April 2026 arXiv papers — page 29

Chenbo Yu

Traffic signal control (TSC) plays a central role in reducing congestion and maintaining urban mobility. This dissertation introduces DGLight, a critic-guided reinforcement-learning framework for adapting a pretrained large language model to TSC. DGLight first trains a CoLight-based Deep Q-Network critic to estimate traffic-aware action values from structure

Huaning Liu, Gokce Dayanikli

We study the control of rumor propagation in large networked populations by using Stackelberg graphon games. We first introduce a principal who wants to incentivize the spread of her preferred news and discourage the spread of non-preferred news. We define the Stackelberg graphon game equilibrium (SGGE), characterize the graphon game Nash equilibrium (GGNE)

Simon R. Anuszczyk, Kyra Phaychanpheng, John O. Dabiri

Measuring energy consumption of marine organisms often requires enclosing the animal in a small, sealed chamber to quantify changes in oxygen concentration of the surrounding water. This can limit measurements of free-swimming organisms by introducing recirculation effects and movement restrictions. We experimentally investigate free-swimming jellyfish energ

Lei Xiong, Kun Luo, Ziyi Xia, Wenbo Zhang

Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to explore existing knowledge for a research problem, or to acquire evidence for verifying assumptions and supporting claims. To assess AI agents' capability in driving this process,

Tianshui Chen, Yujie Zhu, Jianman Lin, Zhijing Yang

Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original speech. A primary challenge in this domain is the scarcity of paired data, namely aligned frames of the same individual with identical speech but different expressions, which impedes direct supervision for emotio

Tao-Tao Sui, Chen Long, Ye zhang

We investigate the dynamics of massive test particles around a static black hole in nonlocal gravity and examine the corresponding properties of HF QPOs, constraining the nonlocal parameter to $\alpha/M \leq 0.452$. The nonlocal parameter $\alpha$ enhances the effective potential $V_{eff}$ and leads to a systematic reduction in the energy E and angular momen

Yan Xu, Zhonghua Yao, Frederic Allegrini, Shengyi Ye

Based on models derived from Earth's magnetotail, other planets with dipole magnetic fields, including Mercury, Jupiter, and Saturn, were expected to possess similar magnetotail configurations. In this traditional picture, the majority of plasma is confined near the magnetic equator within a plasma sheet (or plasma disc), whereas higher-latitude regions feat

Himanshu Tiwari, Binayak Kar, Priyanshu Tiwari

Integrated Sensing and Communication (ISAC) enables joint data transmission and environmental perception for sixth-generation (6G) networks, but centralized and virtualized RAN control loops introduce telemetry latency that yields stale observations and unstable control. This paper proposes a Digital Twin-assisted belief-state reinforcement learning framewor

Xinru Wang, Meghna Bose, Bibhas Chakraborty, Robert Mahar

Dynamic treatment regimes (DTRs) are sequences of decision rules to guide treatment assignments in response to a patient's evolving, time-varying disease status. Sequential multiple assignment randomized trials (SMARTs) are considered the gold standard experimental design for evaluating DTRs. However, SMARTs often require more time to complete compared with

Albert Atserias, Moritz Müller

We prove that the bounded arithmetic theory $S^1_2$ is consistent with EXP $\not\subseteq$ P/poly. More generally, we show that certain separations of $V^1_2$ from a theory $T$ imply the consistency of $T$ with EXP $\not\subseteq$ P/poly. For $T=S^1_2$, Takeuti (1988) established such a separation using a variant of G\"odel's consistency statement. Analogous

Jon-Paul Cacioli

Detecting sandbagging--the deliberate underperformance on capability evaluations--is an open problem in AI safety. We tested whether symptom validity testing (SVT) logic from clinical malingering detection could identify sandbagging through below-chance performance (BCB) on forced-choice items. In a pre-registered pilot at the 7-9 billion parameter instructi

Görkem D. Dinc, Alexander Schnell, Andy M. Martin

We investigate the non-equilibrium topology of a periodically driven, dissipative Su-Schrieffer-Heeger chain using the ensemble geometric phase (EGP) $\phi_{\mathrm{EGP}}$-a generalisation of the Zak phase to open quantum systems. In contrast to earlier work, we use Floquet-Born-Markov theory to describe the coupling to thermal reservoirs microscopically. We

