April 2026 arXiv papers — page 49

Genevieve Markees, Stephen S. Eikenberry, Rodrigo Amezcua-Correa, Miguel Bandres

The next few years will be critical for technology development for Habitable Worlds Observatory (HWO) in its mission to search for and characterize extrasolar planets. To achieve its stated goals with contrasts of one part in ten billion, HWO will require outstanding stability and precision, particularly in measuring and controlling the wavefront of the ligh

Mengke Li, Haiquan Ling, Yiqun Zhang, Yang Lu

Real-world data often exhibit long-tailed distributions with numerous noisy labels, substantially degrading the performance of deep models. While prior research has made progress in addressing this combined challenge, it overlooks the severe label-image mismatch inherent to high-noise settings, thereby limiting their effectiveness. Given that observed labels

Haowei Cheng, Milhan Kim, Chong Liu, Teeradaj Racharak

As software systems grow in complexity, they must satisfy an increasing number of competing quality attributes, making it essential to balance them in a principled manner -- for example, a safety requirement for sensor-fusion verification may conflict with a tight planning-cycle budget. Multi-agent large language model frameworks support this balancing proce

Yan-Song Ma, Yu-Meng Guan, Jian-Xia Jiang, Shao-Jun Li

We construct mean spectral energy distributions (SEDs) for a substantial sample of 56,969 Sloan Digital Sky Survey DR16 quasars with $z < 0.75$, utilizing multiwavelength data from the mid-infrared (MIR) to ultraviolet (UV). These SEDs are built on eigenvector 1 parameters -- the relative optical $\rm Fe~ II$ strength ($R_{\rm Fe~II}$) and the H$\beta$ line

Milliam Maxime Zekeng Ndadji

Simulation is an indispensable tool for validating distributed IoT architectures before physical deployment, and iFogSim has emerged as one of the most widely adopted platform in the fog and edge computing research community. Yet the experience of using iFogSim for non-canonical, application-specific architectures remains incompletely documented, leaving pra

Suning Huang, Jiaqi Shao, Ke Wang, Qianzhong Chen

Have you ever post-trained a generalist vision-language-action (VLA) policy on a small demonstration dataset, only to find that it stops responding to new instructions and is limited to behaviors observed during post-training? We identify this phenomenon as lock-in: after low-data, supervised fine-tuning (SFT), the policy becomes overly specialized to the po

Ching Chang, You-Peng Shih, Tang-An Li

The flow condition at the leading edge governs the dynamics of the leading-edge vortex, which is crucial for understanding the separated flow over an airfoil at high angle of attack. Furthermore, with extensive applications in biomimetic flight, the wings encountering high-angle-of-attack situations in an unsteady manner are of great interest. The leading-ed

Qingqing Peng, Dongxu Chang, Guiying Yan, Guanghui Wang

In this study, we investigate the characteristics of scheduling sequences that enable efficient decoding of generalized low-density parity-check (GLDPC) codes under the layered message-passing algorithm. In particular, we show that scheduling sequences leading to higher decoding efficiency should prioritize the update of constraint nodes corresponding to sub

Yuan Liang, Chao-Wei Tsai, Jingwen Wu

Using \textit{Hubble Space Telescope} images from the PHAT and PHAST surveys, we construct an updated catalogue of 747 OB cluster (OBC) candidates. We introduce a dimensionless structural metric, the trace coefficient of variation ($CV_{\rm tr}$), derived from the Hessian matrix in four \textit{HST} bands, to quantify the internal photometric substructure of

Yusuke Iguchi, Nabhanila Nandi, Mohamed Oudah

Scanning SQUID imaging of CaSb$_2$ reveals dense vortex clusters with enhanced boundary susceptibility and suppressed internal vortex motion, which features inconsistent with both isolated vortex and flux tube behaviors. These measurements provide the first local visualization of magnetic dynamics within vortex clusters in a weakly pinned superconductor, off

Shisong Li, Weibo Liu, Nanjia Li, Elsayed E. E. Qupasie

This paper reports on the status of the Tsinghua tabletop Kibble balance experiment, aiming to deliver a mass calibration instrument for kilogram realizations in accordance with the new International System of Units (SI). Major progress since 2024 in different aspects, i.e., electrical, magnetic, mechanical, and optical, is presented. The primary weighing an

