March 2026 arXiv papers — page 30

Aaron Bembenek, Toby Murray

For high-assurance software, source-level reasoning is insufficient: we need binary-level guarantees. Despite constrained Horn clause (CHC) solving being one of the most popular forms of automated verification, prior work has not evaluated the viability of CHC solving for binary analysis. To fill this gap, we assemble a pipeline that encodes binary analysis

Sobin Alosious, Yuhan Liu, Jiaxin Xu, Gang Liu

The discovery of polymers with targeted properties is challenged by the vast chemical design space and the limited availability of consistent, high-quality data across multiple properties. In this work, an integrated polymer informatics framework is presented that combines the Automated molecular Dynamics Engine for Polymer simulaTions (ADEPT) workflow with

Girish Chandar Ganesan, Yuliang Guo, Liu Ren, Xiaoming Liu

Monocular metric depth estimation (MMDE) is a core challenge in computer vision, playing a pivotal role in real-world applications that demand accurate spatial understanding. Although prior works have shown promising zero-shot performance in MMDE, they often struggle with generalization across diverse camera types, such as fisheye and $360^\circ$ cameras. Re

Hongzhuo Chen, Zhanliang Wang, Quan M. Nguyen, Gongbo Zhang

Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, treatment planning, and outcome prediction. However, most MDTs remain static or passively updated, creating a critical synchronization gap, especially in rare genetic disorders where

Ruosi Wang, Fangwei Zuo, Lei Li, Zhaoqiang Xia

Skeleton-based human action recognition has achieved remarkable progress in recent years. However, most existing GCN-based methods rely on short-range motion topologies, which not only struggle to capture long-range joint dependencies and complex temporal dynamics but also limit cross-modal semantic alignment and understanding due to insufficient modeling of

Wentao Ye, Yuan Luo, Bo Liu, Jianwei Huang

The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and hetero

Gedeon Muhawenayo, Caleb Robinson, Subash Khanal, Zhanpei Fang

Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field mapping are sensitive to illumination, spatial scale, and changes in geographic location. We conduct the first systematic evaluation of segmentation and geospatial foundation models (GFMs) for global field boundary

Dallin Spencer, Kat Volk, Darin Ragozzine, Renu Malhotra

The Small Body Dynamics Tool (SBDynT) is software written for the community of Solar System small body researchers to perform dynamical classification, characterization, and investigation. SBDynT provides advanced simulation analysis capabilities that make it straightforward to determine mean motion resonance occupation, proper orbital elements, and a variet

Yunxiang Wei, Tianlin Li, Yuwei Zheng, Yanni Dong

Large language models (LLMs) have demonstrated remarkable capabilities in generating programs from natural language descriptions, yet ensuring their correctness without an external oracle remains a critical challenge. To solve the challenge, existing methods often rely on uncertainty estimation, measuring the consistency of semantics or execution behaviors a

K. Rajpurohit, E. O'Sullivan, G. Schellenberger, J. M. Vrtilek

We present new MeerKAT 21cm spectral line observations of the neutral hydrogen gas in the compact galaxy group Stephan's Quintet (HCG 92). These data provide a significantly improved view of the atomic gas distribution and kinematics in the group. New features include the first detections of HI associated with member galaxies NGC 7319 and NGC 7320C, the iden

Marriam Naeem, Mohid Farhan

We introduce the Layered Dark Sectors with a Structuring Field (LDS-SF), a structured cosmological framework where the internal architecture of a multi-component dark sector naturally generates scale-dependent growth of structure. In this framework, the characteristic scale dependence is derived from the dominant eigenvalue, $\lambda(k)$, of the dark sector'

Praneel Panchigar, Torlach Rush, Matthew Canabarro

Large Language Models (LLMs) consume vast quantities of human-generated content for both training and real-time inference, yet the creators of that content remain largely invisible in the value chain. Existing approaches to data attribution operate either at the model-internals level, tracing influence through gradient signals, or at the legal-policy level t