Ziming Zhang, Li Li, Guorui Feng, Hanzhou Wu

Large language models (LLMs) are widely deployed in multiple scenarios due to reasoning capabilities. In order to prevent the models from being misused, watermarking is generally employed to ensure ownership. However, most existing watermarking methods rely on superficial modifications to the model's output distribution, rendering the watermark vulnerable to

Rajesh Singh, Shobh Nath Tiwari

Auxiliary information is frequently utilized in survey sampling to improve the efficiency of estimators of the finite population mean. However, the simultaneous use of multiple auxiliary variables often induces multicollinearity, which adversely affects the stability and performance of conventional estimators. To address this issue, the present study propose

Haocheng Tang, Liang Shi, Ya-Shi Zhang, Xixian Liu

Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of dynamic structural data. This survey reviews recent advances in artificial intelligence for protein dynamics from three perspectives: learning from structural ensembles and trajecto

Valentin Massicot

Motivated by branching problems for principal series representations of the Lie group $G = GL(n,\mathbb R)$, we consider all pairs $(G', P)$ with $G'$ being the Levy factor of a parabolic subgroup of $G$ and $P$ a parabolic subgroup of $G$ for which a Borel subgroup $B'$ of $G'$ has finitely many orbits on $G/P$. We classify all such pairs $(G',P)$ for which

Toshiyuki Kobayashi

This expository paper explains, in the case of $\mathfrak{sl}_2$, the ideas introduced in the preprints (arXiv:2509.17007, 2604.22262), which develop a new framework for the study of multiplicities in branching laws of representations, with particular emphasis on their dependence on representation parameters. Taking the Lie algebra $\mathfrak{sl}_2$ as a gui

Naoto Maki, Chia-Min Lin, Kazunori Kohri

We investigate the Wheeler-DeWitt equation for a flat, homogeneous, and isotropic Universe containing a canonical scalar field with a potential. We show that under the constraint $|\Psi|=1$, where the Wheeler-DeWitt equation exactly becomes the classical Hamilton-Jacobi equation, the form of the potential is completely determined depending on the value of th

Joseph Kwong

We characterise the existence of balanced and pluriclosed metrics on compact quotients of real semisimple Lie groups equipped with regular complex structures, in terms of Vogan diagrams. Consequently, such complex manifolds cannot simultaneously admit a balanced metric and a pluriclosed metric. Along the way, we revisit and correct the classification of regu

Edoardo Ballico, Elizabeth Gasparim

Let $\mathcal M_{r,c}$ denote the moduli space of stable bundles with rank $r$ and second Chern class $c>0$ on a Hopf surface. We prove that the subset of $\mathcal M_{r,c}$ formed by irregular bundles is meager.

Yaniv Shulman

Local Polynomial Regression (LPR) is a powerful tool for nonparametric smoothing, yet it traditionally suffers from a "Euclidean tautology": the variables used to define the local neighborhood are identical to those used in the polynomial fit. This restricts its ability to handle complex domains where the regression function varies across non-Euclidean struc

Valery Y. Glizer, Vladimir Turetsky

A finite-horizon zero-sum linear-quadratic differential game is considered. Its features are: (i) the control cost of the minimizing player in the game's cost functional is much smaller than the control cost of the maximizing player and the state cost; (ii) the cost of the fast state variable in the integrand of the cost functional is a positive semi-definit

Divake Kumar, Sina Tayebati, Devashri Naik, Ranganath Krishnan

Vision-language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability. We study this problem through conformal prediction, a distribution-free framework that converts a judge's point score into a calibrated prediction interval using only score-token log-probabilities, with no ret

Qinyuan Zheng, Pengcheng Zhu

As a pivotal usage scenario for 6G networks, integrated sensing and communication (ISAC) has emerged as a focal point of both academic and industrial research. To accommodate the heterogeneous connectivity requirements of future networks while jointly enhancing both the sensing and communication performance, this paper integrates the multi-static ISAC archit

