April 2026 arXiv papers — page 98

Simon Foldvik

We introduce causal-temporal event graphs (CTEGs) as a formal model for fully resolved recursive agent execution records under single-parenthood causal semantics. We formalise direct event emissions and recursive subagent invocations as extension procedures on generic typed temporal graphs and show that the recursive closure $\mathscr{E}_\infty$ of the induc

Faizunnesa Khondaker, Md. Kamrujjaman

Bangladesh exhibits marked year-to-year variability in dengue, partly driven by meteorological fluctuations that shape \textit{Aedes} breeding-site persistence, mosquito development, and transmission. We exploit a contrast between Dhaka (consistently high burden) and Barishal (recently rising burden despite lower population density) and frame feature-set des

Giovanni Apruzzese

Network Intrusion Detection Systems (NIDS) have been studied for decades. Hundreds of papers have, e.g., proposed ways to enhance, harden or bypass NIDS. However, the findings of prior literature are hardly reflected in real-world operational contexts. Such a disconnection is problematic for research itself: it is unclear what scenario envisioned by prior wo

Hansi Zeng, Liam Collins, Bhuvesh Kumar, Neil Shah

Agentic search -- the task of training agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions -- has achieved remarkable progress through reinforcement learning (RL). However, existing approaches such as Search-R1, treat the retrieval system as a fixed tool, optimizing only the reasoning agent while th

Guang Yang, Praveen Kumar

Two-particle two-hole (2p2h) excitations driven by meson-exchange currents (MEC) are among the leading nuclear uncertainties in long-baseline neutrino oscillation experiments. Three models currently implemented in neutrino event generators disagree by 20--40% on the $\omega$-integrated 2p2h cross section in the dip region on carbon (differential disagreement

Yifan Shen, Junlin Chen, Julia Yan, Chiwei Yan

Although shared rides have the potential to increase vehicle utilization and reduce congestion and emissions, these benefits depend heavily on ridesharing platforms' ability to match riders effectively. As such, shared rides have seen limited success outside of dense urban areas -- the sparse outskirts of greater metropolitan areas remain underserved. In the

Jinsun Yoo, Meghan Cowan, Zheng Du, Changhai Man

Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across the stack. We present Flint, a framework that bridges this gap by leveraging the Intermediate Representation of Machine Learning framework compilers. The compiler does the heavy wei

Liyao Jiang, Ruichen Chen, Keith G. Mills

Pre-training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for final evaluation, the model is forced to learn a more general understanding of the input data. While pre-training has been applied to 3D Human Pose Estimation (HPE) previously, the s

Alejandro Duque, Paulina Sepúlveda, Carlos Uriarte, Jamie M. Taylor

This work presents a robust, energy-based deep learning framework for solving transmission problems in heterogeneous media, including cases with discontinuous material scenarios. We introduce a weighted First-Order System Least-Squares (FOSLS) formulation involving an energy-norm Poincar\'e constant and prove its equivalence to a natural energy norm of the u

Andrea Malchiodi, Francesco Malizia

We study the Weyl functional on connected sums of two four-dimensional manifolds $(M,g_M)$ and $(Z,g_Z)$, assuming $g_M$ is Bach-flat and $g_Z$ locally conformally flat. We show that if $g_M$ is neither self-dual nor anti self-dual and if $g_Z$ is of positive Yamabe class, there exists a metric $g_Y$ on $Y := M \# Z$ with Weyl energy lower than that of $g_M$

József Pintér, Regina Stangl

Network caching asks how to place contents in distributed caches so that future requests are served close to their users. Ganian, Mc Inerney and Tsigkari recently initiated the parameterized-complexity study of the problem and, for the homogeneous unit-size variant (HomNC), isolated an unresolved family of six parameterizations: by the number of caches $C$,

James M. Cline

Recently arxiv:2604.12897 urged that the terminology "neutrinoless double beta decay" should be changed to "Majorana double beta decay" to properly give credit to Majorana, and to focus on the positive aspects of the phenomenon -- supposed creation of matter in the laboratory -- rather than the negative: absence of something, embarrassment over false claims

