April 2026 arXiv papers — page 114

Philippe Laban, Tobias Schnabel, Jennifer Neville

Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust - the expectation that the LLM will faithfully execute the task without introducing errors into documents. We introduce DELEGATE-52 to study the readiness of AI systems in delegat

Ryutaro Katsuta, Shun Uchino

We show that the one-dimensional Yang-Gaudin model with two-body loss remains exactly solvable irrespective of whether constituent particles are bosons or fermions. By relating the Liouvillian spectrum to the right eigenvalues of a non-Hermitian effective Hamiltonian obtained by complexifying the interaction strength, we derive a general expression for the i

Chao Li

Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during generation. We propose DALM, a Domain-Algebraic Language Model that replaces unconstrained token generation with structured denoising over a domain lattice. DALM follows a three-phase generation path: it first resolv

Hamet Seydi, Teylama Miabey

In this paper, we investigate the properties of $A$-coherent and $A$-quasi-coherent sheaves within the framework of algebraic geometry over non-algebraically closed fields. We define an $\mathcal{O}_X$-module to be $A$-coherent (resp. $A$-quasi-coherent) if it admits a global presentation by free modules of finite rank (resp. arbitrary rank) over a ringed sp

Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang, Yunfeng Zhao

Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throughput under volatile demand requires deep understanding of real-world serving workloads, yet existing studies often rely on proxy traces or coarse-grained characterizations that fa

Anupreet Walia

Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls. We present BatchDAG, a system in which an LLM generates a typed directed acyclic graph (DAG)

Rikard Rosenbacke, Carl Rosenbacke, Victor Rosenbacke, Martin McKee

Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption while bypassing the slow, reflective processes that underpin sound judgment. This paper introduces Relational Reflective Intelligence (RRI), an inference-time governance layer that operationalizes reflection throug

Tim Dorn, Saara A. Khan, Julie Mumford

As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build. Traditional human brainstorming faces challenges including groupthink, echo chambers, and limited diversity. To address this, we present a multi-agentic architecture that simulates roundtable brainstorming through two phases: divergent thinking to gen

Manh Luong, Tamas Abraham, Junae Kim, Amar Kaur

Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a m

Liu Xiao

We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token interactions without benchmark-specific parsing. The triple-latent family improves a small Transformer baseline on byte-level WikiText-2 and on a tokenizer-based MiniMind language-model benchmark, while a recall-

Arash Ahmadi, Parisa Masnadi Khiabani, Sarah Sharif, Charles Nicholson

Large Language Models (LLMs) have shown strong promise in healthcare applications. Yet deploying general-purpose models in real-world settings remains difficult due to data privacy constraints, inference costs, and limited suitability for edge or on-device use. These challenges motivate the development of smaller, more efficient models that require robust po

Diana Maria Popa, Simona-Vasilica Oprea, Adela Bâra

This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship between learning environments and changes in ICT related career aspirations across countries. The analysis uses country-level data from PISA 2018 and 2022, combining indicators of student autonomy, digital ski

Erin T. Jacques, Erela Datuowei, Elizabeth Quaye, Corey H. Basch

This study seeks to determine the authority signals used by Anthropic's Claude AI in its presentation of sources when answering consumer health questions. While there exists a great deal of discourse around the quality of health citations that LLMs produce, there is limited information on the integrity of the sources the citations originate from, and to

Federico Belotti, Stefano Coniglio, Antonio Cosma, Francesco Fallucchi

Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experimental economics. Their validity, however, rests on the assumption that a human performs them. We study whether this assumption still holds in the era of Artificial Intelligence (AI) and Large Language Models (L

Nelly Dux, Cristina Alaimo, Philippe Roussiere, Abhishek Kumar Mishra

Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving from experimental prototypes to enterprise deployments. This transition introduces tensions in implementation, scaling, and governance: organizations seek scalable autonomy for knowledge and coordination work, y

Joongmin Shin, Jeongbae Park, Jaehyung Seo, Heuiseok Lim

In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true structure. Existing text-centric chunkers and generative hierarchy parsers often miss cross-page parent-child relations, figure/table-caption bindings, and boundary cues, which leads to fragmented or redund

