotlieb

We introduce a tool for rigorous and automated verification of large language model (LLM)- based policies in memoryless sequential decision-making tasks. Given a Markov decision process (MDP) representing the sequential decision-making task, an LLM policy, and a safety requirement expressed as a PCTL formula, our approach incrementally constructs only the re

Katarina Skopalova, Jafet Osuna, Wei Zhang

Most capture-recapture models assume that individuals either do not emigrate or emigrate permanently from the sampling area during the sampling period. This assumption is violated when individuals temporarily leave the sampling area and return during later capture occasions, which can result in biased or less precise inferences under normal capture-recapture

Christian Maurer, Snehal Jauhri, Sophie Lueth, Georgia Chalvatzaki

Comprehensive visual, geometric, and semantic understanding of a 3D scene is crucial for successful execution of robotic tasks, especially in unstructured and complex environments. Additionally, to make robust decisions, it is necessary for the robot to evaluate the reliability of perceived information. While recent advances in 3D neural feature fields have

Wentao Lu, Jun-Xian Wang

We present the first systematic search for "changing-look" ("CL") behavior in the broad He ii $\lambda$4686 emission line in quasars, utilizing repeated spectroscopy from the Sloan Digital Sky Survey (SDSS). The He ii line, originating from high-ionization gas and powered by extreme ultraviolet photons, serves as a sensitive tracer of changes in the ionizing

Yijun Wang, Tao Wang, Junjie Mao, Yerong Xu

Both jets and ionized outflows in active galactic nuclei (AGNs) are thought to play important roles in affecting the star formation and evolution of host galaxies, but their relationship is still unclear. As a pilot study, we performed a detailed spectral analysis for a radio-loud (RL) AGN 3C~59 ($z=0.1096$) by systematically considering various factors that

Junhan Zhu, Hesong Wang, Mingluo Su, Zefang Wang

Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and traini

Rui Hu, Delai Qiu, Yining Wang, Shengping Liu

Omni-modal large language models (OLLMs) offer a promising end-to-end solution for slide-enhanced speech recognition due to their inherent multimodal capabilities. However, we found a fundamental issue faced by OLLMs: \textit{Visual Interference}, where models show a bias towards visible text over auditory signals, causing them to hallucinate slide content t

Jaeseong Lee, Dayoung Kwon, seung-won hwang

Large Reasoning Models (LRMs) excel in structured tasks by emulating deliberate human reasoning but often suffer from overthinking, degrading performance and wasting resources. One possible baseline is to deploy both LLM and LRM, then route input by predicting whether it requires reasoning and may cause overthinking. However, deploying multiple models can be

Eitan Klinger, Zihao Huang, Tran Minh Nguyen, Emma Jayeon Park

Evaluating grammatical error correction requires metrics that reflect the diversity of valid human corrections rather than privileging a single reference. Existing frameworks, largely edit-based and English-centric, rely on rigid alignments between system and reference edits, limiting their applicability in multilingual and generative settings. This paper in

Mareike Hasenpflug

Geodesic slice sampling, introduced in Durmus et al., 2024, is a slice sampling based Markov chain Monte Carlo method for approximate sampling from distributions on Riemannian manifolds. We prove that it is uniformly ergodic for distributions with compact support that have a bounded density with respect to the Riemannian measure. The constants in our converg

I-Fan Lin, Faegheh Hasibi, Suzan Verberne

In this paper, we propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. In customer-facing chatbots, companies are dealing with large amounts of user utterances that need to be clustered according to their intent. In these settings, no labeled data is typically available,

Zhiliang Zhu, Tao Zeng, Tao Yang, Guoliang Luo

Image deraining is crucial for improving visual quality and supporting reliable downstream vision tasks. Although Mamba-based models provide efficient sequence modeling, their limited ability to capture fine-grained details and lack of frequency-domain awareness restrict further improvements. To address these issues, we propose DeRainMamba, which integrates

Somasekhara Goud Sontti, Arnab Atta

We present a CFD based model to understand the Taylor bubble behavior in Newtonian and non-Newtonian liquids flowing through a confined co-flow microchannel. Systematic investigation is carried out to explore the influence of surface tension, inlet velocities, and apparent viscosity on the bubble length, shape, velocity, and film thickness around the bubble.