M. El Azhari

By using a variation of a theorem on $n$-Jordan homomorphisms due to Herstein, we deduce the following G. An's result: Let $ A $ and $ B $ be two rings where $ A $ has a unit and $ char(B)> n. $ If every Jordan homomorphism from $ A $ into $ B $ is a homomorphism (anti-homomorphism), then every $n$-Jordan homomorphism from $ A $ into $ B $ is an $n$-homomorp

Yuting Huang, Yinghao Hu, Qian Xiao, Wenlin Zhong

Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challenging due to hallucinated legal citations, incomplete knowledge coverage, and weak structured reasoning. To address these issues, we propose PoliLegalLM, a domain-specific large language model tailored for poli

Nam Nguyen Phuong, Duc Nguyen The Minh, Phi Le Nguyen, Ehsan Abbasnejad

Conventional test-time adaptation (TTA) approaches typically adapt the model using only a small fraction of test samples, often those with low-entropy predictions, thereby failing to fully leverage the available information in the test distribution. This paper introduces DualTTA, a novel framework that improves performance under distribution shifts by utiliz

Hao Wu, Zhong-Can Ou-Yang

Charged colloidal crystals exhibit a subtle interplay between electrostatic screening and elastic deformation. When an isotropic electrostatic volumetric softening acts on an anisotropic elastic background, the longitudinal acoustic response softens preferentially along specific crystallographic axes. This article provides a self-contained derivation of the

Ilija K. Nikolov, Rong Cong, Adrien Rosuel, Stephen Carr

Coupling between different degrees of freedom (DOF) in an electronic material leads to exotic phases of matter characterized by complex and competing order parameters as well as emergent excitations. Building a microscopic understanding of these order parameters and their mutual relationship is hindered by the fact that different orders often mask each other

Efe Ilker, Kathrin Laxhuber, Jean-Francois Joanny, Frank Jülicher

We present a systematic theory of chemically active emulsions in the hydrodynamic limit by constructing a thermodynamically consistent framework in which the equilibrium is broken by chemo-stating of fuel molecules. For ternary solutions with active chemical reactions, we obtain an effective dynamics of the conserved field dynamics at long length and time sc

Anindita Sarkar, Paranjoy Chaki, Debarupa Saha, Ujjwal Sen

Conventional quantum thermometry assumes completely positive (CP) encoding maps, where the probe is initially uncorrelated with the environment. We consider realistic scenarios with initial probe-environment correlations leading to physically realizable non-completely positive (NCP) encoding, and show how such encodings can significantly impact temperature e

Saptarshi G. Dastider, Amit Singh Negi, Krishnakanta Mondal, Jobin Cyriac

The recent detections of thioformyl cyanide (HCSCN) and propynethial (HCSCCH) in TMC-1 provide critical insights into the interstellar sulfur inventory, yet their sequestration and survivability on dust grain mantles remain poorly constrained. Here, we present a computational study of the site-specific adsorption of HCSCN and HCSCCH on amorphous solid water

Xinsen Zhang, Zhenkai Ding, Tianjun Pan, Run Yang

Extending the effective context length of large language models (LLMs) remains a central challenge for real-world applications. While recent post-training methods have made progress in long-context scaling, they either rely on high-quality supervision data or sparse sequence-level rewards, leading to unstable and inefficient optimization. We propose OPSDL, a

Alexia M. Lopez, Roger G. Clowes

We present the discovery of `A Giant Ring on the Sky' (GR); a ring-like, ultra-large-scale structure at z~0.8, located in the same field that contains the previously-documented Giant Arc (GA) and Big Ring (BR). The GR was predicted from the presence of a Northern Arc (NA) filament (noted in previous work), which looked like it could, with more or enhanced da

Rozita Shafie, Omid Abbasi, Halim Yanikomeroglu, Mohammad Javad Omidi

Spatial correlation poses a significant challenge in massive multiple-input multiple-output (MIMO) high-altitude platform station (HAPS) systems. The inherent spatial correlation among antenna elements on the HAPS induces high correlation and interference among users' channel gains. To mitigate this issue, we propose an integrated approach that combines spat

Abdoulaye Thiam

We develop the convex-analytic structure of the thermodynamic formalism for continuous maps on compact metric spaces. The pressure functional is the Legendre-Fenchel transform of the negative entropy, and the biconjugate recovery of the entropy from the pressure establishes a complete duality. Equilibrium states are elements of the subdifferential of the pre