Junxiao Kong, Chupei Tang, Di Wang, Jixiu Zhai

Accurate prediction of drug-target binding affinity accelerates drug discovery by prioritizing compounds for experimental validation. Current methods face three limitations: sequence-based approaches discard spatial geometric constraints, structure-based methods fail to exploit hydrogen bond features, and conventional loss functions neglect prediction-target

Qishi Zhan, Minxuan Hu, Liang He, Guansu Wang

In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random variable, yet it is routinely reported as if it were a stable property of the method. We study when this practice fails. Using 50 independent repetitions across six regression datasets, we show that CRPS variance

Yang Liu, Bin Chong, Yuhan Lin, Chongyang Zhang

Large language models (LLMs) frequently produce \emph{detail hallucinations} when processing long regulatory documents, including subtle errors in threshold values, units, scopes, obligation levels, and conditions that preserve surface plausibility while corrupting safety-critical parameters. We formalize this phenomenon through a fine-grained \emph{Detail E

Wugeng Zheng, Ziwen Kan, Katie Wang, Chen Chen

Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-modality missingness caused by sensor intermittency or irregular sampling. Existing methods implicitly represent unobserved data via architectural alignment or missing embeddings, often failing to recover the t

Yu Xie, Rikuto Oiwa, Satoru Hayami

We propose a symmetry-based framework for constructing effective harmonic phonon models using a symmetry-adapted multipole basis. By decomposing the force-constant matrix into bond-centered electric multipoles, we identify the minimal microscopic ingredients responsible for phonon angular-momentum splitting. Applying this framework to a minimal zigzag-chain

Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, Ning Wang

Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enforce uniform expert sizes,creating a rigidity that fails to align computational costs with varying token-level complexity. While heterogeneous expert architectures attempt to addre

Siddeshwar Raghavan, Tanwi Mallick

Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Output (I/O) test cases. However, this does not work for scientific workflows, where I/O test cases do not exist, and generating them requires solving the very problem at hand. To address this, we introduce MOSAI

Zihui Zhu, Ziqi Zhou, Yichen Wang, Lulu Xue

Deep learning drives major advances in autonomous driving (AD), where object detectors are central to perception. However, adversarial attacks pose significant threats to the reliability and safety of these systems, with physical adversarial patches representing a particularly potent form of attack. Physical adversarial patch attacks pose severe risks but ar

Linghao Zhang, Ioana Dumitriu, Jiawang Nie

We study the rank one completion problem for tensors of arbitrary orders. The notion of rank one determinable tensors is introduced. We explore its properties and propose a recursive algorithm for computing rank one tensor completion. This algorithm only requires solving linear systems and computing singular vectors. In the absence of noise, it produces a un

Victor Barbosa Martins

The origin of ultra-high-energy cosmic rays (UHECRs) remains a fundamental question in astroparticle physics. While localized 3 $\sigma$ correlations with active galactic nuclei and starburst galaxies have been reported using time-integrated analyses, we propose and implement a spatiotemporal multiplet search method utilizing a pre-defined fixed window of 3

Qishi Zhan, Minxuan Hu, Guansu Wang, Jiaxin Liu

Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity. Method rankings are not only unreliable at small $n$, but also dataset-dependent in ways that point estimates cannot reveal: the same method comparison yields $P(\mathrm{MCD} \prec \mathrm{Ensemble}) = 1.000

Nowfel Mashnoor, Hadi Kamali, Kimia Azar

SystemVerilog Assertions (SVA) are essential for formal verification of digital hardware, yet their manual creation demands significant expertise in both the design under verification and temporal logic. Recent studies have explored using large language models (LLMs) to automate SVA generation, but existing approaches suffer from incorrect signal references,

Lingfeng Li, Zhuoyuan Li, Shun Li, Kaixin Zhan

Building models that generalize across physical systems without retraining remains a central challenge in computational science. Here we introduce In-Context Modeling (ICM), a retrain-free paradigm that infers physical relationships directly from observational fields. Rather than encoding system-specific behavior in fixed parameters, ICM assimilates measurem