Konstantin Batygin, Alessandro Morbidelli

The observed census of resonant extrasolar planets spans a tantalizing display of orbital architectures, ranging from familiar 2:1 and 3:2 mean-motion commensurabilities to nearly co-orbital configurations characterized by period ratios close to unity. While mean-motion resonances are widely recognized as signposts of convergent disk-driven migration, the pr

Sichen Tao, Yifei Yang, Ruihan Zhao, Kaiyu Wang

Multiobjective optimisation in the CEC 2025 MOP track is evaluated not only by final IGD values but also by how quickly an algorithm reaches the target region under a fixed evaluation budget. This report documents RDEx-MOP, the reconstructed differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session) boun

Merham Fouladvand, Peuroly Batra

We propose Domain-Conditioned Meta-Contrastive Learning, a framework for improving the cross-domain generalization of vision-language models. While contrastive models such as CLIP achieve strong performance through large-scale training, they rely on a global objective that does not explicitly account for domain shift. To address this limitation, we formulate

Sichen Tao, Yifei Yang, Ruihan Zhao, Kaiyu Wang

Constrained single-objective numerical optimisation requires both feasibility maintenance and strong objective-value convergence under limited evaluation budgets. This report documents RDEx-CSOP, a constrained differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-CSOP combines success-history

Sichen Tao, Yifei Yang, Ruihan Zhao, Kaiyu Wang

Bound-constrained single-objective numerical optimisation remains a key benchmark for assessing the robustness and efficiency of evolutionary algorithms. This report documents RDEx-SOP, an exploitation-biased success-history differential evolution variant used in the IEEE CEC 2025 numerical optimisation competition (C06 special session). RDEx-SOP combines su

Michael C. Boucher, Peter Sun, Eric W. Moore, John A. Marohn

We recently introduced a new theoretical description of Landau--Zener--St\"{u}ckelberg--Majorana (LZSM) transitions that accounts for both adiabatic and spin-dephasing losses during sweeps through resonance. Here, we use this new description to assess signal loss due to cantilever tip motion in magnetic resonance force microscopy experiments on electron spin

Dogyun Park, Yanyu Li, Sergey Tulyakov, Anil Kag

Scaling video diffusion transformers is fundamentally bottlenecked by two compounding costs: the expensive quadratic complexity of attention per step, and the iterative sampling steps. In this work, we propose EFlow, an efficient few-step training framework, that tackles these bottlenecks simultaneously. To reduce sampling steps, we build on a solution-flow

Shuya Chen, Heng Peng, Min Zhou

In large-scale biomedical research, it's common to gather ultra-high dimensional data that includes right-censored survival times. Feature screening has emerged as a crucial statistical technique for handling such data. In this paper, we introduce a straightforward and robust feature screening approach, leveraging the modified Chatterjee's rank correlation,

Yizuo Peng, Xuelin Chen, Kai Zhang, Xiaodong Cun

Recent diffusion models have achieved remarkable success in image relighting, and this success has quickly been extended to video relighting. However, existing methods offer limited explicit control over illumination in the relighted output. We present LightCtrl, the first controllable video relighting method that enables explicit control of video illuminati

T. Ben Britton, M. Haroon Qaiser, Ruth M. Birch

Scanning electron microscopy combined with electron backscatter diffraction (EBSD) and electron channeling provides rich crystallographic contrast, but the mutual influence of channeling-in and channeling-out is often simplified or neglected in quantitative analyses. In this work, we use selected-area electron channeling patterns (SA-ECPs) acquired from a si

Heling Zhang, Siqi Du, Roy Dong

Follow-the-regularized-leader (FTRL) algorithms have become popular in the context of games, providing easy-to-implement methods for each agent, as well as theoretical guarantees that the strategies of all agents will converge to some equilibrium concept (provided that all agents follow the appropriate dynamics). However, with these methods, each agent ignor

Guillermo F. Quispe Peña, Johor D. Peñalba Quispitupa, José T. Gálvez Ghersi

Features in the primordial power spectrum require numerical methods that are both accurate and scalable across the wide class of multifield inflationary models that produce them. Sharp turns in the background trajectories, induced by either potential or geometric effects, render these computations particularly challenging. In this work, we introduce an effic