Paolo Wang, Michael Zhang, Shrinand Perumal, Ekaterina Tszyao

Posture is a critical factor for beginning instrumental learners. Most students receive instruction only once a week, and during the intervals between lessons they have little or no feedback on their physical posture. As a result, posture often deteriorates, increasing the risk of musculoskeletal injury and inefficient technique. Recent advances in computer

Abdoulaye Thiam

We prove that five characterizations of Gibbs measures for H\"{o}lder potentials on topologically mixing subshifts of finite type are equivalent: the Jacobian condition, the classical cylinder-based Gibbs property, the eigenmeasure of the Ruelle transfer operator, the variational equilibrium state, and the minimizer of the large deviations rate function. The

Akshat Agarwal, Gautam Iyer, Aidan Jameson, Seungjae Son

We study convergence rates of the annealed importance sampling algorithm (Neal '01) combined with Langevin Monte Carlo when the target is a multimodal Gibbs measure. The main result shows that for a fixed error threshold, the time complexity is quadratic in the inverse temperature. We identify a simple and useful quantity that controls the sampling error for

Joan S. Muthu, John Shalen

Medical imaging AI development is fundamentally dependent on annotated datasets, yet no existing standard provides machine-enforceable validation across dataset structure, annotation provenance, quality documentation, and ML readiness within a single framework. DICOM standardizes image acquisition, storage, and communication at the individual study level. BI

Jonathan Holland, George Sparling

Let $(M,g)$ be a Lorentzian manifold whose local space $\mathcal N$ of unparametrized null geodesics is smooth. We show that its Penrose limits assemble into a smooth bundle of plane-wave germs with a canonical gauge-theoretic soldering to spacetime. The incidence correspondence $\mathcal U=\{(x,[γ]):x\inγ\}$ identifies the contact space modulo the tangent s

Mauricio Cataldo

We show that the diffusion-based models proposed in Refs.~\cite{Perez2021,Landau2022} within the framework of Unimodular Gravity (UG) to alleviate the $H_0$ tension are incompatible with the second law of thermodynamics. Starting from the Gibbs equation for a pressureless matter fluid, we derive a general thermodynamic admissibility condition for the $\Lambd

Prabhudarshi Nayak, Gogulakrishnan Thiyagarajan, Debashree Priyadarshini, Vinay Bist

Ransomware continues to evolve as one of the most disruptive cyber threats, with recent variants increasingly leveraging automated and AI-assisted techniques to evade traditional signature-based defenses. Early detection of such attacks remains a significant challenge, particularly when malicious behavior closely resembles legitimate system activity. This st

Christian Klein, Svetlana Roudenko, Nikola Stoilov

We present a novel numerical framework for studying nonlinear dispersive equations in higher-dimensional settings, specifically designed for solutions featuring traveling waves along a preferred axis (or field-aligned traveling waves). Using the three-dimensional generalized Zakharov-Kuznetsov (gZK) equation as a model, we convert it into cylindrical coordin

Anu Dhochak

In this article, we discuss the existence of a 1-parameter infinite genus family of maxfaces having infinitely many planar (spacelike) ends and infinitely many swallowtails. In particular, we show the existence of the following: (1) a period-2 family of maxfaces with infinitely many planar ends and alternating singularity types, where every odd-layer neck ha

Jinyi Li, Juncheng Zheng, Xue Yang, Kainan Hu

Quantum batteries harness non-classical resources, such as quantum coherence and entanglement, to surpass the performance limits of classical energy-storage devices. Here we realize a room-temperature quantum battery based on a collective atomic spin ensemble in a thermal alkali-metal vapor, containing approximately $10^{12}$ $^{87}$Rb atoms with coherence t

Marcelo Fernandez

Autonomous agent systems are governed by enforcement mechanisms that flag hard constraint violations at runtime. The Agent Control Protocol identifies a structural limit of such systems: a correctly-functioning enforcement engine can enter a regime in which behavioral drift is invisible to it, because the enforcement signal operates below the layer where dev