Philip Kilby, Sevvandi Kandanaarachchi, Matthew J. Morgan, Amy M. Paten

Genome-Scale Metabolic Models (GEMs) describe the interactions between genes, proteins, and the biochemical reactions that underpin an organism's metabolism aiming to computationally simulate functions at the cellular level. While many metabolic reactions can be inferred from genome analysis, constructing GEMs often involves incorporating reactions unsupport

Shota Fukushima, Yong-Gwan Ji, Hyeonbae Kang

We consider the transmission problem in presence of interfaces with imperfect bonding. The imperfect bonding condition is characterized by the positive resistance along the interface, which causes discontinuity of the potential across the interface while the flux is continuous. If the interface resistance is zero, then the interface is of perfect bonding, wh

Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Gaurav Najpande, Manan Suri

Diagram question answering (DQA) requires models to interpret structured visual representations such as charts, maps, infographics, circuit schematics, and scientific diagrams. Recent vision-language models (VLMs) often achieve high answer accuracy on these tasks, yet correct answers do not guarantee that models ground their reasoning in the diagram regions

Soonho Kwon, Dong Whi Yoo, Shaowen Bardzell, Younah Kang

We present four conceptual value-sensitive AI systems to examine how the presence of AI could influence praying experiences. Drawing on key values and practices associated with praying identified through a diary study, we designed AI systems intended to "assist" prayer practices. These designs were presented to participants through speculative design workboo

Gautam Sharma, Apurva Tiwari, Niladri Gomes, Jezer Jojo

We present the first quantum-hardware implementation of a Hamiltonian simulation algorithm that produces signed vector-field solutions to the time-domain Maxwells equations using a Schrodingerisation-based approach. The electromagnetic fields are discretized using finite-difference operators, and the resulting non-unitary matrices are mapped to Bell-basis Tr

Toshiyuki Katsura, Matthias Schütt

We describe the possible 3-divisible $A_2^n$ configurations of smooth rational curves on K3 surfaces in characteristic 3 and fully classify the resulting triple covers.

Andreas Gustavsson

We analyze the nonabelian surface holonomy on a bipartite hypercubic lattice following a proposal in arXiv:1002.4636 [hep-th]. The bipartite structure of the lattice enables us to introduce spike string configurations. These spikes play a crucial role for the time evolution of the string when the total number of color indices changes.

Sidi Chang, Peiying Zhu, Yuxiao Chen

LLM-based financial agents increasingly produce investment rationales before the outcomes needed to evaluate them are observable. This creates a delayed-ground-truth evaluation problem: realized returns remain the eventual arbiter of investment quality, but they arrive too late and are too noisy to guide many model-development and governance decisions. LLM j

Ming Chen, Muhammed Tawfiqul Islam, Maria Rodriguez Read, Rajkumar Buyya

Microservice-based cloud applications face changing workloads, evolving request paths, variable network conditions, interference, and failures. These dynamics couple autoscaling, placement, routing, isolation, and remediation. The survey examines dynamics-aware adaptive management for microservices. Its taxonomy covers control locus, modeled dynamics, adapta

Xincheng Lin

This work studies the non-perturbative Coulomb corrections to the He-3 binding energy, magnetic moment, and charge and magnetic radii in leading-order (LO) Pionless Effective Field Theory (Pionless EFT). The splitting between He-3 and H-3 binding energy is found to be 0.85(3) MeV. The Coulomb corrections to the He-3 point charge radius and full magnetic radi

Edgar Cortes-Suárez, Paola Marziani, Héctor Manuel Hernández-Toledo, Miguel Ángel Aragón-Calvo

We present multi-epoch optical spectroscopy of the isolated elliptical galaxy UNAM-KIAS 613, hosting a low-luminosity Type 1 AGN. Analysis of archival Sloan Digital Sky Survey (SDSS) data from 2006 reveals a distinctive double-peaked broad H$\alpha$ profile, tentatively modeled by a relativistic accretion disk. Follow-up observations in 2018 and 2023 show th

Shuichi Sato

We consider $A_1$-weights and prove weighted weak type $(1,1)$ estimates for oscillatory singular integrals with kernels satisfying a Dini condition.