N. Zimniak, C. Zanni, J. Ferreira

Turbulent accretion disks threaded by a large-scale vertical field near equipartition can drive tenuous and fast self-confined jets. Self-similar solutions of these jet-emitting disks (JEDs) have been known for a long time and provide the distributions of all physical quantities, from the turbulent disk to the asymptotic regime of ideal magnetohydrodynamic (

Ziheng Chen, Jiao Liu, Meng Cai

This paper studies the weak convergence order of the stochastic theta method for stochastic differential equations (SDEs) driven by time-changed L\'{e}vy noise under global Lipschitz and linear growth conditions. In contrast to classical L\'{e}vy-driven SDEs, the presence of a random time change makes the weak error analysis involve both the discretization e

David Simmons

We introduce a formal language GDST (gradualist descriptionalist set theory) with a family of interpretations indexed by ordinals, as well as a sublanguage NMID (the language of not necessarily monotonic inductive definitions), and show that the assertion that all propositions in NMID have well-defined truth values is equivalent to the existence for each $k

Tahreem Yasir, Sutapa Dey Tithi, Benyamin Tabarsi, Dmitri Droujkov

Large language models (LLMs) are increasingly used for automated tutoring, but their reliability in structured symbolic domains remains unclear. We study step-level feedback for propositional logic proofs, which require precise symbolic reasoning aligned with a learner's current proof state. We introduce a knowledge-graph-grounded benchmark of 516 unique pro

Marcel Osmond, Thomas Jego

This article presents the first systematic comparative survey of how public bodies, international organisations, national regulators, and the private sector define agentic artificial intelligence, identifying the technical inaccuracies pervading each definition. Analysing eleven regulatory instruments and industry frameworks -- including the EU AI Act, the O

Peter Maurice Catt

Forecasting is usually framed as a problem of model choice. This paper starts earlier, asking how much predictive information is available at each horizon. Under logarithmic loss, the answer is exact: the mutual information between the future observation and the declared information set equals the maximum achievable reduction in expected loss. This paper dev

Neelam Modi Jain, Dan J. Wang

Concerns that interacting with generative AI homogenizes human cognition are largely based on evidence from text-based interactions, potentially conflating the effects of AI systems with those of written communication. This study examines whether these patterns depend on communication modality rather than on AI itself. Analyzing 957 open-ended debates betwee

Anil Kamber, Rahul Parhi

Weight decay is ubiquitous in training deep neural network architectures. Its empirical success is often attributed to capacity control; nonetheless, our theoretical understanding of its effect on the loss landscape and the set of minimizers remains limited. In this paper, we show that $\ell^2$-regularized deep matrix factorization/deep linear network traini

Kaito Sato, Shun Sato, Takayasu Matsuo

Multi-symplectic diamond schemes proposed by McLachlan and Wilkins (2015) provide a framework for the numerical integration of Hamiltonian partial differential equations, combining local implicitness with high-order accuracy and discrete multi-symplectic conservation laws. Despite these advantages, their behavior beyond a limited class of model equations rem

Haoyu He, Yue Zhuo, Yu Zheng, Qi R. Wang

Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whethe

C. Wang, X. R. Wang, Hechen Ren

Anderson (localization) transition is a universal wave phenomenon characterized by a disorder-induced quantum phase transition from extended to localized states, whereas the non-Hermitian skin effect is a generic feature of non-Hermitian systems that causes bulk states to localize at the boundaries. Here, we report an unexpected skin-Anderson localization tr

Zhoujie You, Shu Sun, Ruifeng Gao, Jue Wang

Beam training for extremely large-scale arrays with curvature-reconfigurable apertures (CuRAs) faces the critical challenge of severe, geometry-dependent angle-range coupling. While most existing designs compartmentalize near field and far field scenarios, we propose a unified, distance-adaptive hierarchical codebook framework for 1-D and 2-D CuRAs that seam