Paolo Andreetto, Massimo Casarsa, Alessio Gianelle, Carlo Giraldin

This contribution discusses the physics potential of a future muon collider operating at a center-of-mass energy of $\sqrt{s} = 10$ TeV for precision studies in the Higgs sector. Using a detailed detector simulation that incorporates the dominant sources of machine-induced background, the expected sensitivity to key Higgs processes is evaluated. These includ

Siyuan Luo, Bingyang Zhou, Chong Zhang, Xin Liu

Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulation remains a major bottleneck for deformable object manipulation. The continuously changing geometry of soft materials, together with large numbers of vertices and contact constraints, makes it difficult to ac

Harshavardhanan Deekeswar

Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operational data at scale. A modest dataset of 1,000 IoT sensor readings serialized as JSON requires approximately 80,000 tokens - the majority spent on repeated field names, nested braces, and structural punctuation

Marcelo Fernandez

Autonomous systems increasingly execute actions that directly modify shared state, creating an urgent need for precise control over which transitions are permitted to occur. Existing governance mechanisms evaluate policies prior to execution or reconstruct behavior post hoc, but do not enforce admissibility at the exact moment a state transition is committed

Divya Bajaj, Bin Fu, Ryan Knobel, Austin Luchsinger

Chemical Reaction Networks (CRNs) are a well-established model of distributed computing characterized by quantities of molecular species that can transform or change through applications of reactions. A fundamental problem in CRNs is the reachability problem, which asks if an initial configuration of species can transition to a target configuration through a

Gian Maria Dall'Ara, Adrian Dumitrescu

What is the largest constant $c\in [0,1]$ with the property that every finite collection $\mathcal{C}$ of axis-parallel squares in the plane admits a disjoint sub-collection $\mathcal{S}$ occupying at least a fraction $c$ of the area covered by $\mathcal{C}$? This problem was first raised by T.~Rad\'o in 1928, who was motivated by a classical covering lemma

Katerina Paltoglou, Vassilis E. Zafeiris

We propose a method that employs static and dynamic analysis for augmenting a test suite with automatically generated unit tests. The method is most suitable for test suites where the stratification of unit, integration and system tests does not conform to the recommended test pyramid structure: numerous unit tests providing high code coverage and forming th

Arnaud Amblard, Aurélien Drezet

Relativistic extensions of de Broglie-Bohm theory postulate a preferred foliation of space-time, an additional structure essential for defining simultaneous configurations on Minkowski space-time, but conventionally believed to be empirically undetectable at quantum equilibrium. In this paper, we outline an experimental protocol for empirically detecting the

Wilhelm Hasselbring, Daniel S. Katz, Rob van Nieuwpoort

Research software has been categorized for various goals. One fundamental dimension of such categorizations is the role that the software plays in the research process. Recently, a new role category has emerged: technology research software, which covers research software developed in technology research. Until now, this category of technology research softw

Davin Choo, Paul W. Goldberg, Nicholas Teh

Many high-stakes AI deployments proceed only if every stakeholder deems the system acceptable relative to their own minimum standard. With randomization over a finite menu of options, this becomes a feasibility question: does there exist a lottery over options that clears all stakeholders' acceptability bars? We study a query model where the algorithm propos

Gaozhi Zhou, Hu He, Peng Shen, Jipeng Zhang

Reinforcement learning (RL) post-training substantially improves remote sensing vision-language models (RS-VLMs). However, when handling complex remote sensing imagery (RSI) requiring exhaustive visual scanning, models tend to rely on localized salient cues for rapid inference. We term this RL-induced bias "perceptual inertia". Driven by reward maximization,

Zheng Nie, Ruolin Shen, Xinlei Yu, Bo Yin

Scaling vision-language models into Visual Multiagent Systems (VMAS) is hindered by two coupled issues. First, communication topologies are fixed before inference, leaving them blind to visual content and query context; second, agent reasoning abilities remain static during deployment. These issues reinforce each other: a rigid topology fails to leverage ric

Tairan Fu, Francisco Javier Santos-Martín, Javier Conde, Elena Merino-Gómez

The digital transformation of industrial manufacturing increasingly relies on the ability of autonomous robots to interact with legacy infrastructure, particularly analog gauges. Vision-Language Models (VLMs) have the potential to provide a general solution for gauge reading and have already shown good performance in instrument recognition. However, performi