Mehmet Haklidir

Robotic systems are moving from isolated platforms to interconnected multi-agent ecosystems that operate in human environments. This shift raises a governance problem that existing frameworks do not address: how does consent propagate, degrade, and break down across chains of delegation between embodied autonomous agents? The AI ethics community has begun to

Andrea Bernardi, Marco Clementi, Marcello Bacchi, Matías Rubén Bolaños

Time-bin encoded quantum states of light are crucial for quantum technology applications. The integration of manipulation functionalities into chip-scale devices is essential for deploying scalable, high-performance, and cost-effective quantum networks. Here we develop a fully integrated, high-throughput quantum receiver based on the thin-film lithium niobat

K. Kreckel, O. V. Egorov, N. Drory, G. A. Blanc

The so-called 'Galactic Center' Lobe (GCL) is an extended (~1 deg) radio continuum feature situated above the Galactic Plane, for which the literature contains varying claims about both its nature and location. Using new optical integral field spectroscopic observations from the SDSS-V Local Volume Mapper, we confirm the characterization of the GCL a

Yanlin Zhang, Yan Zhang, Muhua Zheng, Kesheng Xu

Experimental evidence indicates that intrinsic temporal dynamics operating across multiple time scales are closely associated with the emergence of periodic spatial activity of increasing complexity. However, how information encoded in grid-like firing patterns for path integration is processed across these intrinsic time scales remains unclear. To address t

Hamdy Arkoub, Jia-Hong Ke, Miaomiao Jin

Ni-based structural alloys in molten salt environments often experience simultaneous mechanical loading and corrosive attack, yet the mechanisms governing stress-corrosion interactions remain unclear. Prior studies largely emphasize tensile stress, while the role of compressive stress has received limited attention. Here, reactive molecular dynamics simulati

Nahuel Mendez, Maria Cristina Mariani Maria Pia Beccar-Varela, Osei Tweneboah, Sebastian Jaroszewicz

Multifractal Detrended Fluctuation Analysis (MFDFA) is a powerful and widely used technique for characterizing the scaling properties and long-range correlations of complex time series. However, its application often involves significant practical challenges, such as the subjective identification of scaling regions (crossovers) and the disambiguation of the

Claudia P. Gutiérrez, Lluís Galbany, Joseph P. Anderson, Dimitris Souropanis

Understanding supernova (SN) progenitors remains a major challenge in astrophysics, as it involves untangling the complex interplay between stellar physics (e.g., evolution, binarity, explosion) and environments (e.g., metallicity, star formation rate). To address this, we present relative frequencies of core-collapse SNe (CCSNe) as a function of metallicity

Daniel Allepuz-Requena, Jonas Schou Neergard-Nielsen, Alexander Huck, Ulrik Lund Andersen

We present a high-yield method for fabricating concave micromirror templates in silica using feedback-controlled CO2 laser ablation with precise in situ positioning. Real-time monitoring of the white-light emission generated during ablation is used to terminate laser exposure, thereby reducing shot-to-shot variability in mirror depth and radius of curvature.

Jessica H. Zhu, Shayla Stringfield, Vahe Zaprosyan, Michael Wagner

Firearm violence is a pressing public health issue, yet research into survivors' lived experiences remains underfunded and difficult to scale. Qualitative research, including in-depth interviews, is a valuable tool for understanding the personal and societal consequences of community firearm violence and designing effective interventions. However, manual

Eimear Byrne, Alain Couvreur, Lucien François

Tensor codes are a generalisation of matrix codes. Such codes are defined as subspaces of order-r tensors for which the ambient space is endowed with the tensor-rank as a metric. A class of these codes was introduced by Roth, who also outlined a decoding algorithm for low tensor-rank errors that can be generalised to an algorithm with exponential complexity

Haato Watanabe, Nobuyuki Umetani

Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. Its explicit representation with Gaussian primitives enables fast training, real-time rendering, and convenient post-processing such as editing and surface reconstruction. However, 3DGS suffers from a critical

