March 2026 arXiv papers — page 72

Danylo Yakymenko, Maksym Chernyshev, Illia Savchenko, Sergii Strelchuk

For strings of letters from a small alphabet, such as DNA sequences, we present a quantum encoding that empirically provides a strong correlation between the Levenshtein edit distance and the fidelity between quantum states defined by the encodings. It is based on the principles of Rotary Position Embeddings (RoPE), employed in modern large language models.

Javad Vahedi, Stefan Kettemann

Global entanglement in quantum many-body systems is inherently nonlocal, raising the question of whether it can be inferred from local observations. We investigate this problem in monitored quantum circuits, where projective measurements generate classical records distributed across spacetime. Using graph neural networks (GNNs), we represent individual quant

Hamed Vakili, Moaz Ali, Igor Žutić, Alexey A. Kovalev

We predict a supercurrent-driven N\'eel spin-orbit torque in a superconductor/$d$-wave altermagnet heterostructure, associated with the emergence of spin-triplet correlations. The effect can be understood as a consequence of the supercurrent-induced spin polarization, owing to the interplay between spin-orbit coupling and momentum-dependent spin splitting, a

Clémence Chanavat

We extend Campion's pasting theorem for $(\infty, n)$-categories to a larger class of polygraphs, called the directed complexes with frame-acyclic molecules. It follows, for instance, that this pasting theorem applies to any polygraph presented by a semi-simplicial set, and that a large subclass of directed complexes with frame-acyclic molecules is compatibl

Zehua Pei, Hui-Ling Zhen, Weizhe Lin, Sinno Jialin Pan

Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, standard DLM training uses a static, single-step masked prediction objective that never exposes the model to the progressive denoising dynamics of inference, and forces all contextual inform

Chandramouli Haldar

Yoga is widely recognized for improving physical fitness, flexibility, and mental well being. However, these benefits depend strongly on correct posture execution. Improper alignment during yoga practice can reduce effectiveness and increase the risk of musculoskeletal injuries, especially in self guided or online training environments. This paper presents a

Jakob Greilhuber, Roohani Sharma

In this work we study a classic generalization of the Vertex Cover (VC) problem, called the Component Order Connectivity (COC) problem. In COC, given an undirected graph $G$, integers $d \geq 1$ and $k$, the goal is to determine if there is a set of at most $k$ vertices whose deletion results in a graph where each connected component has at most $d$ vertices

Rohit Sahasrabuddhe, Renaud Lambiotte

Many natural and social science systems are described using probability distributions over elements that are related to each other: for instance, occupations with shared skills or species with similar traits. Standard information theory quantities such as entropies and $f$-divergences treat elements interchangeably and are blind to the similarity structure.

Glen Petitpas, Jack Haviland, Te Han, Willie Fong

The Quick-Look Pipeline (QLP; Huang et al. 2020, Kunimoto et al. 2021 and references therein) generates light curves for up to 2 million stars every 27.4 days observed by TESS as part of its planet search. As machine learning methods enable deeper searches and scientific priorities shift toward fainter stars, there is a motivation for QLP to perform better a

A. V. Nazarenko

We examine solutions of the hydrodynamic equations for dark matter (DM) modeled as a Bose-Einstein condensate (BEC) with axionlike interaction, forming a spherically symmetric halo in dwarf galaxies. Small perturbations and decoherence of the BEC DM arise from changes in the gravitational background induced by subgalactic baryonic processes. Focusing on the

Ruihan Ma, Yuqing Cheng, Mengtao Sun

In this work, we theoretically propose a broadband asymmetric transmission (AT) device based on periodic Si nanoring arrays embedded in a SiO2 substrate. Results indicate that the device achieves a remarkable broadband AT effect in the near-infrared region (1750-2400 nm), with forward transmissivity exceeding 0.8 (maximum of 0.98), backward transmissivity le

Yanchen Jiang, Zhe Feng, Christopher P. Mah, Aranyak Mehta

Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However, existing architectures are designed solely for semantic retrieval and do not address concerns such as monetization via ad revenue and incorporation of bids for commercial retrieva

Sopitta Thurachen, Josef Taher, Matti Lehtomäki, Leena Matikainen

Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores the use of Point Transformer v2 (PTv2), an advanced deep neural network architecture designed for point cloud data, for land cover mapping through semantic segmentation of multispect