Zhengyuan Wu, Junhui Zhao, Qingmiao Zhang, Ming Zhang

The development of intelligent and diversified ser vices in urban rail transit (URT) has resulted in an increasing de mand for high-rate communication between vehicles and ground equipment. However, existing URT communication systems strug gle to handle the massive data exchange required for vehicle-to ground (V2G) communication. To address this issue, we pr

Sitao Qin, Yize Liang, Shuai Cao, Changqing Cao

The vectorial structure of light, amplitude, phase, and polarization, encodes essential information for applications ranging from super-resolution microscopy to high-capacity communications and quantum information processing. However, existing characterization methods either rely on multiple sequential measurements or require bulky polarization splitting opt

Jiaqi Wu, Yuchen Zhou, Dennis Tsang Ng, Xingyu Shen

OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can be replaced in under a second for a few cents. We release AIForge-Doc v2, a paired dataset of 3,066 GPT-Image-2 document forgeries with pixel-precise masks in DocTamper-compatible format, and benchmark four lines o

Yuhan Wang, Yuxuan Wei, Li Huang, Chuanshan Tian

Truly chiral phonons are lattice eigenmodes that combine broken mirror symmetry with circular atomic motion. They can mediate angular-momentum-selective interactions in quantum materials, yet directly resolving both their chirality and underlying circular motion remains challenging, especially in high-symmetry crystals. Here we show that symmetry-selective t

Alexander Kolpakov, Igor Rivin

Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top-performing embeddings invent cycles and disconnected islands absent from

Pratincha Singh, Jai Gopal Singla, Prashant Hemrajani, Nitant Dube

Radiometric inconsistencies remain a major challenge in generating seamless lunar mosaics from multi-mission orbital imagery due to variability in illumination geometry, sensor characteristics, and acquisition conditions. This paper presents a deep learning-based radiometric normalization framework for multi-mission lunar mosaics constructed primarily from I

Yichen Wang, Charles Patrick Martin

This performance presents a duet between two intelligent musical instruments, S\`u (to trace back; to go upstream) and Agentier (playing on agentic clavier), and their human performers, connected through feedback loops. Rather than treating AI as a tool that responds predictably to input, both systems operate recursively, where past actions continuously infl

Yao Zhao

Functional autoregressive models of order one (FAR(1)) are predominantly estimated by projecting curves onto leading functional principal components and fitting a vector autoregression in score space, requiring a discrete truncation level $K$ chosen by an \emph{ad hoc} variance threshold. We demonstrate via Monte Carlo experiments that the truncation choice

Arnon Mazza, Elad Levi

Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs suffers from inconsistent boundary-case performance and high inference costs. Training custom classifiers achieves both accuracy and efficiency, yet demands substantial labeled data that is costly to obtain.

Vu Thi Hue, Ngoc Khue Tran, Hoang-Long Ngo

We study the error between the exact solution and its Euler-Maruyama approximation in temporal-spatial H\"older-norms for L\'evy-driven stochastic differential equations.

Dhrumil Bhatt, Anakha Kurup

Reliable and secure communication is essential for mission-critical aerospace and defence operations involving autonomous platforms such as Unmanned Aerial Vehicles (UAVs), satellites, and ground control systems. In contested or dynamic environments, communication links are frequently exposed to jamming, interference, and cyberattacks, making network resilie

Kemal Bicakci

Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical governance problem: the model and scoring rubric should not be exposed in a way that allows applicants to optimize against them, yet the evaluation process must remain auditable, contestable, and accountable. We prop

Xiansheng Cai, Han Wang, Kun Chen

Kohn-Sham (KS) eigenvalues are routinely compared with angle-resolved photoemission (ARPES) and used as input for many-body methods, yet density functional theory (DFT) assigns them no physical meaning. For alkali and alkaline-earth metals, KS bandwidths overestimate ARPES measurements by 20-35%, a discrepancy that persists across all exchange-correlation fu

Takanori Hara, Masahiro Sasabe

Service Function Chaining (SFC) establishes efficient communication paths by ensuring that traffic traverses a predefined sequence of network functions in a specified order to meet particular service requirements. Inspired by this concept, we have proposed an SFC-based architecture for multi-hop split learning (MSL) and split inference (MSI), facilitating di

