March 2026 arXiv papers — page 29

Davi Rodrigues, Andrea Meo, Ali Hasan, Edoardo Piccolo

Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estimating the interfacial Dzyaloshinskii-Moriya interaction strength remains challenging, as existing experimental methods often rely on indirect measurements and can yield inconsistent

Matej Rene Cihlar, Luka Šiktar, Branimir Ćaran, Marko Švaco

Vehicle overtaking is one of the most complex driving maneuvers for autonomous vehicles. To achieve optimal autonomous overtaking, driving systems rely on multiple sensors that enable safe trajectory optimization and overtaking efficiency. This paper presents a reinforcement learning mechanism for multi-agent autonomous racing environments, enabling overtaki

Guohuan Xie, Xin He, Dingying Fan, Le Zhang

Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While diffusion models can synthesize novel-class images at scale, practical gains are often hindered by insufficient coverage and noisy supervision when masks are unavailable or unreliable. We propose Syn4Seg, a gen

Shenao Wang, Junjie He, Yanjie Zhao, Yayi Wang

Skills are increasingly used to extend LLM agents by packaging prompts, code, and configurations into reusable modules. As public registries and marketplaces expand, they form an emerging agentic supply chain, but also introduce a new attack surface for malicious skills. Detecting malicious skills is challenging because relevant evidence is often distributed

Hong Su

Large Language Model (LLM) agents have demonstrated remarkable capabilities in reasoning and tool use, yet they often suffer from rigid, reactive control flows that limit their adaptability and efficiency. Most existing frameworks rely on fixed pipelines or failure-triggered reflection, causing agents to act impulsively or correct errors only after they occu

Pranav Ramesh, Vimala Soundarapandian, KC Sivaramakrishnan

Designing correct replicated data types (RDTs) is challenging because replicas evolve independently and must be merged while preserving application intent. A promising approach is correct-by-construction development in a proof-oriented programming language such as F*, Dafny and Lean, where desired correctness guarantees are specified and checked as the RDTs

Ji Ma, Wei Suo, Peng Wang, Yanning Zhang

Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process. However, visual attention decay is a well-studied problem in Large Vision-Language Model

Yu. S. Lutostansky, A. N. Fazliakhmetov, V. N. Tikhonov, G. A. Koroteev

The interaction of neutrinos with an energy of up to 55~MeV from the Spallation Neutron Source (SNS) accelerator with a perspective ${}^{127}$I detector at the Oak Ridge National Laboratory (United States) has been studied. The resonance structure of the charge-exchange strength function $S(E)$ has been calculated taking into account high-lying resonances, a

Junyoung Koh, Hoyeon Moon, Dongha Kim, Seungmin Lee

Text-to-image models such as Stable Diffusion have achieved unprecedented levels of high-fidelity visual synthesis. As these models advance, personalization of generative models -- commonly facilitated through Low-Rank Adaptation (LoRA) with a dedicated trigger token -- has become a significant area of research. Previous works have naively assumed that fine-

Maxime Breden, Matthieu Cadiot, Antoine Zurek

In this paper, we present a computer-assisted framework for constructive proofs of existence for stationary solutions to one-dimensional parabolic PDEs and the rigorous determination of their linear stability. By expanding solutions in Gegenbauer polynomials, we first develop a general approach for boundary value problems (BVPs), corresponding to the station

David Tschirschwitz, Volker Rodehorst

Progress in object detection benchmarks is stagnating. It is limited not by architectures but by the inability to distinguish model improvements from label noise. To restore trust in benchmarking the field requires rigorous quantification of annotation consistency to ensure the reliability of evaluation data. However, standard statistical metrics fail to han

Kentaro Kameoka, Naoya Yoshida

We study shape resonances of two-dimensional magnetic Stark Hamiltonians in the semiclassical limit. The magnetic field is assumed to be constant and the scalar potential is a perturbation of a linear potential. Under the assumption that the scalar potential has potential wells, the existence of a one-to-one correspondence between shape resonances of the Ham

Zhenyuan Zhao, Yu Xing, Tianyang Xue, Lingxin Cao

Designing microstructures with coupled cross-physics objectives is a fundamental challenge where traditional topology optimization is often computationally prohibitive and deep generative models frequently suffer from physical hallucinations. We introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evoluti