Ruiyao Xu, Mihir Parmar, Tiankai Yang, Zhengyu Hu

Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning

Guandong Li, Mengxia Ye

Scene text editing (STE) has achieved remarkable progress in accurately rendering target text through diffusion-based methods. However, we identify a critical yet overlooked problem: edit spillover -- when editing a target text region, existing methods inadvertently modify non-target regions, particularly neighboring text. Through systematic evaluation on 50

Jiamo Li, Qikai Lu, Minbo Yang

For dimensions $n\geq8$, we are concerned with the quotient functional of the biharmonic Br\'{e}zis-Nirenberg problem under the Navier boundary condition $$ S(\varepsilon V):=\inf_{0\not\equiv u\in H^2(\Omega)\cap H_0^1(\Omega)}\frac{\int_{\Omega}|\Delta u|^2dx+\varepsilon\int_{\Omega}V|u|^2dx}{\big(\int_{\Omega}|u|^{2^\star}dx\big)^{2/2^\star}}, $$ where $2

Feride Baraner, Ovgu Gurel

This paper investigates the q-Stancu operators, which generalize the q-Bernstein operators, by developing a new representation in terms of the q-Pochhammer symbol. Based on this representation, some known properties are re-discovered, and a general recurrence relation for the moments is established. It is shown that higher-order moments can be expressed in t

John T. Behrens

Generative AI systems have entered everyday academic, professional, and personal life with remarkable speed, yet most users encounter them as mysterious artifacts rather than intelligible systems. This chapter discusses large language models within a broader historical shift in computing paradigms and argues that many of the confusions surrounding their use

Eleftheria Malami

The $B \to \pi K$ system provides a rich laboratory for testing the Standard Model and studying CP violation. A particularly important channel is $B^0_d\to\pi^0 K_{\rm S}$, the only mode exhibiting both direct and mixing-induced CP violation. Recent Belle II measurements of the CP asymmetries in this decay provide valuable new input. An updated analysis inco

Francesca Da Lio, Tristan Rivière, Dominik Schlagenhauf

We study the stability of the extended Morse index, defined as the number of negative and zero eigenvalues of the Jacobi operator, for sequences of harmonic maps on degenerating Riemann surfaces. As the conformal structure approaches the boundary of moduli space, collar collapse creates major analytical challenges. We analyze the second variation of the ener

Marcin Kostrzewa, Maciej Zięba, Jerzy Stefanowski

Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Existing methods for generating robust CFEs are often limited to specific types of models, require costly tuning, or inflexible robustness controls. We propose a novel approach that jointly models the data distri

Nicholas Jones, Eytan Modiano

We analyze the problem of scheduling in wireless networks to meet end-to-end service guarantees, defined by instantaneous throughput and hard packet deadlines. Using a network slicing model to decouple the queueing dynamics between flows, we show that the network's ability to meet hard deadline guarantees under interference is largely influenced by the link

Theodoros Kouzelis, Spyros Gidaris, Nikos Komodakis

Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic features extracted from pre-trained visual encoders. However, existing approaches rely on a fixed representation space, constructed independently of the generative objective and kept

V. P. Nair

The coadjoint orbit action for a multifermion system, as an exact description of its dynamics, is considered. A parametrization of the variables involved is given which facilitates the approximation of this by another coadjoint orbit action suitable for expansions around the Fermi surface, recovering various actions which have been used in previous literatur

Nawaf Mohammed

We introduce \emph{joint exclusivity} (JE), a flexible extension of mutual exclusivity for non-negative random vectors that prohibits only the simultaneous positivity of all $n$ components. Unlike mutual exclusivity, JE admits a broad class of marginals while retaining a sharp existence criterion. We prove that a JE random vector with prescribed marginals $F

Zhiyuan Zhang, Bolin Zhang, Yongguang Lv, Ruiqing He

This work aims to address the bottleneck issues of hardware resource limitation and decoherence error in the Hamiltonian simulation of quantum fluids, which are caused by the standard quantum Fourier transform and the evolution of momentum operators, resulting in excessively deep circuits and excessive two-qubit gates. We propose an approximate operator opti