Arjun Sridharkumar, Sara Al Hajj Ibrahim, Jiayuan Zhou, Yuliang Wang

Timely resolution and disclosure of vulnerabilities are essential for maintaining the security of open-source software. However, many vulnerabilities remain unreported, unpatched, or undisclosed for extended periods, exposing users to prolonged security threats. While various vulnerability detection tools exist, they primarily focus on predicting or identify

Saunak Kumar Panda, Yisha Xiang, Ruiqi Liu

Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In particular, capacity sharing has received much attention recently. In this paper, we consider the problem of dynamically matching demand-capacity types of manufac

Chushan Zhang, Ruihan Lu, Jinguang Tong, Yikai Wang

Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground-truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade-off in 2D-3D represent

Zhuoyang Qian, Wei Shi, Xu Lin, Li Ling

Generating scientific manuscripts requires maintaining alignment between narrative reasoning, experimental evidence, and visual artifacts across the document lifecycle. Existing language-model generation pipelines rely on unconstrained text synthesis with validation applied only after generation, often producing structural drift, missing figures or tables, a

Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Isaac Sanchez

Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a

Yixuan Wang, Danyang Li, Matthew Cleaveland, Roberto Tron

Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent approaches have incorporated conformal prediction as a statistical tool for uncertainty quantification. However, most existing methods rely on the assumption that calibration and testi

Josué Meléndez, Eduardo Rodríguez-Romero, Jonatán Torres Orozco

We establish an integral inequality for the Ricci curvature of a certain class of warped products $M\times_fN$, where the equality holds if and only if it is simply a Riemannian product. We also give a sufficient condition for the intersection of a warped product $M=\mathbb{R}\times_fP$ with a totally geodesic hypersurface $N$ in an arbitrary Riemannian spac

Jihwan Hong, Jaeyoung Do

Referring Video Object Segmentation (RVOS) aims to segment target objects in videos based on natural language descriptions. However, fixed keyframe-based approaches that couple a vision language model with a separate propagation module often fail to capture rapidly changing spatiotemporal dynamics and to handle queries requiring multi-step reasoning, leading

Zhihao Zhang, Abhinav Kumar, Xiaoming Liu

Monocular 3D object detection (Mono3D) aims to infer object locations and dimensions in 3D space from a single RGB image. Despite recent progress, existing methods remain highly sensitive to camera intrinsics and struggle to generalize across diverse settings, since intrinsics govern how 3D scenes are projected onto the image plane. We propose MonoIA, a unif

Nikolaus Correll

We compare liquid neural networks with mixture density heads against diffusion policies on Push-T, RoboMimic Can, and PointMaze under a shared-backbone comparison protocol that isolates policy-head effects under matched inputs, training budgets, and evaluation settings. Across tasks, liquid policies use roughly half the parameters (4.3M vs. 8.6M), achieve 2.

Hossein Salemi, Jitin Krishnan, Hemant Purohit

Attribution theory explains how individuals interpret and attribute others' behavior in a social context by employing personal (dispositional) and impersonal (situational) causality. Large Language Models (LLMs), trained on human-generated corpora, may implicitly mimic this social attribution process in social contexts. However, the extent to which LLMs util

Mao Li, Frederick G. Conrad

What does it mean to model a person, not merely to predict isolated responses, preferences, or behaviors, but to simulate how an individual interprets events, forms opinions, makes judgments, and acts consistently across contexts? This question matters because social science requires not only observing and predicting human outcomes, but also simulating inter

Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a signific

Jun Luo, Joerg Thuswaldner, Xiao-Ting Yao, Shuqin Zhang

For any compact set $K$ lying on a closed surface $\mathcal{S}$ we introduce a closed equivalence relation $\sim$, called the {\em Sch\"onflies equivalence} on $K$. We show that every class $[x]_\sim$ of $\sim$ is a continuum and that the resulting quotient space $K\!/\!\sim$ is a {\em Peano compactum}. By definition, all components of a Peano compactum are