Mauri J. Valtonen, Lankeswar Dey, Staszek Zola, Alok C. Gupta

The 136 year long optical light curve of OJ~287 is explained by a binary black hole model where the secondary is in a 12 year orbit around the primary. Impacts of the secondary on the accretion disk of the primary generate a series of optical flares which follow a quasi-Keplerian relativistic mathematical model. The orientation of the binary in space is dete

Maria Levchenko

Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, address

Zi-Yuan Wang, Ying Qin, Georges Meynet, Qing-Zhong Liu

IC 10 X-1, NGC 300 X-1, and Cyg X-3 constitute a unique class of X-ray binaries in which a stellar-mass black hole (BH) accretes material from a Wolf-Rayet (WR). These systems are particularly intriguing because of their short orbital periods, which make them promising progenitors of gravitational-wave (GW) sources detectable by the LIGO-Virgo-KAGRA (LVK) ne

Sören von der Gracht, Eddie Nijholt, Bob Rink

The analysis of network dynamics is oftentimes restricted to networks with one-dimensional internal dynamics. Here, we show how symmetry explains the relation between behavior of systems with one-dimensional internal dynamics and with higher dimensional internal dynamics, when the network topology is the same. Fundamental networks of homogeneous coupled cell

Boyi Zeng, Lin Chen, Ziwei He, Xinbing Wang

Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training proc

Iftach Yakar, Michael Ben-Or

Quantum repeaters are essential for achieving long-distance quantum communication due to photon loss, which grows exponentially with the channel distance. Current quantum repeater generations use entanglement distillation protocols, where the decision of when to perform distillation depends on either local or global knowledge. Recent approaches for quantum r

Christos N. Efrem

This paper focuses on a wide class of Collatz-type arithmetic dynamics, and presents a systematic derivation of recursive formulas and functional equations satisfied by the associated generating functions. The main tools belong to complex analysis, including contour-integral representations, residue calculus, and Cauchy's integral formulas. The basic approac

Zachris Björkman, Jorge Loría, Sophie Wharrie, Samuel Kaski

Bayesian causal discovery benefits from prior information elicited from domain experts, and in heterogeneous domains any prior knowledge would be badly needed. However, so far prior elicitation approaches have assumed a single causal graph and hence are not suited to heterogeneous domains. We propose a causal elicitation strategy for heterogeneous settings,

Fabian Göttsch, Max Franke, Arash Pourdamghani, Giuseppe Caire

We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the correspon

Yajing Wang, Talayeh Aledavood, Juhi Kulshrestha

Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent. While survey-based research has examined social media use and loneliness, findings remain mixed, and little is known ab

Xiaofeng Dong, Nesar Ramachandra, Salman Habib, Katrin Heitmann

The potential of deep learning-based image-to-image translations has recently attracted significant attention. One possible application of such a framework is as a fast, approximate alternative to cosmological simulations, which would be particularly useful in various contexts, including covariance studies, investigations of systematics, and cosmological par

Manuel Frank, Haithem Afli

Current sentence embedding evaluations typically rely on static test beds like the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported scores and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-pr

Fahimeh Khosh-Ahang Ghasr

We provide necessary and sufficient conditions for simplicial complexes whose determinantal facet ideals admit reduced Grobner bases under diagonal term orders. Building on and extending foundational results for binomial edge ideals and determinantal ideals, we introduce two new classes of simplicial complexes-strong closed and poor closed-that generalize th

Cile van Marken, Roxana Petcu

Neural ranking models have shown outstanding performance across a variety of tasks, such as document retrieval, re-ranking, question answering and conversational retrieval. However, the inner decision process of these models remains largely unclear, especially as models increase in size. Most interpretability approaches, such as probing, focus on correlation

Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling

We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we i

I. V. Fomin

The correspondence of single-field cosmological models based on Einstein gravity to modern observational data is considered. A method is proposed to determine possible types of dynamics based on extreme values of the scalar field. It is shown that within the framework of this approach, it is possible to obtain a limited class of known inflationary models at