Alex Salvatierra, José Antonio Sanz, Christian Gutiérrez, Mikel Galar

Recent advances in deep learning have significantly improved 3D semantic segmentation, but most models focus on indoor or terrestrial datasets. Their behavior under real aerial acquisition conditions remains insufficiently explored, and although a few studies have addressed similar scenarios, they differ in dataset design, acquisition conditions, and model s

Sashuai Zhou, Qiang Zhou, Junpeng Ma, Yue Cao

Recent advances in text-to-image (T2I) generation via reinforcement learning (RL) have benefited from reward models that assess semantic alignment and visual quality. However, most existing reward models pay limited attention to fine-grained spatial relationships, often producing images that appear plausible overall yet contain inaccuracies in object positio

Rui Li

This thesis develops a general theoretical and numerical framework for achieving high-contrast atom interferometry based on double Bragg diffraction (DBD). While DBD offers intrinsic symmetry, reduced sensitivity to internal-state systematics, and suitability for microgravity experiments, its performance has long been limited by imperfect diffraction and con

David M. Markowitz

Conversation is ubiquitous in social life, but the empirical study of this interactive process has been thwarted by tools that are insufficiently modular and unadaptive to researcher needs. To relieve many constraints in conversation research, the current tutorial presents an overview and introduction to a new tool, Dyadic (https://www.chatdyadic.com/), a we

Mona Gatzweiler, Fabián Levicán-Santibáñez, Atsuro Yoshida

We give a sufficient combinatorial condition for the non-negativity of the coefficients of polynomial quotients of products of $q$-integers, also known as cyclotomic generating functions (CGFs). This slightly extends work by Iano-Fletcher, Pizzato, Sano and Tasin, who studied this condition as a criterion for quasismoothness of complete intersections in weig

Seyed Amir Mansouri, Kenneth Bruninx

The vision of electrolytic hydrogen as a clean energy vector prompts the emergence of hydrogen-centric companies that must simultaneously engage in electricity, hydrogen, and green certificate markets while operating complex, geographically distributed asset portfolios. This paper proposes a portfolio-level optimization framework tailored for the integrated

Zoltán Buczolich, Jesús Llorente

The Takagi function $T:[0,1]\to \mathbb{R}$ is a classical example of a continuous nowhere differentiable function. In this paper, we study the discrete dynamical system generated by the Takagi function. First, we prove that for almost every point $x\in [0,1]$, the orbit $(T^n(x))_n$ converges to $2/3$. We introduce the family of Takagi maps, given by $\text

Maxwell Norfolk, Dong Xie

Recently, we have seen an increasing need for fresh data exploration, where data analysts seek to explore the main characteristics or detect anomalies of data being actively collected. In addition to the common challenges in classic data exploration, such as a lack of prior knowledge about the data or the analysis goal, fresh data exploration also demands an

Qilin Wang

Modern time series forecasting is evaluated almost entirely through passive observation of single historical trajectories, rendering claims about a model's robustness to non-stationarity fundamentally unfalsifiable. We propose a paradigm shift toward interventionist, exact-statistical benchmarking. By systematically titrating calibrated Gaussian observation

Masanori Nakazato

Affine geometry is usually regarded as a framework in which metric notions such as distance and angle are absent. However, just as projective geometry produces various metric geometries by introducing additional structures on the line at infinity, affine geometry can also serve as a natural basis for an angular geometry once certain directions at infinity ar

Kai Watanabe

Constraints on the unquantized many-body holonomy are less explored than their quantized counterparts. Here we realize an unquantized regime by tuning the bond dimerization $\delta$ and the staggered potential $\Delta$ in a dimerized staggered Hubbard ring at half filling. For the tuned parameter sets, a finite excitation gap persists along the $U(1)$ twist

Tom Biskupski, Stephan Kleber

A Large Language Model (LLM) as judge evaluates the quality of victim Machine Learning (ML) models, specifically LLMs, by analyzing their outputs. An LLM as judge is the combination of one model and one specifically engineered judge prompt that contains the criteria for the analysis. The resulting automation of the analysis scales up the complex evaluation o

Kexian Tang, Jiani Wang, Shaowen Wang, Kaifeng Lyu

While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data-scarce domains, motivating extensive efforts to study synthetic data generation for knowledge injection. We propose SPA (Scaling Prompt-engineered Augmentation), a simple but tough-to-beat baseline that uses a small s