Yannan Chen

Biquadratic forms arise naturally in polynomial optimization, tensor analysis, and quantum information theory. A key problem is determining the minimal number of squares needed in a sum-of-squares (SOS) representation of such a form, known as its SOS rank. For fixed dimensions $(m,n)$, the maximum possible SOS rank over all biquadratic forms in $m$ and $n$ v

Olivier Durif, Barbara Nozière

We present a flow tube reactor design for gas-phase kinetics studies near ambient temperature and pressure. Built entirely from standard tubing, the setup simplifies conventional flow tube configurations based on injector translation while allowing tighter adjustment of reaction time. The reactor spans residence times from sub-second to several minutes throu

Abdulrahman Albaiz, Fathi Amsaad

This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the

Or Ben Yaakov, Haim Diamant, Rudolf Podgornik, David Andelman

We study the effect of asymmetry in solvent and ionic size on the equilibrium properties of multivalent ionic solutions near a charged surface. For a single ionic species in solution, we derive a generalized Grahame equation at the charged surface. For general size ratio between the ions and the solvent, we obtain analytical results for the concentration pro

Abdulrahman Albaiz, Fathi Amsaad

This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feature extraction, clustering, and threshold estimation to identify abnormal appliance behavior. To avo

Ajay Kumar, Anis Biswas, Trevor A. Tyson, Daniel Haskel

We present a comprehensive study of the temperature- and magnetic-field-dependent magnetization, specific heat, and local crystal structure across the first-order ferromagnetic-paramagnetic transition in Eu$_2$In. Anomalies in the magnetocaloric response are observed near $H \approx 25$~kOe, including changes in field scaling of magnetic entropy, local entro

Batu Candan, Simone Servadio

Autonomous proximity operations, such as active debris removal and on-orbit servicing, require high-fidelity relative navigation solutions that remain robust in the presence of parametric uncertainty. Standard estimation frameworks typically assume that the target spacecraft's mass properties are known a priori; however, for non-cooperative or tumbling t

Wei Dai, Taiki Yoda, Yuto Moritake, Masaya Notomi

Valley photonics has emerged as a promising platform in topological photonic systems, yet the topological nature of valley-dependent phenomena remains unsettled. Theoretically, inter-valley scattering may occur with structural imperfections, and global Chern numbers vanish due to time-reversal symmetry. As a result, valley-dependent topology is locally defin

Michael Kaschke, Holger Cartarius

Analyzing the motion of a roller coaster allows for an instructive introduction of various theoretical concepts in a concrete and enjoyable context. We start by modeling the roller coaster train as a point particle. We then develop more realistic models for the train and finally we show how to introduce a continuum limit in a simple way. These studies instru

Yong-Xin Zhang, Chen Wang, Qing-Hu Chen

We investigate nonclassical photon-bundle correlations in the quantum Rabi model and its extended cases, using the quantum dressed master equation. By tuning the light--matter coupling strength at finite temperature, the quantum Rabi model exhibits controllable nonclassical transitions between two-photon bundle bunching and antibunching, allowing for the two

C. J. K. Larkin, J. Mackey, H. Jin, A. A. C. Sander

The evolutionary origin of Wolf-Rayet (WR) stars at Solar metallicity is unclear. Single-star evolution from massive O stars, possibly via a Luminous Blue Variable phase, is challenged by binary period distributions of different WR subtypes. Wd1-72 is a WN7b+O binary embedded in the collective wind of the Galactic young massive cluster Westerlund 1 (Wd 1). I

Tobias Heimig-Elschner, Changyu Du, Anna Scheuvens, André Borrmann

Agentic workflows driven by large language models (LLMs) are increasingly applied to Building Information Modelling (BIM), enabling natural-language retrieval, modification and generation of IFC models. Recent work has begun adopting the emerging Model Context Protocol (MCP) as a uniform tool-calling interface for LLMs, simplifying the agent side of BIM inte

Takeaki Miyamura, Zhiling Wang, Kohei Matsuura, Yoshiki Sunada

Quantum communication between remote chips is essential for realizing large-scale superconducting quantum computers. For such communication, itinerant microwave photons propagating through transmission lines offer a promising approach. However, demonstrations to date have relied on frequency-tunable circuit elements to compensate for fabrication-related para