Yuting Yang, Gang Mei, Feng Chen, Yongshuang Zhang

Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require sufficient conditioning factors and large-scale landslide inventories. However, in practical engineering applications across mountainous and plateau regions, data-scarce conditions are c

Imran Mahboob, Satoshi Sasaki, Takaaki Takenaka

A minimal method to fabricate Al/AlO$_x$/Al Josephson junctions (JJs) using photolithography and argon etching, before metallization and oxidation, is demonstrated. JJs with areas ranging from 1 to 6 $\mu$m$^2$ can be fabricated and, with the appropriate oxidation conditions, the junction resistance can be varied by $\sim$2 orders of magnitude. Transmission

Yufei Zheng, Zihao Gong, Saikat Guha, Don Towsley

Network tomography refers to the use of inference techniques for inferring internal network states from end-to-end probes. Quantum probes, implemented by sending blocks of $n$ coherent-state pulses augmented with continuous-variable (CV) squeezing ($n=1$) or weak temporal-mode entanglement ($n>1$) over a lossy channel to a receiver with homodyne detection ca

Yiwei Qiu, Qingjie Sun, Yangjun Zeng, Ge Chen

In renewable power-to-ammonia (ReP2A) systems, the intermittency of wind and solar generation propagates through electrolytic hydrogen production and induces thermal instability in the ammonia synthesis reactor (ASR). The resulting temperature cycling accelerates fatigue and shortens service life, while reactor thermal inertia limits flexible start-up, shutd

Luis-Armando Rodríguez-Flores, Luciano García-Bañuelos, Abel Armas-Cervantes, Astrid-Monserrat Rivera-Partida

Conformance checking, one of the main process mining operations, aims to identify discrepancies between a process model and an event log. The model represents the expected behaviour, whereas the event log represents the actual process behaviour as captured in information systems' records. Traditionally, the process model and the event log are both accessible

Minghui Xu, Xiaoyu Liu, Yihao Guo, Chunchi Liu

AI agents are autonomous entities that can be instantiated on demand, migrate across platforms, and interact with other agents or services without continuous human supervision. In such environments, identity is critical for establishing reliable interaction semantics among agents that may lack prior trust relationships. However, existing identity and access

Wentao Jiang, Yuanchan Xu, Heng Yuan

Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noise suppression are required simultaneously. Conventional convolutional neural networks, despite their remarkable success in hierarchical feature learning, often struggle with capturing multi-scale contextual

Max Emerick, Saroj Prasad Chhatoi, Bassam Bamieh

We study the problem of distributed control of large-scale robotic swarms which can be modeled as continuum densities evolving under the continuity equation. We propose a formalization of distributed controllers as (generally nonlinear) spatial differential operators, in which control inputs depend only on local information about the state and environment. T

Yongzhong Xu

Tian (2025) proves a repulsion theorem (Theorem 6) for the matrix $ B = (\widetilde{F}^\top \widetilde{F} + \eta I)^{-1} $ during the interactive feature-learning stage of grokking: similar features have negative off-diagonal entries $ B_{j\ell} $, producing an effective repulsive force that drives them apart. However, the theorem does not specify when this

Runze Cui, Fangxin Shang, Yehui Yang, Qing Yang

Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and evidence traceability matter. Compared with static document images, document videos present a temporally redundant and sequentially unfolding evidence stream, require evidence integration across frames, and preserv

Zixin Zhao, Jingzhi Hu, Geoffrey Ye Li

Low Earth orbit (LEO) satellite relays will significantly extend the coverage of mobile networks, enabling users in remote areas to transmit data of real-time events. Nevertheless, the limited power of user devices and the long distance to satellites lead to low signal-to-noise ratio (SNR), which results in high error rates and frequent retransmissions, seve

Robin Geens, Joran Heldens, Joren Dumoulin, Marian Verhelst

Ternary weight quantization (e.g., BitNet b1.58) offers a promising path to mitigate the memory bandwidth bottleneck in Large Language Model (LLM) inference. However, conventional compute platforms lack native support for ternary-weight arithmetic, often relying on inefficient dequantization. Lookup table (LUT)-based hardware architectures provide an effecti