Kai Tian, Jialun Wang, Chuan Lin, Guangjie Han

With the rapid advancement of underwater net-working and multi-agent coordination technologies, autonomous underwater vehicle (AUV) ad-hoc networks have emerged as a pivotal framework for executing complex maritime missions, such as multi-target tracking. However, traditional data-centricarchitectures struggle to maintain operational consistency under highly

Costanza Agazzi, Nick Toledo-García, Estela Martín-Badosa, Mario Montes-Usategui

Fluorescence depletion microscopy techniques such as STED and RESOLFT require optical fields with a well-defined and spatially confined central intensity minimum to achieve sub-diffraction lateral resolution. Here, we present the design and experimental implementation of an azimuthally polarized, doughnut-shaped depletion beam based on super-pupil engineerin

Osama Terra, Adam Sharp, Aidan Connaughton, Mark Ferrier

High-power lasers with narrow linewidth and high beam quality in the visible spectrum are essential for emerging quantum and space technologies. Here we report significant advances in diamond Raman lasers, generating diffraction-limited yellow light at 589 nm with output power up to 35 W and enhanced single-frequency stability. The optical-to-optical efficie

Zheng Zhou, Xiangfei Zhang, Chongguang Tao, Yuhong Yang

Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeability, but may exhibit systematic undercoverage or overcoverage for specific subpopulations. Assessing conditional validity is challenging, as standard stratification methods suffer from the curse of dimensionality. We pro

Jianwei Lou

Dissipative cognitive architectures maintain computation through continuous energy expenditure, where units that exhaust their energy are stochastically replaced with fresh random state. This creates a fundamental challenge: how can persistent, context-specific memory survive when all learnable state is periodically destroyed? Existing memory mechanisms -- i

Zabir Al Nazi, Shubhashis Roy Dipta, Md Rizwan Parvez

Existing omni-modal benchmarks attempt to measure modality-specific contributions, but their measurements are confounded: naturally co-occurring modalities carry correlated yet unequal information, making it unclear whether results reflect true modality reliance or information asymmetry. We introduce OMD-Bench, where all modalities are initially congruent -

Yun Tian, Guili Wang, Jian Bi, Kaixin Han

Accurate prediction of lithium-ion battery remaining useful life (RUL) is essential for reliable health monitoring and data-driven analysis of battery degradation. However, the robustness and generalization capabilities of existing RUL prediction models are significantly challenged by complex operating conditions and limited data availability. To address the

Xiaofeng Tan, Wanjiang Weng, Hongsong Wang, Fang Zhao

Text-to-motion generation has advanced with diffusion- and flow-based generative models, yet supervised pretraining remains insufficient to align models with high-level objectives such as semantic consistency, realism, and human preference. Existing post-training methods have key limitations: they (1) target a specific motion representation, such as joints,

Zhiyang Xu, Tian Qin, Bowen Jin, Zhengfeng Lai

Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning depends on the correct ordering and evolution of events. This deficiency stems in part from training objectives that fail to explicitly reward temporal reasoning a

Ankur Sikarwar, Debangan Mishra, Sudarshan Nikhil, Ponnurangam Kumaraguru

Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether Multimodal Large Language Models (MLLMs) can do the same, aligning distinct egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce COSMIC, a benchmark

Gen-Hui Li, Xiang Ren, Dong-Lin Wang, Shi Pu

We investigate the late-time asymptotic solutions and attractor structure of the spin density in minimal causal spin hydrodynamics in Gubser flow. After deriving the differential equation governing the spin density, we obtain its late-time asymptotic solutions and identify both attractors and repellers in the corresponding numerical solutions. We then map th

Dikai Shang, Jingyue Zhao, Shi Xu, Nanyang Ye

Achieving safe, high-speed autonomous flight in complex environments with static, dynamic, or mixed obstacles remains challenging, as a single perception modality is incomplete. Depth cameras are effective for static objects but suffer from motion blur at high speeds. Conversely, event cameras excel at capturing rapid motion but struggle to perceive static s

Stepan L. Lomaev, Georgii A. Gordeev, Marat A. Timirgazin, Dinara R. Fattalova

The aim of this paper is coupling of temperature oscillations and melt pool dynamics experimentally observed in laser melting. The literature survey has shown that the developed explanations are mainly focused on the capillary and hydrodynamic aspects of the problem. As shown, complete analysis is only possible if the temperature oscillations are properly ac