Pranav Asthana, Alex Hanson, Allen Tu, Tom Goldstein

3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural strategy is to apply super-resolution (SR) to low-resolution (LR) input views, but independently enhancing each image introduces multi-view inconsistencies, leading to blurry rende

Gerhard Schindl

We introduce the new notion of a conjugate weight function and provide a detailed study of this operation and its properties. Then we apply this knowledge to study classes of ultradifferentiable functions defined in terms of fast growing weight functions in the sense of Braun-Meise-Taylor and hence violating standard regularity requirements. Therefore, we tr

Martin Aichele, Igor Cialenco, Damian Jelito, Marcin Pitera

We develop a statistical framework for risk estimation, inspired by the axiomatic theory of risk measures. Coherent risk estimators -- functionals of P\&L samples inheriting the economic properties of risk measures -- are defined and characterized through robust representations linked to $L$-estimators. The framework provides a canonical methodology for cons

Yuxuan Shi, Hongbo Cheng

The observational appearance of a black hole is critically dependent on the surrounding accreting matter, in particular on the central brightness depression and photon ring structure. We perform a systematic comparative analysis of the observational signatures of a Schwarzschild black hole embedded in a Hernquist dark matter (DM) halo under three distinct ac

Zhibin Wang, Rui Ning, Chao Fang, Zhonghui Zhang

Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critical bottleneck with increasing context lengths, is a memory-intensive process requiring heavy memory access on the key-value (KV) cache of the prefixes. Therefore, in this paper, we

Dong Yeap Kang, Jungin Lee, Myungjun Yu

In this paper, we study the distribution of the cokernels of random $p$-adic matrices with fixed zero entries. Let $X_n$ be a random $n \times n$ matrix over $\mathbb{Z}_p$ in which some entries are fixed to be zero and the other entries are i.i.d. copies of a random variable $ξ\in \mathbb{Z}_p$. We consider the minimal number of random entries of $X_n$ requ

Teck-Ghee Lee, Orhan Bayrak, Cheuk-Yin Wong

The fusion of $α$ and $^8$Be to produce a $^{12}$C nucleus is a crucial process in nucleosynthesis. In the laboratory, this process can only be studied theoretically as a $^8$Be target or projectile cannot be prepared experimentally. We use the potential scattering theory in the coupled-channel formalism to study such a process in terms of the collision betw

Qizhi Wang

Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple non-ML policies such as SIEVE and S3-FIFO set a strong baseline, so any learned method must be overhead-aware, robust under drift, and competitive with strong experts. We present SCION, a lightweight policy-orch

Li Zhanchun, Zhang Renwu

This paper, based on the interdisciplinary frontiers of quantum electrodynamics, causal set theory, and the AdS/CFT holographic duality, integrates Keppler's zero point field resonance theory, the discrete causal structure and horizon thermodynamics within causal set theory, and the latest advancements in holographic superconductivity models. For the first t

Carlos Cardona-Giraldo, Cristiano Fanelli, James Giroux, Cole Granger

Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep generative models have emerged as promising surrogates for traditional Monte Carlo simulation, with recent advances drawing inspiration from large language mod

Alberto García-Rodríguez, Tzipe Govezensky, Julia Tagüeña, Kimmo K. Kaski

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals as global challenges for countries of the world to address in their development. However, the progress of countries towards these goals has been much slower than expected. In a previous study, we analyzed the data over two decades (2000--2022), using unsupervised machine lear

Mrdjan Jankovic

Proprioception is a human sense that provides feedback from muscles and joints about body position and motion. This key capability keeps us upright, moving, and responding quickly to slips or stumbles. In this paper we discuss a proprioception-like feature (machine proprioceptive feedback - MPF) for motion control systems. An unexpected response of one actua

Yanjing Zhao, Chanju Kim, Yi Zheng, Chaochao Ye

Optical amplification is critical for optical signal transmission. While the emergence of erbium-doped fiber amplifiers has revolutionized optical communications in fiber-based systems, on-chip amplification remains essential for integrated optics. Nanoscale waveguides enhance nonlinearity by several orders of magnitude, making them promising candidates for