Rui Qi, Fengran Mo, Sijin Lu, Yufeng Chen

A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). However, the vanilla approach that simply concatenates multiple pieces of knowledge from different languages into the context may fail to improve effectiveness due to the potential di

Fabio Pereira dos Santos, Julio de Castro Vargas Fernandes, Adriano Mauricio de Almeida Cortes

Neural operators have emerged as powerful data-driven surrogates for learning solution operators of parametric partial differential equations (PDEs). However, widely used Fourier Neural Operators (FNOs) rely on global Fourier representations, which can be inefficient for resolving anisotropic structures, sharp gradients, and spatially localized discontinuiti

B Ambrosio, A Garroudji, S. Fitzsimons, H Zaag

This article presents a partial differential equation (PDE) of Keller-Segel (KS) type that reproduces patterns commonly observed during the growth of brain microvasculature. We provide mathematical insights into the mechanisms underlying the emergence of these patterns. In addition, we derive a data-driven equation that ensures a consistent temporal evolutio

Baijun Tan, Francesco Moretti

Achieving a desirable balance between rendering quality and real-time performance is a long-standing challenge in modern game and rendering engines, particularly on resource-constrained mobile devices such as laptops, tablets, and smartphones. Existing approaches to automatic rendering parameter optimization either depend on exhaustive per-scene pre-computat

Yunhao Wang, Shuai Ma, Bin Shen, Shouhan Shi

With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. However, existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss. To address

Jie Yan, Jintark Kim, Rakibul Islam, Jiawei Yang

Microelectromechanical (MEMS) resonators are widely used in timekeeping applications, and recent advances in fabrication, materials, and encapsulation technology have advanced their potential as high stability frequency references. However, for holdover applications that require the highest levels of long-term frequency stability, compact vapor atomic clocks

Vijaysinh Gaikwad

The digitization of multi-domain retail billing documents remains a challenging task due to variability in scan quality, layout heterogeneity, and domain diversity across commercial sectors. This paper proposes and benchmarks an intelligent, quality-aware adaptive Optical Character Recognition (OCR) pipeline for retail bill digitization spanning five domains

Ming Wei, Xin Wang, Junyu Lu, Longzhao Liu

Cooperation in groups underpins collective responses to challenges from climate governance to public goods provision, yet how moral evaluation sustains it remains poorly understood. Indirect reciprocity -- cooperating to build a good reputation -- is well characterized for pairwise interactions, but real collective action requires individuals to be judged ag

Joshua Wang

We compute the $k$-colored $\mathfrak{sl}(N)$ homology of the torus knot $T(2,2m+1)$, and we show that it stabilizes as $m\to\infty$ to the integral homology of the free loop space of the complex Grassmannian $\mathrm{Gr}(k,N)$. In particular, when $k = 1$ and $N = 2$, we observe that the Khovanov homology of $T(2,2m+1)$ stabilizes to the homology of the fre

Chunlin Li, Erxiao Wang, Wu Jie, Min Yan

Tilings of a surface of negative Euler characteristic by n-gons with n\ge 7 is a finite problem. We develop the algorithm for finding all the tilings for fixed number of tiles and present the calculation for tilings of surfaces of small genus by two tiles. We also discuss the number of distinct edge lengths in multiple tile tilings.

Douglas Brinkerhoff, Elizabeth Fischer

Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50--100~km where this topographic detail is absent. Dynamical downscaling with high-resolution regional models such as WRF can resolve these processes, but the computational cost -- months of wall-cl

Nhat-Minh Nguyen

Marginalizing over roughly 12 effective-field-theory (EFT) nuisance parameters per tracer per redshift bin is a dominant systematic cost in full-shape galaxy power spectrum analyses. Simulation-based priors (SBP) tighten these parameters but rely on N-body simulations and halo-occupation-distribution (HOD) models. We propose a multi-tracer Fisher analysis as