Alberto Rebassa-Mansergas, Iset Tejero-Gómez, Roberto Raddi

The age-metallicity relation (AMR) is a fundamental observational constraint for understanding the chemical evolution of the Galaxy. As reliable cosmochronometers, white dwarfs in binary systems with main sequence companions (WD+MS binaries) provide excellent laboratories to study this relation, since both components are expected to be coeval. We construct a

Brieg Le Corre, Clothilde Grenèche, Rozenn Bernard, Tony Rohel

Direct orientation contrast imaging of zinc-blende III-V materials is studied using scanning electron microscopy. A quantitative approach is taken using a 3 μm thick orientation-patterned GaP grown on GaAs sample, studying the anti-phase domain contrast with respect to the electron beam energy and the tilt angle. A qualitative approach is taken for III-V gro

Cheol-Yeon Cheon, Kenji Watanabe, Takashi Taniguchi, Alberto F. Morpurgo

Anisotropic magnetoresistance (AMR) offers a robust electrical readout of antiferromagnetic (AFM) states, playing a central role in the rapidly advancing field of AFM spintronics. Despite its great versatility, electrical probing of the Néel vector via AMR remains challenging in the ultrathin limit due to interface disorder and reduced dimensionality. Here,

Oluwaleke Yusuf, M. Tsaqif Wismadi, Adil Rasheed

Urban bike-sharing systems require strategic station expansion to meet growing demand. Traditional allocation approaches rely on explicit demand modelling that may not capture the urban characteristics distinguishing successful stations. This study addresses the need to exploit patterns from existing stations to inform expansion decisions, particularly in da

Hideo Iizuka, Shanhui Fan

A broadband electromagnetic source is important for scientific and technological applications. Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of quantum vacuum fluctuations, by showing that such fluctu

Sidney Wong

This thesis investigates geographic dialect alignment in place-informed social media communities, focussing on New Zealand-related Reddit communities. By integrating qualitative analyses of user perceptions with computational methods, the study examines how language use reflects place identity and patterns of language variation and change based on user-infor

Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa, Jinia Roy

Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent trai

Wenxiang Ying, Carlos M. Bustamante, Franco P. Bonafé, Richard Richardson

Following our recent numerical study [arXiv:2601.16299 (2026)], we investigate vibrational excitation induced by transient optical driving in molecular ensembles strongly coupled to a cavity mode using the field-driven Holstein--Tavis--Cummings model. We analyze how pulsed excitation redistributes energy among electronic, photonic, and vibrational degrees of

Torin Greenwood, Christine Heitsch

Researchers have repeatedly found that the ends of an RNA sequence are significantly closer than expected for a random linear chain. However, we prove that the ends of a branched structure are almost certainly close. Our results are obtained via combinatorial branching models of increasing complexity using tools from multivariate analytic combinatorics. We c

Nilavra Pathak, Samadrita Biswas, Nirmalya Roy

Modern datacenters schedule heterogeneous workloads across geo-distributed sites with diverse compute capacities, electricity prices, and thermal conditions. Compute utilization, heat generation, cooling demand, and energy consumption are tightly coupled, yet most existing schedulers abstract these effects and treat them independently. We present \textit{Dat

Anjary Feno Hasina Rasamimanana, Ravo Tokiniaina Ranaivoson, Roland Raboanary, Raoelina Andriambololona

Advances in the study of relativistic quantum phase space have established the set of Linear Canonical Transformations (LCTs) as a candidate for the fundamental symmetry group associated with relativistic quantum physics. In this framework, for a spacetime of signature $(N_+,N_-)$, the symmetry of the relativistic quantum phase space is described by the LCT

Alexander Plavin, Alexander Pushkarev, Yuri Kovalev

We report the first unambiguous detection of refractive substructure in an active galactic nucleus (AGN) using ground-based Very Long Baseline Interferometry (VLBI). Our analysis of TXS 2005+403 - observed at 1-5 GHz along a line of sight through the Cygnus region - reveals clear signatures of turbulence-induced substructure on long baselines that cannot be