Junhong Liang, Yifan Lu, Ekaterina Kochmar, Fajri Koto

Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require \emph{learner-friendly pedagogical feedback} that is actionable, level-appropriate, and encouraging. We introduce \textbf{SPFG} (\textbf{S}poken \textbf{P}edagogical \textbf{F}eedback \textbf{G}eneration), a dataset built based on the S

Di Hao, Jiawei Hu, Hongwei Yu

In the framework of linearized quantum gravity, we investigate the quantum gravitational interaction induced by the gravitodiamagnetic coupling of two massive objects to vacuum fluctuations of the gravitational field. Starting from the Lagrangian of a particle in a gravitational field and employing the formalism of Weyl gravitoelectromagnetism, we derive the

Yizhou Jin, Yuezhu Feng, Jinjin Zhang, Peng Wang

Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning and perceptual abilities for anomaly detection. However, most approaches remain confined to image-level anomaly detection and textual reasoning, while pixel-level localization still relies on external vision modules and dense annotations. In this work, we activate the in

Sai Swagat Mishra, Soumya Kanta Bhoi, P. K. Sahoo

In this work, we have considered a minimally modified gravity theory that effectively reproduces VCDM-like behavior to investigate its cosmological implications. The model parameters are constrained using a combination of CC, RSD, DESI BAO DR2, and Union3 datasets. The model parameters are constrained using an MCMC framework, ensuring a robust estimation of

Vinay Kathiriya, Saurabh Kumar, Shashi Ranjan Kumar

This paper addresses the three-dimensional path-following guidance problem for unmanned aerial vehicles under explicit actuator constraints. Unlike conventional approaches that assume unbounded control inputs or handle saturation heuristically, the proposed method incorporates bounded lateral acceleration directly into the guidance design. A nonlinear guidan

Arpita Goswami

We investigate localization transitions and mobility edge phenomena in one-dimensional quasiperiodic lattice models using an information theoretic framework based on the Tsallis entropy of single particle eigenstates.We employ the Tsallis entropy as a continuous, normalized functional of wavefunction amplitudes, where the entropic index $q$ provides a tunabl

Sushmita A. Mishra, Gurudatt Gaur

We investigate the stability and nonlinear evolution of localized electron-scale current sheets using fully kinetic, electromagnetic particle-in-cell (PIC) simulations in two and three dimensions. By varying the current-sheet thickness, we examine how it influences the dominant instability and subsequent nonlinear dynamics. In two dimensions, the evolution i

Ruichao Jiang, Long Wen

The transfer algorithm~\cite{jiang} solves the on-chain one-hop swap routing problem. In \cite{jiang}, the convergence is proved but the convergence rate is left open. We prove that the algorithm terminates in at most $\mathcal{O}(N\kappa\log\frac{1}{\varepsilon})$ rounds, where $N$ is the number of AMMs, $\kappa$ a liquidity heterogeneity parameter and $\va

Junhao Chen, Ruowei Li, Zhigang Yao

We study the recovery of geometric structure from data generated by convolving the uniform measure on a smooth compact submanifold $M\subset\mathbb{R}^D$ with ambient Gaussian noise. Our main result is that several fundamental Riemannian quantities of $M$, including tangent spaces, the intrinsic dimension, and the second fundamental form, are identifiable fr

Nobel Dang, Bing Li

Relative Pose Regression (RPR) generalizes well to unseen environments, but its performance is often limited due to pairwise and local spatial views. To this end, we propose MultiLoc, a novel multi-view guided RPR model trained at scale, equipping relative pose regression with globally consistent spatial and geometric understanding. Specifically, our method

Shaodi Feng, Zhuoyi Lin, Yaoxin Wu, Haiyan Yin

Recent research has demonstrated the effectiveness of large language models (LLMs) in solving combinatorial optimization problems (COPs) by representing tasks and instances in natural language. However, purely language-based approaches struggle to accurately capture complex relational structures inherent in many COPs, rendering them less effective at address

Federico Franceschini, Rafe Mazzeo, Paul Minter

Inspired by the Taubes-Wu construction of $\mathcal{C}^{1,\alpha}$ two-valued harmonic functions by the use of symmetry, we construct minimal surfaces with stratified branching sets as graphs of $\mathcal{C}^{1,\alpha}$ two-valued functions. We give three constructions. The first is perturbative and produces branched minimal submanifolds in arbitrary codimen