Mira Chandra Kirana, Patatchona Keyela, Fatemeh Rostamian, Deemah H. Tashman

As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. An examination of the progression of RAN architectures, as well as O-RAN's underlying principles, reveals the importance of machine learning (ML) in addressing various challenges, i

Xander Faber, Niladri Patra

We describe the local and global structure of the fixed locus for the action of a rational function on the Berkovich projective line over a complete nontrivially-valued algebraically closed nonarchimedean field. This includes a bound for the number of connected components that is sharp when the residue characteristic is large or zero. The case of small nonze

Yuxuan Sun

We determine the $\mathrm{PGL}_2$-equivariant Chow ring of $\mathrm{Gr}(2,4)^s$, the $\mathrm{PGL}_2$-stable locus of $\mathrm{Gr}(2,4)$, over any algebraically closed based field of characteristic not equal to 2 or 3. In the process, we demonstrate that the quotient stack $[\mathrm{Gr}(2,4)^s/\mathrm{PGL}_2]$ can be presented as the quotient of an open subs

Jiaqi Tan, Yudong Luo, Sophia Huang, Yifan Yang

Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor

F. L. Carneiro, H. P. de Carvalho, M. P. Lobo, L. A. Cabral

The memory effect of test particles interacting with pp-wave Gaussian pulses is investigated for polarization modes beyond the standard quadrupolar $+$ and $\times$ states. Massive geodesic equations are solved numerically for several values of the multipolar index $m$, allowing the analysis of velocity and energy memory effects. In order to eliminate possib

Wenzhuo Zhang, Anatoly Svidzinsky

Schrodinger path to the quantum mechanical wave equation was heuristic and guided more by physical intuition than formal deduction. Here we derive the Schrodinger equation for the particle wave function, assuming that it has a meaning of the probability amplitude to find the particle at time t at point r and the relations E=hw, p=hk expressing particle energ

Guanhe Huang, Oya Celiktutan

Generative models excel at motion synthesis for a fixed number of agents but struggle to generalize with variable agents. Based on limited, domain-specific data, existing methods employ autoregressive models to generate motion recursively, which suffer from inefficiency and error accumulation. We propose Unified Motion Flow (UMF), which consists of Pyramid M

Alex Yan, Cathal Mills, Augustin Marignier, Younjung Kim

When performing Bayesian inference, we frequently need to work with conditional probability densities. For example, the posterior function is the conditional density of the parameters given the data. Some might worry that conditional densities are ill-defined, considering that for a continuous random variable $Y$, the event $\{Y=y\}$ has probability zero, me

Zachary W. Windom, Daniel Claudino, Vicente Leyton-Ortega

The Tensor Network Quantum Virtual Machine (TNQVM) is a high-performance classical circuit simulation backend for the eXtreme-scale ACCelerator (XACC) framework that leverages the Intelligent Tensor (ITensor) library for tensor network--based quantum circuit simulation. However, TNQVM's original C++ ITensor backend is tied to an older integrated release, lim

Khoa Nguyen, Brendt Wohlberg, Oleg Korobkin, Marc Klasky

We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution {\gamma}-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composit

Weilun Xu, Edward Hall, Martin Rohrmeier

How do different musical traditions achieve tonal coherence? Most computational measures to date have analysed tonal coherence in terms of a single dimension, whereas a multi-dimensional analyses have not been sufficiently explored. We propose a new model drawing on the concept of the Tonnetz -- we define two partially independent measures: \emph{tonal focus

Robert Kwolek, Parash Thapalia, Aditya Tripathi, Pooja Kulkarni

Thin-film lithium niobate (TFLN) has emerged as a leading platform for large-scale programmable photonic circuits for quantum and classical applications. As circuits scale in complexity, low-loss routing of broadband pump and signal fields becomes essential. Here, we present closed-form analytical models and experimentally demonstrate compact, fast-quasi-adi

Logan Lawrence, Mustafa Chasmai, Rangel Daroya, Wuao Liu

Fine-grained bird species identification in the wild is frequently unanswerable from a single image: key cues may be non-visual (e.g. vocalization), or obscured due to occlusion, camera angle, or low resolution. Yet today's multimodal systems are typically judged on answerable, in-schema cases, encouraging confident guesses rather than principled abstention.