Meiqi Wu, Zhixin Cai, Fufangchen Zhao, Xiaokun Feng

Video--based world models have emerged along two dominant paradigms: video generation and 3D reconstruction. However, existing evaluation benchmarks either focus narrowly on visual fidelity and text--video alignment for generative models, or rely on static 3D reconstruction metrics that fundamentally neglect temporal dynamics. We argue that the future of wor

M. Alasli

We prove that P = NP implies #P = FP by exploiting the topological structure of 3SAT solution spaces. The argument proceeds via a dichotomy: any polynomial-time algorithm for 3SAT either operates without global knowledge of the solution-space topology, in which case it cannot certify unsatisfiability for instances with second Betti number b_2 = 2^{Omega(N)}

Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos

This article addresses multi-servicer on-orbit servicing mission planning in geosynchronous Earth orbit, where routing decisions are tightly coupled with time-dependent orbital phasing and strict propellant and mission-duration constraints. We propose a Route-Phasing-Split Genetic Algorithm (RPS-GA) that simultaneously optimizes target sequencing, discrete p

Y. Lalaire, C. Ruyer, A. Debayle, G. Bouchard

We use the theoretical framework introduced in the companion paper to provide simple formulas as regards the resonance conditions for CBET with smoothed laser beams.Our analytical CBET model with optical smoothing shows that these fusion-critical lasers produce a significantly broader resonance than conventional plane wave models predict. In particular, temp

Emmett B. Kendall, Jonathan P. Williams, Curtis B. Storlie, Misty A. Radosevich

Detection of occult hemorrhage (i.e., internal bleeding) in patients in intensive care units (ICUs) can pose significant challenges for critical care workers. Because blood loss may not always be clinically apparent, clinicians rely on monitoring vital signs for specific trends indicative of a hemorrhage event. The inherent difficulties of diagnosing such an

Alessio Zaccone, Tim W. Sirk

Ultrathin films of widely different materials exhibit a dramatic enhancement of projectile penetration resistance under high--velocity impact. Despite extensive simulations and experiments, a unifying physical explanation has remained elusive. Here we show that the thickness dependence of the specific penetration energy obeys a universal law, $E_p^*(h)=E_{p,

Kangqi Ni, Wenyue Hua, Xiaoxiang Shi, Jiang Guo

Multi-agent applications often execute complex tasks as multi-stage workflows, where each stage is an LLM call whose output becomes part of context for subsequent steps. Existing LLM serving systems largely assume homogeneous clusters with identical model replicas. This design overlooks the potential of heterogeneous deployments, where models of different si

Dickson Boahen, Sushant Padhye, Gayan De Silva, Eshanvi Rao

We report on the fabrication and characterization of patterned high-mobility two-dimensional electron gases (2DEG) formed on SrTiO$_3$ (STO) substrate surfaces by hydrogen plasma exposure. The resulting devices consistently showed high electron mobilities up to 7400 cm$^2$/V$\cdot$s. A large range of electron density was systematically explored by controlled

Jacob Fox, Jonathan Tidor

We prove the existence of optimal separators for intersection graphs of balls and spheres in any dimension $d$. One of our results is that if an intersection graph of $n$ spheres in $\mathbb{R}^d$ has $m$ edges, then it contains a balanced separator of size $O_d(m^{1/d}n^{1-2/d})$. This bound is best possible in terms of the parameters involved. The same res

Jan Fornal, Ben Krause

Let $(X,\mu)$ be a probability space equipped with an invertible, measure-preserving transformation $T\colon X \to X$. We exhibit a wide class of weights $w$ so that whenever $f,g \in L^{\infty}(X)$, the bilinear ergodic averages \[ \frac{1}{N} \sum_{n \leq N} w(n)\, T^{an}f \cdot T^{bn}g, \qquad a,b \in \mathbb{Z} \] converge $\mu$-almost surely. This class

Qingrui Zhao, Kaiyue Yang, Xiyu Wang, Shiqi Zhao

Humanoid robots require diverse motor skills to integrate into complex environments, but bridging the kinematic and dynamic embodiment gap from human data remains a major bottleneck. We demonstrate through Hessian analysis that traditional optimization-based retargeting is inherently non-convex and prone to local optima, leading to physical artifacts like jo