Shah Ishmam Mohtashim, Manas Sajjan, Sabre Kais

We present a quantum-dynamical framework for identifying structurally important residues in proteins based on continuous time quantum walks (CTQWs) on weighted residue interaction networks constructed from experimentally resolved structures. By mapping the weighted adjacency matrix to a Hamiltonian, residue importance emerges from the long-time averaged occu

Anum Umer, Ivo Müürsepp, Muhammad Mahtab Alam

This paper addresses the problem of adaptive reconfigurable intelligent surfaces (RIS) configuration design for user localization in rich-scattering environment (RSE), where electromagnetic waves undergo multiple interactions with dynamic scatterers and RIS elements. We propose an adaptive learning-based localization approach for a distributed RIS-assisted n

Haocheng Ju, Leheng Chen, Peihao Wu, Bryan Dai

Retrieving mathematical knowledge is a central task in both human-driven research, such as determining whether a result already exists, finding related results, and identifying historical origins, and in emerging AI systems for mathematics, where reliable grounding is essential. However, the scale and structure of the mathematical literature pose significant

Paul Balmer, Martin Gallauer

Informed by our understanding of the tt-geometry of permutation modules, we investigate the proper definition of the `stable permutation category' of a finite group. Then we prove that this category decomposes over cyclic and generalized quaternion groups and only in those cases.

Li Shen, Yongpeng Wu, Zhen Gao, Yin Xu

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SO-SCL. In this paper, we incorporate node-based fast decoding

Ciaran Bench

In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a separate discriminative model's training data. This can help improve the discriminative model's performance on the test data. However, generative models are prone to producing hallucinations and artefacts tha

Jinqi Cao, Zhiping Yu, Baihong Lin, Chenyang Liu

Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate realistic visual content, their spatial scale remains confined to bounded environments, preventing them from capturing how geographic environments evolve across thousands of kilometers or from modeling the spati

Takuto Kawakami, Igor Kuzmenko, Yshai Avishai, Yigal Meir

We develop a theory of quantum spin Hall insulators with arbitrary spin $J$. Our analysis demonstrates that such systems support $J+\tfrac{1}{2}$ pairs of helical edge modes protected by nontrivial mirror Chern numbers. We establish that the corresponding edge theory is described by a generalized Dirac fermion with higher-order dispersion. These modes produc

Yubing Wang, Quan-feng Wu, Xun-Jie Xu

We present an effective numerical method that can be used to straightforwardly calculate the full spectrum of primordial gravitational waves produced during inflation and reheating. Our method is based on the Bogoliubov approach with several key improvements to overcome its shortcomings such as numerical errors at high frequencies and issues with tachyonic m

Qihao Shen, Jiaxing Xuan, Zhenguang Liu, Sifan Wu

Advanced deepfake technologies are blurring the lines between real and fake, presenting both revolutionary opportunities and alarming threats. While it unlocks novel applications in fields like entertainment and education, its malicious use has sparked urgent ethical and societal concerns ranging from identity theft to the dissemination of misinformation. To

Jianming Tong, Hanshen Xiao, Krishna Kumar Nair, Hao Kang

Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive computational overhead, limiting scalability. We introduce a framework, Privatar, to offload avatar reconstruction from headset to untrusted devices within the same local network while safeguarding attacks against

Ashutosh Bajpai, Tamal Majumder, Akshay Nambi, Tanmoy Chakraborty

Small Vision-Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typically require expensive supervised trajectory tuning to mitigate these deficits. In this work, we propose Self-supervised Perception Enabled by Cascaded Tool Rollout Alignment (SPECTRA), a supervision-free framewo

Anum Umer, Ivo Müürsepp, Muhammad Mahtab Alam

This paper investigates an uplink user equipment (UE) location and orientation estimation problem in an indoor rich-scattering environment (RSE) for a multiple-input-multiple-output (MIMO) narrowband reconfigurable intelligent surfaces (RIS)-assisted communication system. The localization problem in RSE is challenging as the uplink pilot signal undergoes mul

Peng Huang, Yifeng Chen, Zeyu Zhang, Hao Tang

Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D generation (e.g., synthesis, completion, and editing). However, these tasks are often tackled in isolation, resulting in fragmented architectures and representations that hinder knowledge transfer and holist