Erik Burman, Mats G. Larson, Karl Larsson, Jonatan Vallin

We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse problems, and uncertainty quantification. The solution is discretized in the physical domain using finite element methods, while the dependence on a finite-dimensional parameter is approximated separately. We establish exist

Bekir Can Lütfüoğlu, Sardor Murodov, Mardon Abdullaev, Javlon Rayimbaev

Using the convergent Leaver method, we investigate the quasinormal modes of a massive scalar field propagating in the background of the Casadio--Fabbri--Mazzacurati (CFM) brane-world black hole. We show that the spectrum exhibits two distinct types of modes, depending on their behavior as the field mass increases. In one class, the real oscillation frequency

Zhen Li, Yuki Izumida

Heider balance theory provides a fundamental framework for understanding the formation of friendly and hostile relations in social networks. Existing stochastic formulations typically assume a uniform social temperature, implying that all interpersonal relations fluctuate with the same intensity. However, studies show that social interactions are highly hete

Francesco Flammini Dotti, Roberto Capuzzo-Dolcetta, Giovanni Carraro, Alessandro Alberto Trani

Context. In the Local Group, dwarf spheroidal galaxies (dSphs) and ultra-faint dwarf galaxies (UFDs) exhibit large velocity dispersions. These values are generally attributed to the presence of substantial amounts of dark matter (DM), in line with the predictions of the standard model of galaxy formation. However, alternative, more conservative explanations

Emir Bilgili, Nicholas Taormina, Richard Hennig, Simon R. Phillpot

A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong agreement with DFT including lattice constants, interlayer binding energies, and phase-stability. Furthermore, the potential reasonably captures the phonon spectra and the highly a

Argha Debnath, Ayan Sahoo, Debraj Rakshit

We study a one-dimensional lattice model with site-dependent nearest-neighbor hopping amplitudes that follow a power-law profile. The hopping variation is controlled by a grading exponent, $|alpha|$, which serves as the tuning parameter of the system. In the thermodynamic limit, the ground state becomes localized in the limit $|alpha| \to 0$, signaling the p

Jose Luis Alvarez-Perez

Binet's equation provides a direct way to obtain the geometric shape of orbits in a central force field. It is well known that in Newtonian gravitation Binet's equation leads to all the conic curves as solutions for an inverse-square force. In this work, we show how Binet's equation arises from the horizontal and vertical infinitesimal displaceme

Karel van der Marck, Vadim Cheianov

Measuring the electron neutrino mass is a long-standing objective and requires a high energy resolution of certain $β$-decay experiments, as well as a visible cosmic neutrino background (C$ν$B) spectrum. Many quantum mechanical and chemical effects could potentially impair the required resolution/visibility, e.g., the Coulomb interactions between the electro

Ana Almeida, José Alberto Rubiño-Martín, Roke Cepeda-Arroita, Ricardo Tanausú Génova-Santos

We present a parametric component separation forecast for the QUIJOTE-MFI2 instrument (10-20 GHz), assessing its impact on constraining polarised synchrotron emission at $1^\circ$ FWHM and $N_{\rm side}=64$. Using simulated sky maps based on power-law and curved synchrotron spectra, we show that adding QUIJOTE-MFI2 to existing WMAP+$Planck$+MFI data yields s

Zhihao Zheng, Ziming Wang, Jinwen Deng, Yiming Dong

Despite the large uncertainties in the equation of state for neutron stars (NSs), a tight universal ``Love-Q'' relation exists between their dimensionless tidal deformability, $Λ$, and the dimensionless quadrupole moment, $Q$. However, this relation has not yet been directly measured through observations. Gravitational waves (GWs) emitted from binary

Koon Siang Gan, Vijay Pal Singh, Luigi Amico, Rainer Dumke

We investigate Josephson transport in a fully closed, two-dimensional superfluid circuit formed by a ring-shaped 87Rb Bose-Einstein condensate that contains two optical barriers acting as movable weak links. Translating these barriers at controlled speeds imposes a steady bias current, enabling direct mapping of the current-chemical-potential (I-Δμ) characte