Andrea Sciandra, Zhenbang Zuo

By providing a suitable generalization of Newman's bijective correspondence known for cocommutative Hopf algebras, we prove that the category of cocommutative Hopf monoids in any abelian symmetric monoidal category is semi-abelian, once faithful (co)flatness conditions are satisfied. This result unifies and generalizes the semi-abelianness of cocommutative H

Daniel Shao, Joel Runevic, Richard J. Chen, Drew F. K. Williamson

Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer to obtain task-specific patch features, and 3) aggregating the patches into a slide feature for classification. While substantial efforts have

Sikandar Akbar, Zahir Shah

We present a systematic analysis of high-cadence optical light curves of the non-blazar AGN PKS~0521$-$36 obtained with \textit{TESS} across three sectors: Sectors~5 and~6 (Cycle~1, 30~min cadence) and Sector~32 (Cycle~3, 10~min cadence). The source exhibits moderate variability with $F_\mathrm{var} \approx 0.69$--$1.19\%$, consistent with a mildly beamed je

Andras Batkai

This paper re-evaluates Jozsef Sutak (1865-1954), a Hungarian scholar-priest and professor, as a grey eminence rather than a genius, offering a counter-narrative to the history of Hungarian university mathematics. By examining his career - including his 1897 Bolyai translation and his defense of set theory during the 1911 Grundlagenkrise - the study illumina

A. Chervov, F. Levkovich-Maslyuk, A. Smolensky, F. Khafizov

This is the fourth paper in the CayleyPy project, which applies AI methods to the exploration of large graphs. In this work, we suggest the existence of a new discrete version of holographic string dualities for this setup, and discuss their relevance to AI systems and mathematics. Many modern AI tasks -- such as those addressed by GPT-style language models

Siarhei Finski

We express the Kodaira-Iitaka dimension and the multiplicity of graded linear series in terms of the intersection theory of the plurisubharmonic envelope associated with the linear series, and obtain two refined versions of these formulas at the pointwise and at the metric levels. At the pointwise level, we focus on the weak convergence of the partial Bergma

Mingju Gao, Kaisen Yang, Huan-ang Gao, Bohan Li

Hand-object interaction (HOI) reconstruction and synthesis are becoming central to embodied AI and AR/VR. Yet, despite rapid progress, existing HOI generation research remains fragmented across three disjoint tracks: (1) pose-only synthesis that predicts MANO trajectories without producing pixels; (2) single-image HOI generation that hallucinates appearance

Xifan Yu, Ilias Zadik

In this work, we show that for all statistical estimation problems, a natural MMSE instability (discontinuity) condition implies the failure of stable algorithms, serving as a version of OGP for estimation tasks. Using this criterion, we establish separations between stable and polynomial-time algorithms for the following MMSE-unstable tasks (i) Planted Shor

Danielle Hanson, Jeremy Straub

The use of Internet of Things (IoT) devices is growing at a rapid rate. While much of this growth is consumer devices, IoT devices are also commonly found in corporate and industrial environments, as well. These devices can be organization-owned and managed by an information technology unit, deployed organizationally without the knowledge and involvement of

Shukesh Reddy, Abhijit Das

In this work, we benchmark with different backbones and study their impact for self-supervised learning (SSL) as an auxiliary task to blend texture-based local descriptors into feature modelling for efficient face analysis. It is established in previous work that combining a primary task and a self-supervised auxiliary task enables more robust and discrimina

Philip O'Sullivan, Kosuke Imai, Cory McCartan

Simulation methods have become important tools for quantifying partisan and racial bias in redistricting plans. We generalize the Sequential Monte Carlo (SMC) algorithm of McCartan and Imai (2023), one of the commonly used approaches. First, our generalized SMC (gSMC) algorithm can split off regions of arbitrary size, rather than a single district as in the

Junrong Guo, Shancheng Fang, Yadong Qu, Hongtao Xie

Recent advances in Multimodal Large Language Models (MLLMs) have enabled automated generation of structured layouts from natural language descriptions. Existing methods typically follow a code-only paradigm that generates code to represent layouts, which are then rendered by graphic engines to produce final images. However, they are blind to the rendered vis