Marina Palaisti

We introduce the notion of asymptotic universal Koszulity for graded-commutative algebras generated in degree~$1$, capturing the idea that an infinite-dimensional algebra can be approximated by a filtered system of finite-type universally Koszul quadratic subalgebras. We establish basic structural properties of this class, including stability under filtered

Sean C. Smithson, Naoya Onizawa, Brett H. Meyer, Warren J. Gross

Invertible logic can operate in one of two modes: 1) a forward mode, in which inputs are presented and a single, correct output is produced, and 2) a reverse mode, in which the output is fixed and the inputs take on values consistent with the output. It is possible to create invertible logic using various Boltzmann machine configurations. Such systems have b

Patryk Niżeniec, Marcin Iwanowski, Marcin Gahbler

YOLO object detectors recently became a key component of vision systems in many domains. The family of available YOLO models consists of multiple versions, each in various variants. The research reported in this paper aims to validate the applicability of members of this family to detect objects located within the robot workspace. In our experiments, we used

Qin-Qin Wang, Xiao-Ye Xu, Chuan-Feng Li, Guang-Can Guo

While remarkable progress has been achieved in engineering nontrivial Hamiltonians across a wide range of physical platforms, preparing their corresponding nontrivial ground states remains a major experimental challenge. The commonly used strategy for state preparation relies on adiabatic protocols. However, when a trivial initial state is unitarily driven t

Mohamad Zbib, Mohamad Bazzi, Ammar Mohanna, Hasan Abed Al Kader Hammoud

Speculative decoding accelerates autoregressive generation by letting a lightweight draft model propose future tokens that a larger target model then verifies in parallel. In practice, however, draft models are usually trained on broad generic corpora, which leaves it unclear how much speculative decoding quality depends on the draft training distribution. W

Mihai-Silviu Lazorec

Let $G$ be a finite group. The aim of this paper is to study the number of solutions $S\subseteq G$ of the equation $\mho^{\{n\}}(S)=L$, where $L$ is a non-empty subset of $G$, $n$ is a positive integer and $\mho^{\{n\}}(S)=\{ s^n \ | \ s\in S\}$. Besides our findings obtained in this general frame, we also outline some results which hold for some particular

Ike Griss Salas, Ethan King

Many engineered physical processes exhibit nonlinear but asymptotically stable dynamics that converge to a finite set of equilibria determined by control inputs. Identifying such systems from data is challenging: stable dynamics provide limited excitation and model discovery is often non-unique. We propose a minimally structured Neural Ordinary Differential

Gitan Balogh, June Cagan, Bea Fatima, Auguste H. Gezalyan

Neighborhood graphs and clustering algorithms are fundamental structures in both computational geometry and data analysis. Visualizing them can help build insight into their behavior and properties. The Ipe extensible drawing editor, developed by Otfried Cheong, is a widely used software system for generating figures. One particular aspect of Ipe is the abil

Madeline Clyburn, Jonathan Zrake

We present a study of the gas-driven orbital evolution of unequal mass black hole binaries with circumbinary gas disks (CBDs), varying Mach number and viscosity (nu). Using two-dimensional grid-based hydrodynamics simulations spanning a thousand binary orbits at fixed separation, we explore low to moderate mass ratios (q = 0.05-1.0) and examine how variation

Hanif Rahman, Shafeeq ur Rehman

We present the Pashto Common Voice corpus -- the first large-scale, openly licensed speech resource for Pashto, a language with over 60 million native speakers largely absent from open speech technology. Through a community effort spanning 2022-2025, the corpus grew from 1.5 hours and 5 contributors to 147 total hours and 1,483 unique speakers across ten Moz

Deemah H. Tashman, Soumaya Cherkaoui

Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adap