Daniele Bartoli, Giovanni Giuseppe Grimaldi, Pantelimon Stănică

Motivated by entanglement-assisted quantum error-correcting codes, where the hull dimension determines the number of required pre-shared entangled pairs, we study hulls of two families of $\mathbb{F}_q$-linear codes defined by $q$-polynomial operators over $\mathbb{F}_{q^m}$. Our main tool is a unified Gram-matrix method. For image codes $\mathcal{C}(\boldsy

Alexander Nikitas Dimopoulos, Joseph Grasso, John Beltz

Indoor environments lack the spatial intelligence infrastructure that GPS provides outdoors; first responders arriving at unfamiliar buildings typically have no machine-readable map of safety equipment. Prior work on 3D semantic segmentation for public safety identified two barriers: scarcity of labeled indoor training data and poor recognition of small safe

Qian Huang, Mayoore Selvarasa Jaiswal, Zhen Zhong, Rochelle Pereira

Portrait relighting is a low-level vision problem in which physically plausible illumination transfer, identity preservation, and compact real-time inference must be considered together. Iterative diffusion-style methods can synthesize fine detail, but stochastic inference and cost complicate deterministic live video creation; physically grounded relighting

Muhammad Akmal Husain, Ferdinand, Mochamad Ikbal Arifyanto, Muhammad Irfan Hakim

A rare multiple open cluster system has been analyzed using Gaia DR3 astrometry and photometry data. Using Agglomerative Hierarchical clustering and Bayesian-HDBSCAN, we identify a compact core consisting of seven known open clusters and two additional components, including a new candidate, forming a nine-member association. Membership probabilities are refi

Kuntal Kokate, Bruno Aristimunha, Dung Truong, Arnaud Delorme

Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We present the first systematic comparison of four channel adaptation methods (Conv1d projection, spherical spline interpolation (SSI), source-space decomposition, and Riemannian re-cen

Abid Talukder, Maruf Ahmed Mridul, Oshani Seneviratne

Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language models (LLMs) show promise, it remains unclear which architectural design choices drive generation quality and why current approaches fail. We present a controlled experimental study using domain-specific in

Vladislav Kargin

Let $A_1, \ldots, A_r$ be Hermitian $n \times n$ matrices and $S = \sum A_i \otimes s_i$ the associated matrix semicircle, where $s_1, \ldots, s_r$ are free semicircular variables. We prove that the following are equivalent: (i) the matrix pencil $A = \sum A_i x_i$ is LR-semisimple (decomposes, up to left--right equivalence, as a direct sum of unsplittable p

Samer Attrah

We present Code Broker, a multi agent system built on Google s Agent Development Kit ADK that analyses Python source code from individual files, local directory trees, or remote GitHub repositories and generates structured, actionable quality assessment reports. The system realises a hierarchical five agent architecture in which a root orchestrator coordinat

Yunqi Liang, Hasan Ugur Koyluoglu, Fuat Alican, Yigit Ihlamur

We propose a Gaussian-copula-based framework that learns deal-level dependence directly from observed joint success frequencies across founder, geography, and market attributes. Holding marginal deal success probabilities fixed, deal-level correlation preserves expected portfolio outcomes but shifts the portfolio distribution toward heavier right tails and h

Hiroo Azuma

In this paper, we study the non-Gaussianity of the eigenstates of the Pegg-Barnett phase observable. By computing the Wigner functions of the eigenstates, we confirm that they take negative values in specific regions of the phase space. The Pegg-Barnett phase-operator eigenstates are defined in a finite-dimensional Hilbert space. Thus, we examine how their n

Samuel Anyaso-Samuel, Thong Luong, Fei Qin, Jiyeon Choi

Pooling genome-wide association studies of multiple related traits can substantially increase power for detecting genetic variants with pleiotropic effects. ASSET, which exhaustively searches all subsets of studies for association signals, has been widely used to detect modest effects and improve interpretability. Under a normality assumption, ASSET computes

Bui D. Hoi

Altermagnets, characterized by time-reversal symmetry breaking without net magnetization and momentum-dependent spin-split bands, offer a promising platform for spintronics due to their anisotropic spin textures and potential for tunable magnetic interactions. Here, we theoretically investigate the slow phonon-renormalized Ruderman-Kittel-Kasuya-Yosida (RKKY