Ireh Kim, Tesia Sker, Chanwoo Kim

In Machine Translation, Large Language Models (LLMs) have generally underperformed compared to conventional encoder-decoder systems and thus see limited adoption. However, LLMs excel at modeling contextual information, making them a natural fit for document-level translation tasks where coherence across sentences is crucial. Despite this potential, document-

Sanjit Bhowmick, Javier de la Cruz, Edgar Martínez-Moro

Let $\mathbb{F}_\ell$ be a finite field with $\ell$ elements and let $G = C_p \rtimes C_m$ be a faithful split metacyclic group. In this paper, we develop a complete theory for the twisted group algebra $\mathbb{F}_\ell^\alpha G$. Using the Lyndon--Hochschild--Serre spectral sequence, we prove that the second cohomology group of $G$ is isomorphic to $\mathbb

Oscar Novo, Oscar Bastidas-Jossa, Alberto Calvo, Antonio Peris

Recent advances in large language models (LLMs) have enabled the automation of an increasing number of programming tasks, including code generation for scientific and engineering domains. In rapidly evolving software ecosystems such as quantum software development, where frameworks expose complex abstractions, a central question is how best to incorporate do

Andrew Renninger

Marathons are now common ways of seeing cities, yet little is known about how representative their routes are. Using 311 marathon routes across five continents, we compare landmarks and amenities along the course with those elsewhere in the same city, finding that museums are 15.7 times denser near the route and that the median city has about 8.5 times more

Davide Di Gioia

Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience, replacing ad hoc biologically inspired timers with a principled learned policy. The policy state is augmented with a pred

Zeping Zhang, Robert Laganière

LiDAR perception is severely limited by the distance-dependent sparsity of distant objects. While diffusion models can recover dense geometry, they suffer from prohibitive latency and physical hallucinations manifesting as ghost points. We propose Scanline-Consistent Range-Aware Diffusion, a framework that treats densification as probabilistic refinement rat

Omkar Sawant, Luca Zanatta, Grzegorz Malczyk, Kostas Alexis

This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasserstein Autoencoder that learns shared latent representations by enforcing cross-modal consistency, enabling the system to infer depth-relevant features from grayscale observations

Giorgio Comitini

Evidence from lattice and continuum studies supports the existence of a fully nonperturbative Higgs mechanism generating mass for gluons in linear covariant gauges. The broken charge is the Kugo-Ojima charge. The corresponding unphysical Goldstone boson is a bound-state superposition of two gluons, three gluons, a ghost-antighost, and a quark-antiquark pair.

Y. Lalaire, C. Ruyer, A. Debayle, G. Bouchard

In the context of inertial confinement fusion (ICF) experiments, spatial and temporal laser beam smoothing techniques are used to control the beams propagation in hohlraum plasmas. Currently, spatial and temporal smoothing are either neglected or not properly taken into account in the inline cross beam energy transfer (CBET) models included in the hydrodynam

Jack W O'Sullivan, Mohammad Asadi, Lennart Elbe, Akshay Chaudhari

Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Current AI vision-language models are limited to single-modality inputs and are non-interactive. We present MARCUS (Multimodal Autonomous Reasoning and Chat for Ultrasound and Signals), an agentic vision-language syst

Brocchieri Elisabetta, Soresina Cinzia

Cross-diffusion systems play a central role in mathematical modelling, in which density-dependent dispersal and multiscale mechanisms can lead to spatial segregation and diffusion-driven instabilities. In several relevant examples, including generalised SKT-type competition models, cross-diffusion terms can be rigorously derived as fast-reaction limits, ther

Florian Mangold, Enrico Baù, Lin Nan, Julian Schwab

Optical skyrmions are members of the emerging topological branch of solid-state physics and photonics, allowing for control over topological light textures through light-matter interactions. However, in nanophotonics their practical application has been severely limited by high inherent losses in plasmonic materials, resulting in the lack of tunability betwe

Benjamin Cross, Léo Garcia, Elisabeth Charlaix, Patrick Kékicheff

Previous experimental reports of long-range interactions in ionic liquids (ILs) stand in contradiction with theoretical predictions and numerical simulations. To provide insights into the literature discrepancies regarding the experimental ranges of electrostatic screening, claimed with orders of magnitude larger, the interactions between pairs of mica and b

