October 2025 arXiv papers — page 182

Yuni Lai, Xiaoyu Xue, Linghui Shen, Yulun Wu

Language-empowered foundation models (LeFMs), such as CLIP and GraphCLIP, have transformed multimodal learning by aligning visual (or graph) features with textual representations, enabling powerful downstream capabilities like few-shot learning. However, the reliance on small, task-specific support datasets collected in open environments exposes these models

Jinling Gan, Churong Liang, Runnan Li

The latency-quality tradeoff is a fundamental constraint in open-domain dialogue AI systems, since comprehensive knowledge access necessitates prohibitive response delays. Contemporary approaches offer two inadequate solutions: lightweight instruct models achieve sub-second latency but lack reasoning depth, while tool-augmented ReAct agents enhance factualit

Artsiom Patarusau, Nikita Puchkin, Maxim Rakhuba, Fedor Noskov

We consider a problem of covariance estimation from a sample of i.i.d. high-dimensional random vectors. To avoid the curse of dimensionality, we impose an additional assumption on the structure of the covariance matrix $\Sigma$. To be more precise, we study the case when $\Sigma$ can be approximated by a sum of double Kronecker products of smaller matrices i

Shuige Liu

We examine how misinformation spreads in social networks composed of individuals with long-term offline relationships. Especially, we focus on why misinformation persists and diffuses despite being recognized by most as false. In our psychological game theoretical model, each agent who receives a piece of (mis)information must first decide how to react -- by

Haolin Yang, Yuxing Long, Zhuoyuan Yu, Zihan Yang

Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigation agents' spatial perception and reasoning capabilities. In this work, we introduce the NavSpace benchmark, which contains six task categories and 1,228 trajectory-instruction pai

Maurizio Vassallo, Adrien Bolland, Alireza Bahmanyar, Louis Wehenkel

Distributed energy resources (DERs) are transforming power networks, challenging traditional operational methods, and requiring new coordination mechanisms. To address this challenge, this paper introduces SecuLEx (Secure Limit Exchange), a new market-based paradigm to allocate power injection and withdrawal limits that guarantee network security during time

Ignacio Sardinero, Jorge Cayao, Rubén Seoane Souto, Pablo Burset

Josephson junctions coupled through magnetic textures provide a controllable platform for odd-frequency superconductivity and Majorana physics. Within a tight-binding Green function framework, induced pair correlations and spectral properties are analyzed under various magnetic and geometric conditions. When the junction is in the topologically trivial regim

Jia-Lin Chen, Yuan-Hao Yang, Zheng-Xu Zhu, Jia-Qi Wang

Thin-film lithium niobate on sapphire provides an excellent platform for simultaneously confining acoustic and optical modes without suspended structures, enabling efficient acousto-optic modulation through strong piezoelectric coupling. Here, we identify the challenges in realizing the forward Brillouin interaction at visible wavelengths, and overcome the l

Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu

Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive (NAR) models offer holistic, bidirectional representations but face challenges with gener

Chaoqun Gao, Yong Wei, Rong Zhou

We first establish a local gradient estimate for anisotropic $p$-harmonic functions. A key feature of our estimate is that the constant remains bounded as $p\to 1$; consequently, in the limit $p\to 1$, this estimate yields the local gradient estimate for weak solutions of the inverse anisotropic mean curvature flow (IAMCF). As an application, we show that th

Elias Kristmann, Markus Schütz, Michael Wimmer

Although variable-rate compressed image formats such as JPEG are widely used to efficiently encode images, they have not found their way into real-time rendering due to special requirements such as random access to individual texels. In this paper, we investigate the feasibility of variable-rate texture compression on modern GPUs using the JPEG format, and h

Ivan Tambovtsev, Óskar Kristinsson, Hannes Jónsson

The effect of substituting a hydrogen atom by a chlorine atom or a methyl group by a trichloromethyl (CCl3) group at the stereogenic center of light-driven second generation molecular motors is calculated in order to assess the effect on rotational speed and the separation of the absorption peaks of the isomers. While experimental and theoretical studies hav