Michael Dobinson, Felix Hufnagel, Simon A. Meynell, Camille Bowness

Among the many solid-state emitters being explored for scalable quantum technologies, the silicon T centre is a leading candidate offering long-lived spin qubits, a telecommunications-band spin-photon interface, and integration with on-chip photonic circuits. However, nanophotonic integration broadens both the inhomogeneous spectral distribution and individu

Hiroto Mizoguchi, Yutaro Nagae, Yasuhiro Asano, Satoshi Ikegaya

We identify two fundamental properties of the Gor'kov Green's function of finite one-dimensional topological superconductors. In the low-frequency (low-energy) regime, the normal and anomalous Green's functions, which describe single-particle and Cooper-pair correlations, respectively, become identical up to a phase factor. Moreover, they exhibit pronounced

Abdoulaye Thiam

The top Lyapunov exponent $\lambda_+(A, p)$ of a random product of matrices in $\mathrm{GL}(d, \mathbb{R})$, $d \geq 2$, with simple top spectrum, depends real-analytically on the probability weights $p$ and the matrix coefficients $A$. We establish a quantitative form of this analyticity through a single Kato perturbation argument on the complexified Markov

Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang

While mechanistic interpretability tools like Sparse Autoencoders (SAEs) can uncover meaningful features within Large Language Models (LLMs), a critical gap remains in transforming these insights into practical actions for model optimization. We bridge this gap with the hypothesis that data selection guided by a model's internal task features is a effective

Ruize Xu, Chenxiao Yang, Yanhong Li, David McAllester

We demonstrate that a small transformer can learn to execute programs in MicroPy, a simplified yet computationally universal programming language. Given procedure definitions together with an expression to evaluate, the transformer predicts small-step execution using PENCIL scaffolding for space-efficient execution within a bounded context window. After trai

Fu-Ming Liu

The anisotropic emission of direct photons from Au+Au collisions at $\sqrt{s_{NN}}$=200 GeV was calculated using a (3+1)-dimensional viscous hydrodynamic model with the impact parameter Glasma initial condition. The transverse momentum spectra of direct photons in different centrality bins (0-20\%, 20-40\%, and 40-60\%) are in good agreement with experimenta

Wenqi Jia, Zekun Li, Abhay Mittal, Chengcheng Tang

Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attri

Jiancheng Wu, Haicheng Feng, Qingwen Wu, Xinwu Cao

The virial factor $f$ is critical for accurate supermassive black hole (SMBH) mass measurements using reverberation mapping (RM) and the radius--luminosity ($R$--$L$) relation, yet its value remains highly uncertain. While traditional models assume axisymmetric broad-line region (BLR) geometries, growing evidence suggests that BLRs may possess more complex,

Prashanti Priya Angara, Luis F. Rivera, Ulrike Stege, Hausi Müller

Demonstrating quantum advantage for combinatorial optimization requires more than standalone algorithmic results; it calls for end-to-end case studies that integrate problem modelling, quantum execution, and classical refinement into practical workflows. This paper presents a sandbox platform for experimenting with hybrid quantum-classical workflows in graph

Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie

Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision), making failures difficult to localize and attribute. Existing testing methods are largely system-level and provide limited insight into which capability deficiencies cause task f

Shuo Wang, Deyu Zhu, Chenjie Xiong, Bin Hu

Accurate and real-time sensing of targets in three-dimensional (3D) environments is essential for modern machine vision, underpinning emerging technologies such as autonomous systems, robotic manipulation, augmented reality, and intelligent surveillance. However, state-of-the-art 3D sensing approaches typically rely on complex postprocessing of multi-view im

Kaixuan Shao, Gang Mei, Yinghan Wu, Nengxiong Xu

Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Available landslide inventories are often sparse and imbalanced, which limits understanding of triggering conditions and failure mechanisms. Data generation provides an effective approach to help capture feature

Mohammad Jafari Jozani, Bahram Moeinianfar

Support vector machines (SVMs) are a standard tool for binary classification, but their classical formulations are purely data-driven and offer no direct way to encode trusted benchmark models or structured preferences on selected subsets of the data. We propose Elite-Driven Support Vector Machines (EDSVM), a general framework that augments regularized empir

Zhang Jiang, Marios Andreou, Sebastian Reich, Nan Chen

Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underlying dynamics evolve rapidly or undergo regime transitions. Smoothing, which add