Buyan Li, Jie Xiao

In [4], we use the root categories to realize Chevalley groups. Lusztig's theory of total positivity for reductive groups can be naturally applied to Chevalley groups. In this paper, we explicitly determine regions of $\mathbb{R}_{>0}^t$ for describing the size of monoids of totally positive elements, with respect to the root subgroups corresponding to the i

Vedanta Thapar, Abhinav Gupta

Machine learning has become a powerful tool for discovering governing laws of dynamical systems from data. However, most existing approaches degrade severely when observations are sparse, noisy, or irregularly sampled. In this work, we address the problem of learning symbolic representations of nonlinear Hamiltonian dynamical systems under extreme data scarc

Malaika Arshad, Jehanzad Zafar, Ahdab Althukair, Bilal Ahmad

We show that a Weyl-flat null origin of inflation need not be in tension with present observations. For canonical single-field inflation, any background with $\epsilon(N)\to \epsilon_\infty\in(0,1)$ as $N\to\infty$ is asymptotically power-law, inherits the same Weyl-flat null past boundary, and reconstructs an exponential tail in field space. This identifies

Frank Redig, Berend van Tol

In this paper we study detailed fluctuation results for a class of non-equilibrium steady states. The main example is the boundary driven harmonic model \cite{frassek2022exact}. In this model, the non-equilibrium steady state (NESS) is a mixture of products of geometric distributions, of which the local parameters are in turn distributed as uniform order sta

Narayan Kundu, Soumya Ghosh, Dhananjay Nandi

We present velocity map imaging data on intramolecular ion-pair dissociation (IPD) of carbonyl sulfide (OCS) induced by electron impact over the 20 eV to 45 eV energy range. Two distinct IPD pathways were resolved: CO+ + S- (threshold 14.8 +- 0.7 eV) and CS+ + O- (threshold 16.8 +- 0.7 eV). The kinetic energy release spectra display a single peak for S- but

Wei Cheng, Xin Peng, Jia-wei Zhang, Ruiyu Zhou

Within the framework of warm Higgs inflation, a systematic comparison is carried out among seven effective dissipation channels (EDC) constructed from combinations of the three basic dissipation channels, namely the low temperature (LT), high temperature (HT), and threshold (Th) channels. Adopting a unified treatment of warm background evolution, complexity

Y. Friedman

We develop a framework for superposition in relativistic gravity within Extended Relativity (ER), a Lorentz-covariant theory formulated in flat spacetime. In this approach, gravitational fields are described by deviations from the Minkowski metric associated with individual sources, and multi-source configurations are constructed through a superposition prin

Robin Bartlett, Bao V. Le Hung, Brandon Levin

We introduce a new partial resolution of crystalline spaces of Galois representations when the gaps in Hodge--Tate weights are smaller than $p$, with no bound on ramification. Furthermore, when $n =3$ in the case of minimal regular weight, we are able to show that the resolution is normal (assuming the ramification index is divisible by 3). Employing base ch

Damiano Fornasiere, Mirko Bronzi, Spencer Kitts, Alessandro Palmas

We provide evidence that language models can detect, localize and, to a certain degree, verbalize the difference between perturbations applied to their activations. More precisely, we either (a) mask activations, simulating dropout, or (b) add Gaussian noise to them, at a target sentence. We then ask a multiple-choice question such as "Which of the previous

Yongchao Wang, Zhiqiu Huang

The transition from neural machine translation to agentic workflows has revolutionized Automated Program Repair (APR). However, existing agents, despite their advanced reasoning capabilities, frequently suffer from the ``Intent Gap'' -- the misalignment between the generated patch and the developer's original intent. Current solutions relying on natural lang

Elisa Bellantoni, Fabio Guglietta, Andreas Demou, Francesca Pelusi

We study the hydrodynamic behaviour of a mesoscale numerical model for wetting dynamics based on the immersed boundary - lattice Boltzmann (IBLB) method. This IBLB model features a wetting potential to capture the interaction between a non-ideal droplet interface and a solid boundary; it is designed to prevent abrupt curvature changes near the contact line.