Tianyu Song, Felix Pabst, Feng Li, Yordanka Velikova

Accurate needle placement in spine interventions is critical for effective pain management, yet it depends on reliable identification of anatomical landmarks and careful trajectory planning. Conventional imaging guidance often relies both on CT and X-ray fluoroscopy, exposing patients and staff to high dose of radiation while providing limited real-time 3D f

Nehal Mittal, Tristan Villain, Mathis Demouchy, Quentin Redon

Quantum anomalies arise when symmetries of a classical theory cannot be preserved upon quantization, leading to unconventional topological responses. A prominent example is the parity anomaly of a single two-dimensional Dirac fermion, which enforces a half-quantized Hall response. Anomaly inflow mechanism allows this effect to be observed at the surfaces of

Maurizio Grasselli, Bohan Ouyang, Andrea Poiatti, Hao Wu

We consider a diffuse-interface model for two-phase incompressible viscous flows with a soluble surfactant in a bounded porous medium. This hydrodynamic system consists of a Darcy--Forchheimer equation for the seepage velocity $\boldsymbol{u}$ coupled with two Cahn--Hilliard equations involving Flory--Huggins type singular potentials, one for the phase-field

Luca Terenzi

To every Nori motivic local system over a smooth, connected complex algebraic variety, we associate an exceptional locus controlling the variation in the complexity of its stalks; the definition is given explicitly in terms of motivic Galois groups and Artin motives. We prove a motivic analogue of the Cattani--Deligne--Kaplan Theorem, asserting that the exce

Jingfeng Shan, Francesco Guidi, Mehrdad Saeidi, Enrico Testi

Autonomous agents operating in uncertain environments must balance fast responses with goal-directed planning. Classical MF RL often converges slowly and may induce unsafe exploration, whereas MB methods are computationally expensive and sensitive to model mismatch. This paper presents a human-inspired hybrid RL architecture integrating Pavlovian, Instrument

P. Schillemans, J. O. Sundqvist, D. Debnath, L. Delbroek

Mass loss from massive stars located in the part of the Hertzsprung-Russell diagram (HRD) where we find luminous blue variables (LBVs) is profoundly important for stellar evolution yet poorly understood. We use time-dependent radiation-hydrodynamic (RHD) simulations to examine the atmosphere and wind properties of such massive stars, computing 2D and 1D RHD

Yurong Chen, Zhiyi Huang, Michael I. Jordan, Haipeng Luo

We study calibeating, the problem of post-processing external forecasts online to minimize cumulative losses and match an informativeness-based benchmark. Unlike prior work, which analyzed calibeating for specific losses with specific arguments, we reduce calibeating to existing online learning techniques and obtain results for general proper losses. More co

Sepideh Eskandarlou, Mohammad Akhlaghi, Francisco Arizo-Borillo, Johan H. Knapen

Galaxies frequently interact with nearby systems, a process that can significantly alter their morphology and star formation activity. However, spectroscopic studies of their faint and diffuse remnants require very long exposure times and often exceed the limited field of view of integral field units (IFUs). On the other hand, broad-band imaging can have a m

Kaili Huang, Hongming Zhang, Rui Shen, Linjun Dai

While Direct Preference Optimization (DPO) has become the de facto approach for aligning Large Vision-Language Models (LVLMs), it suffers from Likelihood Displacement, where the probability of both chosen and rejected responses collapses. This optimization flaw is especially detrimental in multimodal settings: the erosion of chosen likelihoods -- a failure w

Yuhan Wang, Rajesh Kumar, Joaquin Grefa, Konstantin Maslov

We investigate s-wave kaon condensation in dense matter and neutron stars within the updated Chiral Mean Field model with an improved meson description (mCMF), which incorporates dynamically generated in-medium meson masses arising from explicit chiral symmetry breaking and vector-meson self-interactions. In contrast to conventional relativistic mean-field d

Kohei Fukai, Hironobu Yoshida, Hosho Katsura

Recently, a class of spin chains known as ``free fermions in disguise'' (FFD) has been discovered, which possess hidden free-fermion spectra even though they are not solvable via the standard Jordan-Wigner transformation. In this work, we extend this FFD framework to open quantum systems governed by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation.