Marco Picone, Samuele Burattini, Marco Melloni, Prasad Talasila

The increasing capabilities of Digital Twins (DTs) in the context of the Internet of Things (IoT) and Industrial IoT (IIoT) call for seamless integration with simulation platforms to support system design, validation, and real-time operation. This paper introduces the concept, design, and experimental evaluation of the DT Simulation Bridge - a software frame

Jian Xie, Zhendong Chu, Aoxiao Zhong, Kai Zhang

Large Reasoning Models (LRMs) often suffer from the ``over-thinking'' problem, generating unnecessarily long reasoning on simple tasks. Some strategies have been proposed to mitigate this issue, such as length penalties or routing mechanisms, but they are typically heuristic and task-specific, lacking a general framework for adaptive reasoning. In this paper

Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati, Eilam Gross

Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background pr

Yaakov Libero, Itzik Klein

Attitude and heading reference systems (AHRS) play a central role in autonomous navigation systems on land, air and maritime platforms. AHRS utilize inertial sensor measurements to estimate platform orientation. In recent years, there has been increasing interest in multiple inertial measurement units (MIMU) arrays to improve navigation accuracy and robustne

Jimmy Joseph

We offer a pragmatic model to operationalize responsible, secure, and sustainable healthcare AI, aligning world-class technical excellence with organizational readiness. The framework includes five key pillars - Leadership & Strategy, MLOps & Technical Infrastructure, Governance & Ethics, Education & Workforce Development, and Change Management & Adoption -

Nabeel Nisar Bhat, Maksim Karnaukh, Jakob Struye, Rafael Berkvens

Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person's unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers

Iordanis Kerenidis, El-Amine Cherrat

We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search algorithm; optimal cheating strategies for strong coin flipping; and

Shuzhou Yuan, Ercong Nie, Yinuo Sun, Chenxuan Zhao

Large language models (LLMs) frequently produce false refusals, declining benign requests that contain terms resembling unsafe queries. We address this challenge by introducing two comprehensive benchmarks: the Exaggerated Safety Benchmark (XSB) for single-turn prompts, annotated with "Focus" keywords that identify refusal-inducing triggers, and the Multi-tu

Zhentao Zou, Zhengrong Yue, Kunpeng Du, Binlei Bao

Image editing with natural language has gained significant popularity, yet existing methods struggle with intricate object intersections and fine-grained spatial relationships due to the lack of an explicit reasoning process. While Chain-of-Thought (CoT) has been explored to enhance reasoning, purely textual CoT or CoT augmented with coordinate information i

Sayooj P, Awadhesh Narayan

The dynamics of open quantum systems described by the Lindblad master equation follows according to non-Hermitian operators. As a result, such systems can host non-Hermitian degeneracies called Liouvillian exceptional points (EPs). In this work, we show that Newton polygons and tropical geometric approach allow identification and characterization of Liouvill

Mingyu Sun, Gabriel Waite, Michael Bremner, Christopher Ferrie

As quantum devices scale, quantifying how close an experimental state aligns with a target becomes both vital and challenging. Fidelity is the standard metric, but existing estimators either require full tomography or apply only to restricted state/measurement families. Huang, Preskill, and Soleimanifar (Nature Physics, 2025) introduced an efficient certific

Laura Mančinska, Elias Theil

Many quantum information tasks use inputs of the form $\rho^{\otimes m}$, which naturally induce permutation and unitary symmetries. We classify all quantum channels that respect both symmetries - i.e. unitary-equivariant and permutation-invariant quantum channels from $(\mathbb{C}^{d})^{\otimes m}$ to $(\mathbb{C}^{d})^{\otimes n}$ - via their extremal poin

Vincent Eichenseher, Maja Franz, Christian Wolff, Wolfgang Mauerer

Structured variational quantum algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) have emerged as leading candidates for exploiting advantages of near-term quantum hardware. They interlace classical computation, in particular optimisation of variational parameters, with quantum-specific routines, and combine problem-specific advantages

Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar, Xue-Yong Fu

The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot inst

Larissa Reichart, Cem Ata Baykara, Ali Burak Ünal, Harlin Lee

Unsupervised multi-source domain adaptation (UMDA) leverages labeled data from multiple source domains to generalize to an unlabeled target. While federated UMDA addresses privacy by avoiding raw data sharing, existing methods scale poorly as the number of sources increases, often suffering from high computational overhead or training instability. We propose

Md Tahmid Rahman Laskar, Julien Bouvier Tremblay, Xue-Yong Fu, Cheng Chen

The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact ce

Stefan Takacs, Stefan Tyoler

Isogeometric Analysis is a variant of the finite element method, where spline functions are used for the representation of both the geometry and the solution. Splines, particularly those with higher degree, achieve their full approximation power only if the solution is sufficiently regular. Since solutions are usually not regular everywhere, adaptive refinem

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $(10087 \pm 44)\times10^6$ $J/\psi$ events collected with the BESIII detector at the BEPCII, the decays $J/\psi\to \Xi^0\bar\Lambda K^0_S+c.c.$, $J/\psi\to \Xi^0\bar\Sigma^0 K^0_S+c.c.$, and $J/\psi\to \Xi^0\bar\Sigma^- K^++c.c.$ are observed for the first time. Their branching fractions are determined to be $\mathcal{B}(J/\psi\to \Xi^0\bar\Lamb

Aman Sharma, Paras Chopra

We introduce a simple, yet novel entropy-based framework to drive token efficiency in large language models during reasoning tasks. Our approach uses Shannon entropy from token-level logprobs as a confidence signal to enable early stopping, achieving 25-50% computational savings while maintaining task accuracy. Crucially, we demonstrate that entropy-based co

Shuliang Liu, Zhipeng Xu, Zhenghao Liu, Yukun Yan

Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LLMs with human judgments, providing accurate and reliable assessments. However, LLM-based judgment models often exhibit judgment preference bias during the evaluation phase, tending

Jiawei Zhang, Shihan Wang, Jienan Chen, Fan Wu

In the beyond fifth-generation (B5G) and upcoming sixth-generation (6G) wireless communication systems, millimeter (mmWave) wave technology is a promising solution for offering additional bandwidth resources and mitigating spectrum congestion. Beam tracking is an essential procedure for providing reliable communication services in the mmWave communication sy

Shian Du, Menghan Xia, Chang Liu, Quande Liu

Cascaded video super-resolution has emerged as a promising technique for decoupling the computational burden associated with generating high-resolution videos using large foundation models. Existing studies, however, are largely confined to text-to-video tasks and fail to leverage additional generative conditions beyond text, which are crucial for ensuring f

Joona V. Pankkonen, Matti Raasakka, Andrea Marchesin, Ilkka Tittonen

Parameterized quantum circuits (PQCs) play an essential role in the application of variational quantum algorithms (VQAs) in noisy intermediate-scale quantum (NISQ) devices. The PQCs are a leading candidate to achieve a quantum advantage in NISQ devices and have already been applied in various domains such as quantum chemistry, quantum machine learning, combi

Zipo Jibao, Yingyi Fu, Xinyang Chen, Guoting Chen

Real-world time series are influenced by numerous factors and exhibit complex non-stationary characteristics. Non-stationarity can lead to distribution shifts, where the statistical properties of time series change over time, negatively impacting model performance. Several instance normalization techniques have been proposed to address distribution shifts in

Yancheng Wang, Chuan Huang, Songyang Zhang, Guanying Chen

The substantial communication resources consumed by conventional pilot-based channel sounding impose an unsustainable overhead, presenting a critical scalability challenge for the future 6G networks characterized by massive channel dimensions, ultra-wide bandwidth, and dense user deployments. As a generalization of radio map, channel knowledge map (CKM) offe

Wenyang Jia, Jingjing Wang, Ziwei Yan, Xiangli Peng

The scalability of blockchain systems is constrained by inefficient P2P broadcasting, as most existing optimizations focus only on the logical layer without considering physical network conditions. To address this, we propose BlockSDN, the first SDN-based integrated architecture for blockchain. BlockSDN employs a distributed control plane for a global networ