Beatrice Donelli, Gabriele De Chiara, Francesco Scazza, Stefano Gherardini

We introduce a nonequilibrium phenomenon, reminiscent of Anderson's orthogonality catastrophe (OC), that arises in the transient dynamics following an interaction quench between a quantum system and a localized defect. Even if the system comprises only a single particle, the overlap between the asymptotic and initial superposition states vanishes accordi

Satoshi Yoshida, Jisho Miyazaki, Mio Murao

Storage and retrieval refer to the task of encoding an unknown quantum channel $Λ$ into a quantum state, known as the program state, such that the channel can later be retrieved. There are two strategies for this task: classical and quantum strategies. The classical strategy uses multiple queries to $Λ$ to estimate $Λ$ and retrieves the channel based on the

Raphael Kaubruegger, Diego Fallas Padilla, Athreya Shankar, Christoph Hotter

We explore a two-node, entanglement-enhanced sensor network for differential phase sensing that exploits decoherence-free subspaces to suppress common-mode noise, a primary limitation of many state-of-the-art quantum sensors. We identify a class of entangled states that, while not strictly optimal, achieve the same asymptotic sensitivity scaling as optimal s

Daniele Gerosa, Rui Hou, Vimar Björk, Ulf Gustavsson

This paper addresses the mathematical modeling and compensation of stochastic discrete-time clock jitter in analog-to-digital converters (ADCs). We model the stochastic clock jitter as a first-order autoregressive (AR(1)) process, and we propose two novel, computationally efficient, pilot-assisted dejittering algorithms for baseband signals: one based on sol

Euclid Collaboration, C. Cleland, S. Mei, G. De Lucia

The extent to which the environment affects galaxy evolution has been under scrutiny by researchers for decades. With the first data from Euclid, we can begin to systematically study a wide range of environments and their effects as a function of redshift, using 63 deg2 of space-based data. In this paper, we present results from Euclid Q1, where we measured

Michele Cicoli, Antonella Grassi, Osmin Lacombe, Francisco G. Pedro

We carefully analyse the challenges posed by the construction of type IIB chiral global embeddings of Fibre Inflation with $\overline{ \rm D3}$ uplift to a de Sitter vacuum. We present an explicit example involving an $h^{1,1}=4$ Calabi-Yau manifold with a K3 fibration and a del Pezzo divisor supporting non-perturbative effects. The chosen orientifold involu

Donggyun Seo

We introduce the concept of a pants decomposition for a finitely generated free group and construct the corresponding pants graph. A pants decomposition of a free group leads to the formation of a simplicial graph, referred to as the pants graph of a free group, consisting of all possible pants decompositions. The natural isometric action of the outer automo

Kim Hammar

Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement learning solutions for security management are mostly limited to simulation environments and it is unclear how they generalize to operational systems. In this paper, we address this limitation by presenting CSLE:

Jack Wei Lun Shi, Minghao Dang, Wawan Solihin, Justin K. W. Yeoh

Existing research on large language models (LLMs) for automated code compliance has primarily focused on performance, treating the models as black boxes and overlooking how training decisions affect their interpretive behavior. This paper addresses this gap by employing a perturbation-based attribution analysis to compare the interpretive behaviors of LLMs a

Hitaishi Chillara

The possibility of over-massive black holes suggested by James Webb Space Telescope photometric discoveries of 'little red dots', may disfavor light supermassive black hole (SMBH) seeds. However, what should constitute the mass (range) of 'heavy' seeds remains relatively unconstrained. Moreover, Vera C Rubin Observatory's Legacy Survey of

Yang Wu, Jinhong Yu, Jingwei Xiong, Zhimin Tao

The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However, the reactive nature of LLMs, which respond only when prompted, limits their effectiveness in collaborative settings that demand foresight and autonomous engagement. In this study, we introduce CoLabScience, a

Jun Lu, Kwangmin Kim, Iain Dixon, Justin Deterding

Resistively insulated (RI) REBCO magnets feature short ramp times and low ramp losses while maintaining the advantages of no-insulation coils with high engineering current density and tolerance for defects in the REBCO conductor. Control of the turn-to-turn contact resistivity Rc is key to RI technology. Rc must be sufficiently high to prevent a large transi