Wanru Zhao, Yihong Chen, Yuzhi Tang, Wentao Ma

Data curation is a critical yet under-explored area in large language model (LLM) training. Existing methods, such as data selection and mixing, operate in an offline paradigm, detaching themselves from training. This separation introduces engineering overhead and makes the curation brittle: the entire pipeline must be re-run under model/task shifts. Moreove

Shoki Sato

The classification of finite groups under monoidal equivalence is a fundamental topic in the study of finite quantum groups. While a complete classification has been established for all groups of order strictly less than 64, the case for order 64 has remained limited to the construction of specific examples. In this study, we achieve the complete classificat

Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan Luo, Konstantin Makarychev

Quartet Reconstruction, the task of recovering a phylogenetic tree from smaller trees on four species called \textit{quartets}, is a well-studied problem in theoretical computer science with far-reaching connections to statistics, graph theory and biology. Given a random sample containing $m$ noisy quartets, labeled by an unknown ground-truth tree $T$ on $n$

Zain Naboulsi

AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face a gap between tool documentation and practical mastery, relying on fragmented resources such as blog posts, video tutorials, and trial-and-error. We present cc-self-train, a modular interactive curriculum for learning

Chi Zhang, Zhipeng Xu, Jiahao Liu, Dongsheng Li

While personalized recommender systems excel at content discovery, they frequently expose users to undesirable or discomforting information, highlighting the critical need for user-centric filtering tools. Current methods leveraging Large Language Models (LLMs) struggle with two major bottlenecks: they lack multimodal awareness to identify visually inappropr

Yifan Song, Xingjian Tao, Zhicheng Yang, Yihong Luo

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recent lightweight approaches reduce indexing costs by leveraging Named Entity Recognition (NER), they rely strictly on structural co-occurrence, failing to capture latent semantic connections between disjoint entitie

Donghwan Lee

Q-value iteration (Q-VI) is usually analyzed through the \(\gamma\)-contraction of the Bellman operator. This argument proves convergence to \(Q^*\), but it gives only a coarse account of when the induced greedy policy becomes optimal. We study discounted Q-VI as a switching system and focus on the practically optimal solution set (POSS), the set of \(Q\)-fu

Evren Çetinkaya, Sangmin Lee, Jung Uk Kim, Hong Joo Lee

Visual prompting has emerged as a powerful method for adapting pre-trained models to new domains without updating model parameters. However, existing prompting methods typically optimize a single prompt per domain and apply it uniformly to all inputs, limiting their ability to generalize under intra and inter-domain variability, which is especially critical

Y. Friedman

We introduce a relativistic action that provides a unified and physically meaningful description of particle dynamics in external fields. The proposed action is constructed to be Lorentz covariant and reduces to the standard classical action in the appropriate limit. It incorporates gravitational and electromagnetic interactions within a single framework and

Liyang Wang, Zeyu Zhang, Hao Tang

Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview and 3D scene reasoning. Existing methods such as MSG learn scene graph embeddings in Euclidean space using contrastive learning and attention based association. However, Euclidean geometry does not explicitly

Marco Sánchez-Beeckman, Antoni Buades

Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning. Yet, most modern denoising architectures make limited use of the technical knowledge acquired researching the classical denoisers that came before the mainstream use of neural networks, instead relying on dept

Pushkar Dasika, Liza Jain, Varun Srivatsav Kondapally, Md Arif Ali

A Single-Photon Detector (SPD) can detect extremely low intensity of electromagnetic wave - down to a single photon. Driven by the rapid developments in quantum information science and an increasing demand for ultra-low-light sensing across various domains, there is a need for transformative advancements in the design and development of SPDs. In this context

Yihong Yao, Chunlei Li, Canxuan Gang, Wenzhi Hu

Increasingly advanced data augmentation techniques have greatly aided clinical medical research, increasing data diversity and improving model generalization capabilities. Although most current basic models exhibit strong generalization abilities, image quality varies due to differences in equipment and operators. To address these challenges, we present SegT

Zan Kai Chong, Hiroyuki Ohsaki, Bryan Ng

Enterprise SLM deployment faces epistemic asymmetry: small models cannot self-correct reasoning errors, while frontier LLMs incur prohibitive costs and data sovereignty risks at scale. We propose Semantic Gradient Descent (SGDe), a teacher-student framework that compiles agentic workflows into discrete execution plans--DAG topologies, system prompts, and det