Jan-Fredrik Olsen, Tor Ole B Odden

Programming is deeply embedded in contemporary mathematical practice, yet its epistemic status in university mathematics teaching remains contested. Little is known about how mathematicians themselves understand the legitimacy of programming in their professional work, and how these views shape their teaching. We address this gap through semi-structured inte

Kiriko Kato, Ryo Takahashi

Let R be a commutative noetherian ring. Let D^b(R) be the bounded derived category of finitely generated R-modules. Let X and Y be thick subcategories of D^b(R). In this paper, we consider the question asking when the equality Supp(X\cap Y)=Supp X\cap Supp Y holds, and give several answers. As applications, we obtain a characterization of the proxy small sub

Yiyang Zou, Tianhao Zhao, Peilun Xiao, Hongyu Jin

Accident anticipation aims to predict impending collisions from dashcam videos and trigger early alerts. Existing methods rely on binary supervision with manually annotated "anomaly onset" frames, which are subjective and inconsistent, leading to inaccurate risk estimation. In contrast, we propose RiskProp, a novel collision-anchored self-supervised risk pro

Yeping Hu, Ruben Glatt, Shusen Liu

Graph-based surrogate models provide fast alternatives to high-fidelity CFD solvers, but their opaque latent spaces and limited controllability restrict use in safety-critical settings. A key failure mode in oscillatory flows is phase drift, where predictions remain qualitatively correct but gradually lose temporal alignment with observations, limiting use i

Yiwei Qin, Yixiu Liu, Tiantian Mi, Muhang Xie

The foundational pretraining phase determines a model's capability ceiling, as post-training struggles to overcome capability foundations established during pretraining, yet it remains critically under-explored. This stems from a structural paradox: organizations with computational resources operate under commercial pressures that inhibit transparent disclos

Xiao Fan, Yi Zhang

Visualizing brain functional connectivity (FC) patterns is essential for understanding neural organization, yet existing tools such as Circos and BrainNet Viewer require complex configuration files or proprietary software environments. We present BrainRing, a free, open-source, browser-based interactive tool for generating publication-quality chord diagrams

Yujia Li, Alexandre Moreira, Miguel Heleno

Mechanisms to coordinate transmission and distribution planning should be regulatory compliant and keep the spheres of DSO and TSO decisions separate, without requiring disclosure of proprietary data or unrealistic computationally expensive T&D co-simulations. The concept of Netload Range Cost Curves (NRCC) has been recently proposed as simple non-invasive f

Lintao Ye, Ankang Zhang, Ming Chi, Bin Du

In this paper, we study the problem of learning Kalman filtering with unknown system model in partially observed linear dynamical systems. We propose a unified algorithmic framework based on online optimization that can be used to solve both the output estimation and state estimation scenarios. By exploring the properties of the estimation error cost functio

German Shâma Wache, Chaithya G R, Asma Tanabene, Sebastian Neumayer

While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep learning based reconstruction methods can alleviate this, but often lack stability and robustness to distribution shifts. As an alternative, we train a rotation invariant weakly convex ridge regularizer (WCRR). The r

Hemant Kumar Prajapati, Rahul Srivastava

Model independent phenomenological studies, ranging from neutrino to B-physics, often consider effective interactions involving either purely vector (V), purely axial vector (A), or mixed vector and axial vector (V, A) couplings. While pure vector $Z'$ interactions can naturally emerge in gauged $U(1)_X$ extensions of the Standard Model, such as the $B-L$ mo

Yuebo Luo, Shiyang Li, Yifei Feng, Vishal Kancharla

Graph Neural Networks (GNNs) show strong promise for circuit analysis, but scaling to modern large-scale circuit graphs is limited by GPU memory and training cost, especially for deep models. We revisit deep GNNs for circuit graphs and show that, when trainable, they significantly outperform shallow architectures, motivating an efficient, domain-specific tra

Gergely Csáji, Rareş-Ioan Mateiu, Alexandru Popa, Ildikó Schlotter

We study financial networks where banks are connected through bilateral liabilities and may default when resources are insufficient to meet obligations. We consider both the standard proportional clearing model and a priority-proportional clearing model in which banks repay creditors according to exogenously given priority classes. In such markets, portfolio