Taira Kaminaga, Hampei Sasahara

Matrix ellipsoids provide a standard framework for representing bounded uncertainties in data-driven control. Since noise models for sequential observations are naturally represented as the Minkowski sum of multiple matrix ellipsoids, applying existing robust control methods, which typically assume a single ellipsoidal set, requires a tight outer approximati

Ethan Tang, Hasan Davulcu, Jia Zou, Zhongju Zhang

Predicting the relative value of any given chess piece in a position remains an open challenge, as a piece's contribution depends on its spatial relationships with every other piece on the board. We demonstrate that incorporating the state of the full chess board via latent position representations derived using a CNN-based autoencoder significantly improves

Asif Alif, Khondokar Fida Hasan, Basker Palaniswamy, Md. Morshedul Islam

Smart healthcare industry is increasingly relying on Internet of Things (IoT) devices to improve patient care and operational efficiency. However, the cryptographic algorithms that enable fundamental security and are widely used in these cyber systems are vulnerable to attacks by emerging quantum computers - known as Quantum Threat. This paper examines the q

Xinzhi Wang, Peter Baile Chen, Gerardo Vitagliano, Matthew Russo

Large language models with long context windows can answer complex questions directly from full-length academic, technical, and policy documents, but passing entire documents is often costly, slow, and can degrade answer quality while increasing the risk of unnecessary data leakage. This paper targets the common setting of answering many heterogeneous questi

Colton Sandvik

We introduce two 2-categories which categorify the monodromic Hecke algebra. The first is algebraic in nature and generalizes Abe's theory of Soergel bimodules. The second is a diagrammatic category defined via generators and relations which generalizes the Elias-Williamson diagrammatic calculus. As our first main result, we prove that these algebraic and di

Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo, Tiago A. Almeida

Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as thos

Hui Wu

We develop a stochastic free-boundary model of housing tenure decisions in markets with high mobility risk, such as areas near military installations. Housing prices and rents follow correlated diffusion processes, and households face an uncertain relocation horizon. We derive a closed-form characterization of the optimal buy-versus-rent boundary in terms of

MD Nahidul Hasan Sabit, Faija Anjum

We study clustering through the partitions it induces on a finite labeled set $[n]=\{1,\dots,n\}$, and analyze how these partitions change under perturbations of a point configuration $X=(x_1,\dots,x_n)\in(\mathbb{R}^d)^n$. We equip the space of partitions $\Pi_n$ with a normalized pairwise disagreement metric $d(\cdot,\cdot)$, and define the stability radiu

Alexander Peysakhovich, William Berman

Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vector y (e.g., helpfulness vs. harmlessness, or bio-availability vs. lipophilicity). An arbitrary reward function r(y) encodes tradeoffs between these properties. Typically, tilting the model's sampling distributi

Mattia Mosso, George Rapakoulias, Yue Guan, Panagiotis Tsiotras

The paper studies the optimal density steering problem for nonlinear continuous-time stochastic systems. To accurately capture nonlinear dynamics in high-uncertainty regions that deviate significantly from a nominal linearization point, we introduce the concept of Multiple Distribution-to-Distribution Linearization. The proposed approach first approximates t

Guy Kaplan, Zorik Gekhman, Zhen Zhu, Lotem Rozner

Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning (SFT), which can increase hallucinations w.r.t.~knowledge acquired during pre-training. Since these errors arise as a by-product of knowledge degradation, we explore whether establ

Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo, Tiago A. Almeida

In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has demonstrated that traditional matrix factorization methods can remain competitive, offering simplicity and reduced computational overhead. Hybr

Xiaozhu Sun, Bilal Farooq

This paper proposes a bi-level optimization framework to coordinate Automated Guided Vehicle (AGV) flexible operations in smart independent warehouses, addressing the critical challenge of balancing high-throughput order fulfillment with stringent cost control. The framework is designed to simultaneously optimize flexible customer service level, system cost,