Chengzhi Li, Heyan Huang, Ping Jian, Zhen Yang

Large language models (LLMs) often generate self-contradictory outputs, which severely impacts their reliability and hinders their adoption in practical applications. In video-language models (Video-LLMs), this phenomenon recently draws the attention of researchers. Specifically, these models fail to provide logically consistent responses to rephrased questi

Anastasios Petropoulos, Theodore Antonakopoulos

Deep neural network (DNN) inference relies increasingly on specialized hardware for high computational efficiency. This work introduces a field-programmable gate array (FPGA)-based dynamically configurable accelerator featuring systolic arrays, high-bandwidth memory, and UltraRAMs. We present two processing unit (PU) configurations with different computing c

Yiyi Yao, Shijie Kang, Aoning Luo, Jiusi Yu

Chiral responses in electromagnetic metasurfaces are typically categorized as extrinsic, resulting from asymmetric interactions between the structure and incident waves, and intrinsic, arising from three-dimensional symmetry breaking of the unit cell. However, most existing metasurface designs target only one type of chirality and lack a unified, continuousl

Xiaoqiang Cheng, Jianfeng Wu, Qiaoya Wu

Weak-line quasars (WLQs) are a subset of type 1 quasars with remarkably weak high-ionization broad emission lines but normal optical/UV continua. Using 371,091 quasars from SDSS DR16, we define WLQs by analyzing outliers in three relations: the L1350-CIV blueshift, the Baldwin effect, and the logL2500-alpha_ox. We find two CIV EW thresholds: $8.9\pm0.2${\AA}

Sudipta Sahu, Emanuele Macca, Rathan Samala

Quasi-linear hyperbolic systems with source terms introduce significant computational challenges due to the presence of a stiff source term. To address this, a finite volume Nessyahu-Tadmor (NT) central numerical scheme is explored and applied to benchmark models such as the Jin-Xin relaxation model, the shallow-water model, the Broadwell model, the Euler eq

Rina Miyajima, Yuki Takeuchi, Seiseki Akibue

We show (i) the existence of universal resource states for a certain class of linear Hamiltonians and (ii) the uselessness of highly entangled states for quantum metrology of linear Hamiltonians. We also show that random pure states are basically not useful even if we consider more general Hamiltonians. Since random pure states have high entanglement, this r

Kodai Kawamura, Yuta Goto, Rintaro Yanagi, Hirokatsu Kataoka

Pre-trained Vision-Language Models (VLMs) exhibit strong generalization capabilities, enabling them to recognize a wide range of objects across diverse domains without additional training. However, they often retain irrelevant information beyond the requirements of specific downstream tasks, raising concerns about computational efficiency and potential infor

Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-s

Muhammad Imran, Sikander Azam, Qaiser Rafiq, Amin Ur Rahman

Perovskite oxides are promising for energy and quantum technologies, but wide-gap hosts such as NaAlO3 suffer from deep-UV absorption and limited carrier transport. Using first-principles GGA+U+SOC calculations, we investigate Eu3+-, Gd3+-, and Tb3+-doped NaAlO3 and evaluate their electronic, optical, elastic, and thermoelectric properties. Rare-earth substi

Jack Esson, Eleftherios Kastis, Bernd Schulze

This paper establishes combinatorial characterisations of forced-symmetric and forced-periodic rigidity (under a fixed lattice) of bar-joint frameworks in non-Euclidean normed planes. In $\ell_q$-planes for $q\in(1,\infty)\backslash\{2\}$, we prove characterisations for forced-periodic rigidity and forced-reflectionally-symmetric rigidity. We also characteri

Wenxing Zhu, Hao Ai

Arranging many modules within a bounded domain without overlap, central to the Electronic Design Automation (EDA) of very large-scale integrated (VLSI) circuits, represents a broad class of discrete geometric optimization problems with physical constraints. This paper develops a variational and spectral framework for Poisson energy-based floorplanning and pl

Nikola Herceg, Tajron Jurić, A. Naveena Kumara, Andjelo Samsarov

We study the gravitational perturbation theory of black holes in noncommutative spacetimes with noncommutativity of the type $[t\stackrel{\star}{,} r] = i a \alpha A(r)$ and $[\varphi \stackrel{\star}{,} r] = i a \beta A(r)$ for arbitrary $A(r)$, which includes several Moyal-type spaces and also the $\kappa$-Minkowski space. The main result of this paper is