Ashwin Ganesan

Entity resolution -- identifying database records that refer to the same real-world entity -- is naturally modelled on bipartite graphs connecting entity nodes to their attribute values. Applying a message-passing neural network (MPNN) with all available extensions (reverse message passing, port numbering, ego IDs) incurs unnecessary overhead, since differen

Hemanth Saratchandran

Central to the success of Transformers is the attention block, which effectively models global dependencies among input tokens associated to a dataset. However, we theoretically demonstrate that standard attention mechanisms in transformers often produce ill-conditioned matrices with large condition numbers. This ill-conditioning is a well-known obstacle for

S M Asif Iqbal, Hang Zhang, Lin Yang, Aoyi Luo

A single-step, single-material 4D printing method is developed for programmable structures featuring spatially patterned strain trapping for one-way actuation. This approach enables fabrication on desktop fused filament fabrication 3D printers through a recently developed shape-memory strain programming method, Programming via Printing (PvP), which eliminate

Jaden Zhang, Gardenia Liu, Oliver Johansson, Hileamlak Yitayew

We introduce Prediction Arena, a benchmark for evaluating AI models' predictive accuracy and decision-making by enabling them to trade autonomously on live prediction markets with real capital. Unlike synthetic benchmarks, Prediction Arena tests models in environments where trades execute on actual exchanges (Kalshi and Polymarket), providing objective groun

Kenji Tojo, Bernd Bickel, Nobuyuki Umetani

Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, enable real-time rendering with high visual fidelity on sufficiently powerful graphics hardware. However, efficient online transmission and rendering across diverse platforms requires drastic model simplifi

Yibo Wang, Jiale Lao, Chen Zhang, Cehua Yang

Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs

Fritz Grimpen, Matthias Orth, Anastasios Stefanou

Finitely generated modules over the polynomial ring in $n$ indeterminates are isomorphic to quotients of finite rank free modules. We introduce a theory of relative Gr\"obner bases for those quotients of free modules and, equivalently, for pairs of submodules; we prove corresponding Buchberger- and Schreyer-type theorems. As applications of this theory, we c

Aditya Dhodapkar, Farhaan Pishori

When an LLM agent reads a confidential file, then writes a summary, then emails it externally, no single step is unsafe, but the sequence is a data leak. We call this safety drift: individually safe actions compounding into violations. Prior work has measured this problem; we predict it. SafetyDrift models agent safety trajectories as absorbing Markov chains

Shigeyuki Karino, Kenji Nakamura

Tight and compact binary systems, such as double neutron star binaries, are believed to undergo a common envelope evolution phase, resulting in strongly bound orbits. During this phase, the outer layers of the primary star are expelled, resulting in orbital shrinkage. However, a part of the expelled material may remain as a circumbinary disk, which can furth

Soham Chatterjee

As immunotherapies become standard cancer treatments, it is increasingly important to identify a patient's immune profile, which encompasses the activity of immune cells within the tumor microenvironment and the presence of specific biomarkers. However, we lack mechanistic explanations drivers of immune phenotypes. Despite advances in immune profiling with h

Daniel Hadas, Ron Peled

We establish long-range order for the hard-core model on a finite, regular bipartite graph above a threshold fugacity given in terms of expansion parameters of the graph. The result applies to the $d$-dimensional hypercube graph and, more generally, to $d$-dimensional discrete tori of fixed side length, proving long-range order at fugacities $\lambda\ge\Omeg

Zaiyang Guo, Jessie N. Dong, Filippos Bellos, Jilei Hao

Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pos

Amuche Ibenegbu, Pierre Lafaye de Micheaux, Rohitash Chandra

Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring. Multiple Imputation by Chained Equations (MICE) has been prominent for imputing missing values through "fully conditional specification". We extend MICE using the Bayesian framework (tBayes-MICE), utilising Bayesi

Junhyeok Lee, Kyu Sung Choi

Mixture-of-Experts (MoE) language models are universally sensitive to demographic content at the routing level, yet exploiting this sensitivity for fairness control is structurally limited. We introduce Fairness-Aware Routing Equilibrium (FARE), a diagnostic framework designed to probe the limits of routing-level stereotype intervention across diverse MoE ar