Alexander Falk, Andreas Habring, Christoph Griesbacher, Thomas Pock

We consider the \emph{inertial Langevin algorithm} (ILA), a simple momentum-based method for sampling from Gibbs distributions of the form $π(x) \propto \exp(-U(x))$. ILA augments the unadjusted Langevin algorithm with an inertia term and a matching noise rescaling, yielding a sampling analogue of Polyak's heavy-ball method from optimization. This modifi

M. Koshelev, A. Raigorodskii

The spectrum of a graph $G$ is the set of the eigenvalues of its adjacency matrix. It turns out that one can say a lot about a graph with the only knowledge being the spectrum of this graph. In this paper we obtain new results about the spectrum of $G(n, \alpha n, \alpha^2 n)$ graphs. We then apply these results to get a giant component theorem for them.

Benedikt Jung, Maximilian Kalcher, Merlin Marinova, Piper Powell

With traditional computing technologies reaching their limit, a new field has emerged seeking to follow the example of the human brain into a new era: neuromorphic computing. This paper provides an introduction to neuromorphic computing, why this and other new computing systems are needed, and what technologies currently exist in the neuromorphic field. It b

David Kogan, Moshe Gottlieb

In this work, we have studied the viscoelastic behavior of chemically and physically crosslinked Poly(vinyl alcohol) (PVA) hydrogels near the critical gel point (GP) as well as further away from it, by means of small amplitude (SAOS) and large amplitude (LAOS) oscillatory shear experiments. Chemical crosslinking involved covalent bonding by means of glutaral

Junki Mori, Kazuya Kakizaki, Taiki Miyagawa, Jun Sakuma

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application in sensitive domains is limited by privacy risks. Existing private RAG methods typically rely on query-time differential privacy (DP), which requires repeated noise injection and leads to accumulated privacy loss. To ad

Ranim Khojah, Mazen Mohamad, Linda Erlenhov, Francisco Gomes de Oliveira Neto

The risks associated with adopting large language model (LLM) chatbots in software organizations highlight the need for clear policies. We examine how 11 companies create these policies and the factors that influence them, aiming to help managers safely integrate chatbots into development workflows.

Jie Ji, Jingang Xiong

We establish a global weighted $L^p$ estimate for the gradient of the solution to a divergence-form elliptic equations, where the coefficients are in a weighted VMO space and the equations have singularities on a co-dimension two boundary.

Dongfen Bian, Emmanuel Grenier, Gérard Iooss

It is well-established that shear flows are linearly unstable provided the viscosity is small enough, when the horizontal Fourier wave number lies in some interval, between the so-called lower and upper marginally stable curves. In this article, we prove that, under a natural spectral assumption, shear flows undergo a Hopf bifurcation near their upper margin

Seohong Park, Deepinder Mann, Sergey Levine

In this work, we introduce dual goal representations for goal-conditioned reinforcement learning (GCRL). A dual goal representation characterizes a state by "the set of temporal distances from all other states"; in other words, it encodes a state through its relations to every other state, measured by temporal distance. This representation provides several a

F. Marin, T. Pursimo, I. Liodakis, E. Lindfors

Concomitant with the Imaging X-ray Polarimetry Explorer (IXPE) observation of the Perseus cluster, we obtained optical spectropolarimetry of its central active galactic nucleus, NGC 1275, using the Alhambra Faint Object Spectrograph and Camera (ALFOSC) on the Nordic Optical Telescope (NOT). While the total-light spectrum confirms its edge-on, core obscured (

Fabian Rennecke, Shi Yin

Dense QCD matter can feature a moat regime, where the static energy of mesons is minimal at nonzero momentum. Valuable insights into this regime can be gained using low-energy models. This, however, requires a careful assessment of model artifacts. We therefore study the effects of renormalization and in-medium modifications of quark-meson interaction on the

Batu El, Mert Yuksekgonul, James Zou

Recent works began to automate the design of agentic systems using meta-agents that propose and iteratively refine new agent architectures. In this paper, we examine three key challenges in a common class of meta-agents. First, we investigate how a meta-agent learns across iterations and find that simply expanding the context with all previous agents, as pro