Ibrahim Bilau, Eunhwa Yang, Hyeokhyen Kwon, Stacie Smith

This study examines how visual accessibility through cabinet design influences task performance, cognitive load, physical activity level, motivation, and user experience in a virtual kitchen among older adults with and without mild cognitive impairment (MCI). Seventeen older adults (7 with MCI, 10 without) completed a repeated-measures item retrieval task un

Patrizio Dazzi, Emanuele Carlini, Matteo Mordacchini, Saul Urso

Large-scale agentic systems run on distributed infrastructures where many software agents share physical hosts and are discovered via peer-to-peer mechanisms. Discovery must handle node-level churn from failures and host departures and agent-level churn from demand-driven activation, deactivation, and state changes. Their interaction reshapes classic trade-o

Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba, Sule Yildirim Yayilgan

The quality of diabetic retinopathy (DR) screening relies on the ability to correctly grade severity; however, many deep-learning (DL) classifiers cannot be easily interpreted in the clinical context. This study presents a methodology that combines strong discriminative models with multimodal explanations, converting retinal pixels into clinically interpreta

Yan-Martin Tamm, Anna Aljanaki

Over the years, Music Information Retrieval (MIR) research community has released various models pretrained on large amounts of music data. Transfer learning showcases the proven effectiveness of pretrained backend models for a broad spectrum of downstream tasks, including auto-tagging and genre classification. However, MIR papers generally do not explore th

Ivan Etoku Oiye, Ajay Sharma, Sairam Geethanath

Low-field MRI is increasingly considered accessible for imaging owing to its lower cost, simpler infrastructure requirements, and potential for mobile and point-of-care deployment. A central challenge is achieving clinically useful field strength and homogeneity while keeping the magnet lightweight and maintaining patient accessibility. This work presents th

Alexander Liss, Nicholas Desmond, Santiago Gil Gallego

When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversation terminates without achieving either agent&#39;s stated objective. This paper asks whether a control-theor

Xiang Yin, Tim Miller, Nico Potyka, Antonio Rago

Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each. In this position paper, we advocate (computational) argumentation as a particularly suitable paradigm to provide a formal, computable foundation

Ilia Zhavoronkov

We present a graph-theoretic and convex optimization framework for multi-path routing in decentralized exchange networks, together with its implementation and empirical evaluation on Ethereum mainnet. The framework models the market as a directed token multigraph whose arcs carry AMM exchange functions. Routing is decomposed into two implemented layers: cand

Ayman Ali Sharara

Idiomatic expressions remain a persistent challenge for natural language processing because their meanings are often non-compositional, context-dependent, and difficult to align across languages. Existing idiom resources are often limited in scale, contextual diversity, or multilingual coverage, restricting their utility for modern language models. We introd

Dave Banerjee, Onni Aarne

AI integrity means ensuring AI systems are free from secret or unauthorized modifications that could compromise their behavior. Integrity represents one pillar of the confidentiality, integrity, and availability (CIA) triad in information security: confidentiality preserves secrecy of sensitive information, integrity ensures data remain authentic and uncorru

Yulu Pi, Wenlong Li, Jatinder Singh

Algorithm registers are increasingly being both considered and deployed as instruments in AI governance. They are often expected to deliver transparency; however, in practice their design, scope, and implementation vary substantially. Currently, we lack a holistic understanding of the potential roles that registers might play in AI governance, and how differ

Uloma Egondu Nelson, Gil Gallegos

Artificial Intelligence AI has emerged as a transformative innovation in inclusive science education for disabled learners in rural New Mexico. Using a mixed method design that combined multiple linear regression and an Artificial Neural Network ANN model, this study examined 120 students in grades 6 to 10 and 15 instructors across four rural schools. The AI

Takaaki Fujita, Florentin Smarandache

This book presents a comprehensive and systematic survey of graph theory under uncertainty, with particular emphasis on the unifying role of the uncertain graph framework. It reviews fundamental concepts, structural properties, graph classes, and graph parameters within fuzzy, neutrosophic, and related models, while also introducing a wide range of extension