Anton Herrmann, Christian Komusiewicz, Nils Morawietz, Frank Sommer

A temporal graph is a finite sequence of graphs, called snapshots, over the same vertex set. Many temporal graph problems turn out to be much more difficult than their static counterparts. One such problem is \textsc{Timeline Vertex Cover} (also known as \textsc{MinTimeline$_\infty$}), a temporal analogue to the classical \textsc{Vertex Cover} problem. In th

Parham Rezaei, Filip Kovacevic, Francesco Locatello, Marco Mondelli

Despite the progress in the development of generative models, their usefulness in creating synthetic data that improve prediction performance of classifiers has been put into question. Besides heuristic principles such as "synthetic data should be close to the real data distribution", it is actually not clear which specific properties affect the generalizati

Umberto Zucchelli, Miguel Alfonso Mendez, Annafederica Urbano, Sebastien Vincent-Bonnieu

New-generation space missions require satellites to carry substantial amounts of liquid propellant, making it essential to analyse the coupled control-structure-propellant dynamics in detail. While Computational Fluid Dynamics (CFD) offers high-fidelity predictions, its computational cost limits its use in iterative design. Equivalent Mechanical Models (EMMs

Jasmina Gajcin, Erik Miehling, Rahul Nair, Elizabeth Daly

Using LLMs to evaluate text, that is, LLM-as-a-judge, is increasingly being used at scale to augment or even replace human annotations. As such, it is imperative that we understand the potential biases and risks of doing so. In this work, we propose an approach for extracting high-level concept-based global policies from LLM-as-a-Judge. Our approach consists

Frank Mukwege, Tam Willy Nguyen, Emanuele Garone

This paper presents a general framework for solving the control allocation problem (CAP) in thrust-vector controlled rigid-bodies with an arbitrary number of thrusters. Two novel solutions are proposed: a closed-form, Lipschitz continuous mapping that ensures smooth actuator orientation references, and a convex optimization formulation capable of handling pr

Massimiliano de Leoni, Faizan Ahmed Khan, Simone Agostinelli

Robotic Process Mining focuses on the identification of the routine types performed by human resources through a User Interface. The ultimate goal is to discover routine-type models to enable robotic process automation. The discovery of routine-type models requires the provision of a routine log. Unfortunately, the vast majority of existing works do not dire

Frédéric Zheng, Yassir Jedra, Alexandre Proutiere

We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matrix by estimating its singular values and the corresponding singular vectors up to an effective rank, adaptively determined based on the data. We establish instance-specific lower b

Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer

Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable automated medical image analysis for detecting and delineating tumors in CT images, thereby reducing clinicians' workload. Achieving generalization capabilities in limited data do

Mahboubeh Shahrbaf, Prashant Thakur, Davood Rafiei Karkevandi

We investigate the impact of a hypothetical bosonic dark matter (DM) candidate, the sexaquark, on the fundamental ($f$-mode) oscillations of neutron stars (NSs). By varying the DM particle mass and considering different core compositions including hypernuclear matter, sexaquark DM, and deconfined quark matter (QM), we construct hybrid equations of state (EOS

Ali Mazyaki, Mohammad Naghizadeh, Samaneh Ranjkhah Zonouzaghi, Amirhossein Farshi Sotoudeh

Responsible AI demands systems whose behavioral tendencies can be effectively measured, audited, and adjusted to prevent inadvertently nudging users toward risky decisions or embedding hidden biases in risk aversion. As language models (LMs) are increasingly incorporated into AI-driven decision support systems, understanding their risk behaviors is crucial f

Daniel Jarne Ornia, Joel Dyer, Nicholas Bishop, Anisoara Calinescu

Complex learning agents are increasingly deployed alongside existing experts, such as human operators or previously trained agents. However, it remains unclear how should learners optimally incorporate certain forms of expert data, which may differ in structure from the learner's own action-outcome experiences. We study this problem in the context of Bayesia