Jose Blanchet, Zhenyuan Zhang

We study a continuous-time nearest-neighbor branching random walk on the $d$-dimensional $b$-ary hypercube $\{0,1,\dots,b-1\}^d$ as a model for viral quasispecies evolution under mutation and replication. Motivated by mutagenic antiviral treatments and evolutionary-safety questions, we analyze the first passage time to a fixed target genotype at Hamming dist

Artur Kawalec

We analytically continue the Euler prime product for $\Re(s)>\tfrac{1}{2}$ (except for its pole $s=1$) assuming (RH) by introducing a new factor to the Euler product. We also discuss how to recover the Mertens's 3rd Theorem at $s=1$ case, and how to apply the same technique to analytically continue other similar Euler products. In the last part, we also cons

Shivang Chopra, Shaunak Halbe, Chengyue Huang, Brisa Maneechotesuwan

Fine-tuning approaches for Vision-Language Models (VLMs) face a critical three-way trade-off between In-Distribution (ID) accuracy, Out-of-Distribution (OOD) generalization, and adversarial robustness. Existing robust fine-tuning strategies resolve at most two axes of this trade-off. Generalization-preserving methods retain ID/OOD performance but leave model

V. Kumaran

When an electrically conducting non-magnetic particle is subjected to a spatially varying and oscillating applied magnetic field of amplitude $\mathcal{H} + \mathcal{G} \cdot x$ and frequency $\omega$, an oscillating eddy current is induced. The Lorentz force density, the cross product of the current density and the magnetic field, consists of a steady compo

Berkin Durmus, Chen Cen, Eduardo Pacheco, Arda Okan

The accuracy frontier of speech-to-text systems has plateaued on academic benchmarks.1 In contrast, industrial benchmarks and adoption in high-stakes domains suggest otherwise. We hypothesize that the primary difference between the two is contextual conditioning: Academic benchmarks are dominated by frequently encountered general vocabulary that is relativel

Qiuyang Zhang, Kai Zhou, Ding Tang, Kai Lu

Large language models encounter critical GPU memory capacity constraints during long-context inference, where KV cache memory consumption severely limits decode batch sizes. While existing research has explored offloading KV cache to DRAM, these approaches either demand frequent GPU-CPU data transfers or impose extensive CPU computation requirements, resulti

Haoyu Wang, Zibo Xiao, Yedi Zhang, Christopher M. Poskitt

LLM-based multi-agent systems (MASs) are transforming personal productivity by autonomously executing complex, cross-platform tasks. Frameworks such as OpenClaw demonstrate the potential of locally deployed agents integrated with personal data and services, but this autonomy introduces significant safety and security risks. Unintended actions from LLM reason

Alessio Basti, Fabio Camilli, Adriano Festa

We propose a control-theoretic framework for evolutionary clustering based on Mean Field Games (MFG). Moving beyond static or heuristic approaches, we formulate the problem as a population dynamics game governed by a coupled Hamilton-Jacobi-Bellman and Fokker-Planck system. Driven by a variational cost functional rather than predefined statistical shapes, th

Yichen Hao, Weiwei Xu, Kai Gao, Xiaofang Zhang

Sustaining newcomer participation is critical for the long-term health of open-source communities. Although prior research has explored various task recommendation approaches to help newcomers resolve their first-issue, these methods overlook how characteristics of first-issues may influence newcomers' long-term retention, limiting our understanding of wheth

Shijie Zhang

Text-to-time-series generation is particularly important in meteorology, where natural language offers intuitive control over complex, multi-scale atmospheric dynamics. Existing approaches are constrained by the lack of large-scale, physically grounded multimodal datasets and by architectures that overlook the spectral-temporal structure of weather signals.

Ankan Ghosh Dastider, Matt Grupen, Nicholas C. Miller, Shaloo Rakheja

Hot longitudinal optical (LO) phonons in GaN have recently been identified as a major factor degrading the DC performance of GaN high-electron-mobility transistors (HEMTs) by 30-60%, despite their ultrafast decay. However, their impact on large-signal RF performance, particularly RF linearity, remains poorly understood. Using full-band transport simulations

Alexander J. Shook, Daksh Malhotra, Aymar Muhikira, John P. Davis

Superfluidity in $^3$He exhibits many unique properties that are of interest to modern condensed matter research, including multiple superfluid phase transitions, topological defects, and exotic classes of excitations like Majorana and Weyl fermions. Many of the most interesting theoretical proposals, which remain underexplored, are realized in highly confin