Alizishaan Khatri, Chiquita Prabhu

The rapid proliferation of AI-powered video generation systems has introduced significant challenges in content moderation, particularly with respect to adult and sexually explicit material. Existing detection methods operate on either prompts or decoded pixel-space outputs. Therefore, both approaches are blind to the rich internal representations formed dur

Arthur Amalvy, Vincent Labatut, Xavier Bost, Hen-Hsen Huang

While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange among researchers. Yet, such corpora are necessary to fully represent the diversity of data found in the wild in the context of NLP tasks. We tackle this issue by proposing a method to lawfully and publicly sh

Kidist Amde Mekonnen, Yongkang Li, Yubao Tang, Simon Lupart

Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to early pruning of relevant prefixes under finite-beam decoding. Planning Ahead in Generative Retrieval (PAG) mitigates this failure mode by using simultaneous decoding to compute a

Kaixian Qu, Han Wang, Victor Klemm, Cesar Cadena

Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move and what to sense in order to obtain the most informative observations for accomplishing its mission. Existing approaches either solve a computationally expensive traveling salesman problem over heuristically selected informative nodes, or adopt a m

Hao Chen, Jumin Shi, Yinghao Li, Xiangyu Luo

High temperature superconductivity in cuprate superconductors is generally considered to be generated from doping the Mott insulators. The fundamental nature of the doped parent compounds as well as the microscopic origin of electron pairing remain critical issues in understanding the emergence of superconductivity. Here, using high-resolution spatially-reso

Tingyu Gou, Katharine K. Reeves, Peter R. Young, Astrid M. Veronig

Solar eruptions are sudden ejections of coronal mass and magnetic fields accompanied by intense energy release. The eruptive structure does not always erupt successfully, but sometimes fails to escape the Sun after initiation. The failure of an eruption, however, provides an invaluable opportunity for understanding the intricate mechanism of eruptions. We pr

J. A. Manuel, D. Jones, M. Santander-García, N. Reindl

As planetary nebulae evolve, they fade and dissipate into the surrounding interstellar medium making them harder to detect. Modern, advanced amateur equipment can help to uncover this hidden population of ancient &#39;ghost&#39; planetary nebulae. Via careful processing of long-integration, narrow-band imagery with modest aperture telescopes at a dark-sky si

Mehdi Maboudi, Said Harb, Jackson Ferrao, Kourosh Khoshelham

Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal data into a unified representation. This is essential in applications such as construction monitoring, autonomous driving, robotics, and virtual or augmented reality (VR/AR). With the increasing accessibility

Satori Tsuzuki

High-curvature observables in incompressible flows, including $k^4$-weighted spectra, can arise from explicit internal rotation, elimination of a fast spin variable, or polynomial higher-gradient closure. Building on a retained-spin micropolar closure derived separately from the Boltzmann--Curtiss equation, we show that these mechanisms are dynamically disti

Yue Liu

This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model via trajectory-splitting SFT, then scale it via progressive RL training. Specifically, we first activate basic agentic abilities of a base model with a comprehensive SFT recipe. Then, we introduce Research-Factor

Rafael A. Molina

Non-Hermitian degeneracies of Lindblad generators (Liouvillian exceptional points) can induce non-exponential relaxation and higher-order poles in dynamical response functions. A collective spin coupled to a polarized Markovian bath exhibits an \emph{exceptional spectral phase} in which defective Liouvillian modes imprint super-Lorentzian features in frequen

Lech Madeyski, Barbara Kitchenham, Martin Shepperd

Context: Large language models (LLMs) are increasingly used to screen literature for systematic reviews (SRs), but the standard confusion-matrix metrics used to evaluate them can mislead under the imbalanced, cost-asymmetric conditions of screening. Objective: We develop and justify LLM4SCREENLIT-practical recommendations for researchers conducting LLM-scree

Johnnie Gray, Gunhee Park, Glen Evenbly, Nicola Pancotti

We analyze the tensor network loop cluster expansion, introduced in [G. Park, J. Gray, and G. K.-L. Chan, Phys. Rev. B 112, 174310 (2025)] as a systematic correction to belief propagation, in the context of general quantum many-body problems. We provide numerical examples of the accuracy and practical applicability of the approach for the computation of grou