Jieun Lee, Esfandiar Maasoumi

We develop inference under model uncertainty due to weak, noisy, multiple candidate restrictions and theories, and nuisance control covariates. A unified framework is given with degrees of misspecification and corresponding shadow prices, based on a Lagrangian constrained optimization approach, and a data$-$driven tolerance parameter selected via a Stein$-$t

Hui Wu

We study delay-induced transitions in consensus dynamics on signed networks with a ring topology. The proposed model is formulated as a system of delay differential equations incorporating both cooperative and antagonistic interactions, as well as heterogeneous time delays. We perform a stability analysis by deriving the associated characteristic equation an

Yirui Wang, Xiuwei Xu, Angyuan Ma, Bingyao Yu

Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order to function robustly under such variations, policies need to work in a category-level manner, i.e. knowing how to interact with any object in a certain category, instead of only a specific one seen during trai

Peter Constantin, Zhongtian Hu

We consider inertial magneto-hydrodynamic systems in 2D. We show global existence and uniqueness of smooth solutions and global existence and uniqueness of weak solutions in Yudovich class. We prove magnetic reconnection without magnetic resistivity, for smooth solutions and for patch solutions. This is obtained by proving merger in corresponding systems of

Francisco Ruiz-Escobedo, Michael G. Richer, José Alberto López

We present an analysis of the residual velocities from a sample of 105 Galactic planetary nebulae (PNe), the largest done to date on this subject. The analysis has been carried out with long-slit, high dispersion echelle spectra. The data were drawn from the San Pedro M\'artir Kinematic Catalogue of Galactic Planetary Nebulae. The residual velocity is identi

Lorenzo Toni

We study the one-loop correction to the near-extremal quantum entropy of the charged two-dimensional black hole introduced in \cite{McGuigan:1992}. In target space this background can be understood as arising from the dimensional reduction of a three-dimensional solution of the low-energy string effective action. On the other hand, its worldsheet description

S. Ahmed, B. Algohi, D. Anthony, P. Berard

The TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration has commissioned a large magnetically shielded room to be used for measuring the neutron electric dipole moment (nEDM) to a precision of $10^{-27}~e\mathrm{cm}$. The room is composed of five layers of MuMetal and one layer of copper and sits within the $\lesssim 370~\mu\mathrm{T}$ ambient field prod

Tareq Alsaleh, Bilal Farooq, Zachary Patterson

We examine these relationships using a panel dataset of more than 80,000 trip observations from 100 participants through a custom-built mobile application. A joint revealed preference (RP) and stated preference (SP) framework is used to estimate multinomial logit (MNL) and mixed logit (MXL) models. The level of integration is represented through a composite

Belal Jahannia, Abdolah Amirany, Hamed Dalir

The rapid growth of deep neural networks (DNNs) has exposed fundamental limitations in electronic accelerators, where data movement dominates energy consumption, commonly referred to as the memory wall. Photonic accelerators offer a compelling alternative due to their inherent parallelism and high-speed matrix operations. However, existing research largely f

Isaiah Andrews, Harvey Barnhard, Jacob Carlson

Parameter estimates in misspecified models converge to pseudo-true parameter values, which minimize a population objective function. Pseudo-true values often differ from quantities of economic interest, raising questions of how, if at all, they are relevant for decision-making. To study this question we consider Bayesian decision-makers facing a linear popul

Alexandre Guillemot, John Voight

We provide an end-to-end workflow to rigorously compute the monodromy of Belyi maps from exact equations over number fields using certified homotopy continuation. We then apply this method at scale to certify the monodromy triples of Belyi maps in the $L$-functions and Modular Forms Database (LMFDB).