Foster Tom, Aarush Vailaya

We provide a matrix-based formula for the Tutte symmetric function of a graph. In particular, for any graph $G$ with a designated head and tail vertex, we describe an infinite matrix $M_G$ from which the Tutte symmetric function can be easily recovered. We prove gluing graphs together corresponds to matrix multiplication, gluing the head and tail of a single

Molla Basir Ahamed, Rajesh Hossain

Let $\mathcal{H}$ be the class of all complex-valued harmonic mappings $f=h+\overline{g}$ defined on the unit disc $\mathbb{D}=\{z\in\mathbb{C}:|z|<1\}$ with the normalization $h(0)=0=h'(0)-1$, here $h$ and $g$ are analytic functions in $\mathbb{D}$. In this paper, we investigates Landau-type theorems for several significant subclasses of sense-preserving ha

Jeremy Chizewer, Samuel Everett, Deven Mithal, Youming Qiao

We study the problem of testing whether two tensors in $\mathbb{R}^\ell\otimes \mathbb{R}^m\otimes \mathbb{R}^n$ are isomorphic under the natural action of orthogonal groups $\textbf{O}(\ell, \mathbb{R})\times\textbf{O}(m, \mathbb{R})\times\textbf{O}(n, \mathbb{R})$, as well as the corresponding question over $\mathbb{C}$ and unitary groups. These problems n

Tran Vy Khang, Nguyen Dang Nguyen Khang, Nghi Hoang Khoa, Do Thi Thu Hien

Web applications remain the dominant attack surface in cybersecurity, where vulnerabilities such as SQL injection, XSS, and business logic flaws continue to cause significant data breaches. While penetration testing is effective for identifying these weaknesses, traditional manual approaches are time-consuming and heavily dependent on scarce expert knowledge

Yusuke Sato

We study two-dimensional cyclic quotient singularities defined by $k$-Wahl chains, a class of Hirzebruch--Jung continued fractions obtained inductively starting from $[k+2]$. This class includes the classical Wahl singularities in the case $k=2$ and also contains cyclic quotient singularities arising from $k$-generalized Markov triples. For singularities def

Elena Baskakova, William Bergeron, Matthew Hubbell, Hayden Jananthan

Supercomputers are complex, dynamic systems that serve thousands of users and are built with thousands of compute nodes. Due to the vast amounts of system and performance data needed to accurately capture their status, supercomputers require complex methods to monitor, maintain, and optimize. Data visualization is a powerful technique for overseeing these la

Yan Liu, Abhinav K. Jha

Objective evaluation of quantitative imaging (QI) methods with patient data is often hindered by the lack of gold standards. To address this challenge, a class of regression-without-truth (RWT) techniques have been developed. These techniques assume that the true and measured values are linearly related and estimate the linear-relationship parameters without

Federico Buccioni, Hanyu Fang, Kai Yan

The study of QCD scattering amplitudes in the collinear regime provides crucial insight into the factorization properties of hadronic cross sections. In this paper, we present the first complete results for two-loop spacelike splitting amplitudes in full-color QCD, in all partonic channels and helicity configurations. We confirm the universality of a class o

Yan Liu, Abhinav K. Jha

Clinical translation of quantitative imaging (QI) methods requires objective evaluation of these methods on reliably measuring the underlying true quantitative values. Ideally, such evaluation would be performed using ground truth or gold standards. However, in clinical practice, ground truth is generally unavailable, and obtaining gold standards for large p

Yongyun Chen, Qiusheng Gu, Luis. C Ho, Junhui Fan

The physical quantities that directly regulate AGN feedback in massive galaxies remain poorly understood. Observations of molecular gas surrounding AGNs suggest that this gas serves as a fuel source for AGN activity. Accordingly, we study the relationship between AGN activity and molecular gas properties. In this study, we analyze a large sample of nearby AG

Suyu Xiao, Hui Liang, Congcong Wang, Zhenyu Jiang

Silicon carbide detectors exhibit good detection performance such as fast time resolution, high radiation tolerances, high breakdown voltage and low temperature sensitivity and have been studied for detection applications. Meanwhile, transient current technique (TCT) is a direct and effective method to evaluate the time resolution of semiconductor detectors.