Hongzhi Zang, Mingjie Wei, Si Xu, Yongji Wu

Recent studies have demonstrated the potential of reinforcement learning (RL) to improve the task performance of vision-language-action (VLA) models through interaction. However, current efforts remain fragmented, lacking a unified platform for fair comparison across architectures and algorithms, as well as an efficient system design for scalable training. T

Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan, Morteza Hashemi

In this paper, we propose an Expectation-Maximization-based (EM) Personalized Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Communication (ISAC) systems. In contrast to standard federated learning (FL) methods that handle all clients uniformly, the proposed approach enables each base station (BS) to adapt

Aleksi Huotala, Miikka Kuutila, Olli-Pekka Turtio, Simo Sipilä

Conducting systematic reviews is laborious. In the screening or study selection phase, the number of papers can be overwhelming. Recent research has demonstrated that large language models (LLMs) can perform title-abstract screening and support humans in the task. To this end, we developed AISysRev, an LLM-based screening tool implemented as a containerized

Katharina Arms

The linear decomposition attack reveals a vulnerability in encryption algorithms operating within groups or monoids with excessively small representations. The representation gap, defined as the size of the smallest non-trivial representation, therefore serves as a metric to assess the security of these algorithms. This paper will demonstrate that the diagra

Phuong Tuan Dat, Tran Huy Dat

Recent advancements in speech synthesis technologies have led to increasingly sophisticated spoofing attacks, posing significant challenges for automatic speaker verification systems. While systems based on self-supervised learning (SSL) models, particularly the XLSR-Conformer architecture, have demonstrated remarkable performance in synthetic speech detecti

Jianlong Chen, Yu Xu, Jiarui Du, Xiaoqun Wang

Many problems can be formulated as high-dimensional integrals of discontinuous functions that exhibit significant boundary growth, challenging the error analysis and applications of randomized quasi-Monte Carlo (RQMC) methods. This paper studies RQMC methods for super-fast growing functions satisfying generalized exponential growth conditions, with a special

Zafrin Jahan Nikita, Mohammad Tahsin Alam, Yasir Mahmud, Rafichha Yasmin

The paper demonstrates the design and execution of a low-cost optical spectrometer that employs a webcam, diffraction grating & Python (a free, open-source programming language). The device's design prioritized economy and usability, with a black box casing to reduce stray light and increase measurement accuracy. A diffraction grating made from a DVD was use

Ivana Miháliková, Joseph Carlson, Duff Neill, Ionel Stetcu

We demonstrate the importance of symmetries in Variational Quantum Eigensolver (VQE) algorithms to prepare the ground or specific low-lying states of quantum Hamiltonians. We examine two spin problems, one with random all-to-all couplings inspired by neutrino flavor evolution in supernovae, and the standard Heisenberg spin Hamiltonian on a $4 \times 3$ latti

Antonio Bjelčić, Nicolas Schunck, Marc Verriere

The number and properties of the neutrons and photons emitted in nuclear fission are directly related to the excitation energy of the fission fragments when they are formed at scission. Though not observable experimentally because of the extremely short time scales, the excitation energy of fission fragments can be predicted by microscopic theory based on ti

Leonardo Bertolazzi, Sandro Pezzelle, Raffaella Bernardi

Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning problem influences judgments regarding its logical validity. While this phenomenon in humans is best explained by the dual-process theory of reasoning, the mechanisms behind content effects in LLMs remain unclear. In t

Gal Fadlon, Idan Arbiv, Nimrod Berman, Omri Azencot

Generating realistic time series data is critical for applications in healthcare, finance, and science. However, irregular sampling and missing values present significant challenges. While prior methods address these irregularities, they often yield suboptimal results and incur high computational costs. Recent advances in regular time series generation, such

Raquel M. Gaspar, Thorsten Schmidt

To make medium- and long-term insurance products attractive, it is essential to enable participation in stock market returns. However, to eliminate downside risk, guarantees must be included, which naturally leads to the challenge of valuing such contracts within a unified insurance-finance framework. We develop a general setup that allows for the joint mode