Anton Ivchenko

Reported chest CT segmentation performance can be strongly inflated when train and test partitions mix slices from the same study. We present CTSCAN, a reproducible multi-source chest CT benchmark and research stack designed to measure what survives under patient-disjoint evaluation. The current four-class artifact aggregates 89 cases from PleThora, MedSeg S

Md. Rashadul Islam

The discovery of exoplanets at scale has become one of the defining data science challenges in modern astrophysics. NASA's Transiting Exoplanet Survey Satellite (TESS) had catalogued over 7,800 planet candidates by early 2026, yet confirmation stands at fewer than 720. This paper introduces ExoNet, a multimodal deep learning framework that jointly processes

Jacob Dang, Brian Y. Xie, Omar G. Younis

Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those traits. However, it remains unclear whether behavioral traits can transfer in agentic systems, where policies are learned from trajectories rather than static text. In this work, we provide the first empirical

Saad Alqithami

Deliberative multi-agent systems allow agents to exchange messages and revise beliefs over time. While this interaction is meant to improve performance, it can also create dangerous conformity effects: agreement, confidence, prestige, or majority size may be treated as if they were evidence, producing high-confidence convergence to false conclusions. To addr

Jayadev Billa

Steering vectors work for some concepts and layers but fail for others, and practitioners have no way to predict which setting applies before running an intervention. We introduce the Linear Accessibility Profile (LAP), a per-layer diagnostic that repurposes the logit lens as a predictor of steering vector effectiveness. The key measure, $A_{\mathrm{lin}}$,

Oriel Savir, Zhenghan Fang, Jeremias Sulam

Proximal operators are fundamental across many applications in signal processing and machine learning, including solving ill-posed inverse problems. Recent work has introduced Learned Proximal Networks (LPNs), providing parametric functions that compute exact proximals for data-driven and potentially non-convex regularizers. However, in many settings it is i

Hexin Dong, Yi Lin, Pengyu Zhou, Fengnian Zhao

Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single institution, failing to capture the prevalence of rare diseases or the appearance of novel findings. To address this, we present the CXR-LT challenge

Anthony Nouy, Agustín Somacal

We consider the problem of approximating a function by an element of a nonlinear manifold which admits a differentiable parametrization, typical examples being neural networks with differentiable activation functions or tensor networks. Natural gradient descent (NGD) for the optimization of a loss function can be seen as a preconditioned gradient descent whe

Jaden R. Love, Carlos A. Armenta, Atlantis K. Moses, Haley B. Woolf

Gray tin ({\alpha}-Sn) layers with 30 nm thickness were grown on InSb (001) substrates using molecular beam epitaxy. The surface preparation of the substrates was adjusted to achieve either n-type or p-type doping in the {\alpha}-Sn layer. Fourier-transform infrared ellipsometry was used to find the temperature-dependent dielectric function of the {\alpha}-S

Maureen Krumtünger, Martin Sevior, Muhammad Usman

Group-equivariant quantum models are designed to exploit symmetry and can improve trainability, but it remains unclear how symmetry constraints shape their adversarial robustness. We study this question through a feature-level analysis of equivariant quantum models in a transfer-attack setting. Under equivariance with an invariant readout, predictions depend

Qian Huang, Simon Görtz, Paul Hollmann, Johannes Conrad

We consider the statistics for the vorticity field in two-dimensional homogeneous isotropic turbulence (HIT). First, we exploit the invariance properties to derive dimensionally reduced governing equations for the one-point and two-point probability density functions (PDFs). These take the form of linear kinetic transport equations, but with an unclosed oper

Amin Hashemi, Vinzenz Zimmermann, Armando Perez-Leija, Andrea Blanco-Redondo

We introduce and experimentally demonstrate an approach for selective mode excitation in non-Hermitian resonant systems using incoherent light. This method eliminates the need for precise phase control that is often required in coherent excitation schemes. Using this technique on a silicon photonic platform with coupled ring resonators, we successfully excit

Tuan Nguyen, Ting He

We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communications, with focus on mixing matrix design. The mixing matrix is a critical hyperparameter for DFL that simultaneously controls the convergence rate across iterations and the communication demand per iteration, bo

Paul Shick

We study $v_n$-periodic phenomena in $C_2$-equivariant stable homotopy through the lens of the $C_2$-equivariant Adams spectral sequence at the prime 2. In particular, we construct/detect certain classes related to powers of the $v_n$ generators of $\pi_*(BP)$ in the cohomology of certain finitely generated subalgebras $A^{C_2}(m)$ of the $C_2$-equivariant S