Minna Hirvonen

We study various notions of dependency in semiring team semantics. Semiring teams are essentially database relations, where each tuple is annotated with some element from a positive semiring. We consider semiring generalizations of several dependency notions from database theory and probability theory, including functional and inclusion dependencies, margina

Haoyang Gui, Thales Bertaglia, Taylor Annabell, Catalina Goanta

The rise of influencer marketing has blurred boundaries between organic content and sponsored content, making the enforcement of legal rules relating to transparency challenging. Effective regulation requires applying legal knowledge with a clear purpose and reason, yet current detection methods of undisclosed sponsored content generally lack legal grounding

F. Colangelo, M. Modestino, F. Avitabile, A. Galluzzi

NbRe is a non-centrosymmetric superconductor that has been proposed as a candidate for intrinsic spin-triplet pairing. However, a conclusive demonstration of triplet pairing in NbRe is yet to be found. To probe the presence of equal-spin triplet Cooper pairs, we fabricated Py/NbRe/Py trilayers capped with an antiferromagnetic layer. Magnetic and electrical m

Daniel Huwiler, Kurt Stockinger, Jonathan Fürst

Retrieval-Augmented Generation (RAG) systems fail when documents evolve through versioning-a ubiquitous characteristic of technical documentation. Existing approaches achieve only 58-64% accuracy on version-sensitive questions, retrieving semantically similar content without temporal validity checks. We present VersionRAG, a version-aware RAG framework that

G. V. Ravindra, Debaditya Raychaudhury

We exhibit a class of extendable codimension $2$ subvarieties in a general hypersurface of dimension at least $4$ in projective space. As a consequence, we prove that a general hypersurface of degree $d$ and dimension at least $4$ does not support globally generated indecomposable ACM bundles of any rank if their first Chern class $e \ll d$.

Zihan Li, Yixiao Xu, Lei Zhang, Taiyu Han

Liver disease is a major global health burden. While ultrasound is the first-line diagnostic tool, liver sonography requires locating multiple non-continuous planes from positions where target structures are often not visible, for biometric assessment and lesion detection, requiring significant expertise. However, expert sonographers are severely scarce in r

Hao Wang, Bin Liu, Yuan-Chuan Zou, Qing-Wen Wu

In studies of binary black hole (BBH) mergers in eccentric orbits, the mean anomaly, traditionally regarded as less significant than eccentricity, has been thought to encode only the orbital phase, leading to the assumption that it exerts minimal influence on the dynamics of eccentric mergers. In a previous investigation, we identified consistent oscillation

Joshua Holstein, Gerhard Satzger

Artificial intelligence has become integral to organizational decision-making and while research has explored many facets of this human-AI collaboration, the focus has mainly been on designing the AI agent(s) and the way the collaboration is set up - generally assuming a human decision-maker to be "fixed". However, it has largely been neglected that decision

Andrei Neguţ

In this short paper, we prove a conjecture of Frenkel-Hernandez, which states that $q$-characters of finite-dimensional simple modules of the quantum affine algebra $U_q(\widehat{\mathfrak{g}})$ are bounded by the Weyl group orbit of the leading monomial under Chari's braid group action. This generalizes the Weyl group invariance of characters of finite-dime

Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Shin'ya Yamaguchi

Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models. Particularly, auto-regressive language models generate texts token by token, i.e., by predicting the next-token distribution given the previous ones, and thus tokenization directly affects th

Simone Bozzolan, Stefano Calzavara, Lorenzo Cazzaro

Web measurements are a well-established methodology for assessing the security and privacy landscape of the Internet. However, existing top lists of popular websites are unlabeled and lack semantic information about the nature of the included websites, making targeted web measurements challenging, as researchers often rely on ad-hoc techniques to bias datase

Andrey V. Panov

Recent advances in nanophotonics have demonstrated that various optical resonances in nanostructures can achieve strong field confinement with substantially suppressed scattering. This study investigates the optical Kerr effect (OKE) enhancement in high-refractive-index metasurfaces featuring non-BIC trapped-mode resonances, using gallium phosphide (GaP) as