Yu Wang, Leyi Lao, Langchu Huang, Gabriel Skantze

Backchannels and fillers are important linguistic expressions in dialogue, but often treated as &#39;noise&#39; to be bypassed in modern transformer-based language models (LMs). Here, we study how they are represented in LMs using three fine-tuning strategies on three dialogue corpora in English and Japanese, in which backchannels and fillers are both preser

Mingwei Yang, Jiayin Tang, Xianfeng Wu, Heng Wang

Intertwined superconducting and magnetic orders may give rise to exotic quantum phases, including field-induced and re-entrant superconductivity. However, such magnetism-enhanced superconductivity has remained elusive in superconductors with higher transition temperatures. While infinite-layer nickelates represent a new class of unconventional superconductor

Boou Jiang, Jongho Park, Jinchao Xu

This paper introduces an abstract framework for randomized subspace correction methods for convex optimization, which unifies and generalizes a broad class of existing algorithms, including domain decomposition, multigrid, and block coordinate descent methods. We provide a convergence rate analysis ranging from minimal assumptions to more practical settings,

Zeynep Engin

This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI Governance (AIG) field by foregrounding the relational dynamics between human and AI actors rather than treating AI systems as objects of governance alone. Current categorical frameworks (e.g., human-in-the-loop models) inadequately capture how AI systems evolve from tools

Eeshan Ketkar, Giovanni Marini, Pietro Maria Forcella, Giorgio Sangiovanni

Mercury telluride is a canonical material for realizing topological phases, yet a full understanding of its electronic structure remains challenging due to subtle competing effects. Using first-principles calculations and $\mathbf{k}\cdot\mathbf{p}$ modelling, we study its topological phase diagram under strain. We show that linearly $k$-dependent higher-ord

Teighin Nordholt, Melissa Greeff

Autonomous multirotor landings on uncrewed surface vessels (USVs) are critical for persistent maritime operations but remain challenging due to wave-induced tilt, wind disturbances, and limited landing area. Many existing approaches exhibit small pose tolerance for reliable landing. This paper presents a lightweight toggleable adhesion mechanism to improve l

Charles Xu, Jost Tobias Springenberg, Michael Equi, Ali Amin

Vision-language-action (VLA) models can learn to perform diverse manipulation skills "out of the box," but achieving the precision and speed that real-world tasks demand requires further fine-tuning -- for example, via reinforcement learning (RL). We introduce a lightweight method that enables sample-efficient online RL fine-tuning of pretrained VLAs using j

Junyan Cheng, Kyle Richardson, Peter Chin

Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet their reasoning suffers from stochastic instability and lacks a verifiable, compositional structure. To address this, we introduce Analytica, a novel agent architecture built on the principle of Soft Propositi

Taeju Lee

Oscillation and frequency modulation have been leveraged for applications in detection (or sensing), data processing, and telemetry. This work provides a theoretical analysis of oscillation phenomena with negative impedance implemented using a cross-coupled transconductance pair. The negative impedance can consist not only of a negative resistance but also o

Sol Lim, Min-Seung Ko, Farnaz Safdarian, Hao Zhu

This paper proposes a weather-to-voltage (W2V) predictive modeling framework to learn the underlying weather-grid nexus. Unlike existing approaches on weather-informed grid operations, our proposed W2V model can achieve the joint analysis of weather and grid states, and further leverage this coupling to enhance grid-aware weather forecasting (GAWF) as a key

Yating Wu, Yuhao Zhang, Sayan Ghosh, Sourya Basu

Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management approaches such as sliding window and prompt compression may omit earlier structured information that later steps rely on. Recent retrieval-based memory systems surface relevant content but still overlook the ca

Sudhir P. Jodha, Konstantinos G. Papakonstantinou

The safety and resilience of civil infrastructure systems are increasingly threatened by compounded risks from various hazard events and structural deterioration due to environmental stressors. This study presents a comprehensive risk-informed, life-cycle optimization framework that extends the Performance-Based Earthquake Engineering (PBEE) and probabilisti

Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea, Christopher Parisien

Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover weaknesses unique to it. Most current automated red teaming methods are intended for tackling safety and content moderation. Thus, t

João Carnevale

Inspired by constructions of Kova\v{c}evi\'{c}, we introduce the amalgamated free product of circle actions, obtained by blowing up two actions along prescribed orbits and rearranging the inserted intervals. Under natural orbit and index assumptions, we prove that this construction is well defined, yields a minimal action on the circle, and is unique up to t

Emil Y. Sidky, John Paul Phillips, Zheng Zhang, Dan Xia

The primal-dual hybrid gradient (PDHG) algorithm for solving convex optimization problems that arise in tomographic imaging is revisited. In particular, simplification of the selection of step-size parameters is developed for optimization problems with multiple terms, each containing a linear transform subject to splitting. This simplification maintains algo

Atanu Patra, Subhamoy Sahoo, Johannes Düreth, Simon Betzold

Optical cavities play a central role in photonic and quantum technologies by enhancing light-matter interactions. In semiconductor microcavities, achieving high quality (Q) factors typically relies on sophisticated epitaxial growth techniques, such as molecular beam epitaxy, which offer atomic-scale precision but are costly and limited in material compatibil

Andrey A. Popov

AI and data-driven models have large potential for data assimilation applications by creating fast and accurate forecasts. Their tendency to produce spurious inaccurate, nonphysical results -- hallucination -- however, raises a serious question about their long-term use, and can be categorized as untrustworthy methods. Theory-driven methods on the other hand

Baris Karacan, Barbara Di Eugenio, Patrick Thornton, Joanna Tess

Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with important implications for healthcare outcomes. Obstetrics is a high-stakes domain in which physicians counsel patients on delivery mode choices such as vaginal birth after cesarean (VBAC) and repeat cesarean s

Sukwoong Choi

Firms are deploying more capable AI systems, but organizational controls often have not kept pace. These systems can generate greater productivity gains, but high-value uses require broader authority exposure -- data access, workflow integration, and delegated authority -- when governance controls have not yet decoupled capability from authority exposure. We

Zixuan Xia, Quanxi Li

We propose a simple yet effective alternative to reward normalization in policy gradient reinforcement learning by integrating a 1D Kalman filter for online reward estimation. Instead of relying on fixed heuristics, our method recursively estimates the latent reward mean, smoothing high-variance returns and adapting to non-stationary environments. This appro

J. H. Peterson, M. Jacquart

Earth's matter affects the oscillation of atmospheric neutrinos and antineutrinos differently depending on the neutrino mass ordering (NMO). As more neutrinos than antineutrinos are expected to be detected in the IceCube detector, this matter effect can be used to probe the NMO. The fraction of energy transferred to the nucleon during a neutrino interaction,

Prasoon Suchandra, Shabnam Raayai-Ardakani

Bio-inspired $\pmb \vee$ flight formation is a well known technique for energy saving among groups of fixed-wing aircraft, and as of recently, for groups of quad-rotors. Here, we study the effect of the formation angle on the performance of each of the members of a 5-member $\pmb \vee$-formation in terms of the flow field, and drag force. We employ axisymmet

Yash Kumar Atri, Steven L. Johnson, Tom Hartvigsen

Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios, correct behavior requires models to maintain and update implicit temporal assumptions established earlier in a conversation. We study this challenge through the lens of temporal scope stability: the ability t

Edward Cheng, Jeshua Cheng

AI agents are increasingly deployed to execute tasks and make decisions within agentic workflows, introducing new requirements for safe and controlled autonomy. Prior work has established the importance of human oversight for ensuring transparency, accountability, and trustworthiness in such systems. However, existing implementations of Human-in-the-Loop (HI

Teal Amore, Nathan Berman, Siyuan Jiang

Automated testing is crucial for maintaining open-source software quality. However, motivating contributors to include tests for code changes remains a challenge. While existing interventions, such as code coverage metrics and reviewer feedback, are often reactive and applied only after a pull request is opened, this study investigates whether documentation