Fan Wang, Haotian Xu, Yi Yu

Dynamic multilayer networks arise in many applications where multiple types of relations among a common set of nodes evolve over time. Existing approaches often assume temporal independence, focus on single-layer networks or impose stationarity, limiting their applicability in practice. In this paper, we introduce a first-order autoregressive multilayer stoc

Changyuan Zhao, Jiacheng Wang, Dusit Niyato, Zan Li

Mathematical analysis has long underpinned wireless communication theory, yet the growing complexity of next-generation systems demands increasingly sophisticated reasoning from domain experts. Recent advances in AI mathematical reasoning, from formal theorem proving to large language model (LLM)-based derivation, offer a promising but largely unexplored pat

Laure Berti-Equille

Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating processes -- making them highly attractive for practitioners who cannot afford large annotated corpora. However, their in-context learning mechanism assumes approximately clean inputs: missing values, outliers

Hien Duy Nguyen, Jacob Westerhout, Xin Guo

Sample average approximation (SAA) replaces an intractable expected objective by an empirical average and is a basic device of modern stochastic optimization. We develop a rate theory for optimal values and empirical $\varepsilon$-minimizers that does not assume continuity, lower semicontinuity, or smooth perturbation structure of the sample objectives. Work

Yuanfan Li, Qi Zhou, Chengzhengxu Li, Zhaohan Zhang

We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detection, existing evaluations are often fragmented across datasets, preprocessing, attacks, and metrics, making results hard to compare and reproduce. MGTEVAL organizes the workflow into four components: Dataset Buil

Igor Rivin

For a sequence $\gamma=(\gamma_n)_{n\ge 1}$, define \[ L_\gamma(z):=\sum_{n\ge 1}\gamma_n\frac{z^n}{1-z^n} =\sum_{n\ge 1}\Bigl(\sum_{d\mid n}\gamma_d\Bigr)z^n. \] We prove a short rigidity theorem: if $\gamma$ is eventually linearly recurrent and $L_\gamma(z)$ is rational, then $\gamma$ is finitely supported. Equivalently, among sequences with rational ordin

Kusha Sareen, Mohammad Pedramfar, Sékou-Oumar Kaba, Mehran Shakerinava

Overparameterization is central to the success of deep learning, yet the mechanisms by which it improves optimization remain incompletely understood. We analyze weight-space symmetries in neural networks and show that overparameterization introduces additional symmetries that benefit optimization in two distinct ways. First, we prove that these symmetries ac

Michael Rumiantsau, Ivan Fokeev

LLMs deployed for natural-language querying of analytical databases suffer from two intertwined failures - incorrect answers and confident hallucinations - both rooted in the same cause: the model is forced to infer business semantics that the schema does not encode. We test whether supplying those semantics as context closes the gap. We benchmark three fron

Reece Robertson, Ameya Bhave

In an extension of the Unconventional Noiseless Intermediate Quantum Emulator, this work introduces a classical emulation of the quantum Harrow-Hassidim-Lloyd algorithm for sampling from the solution space of linear systems. The emulated HHL algorithm scales exponentially with the number of qubits required to represent the linear system, which is an advantag

Zhao Zhang, Zhuopeng Hou

Backward stochastic differential equation (BSDE) provides probabilistic solutions for a class of parabolic partial differential equations (PDEs). DeepBSDE and FBSNN are two deep learning approaches for solving high-dimensional PDEs through approximating the solution of BSDEs. The conventional approach for learning functions defined on continuous domains is v

Vedran Jelic, Kaedon Cleland-Host, Stefanie Adams, Mohamed Hassan

Atomic-scale disorder shapes the potential energy landscape traversed by photoexcited charge carriers, while the carriers themselves also dynamically reshape this landscape. However, resolving ultrafast photocarrier motion at atomic length scales has remained a central challenge in materials science. Here, we demonstrate that lightwave-driven terahertz scann

Mohammad Jafari Jozani, Jingyu Wang

Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but existing formulations assume simple random sampling. In many applications, however, the retained observation is an extreme order statistic from a set rather than a randomly selected unit. This is particularly ap