Sherzod Hakimov, Roland Bernard, Tim Leiber, Karl Osswald

Negotiation is a fundamental challenge for AI agents, as it requires an ability to reason strategically, model opponents, and balance cooperation with competition. We present the first comprehensive study that systematically evaluates how explicit reasoning training affects the negotiation abilities of both commercial and open-weight large language models, c

Dalga Merve Özkan, Sergio Lucia, Sebastian Engell

This paper presents a general mathematical programming framework for the design and optimization of supply chain infrastructures for the upcycling of plastic waste. For this purpose, a multi-product, multi-echelon, multi-period mixed-integer linear programming (MILP) model has been formulated. The objective is to minimize the cost of the entire circular supp

Ankit Gahlawat, Anirban Mukherjee, Dinesh Babu Jayagopi

Accurate face parsing under extreme viewing angles remains a significant challenge due to limited labeled data in such poses. Manual annotation is costly and often impractical at scale. We propose a novel label refinement pipeline that leverages 3D Gaussian Splatting (3DGS) to generate accurate segmentation masks from noisy multiview predictions. By jointly

Amitis Shidani, Tyler Farghly, Yang Sun, Habib Ganjgahi

Synthetic data can improve generalization when real data is scarce, but excessive reliance may introduce distributional mismatches that degrade performance. In this paper, we present a learning-theoretic framework to quantify the trade-off between synthetic and real data. Our approach leverages algorithmic stability to derive generalization error bounds, cha

Tejash Varsani

Federated learning is distributed model training across several clients without disclosing raw data. Despite advancements in data privacy, risks still remain. Differential Privacy (DP) is a technique to protect sensitive data by adding noise to model updates, usually controlled by a fixed privacy budget. However, this approach can introduce excessive noise,

Morteza Sargolzaei Javan

Cloud computing has become the foundation of modern digital infrastructure, yet the absence of a unified architectural and compliance framework impedes interoperability, auditability, and robust security. This paper introduces a formal, machine-readable semantic model for Cloud Engines, integrating the architectural taxonomy of ISO/IEC 22123 (Cloud Reference

Ziqi Zhou, Menghao Deng, Yufei Song, Hangtao Zhang

Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstrated the vulnerability of deep hashing models to backdoor attacks. Although these studies have shown promising attack results, they rely on access to the training dataset to implan

Da Wei, Shiyuan Hu, Tangmiao Tang, Yaochen Yang

Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation a

Vivek Banerjee, Sasmita Mishra

Neutrinoless double beta ($0\nu\beta\beta$) decay, an important low-energy process, serves not only as a potential test of the Majorana nature of neutrinos, but also as a sensitive probe for new physics beyond the Standard Model. In this study, the supersymmetric left-right model is explored to investigate its impact on $0\nu\beta\beta$ decay. Although the p

Chun-Tse Li, Tzen Ong, Chih-Yun Lin, Yu-Cheng Chen

Subspace eigenvalue problems arise ubiquitously in quantum chemistry and condensed-matter physics, where the relevant object is often a low-energy manifold rather than a single ground-state wavefunction. In this work, we propose a fault-tolerant quantum algorithm for this subspace-level task based on the Feshbach effective-Hamiltonian formalism. Given block-

Vaishakh Kargudri, Sandra M. Jose, Rejish Nath

We study the formation of transient Faraday patterns and spin textures in driven quasi-one-dimensional and quasi-two-dimensional spin-1 Bose-Einstein condensates under the periodic modulation of $s$-wave scattering lengths $a_0$ and $a_2$, starting from the anti-ferromagnetic phase. This phase is characterized by a Bogoliubov spectrum consisting of three mod

Sukriti Bhattacharya, Chitro Majumdar

This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that capture

Sohaib El Karmi

We present a reproducible research framework for market microstructure combining a deterministic C++ limit order book (LOB) simulator with stochastic order flow generated by multivariate marked Hawkes processes. The paper derives full stability and ergodicity proofs for both linear and nonlinear Hawkes models, implements time-rescaling and goodness-of-fit di