Falk L Wiegmann, Nancy L Ford

Preclinical micro-CT reconstruction involves large projection sizes and volumes that make iterative methods costly - 5x to 50x slower than analytic alternatives on modern GPUs. Whether this cost is justified depends on the imaging task, yet head-to-head comparisons using task-based metrics on identical preclinical data are lacking. We benchmark four reconstr

Kennon Stewart

We argue that current definitions of machine unlearning are underspecified for second-order optimizers. We compare first-order and second-order learners for their ability to handle the data deletion task with varying degrees of eigendecomposition to mimic the loss model memory. While both first and second-order methods realign with the ideal counterfactul in

Kamlesh Sawadekar, Seth McGinnis, Peijun Li, Kathryn Lawson

Systematic biases in General Circulation Model (GCM) outputs limit their direct applicability in regional planning, making bias correction a technically demanding but necessary step for both short-term and long-term impact assessment. Correcting precipitation is particularly challenging due to its non-Gaussian distribution, intermittent nature, and heavy-tai

Nicholas A. Corbin, Boris Kramer

The theory of nonlinear balanced truncation provides a system-theoretic framework for model reduction that preserves important properties such as stability, controllability, and observability. We present a scalable algorithm for computing reduced-order models based on the nonlinear balancing theory. The approach is based on polynomial approximations using th

Sina Sadeghzadeh, Frank Toffoletto, Vassilis Angelopoulos, Richard Wolf

Plasma sheet bubbles, defined as entropy-depleted flux tubes, are widely regarded as an efficient mechanism for transporting plasma into the inner magnetosphere during geomagnetic storms. Equilibrium simulations using the Rice Convection Model (RCM-E) predict that bubbles can account for up to 60% of storm-time ring current energy during intense storms. Howe

Xiangdong Wen

We give a constructive proof that Young's lattice L(6,n) has a partition into saturated symmetric chains.

Merve Cerit, Andrea Mock, Vryan Almanon Feliciano, Thomas N. Robinson

Predicting whether an individual's depressive symptoms will worsen, remain stable, or improve over the coming weeks can enable earlier and more targeted care, yet prospective within-person trajectory prediction remains largely unaddressed in digital phenotyping. We combine fortnightly CES-D assessments with over 100 million screenshots captured every five se

Rui Luo, Jonas Mariager Jakobsen, Wesley Roozing, Federico Califano

Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting applications, e.g., collaborative robots for rehabilitation. Safety is critical for the successful deployment of this kind of robotic system. In recent years, Control Barrier Function (CBF) has emerged as an effec

Ali Cici, Shaaban Khalil, Stefano Moretti, Cem Salih Un

The B-L Supersymmetric Standard Model with Inverse Seesaw (BLSSM-IS) extends the Minimal Supersymmetric Standard Model (MSSM) by incorporating a gauged B-L symmetry, right-handed neutrinos and an additional neutral gauge boson Z'. Searches at the Large Hadron Collider (LHC) constrain the mass of this gauge boson to be as low as only ~ 2.2 TeV in the BLSSM-IS

Ali Keramatipour

The Conway-99 problem questions the existence of a strongly regular graph with 99 vertices and specific parameters. A \textit{strongly} regular graph is a regular graph that exhibits two additional properties: vertices must share a fixed number of neighbours, depending on whether they are adjacent or not, given by two parameters. Despite the search space for

Haoze He, Xingyuan Ding, Xuan Jiang, Xinkai Zou

Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMixer and ESFT mitigate router collapse with dense mixing or auxiliary load-balancing losses, but these introduce noisy gradients that often degrade performance. In preliminary experi

Maarten Steevens, Tom Lauwaerts, Christophe Scholliers

Debugging nondeterministic programs is inherently difficult, particularly in microcontroller environments where execution paths can diverge unpredictably due to external sensor inputs. Traditional debugging techniques often fail to capture or reproduce this nondeterministic behavior effectively. Multiverse debugging has emerged as a compelling technique to d

Alexander Murray-Watters, Cheng Wang, John R. Hipp, Cynthia Lakon

Many processes related to status, power, and influence within social networks have been modeled using forced linear diffusion models; examples include the highly successful Friedkin-Johnsen model of social influence, the status/power scores of Katz and Bonacich, and the widely used network autocorrelation model. While a basic assumption of such models is tha