Krisanu Sarkar

We study cross-modal alignment between independently pretrained vision (DINOv2) and language (all-MiniLM-L6-v2) encoders using the functional map framework from computational geometry, which represents correspondence between representation manifolds as a compact linear operator between graph Laplacian eigenbases. While the framework underperforms Procrustes

Phong Lam, Ha-Linh Nguyen, Thu-Trang Nguyen, Son Nguyen

High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-prone. Programmatic labeling addresses this challenge by using label functions (LFs), i.e., heuristic rules that automatically generate weak labels for training datasets. However, existing automated LF generati

Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. Loke

Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not only accurate but also uncertainty-aware. In this work, we propose a loosely coupled neuro-symbolic framework that integrat

Zhendong Cao, Hongji Dai, Zhida Li, Ash Parameswaran

This paper presents a smartphone-based imaging system capable of quantifying the concentration of an assortment of biological/chemical assay samples. The main objective is to construct an image database which characterizes the relationship between color information and concentrations of the biological/chemical assay sample. For this aim, a designated optical

Sambartha Ray Barman, Andrey Starenky, Sofia Bodnar, Nikhil Narasimhan

Every major AI memory system in production today organises information by meaning. That organisation enables generalisation, analogy, and conceptual retrieval -- but it comes at a price. We prove that the same geometric structure enabling semantic generalisation makes interference, forgetting, and false recall inescapable. We formalise this tradeoff for \tex

Bingqi Shan, Baoquan Zhang, Xiaochen Qi, Xutao Li

Speculative Jacobi Decoding (SJD) has emerged as a promising method for accelerating autoregressive image generation. Despite its potential, existing SJD approaches often suffer from the low acceptance rate issue of speculative tokens due to token selection ambiguity. Recent works attempt to mitigate this issue primarily from the relaxed token verification p

Yuying Lu, Wenbo Fei, Yuanjia Wang, Molei Liu

Precision mental health requires treatment decisions that account for heterogeneous symptoms reflecting multiple clinical domains. However, existing methods for estimating individualized treatment effects (ITE) rely on a single summary outcome or a specific set of observed symptoms or measures, which are sensitive to symptom selection and limit generalizabil

Urvi Awasthi, Alexander Arjun Lobo, Leonid Zhukov

Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecules with long-range constraints. Many unconditional 3D generators emphasize coordinates and then infer bonds or rely on post-processing, leavi

Sen Zhang, Runmei Li, Shizhuang Deng, Zhichao Zheng

As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and decision-oriented inference to ensure safe operation in complex and dynamic railway environments. However, existing approaches focus primarily on basic perception and often generalize poorly to rare yet safety-crit

Jialu Wang, Chengbin Xu, Fang Zhang, Junyong Zhang

This paper investigates $L^p$-estimates for solutions to the wave equation perturbed by a scaling-critical partial inverse-square potential. We study a model in which the singularity of the potential appears only in a subset of the variables, corresponding to the Schr\"{o}dinger operator $\mathcal{H}_a = -\Delta_x - \Delta_y + a/|x|^2$ on $\mathbb{R}^{2+n}$.

Louis DeBiasio, Tucker Wimbish

Let $F_n$ be the graph on $2n+1$ vertices consisting of $n$ triangles meeting at a single vertex. After a number of improvements over the years, it is currently known that the Ramsey number of $F_n$ is between $4.5n-5$ (Chen, Yu, Zhao) and $(5+\frac{1}{6})n+O(1)$ (Dvo{\v{r}}{\'a}k and Metrebian). We improve both of these bounds as follows $$4.732n\approx (3+

Wentao Ye, Yuan Luo, Bo Liu, Jianwei Huang

The high-definition map is a cornerstone of autonomous driving. Unlike constructing a costly fleet of mapping vehicles, the crowdsourcing paradigm is a cost-effective way to keep an HD map up to date. Achieving practical success for crowdsourcing-based HD maps is contingent on addressing two critical issues: freshness and recruitment costs. Given that crowds

Haohua Que, Mingkai Liu, Jiayue Xie, Haojia Gao

High-resolution sensors are critical for robust autonomous perception but impose a severe memory wall on battery-constrained electric vehicles. In these systems, data movement energy often outweighs computation. Traditional image compression is ill-suited as it is semantically blind and optimizes for storage rather than bus switching activity. We propose Mot