Hikmat A. M. Abdeljaber, Md. Alamgir Hossain, Sultan Ahmad, Ahmed Alsanad

The rapid expansion of Internet of Things (IoT) devices has transformed industries and daily life by enabling widespread connectivity and data exchange. However, this increased interconnection has introduced serious security vulnerabilities, making IoT systems more exposed to sophisticated cyber attacks. This study presents a novel ensemble learning architec

Janos Hajdu, Martin Janßen

A general approach to modeling irreversibility starting from microscopic reversibility is presented. The time $t_s$ up to which relevant degrees of freedom of a system are tracked is extremely much shorter than the spectral resolution time $t_e$ that would be necessary to resolve the spectrum of all degrees of freedom involved. A relaxator that breaks revers

Aniana Cruz, Marko Kuzmanoski, Gabriel Pires

Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and

Xiaochong Lan, Jie Feng, Yinxing Liu, Xinlei Shi

Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains an

Abdikarim Mohamed Ibrahim, Rosdiadee Nordin, Yahya S. M. Khamayseh, Angela Amphawan

The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and resilient industrial systems. Sixth-generation (6G) wireless networks aim to meet these requirements through sub-millisecond end-to-end delays, microsecond level jitter, and near per

Liyang Chen, Hongkai Chen, Yujun Cai, Sifan Li

Video-to-Audio generation has made remarkable strides in automatically synthesizing sound for video. However, existing evaluation metrics, which focus on semantic and temporal alignment, overlook a critical failure mode: models often generate acoustic events, particularly speech and music, that have no corresponding visual source. We term this phenomenon Ins

Paing Thit Nyein, Kyaw Kyaw Naing, Htun Htun Oo, M. Yamaguchi

The recent observation of the deeply bound \Xi - hypernucleus ^15_\Xi^-C through the IRRAWADDY and KINKA events provided a crucial benchmark for determining the \Xi-nucleus interaction. This work aims to constrain the depth of this potential by calculating the binding energy B_\Xi of the ^15_\Xi C system, which forms a \Xi^- -^14 N bound state. We achieve th

Lujun Hong, Chao Liu, Jun Wu, Chaojian He

Beam splitters are pivotal components in integrated microwave and photonic systems. However, conventional designs based on directional coupling or multi-mode interference often suffer from back scattering, frequency-dependent splitting ratios, and limited bandwidth. To overcome these limitations, here, we propose a new physical mechanism to achieve a broadba

Qingyang Hao, Wenbo Liao, Bingyi Jing, Hongxin Wei

Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignment of large language models. While conformal selection methods offer a rigorous solution with False Discovery Rate (FDR) control, their applicability is confined to single-threshol

Ian D. Morris

R. Shorten, F. Wirth, O. Mason, K. Wulff and C. King have asked whether a linear switched system is guaranteed to be globally uniformly stable under arbitrary switching if it is known that every trajectory induced by a periodic switching law converges exponentially to the origin. Positive answers to this question have previously been announced for linear swi

Vamsi Addanki

As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, collective communication has emerged as a critical bottleneck in scale-up systems. Programmable photonic interconnects offer a promising path forward: by dynamically reconfiguring the fabric, they can establish direct, high-bandwidth optical paths between communicating

Jan Nöller, Viet T. Tran, Mariami Gachechiladze, Richard Kueng

Learning properties of quantum states from measurement data is a fundamental challenge in quantum information. The sample complexity of such tasks depends crucially on the measurement primitive. While shadow tomography achieves sample-efficient learning by allowing entangling measurements across many copies, it requires prohibitively deep circuits. At the ot

Prasanta Gorai, Maryam Saberi, Theo Khouri, Taïssa Danilovich

Sulfur and its isotopic ratios play a crucial role in understanding astrophysical environments, providing insights into nucleosynthesis, ISM processes, star formation, planetary evolution, and galactic chemistry. We investigate the distribution of sulfur bearing species $\rm{SO_2}$, $\rm{^{34}SO_2}$, SO, and $\rm{^{34}SO}$ towards five oxygen rich Asymptotic

Aadi Singhi

This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Language Models (LLMs) for alpha generation and portfolio management in the cryptocurrencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to m