Shih-Yu Chang

We show that the Baum--Connes assembly map factors canonically through the unitary conjugation groupoid, which serves as a universal mediator among groupoid models that are Morita equivalent to a given transformation groupoid. This establishes a structural link between groupoid-based index theory and the Baum--Connes program at the level of K-theory. Buildin

Yukun Wu, Lihui Liu

Textual graph-based retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) in domain-specific question answering. While existing approaches primarily focus on zero-shot GraphRAG, selecting high-quality demonstrations is crucial for improving reasoning and answer accuracy. Furthermore, recent st

Joanna Zou, Youssef Marzouk

The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly

Oliver Rice, Anthony Yeates

We expand upon our paper (Rice and Yeates, 2021) which introduced `Outflow Fields': alternatives to the widely-used potential field source surface (PFSS) extrapolations of the Sun's coronal magnetic field which take into account the effect of the solar Wind. We showed that our fields have several advantages over PFSS, namely more accurate measurements of the

Moritz Gögl, Christopher Yau

We study multimodal survival analysis integrating clinical text, tabular covariates, and genomic profiles using locally deployable large language models (LLMs). As many institutions face tight computational and privacy constraints, this setting motivates the use of lightweight, on-premises models. Our approach jointly estimates calibrated survival probabilit

Adrien Kassel, Thierry Lévy

We give an expression for the determinant of the twisted Laplacian associated with any linear representation of a finite quiver in terms of traces of the holonomy of its cycles. To establish this expression, we prove a general identity for the determinant of a block matrix in terms of traces of products of its blocks. We give two proofs, one purely enumerati

Zhankun Luo, M. Berk Sahin, Antesh Upadhyay, Behzad Sharif

A celebrated method for Variational Inequalities (VIs) is Extragradient (EG), which can be viewed as a standard discrete-time integration scheme. With this view in mind, in this paper we show that EG may suffer from discretization bias when applied to non-linear vector fields, conservative or otherwise. To resolve this discretization shortcoming, we introduc

Alois Bachmann

This paper proposes $\mathrm{dynActivation}$, a per-layer trainable activation defined as $f_i(x) = \mathrm{BaseAct}(x)(\alpha_i - \beta_i) + \beta_i x$, where $\alpha_i$ and $\beta_i$ are lightweight learned scalars that interpolate between the base nonlinearity and a linear path and $\mathrm{BaseAct}(x)$ resembles any ReLU-like function. The static and dyn

Kejia Liu, Haoyang Zhou, Ruoyu Xu, Peicheng Wang

Recent advances in cross-view geo-localization (CVGL) methods have shown strong potential for supporting unmanned aerial vehicle (UAV) navigation in GNSS-denied environments. However, existing work predominantly focuses on matching UAV views to onboard map tiles, which introduces an inherent trade-off between accuracy and storage overhead, and overlooks the

Yuta Tsuchiya, Yukino Baba

Just as people improve decision-making by consulting diverse human advisors, they can now also consult with multiple AI systems. Prior work on group decision-making shows that advice aggregation creates pressure to conform, leading to overreliance. However, the conditions under which multi-AI consultation improves or undermines human decision-making remain u

Vitor H. Ribeiro, Andre S. Sunahara, Golnaz Shahtahmassebi, Matjaz Perc

Gender disparities in academia manifest and persist in various aspects of the scientific enterprise, yet their influence on the interplay between research productivity and journal prestige remains underexplored. Here we analyze the academic trajectories of over 6,000 elite Brazilian researchers by jointly tracking their annual productivity and the average pr

Zahra Ghadiri, Jari Saramäki, Takayuki Hiraoka

The basic and effective reproduction numbers are widely used metrics for characterizing the dynamics of infectious disease epidemics. However, the interpretation of these numbers is based on the assumption of homogeneous mixing and may not hold in real-world populations where the contact patterns deviate from that assumption. In this paper, we present a netw

Taiwo O. Sangodapo

In this paper, the notion of convexity of picture fuzzy multisets was introduced and some of their properties were presented after studying the concept of picture fuzzy multisets.

Sijie Zhao, Feng Liu, Xueliang Zhang, Hao Chen

Earth Observation (EO) is essential for perceiving dynamic land surface changes, yet deploying autonomous EO in open environments is hindered by the immense diversity of multi-source data and heterogeneous tasks. While remote sensing agents have emerged to streamline EO workflows, existing tool-calling agents are confined to closed environments. They rely on

Amrita Singh, Guillermo A. Blanc, Nimisha Kumari, J. E. Méndez-Delgado

The abundance discrepancy problem refers to the systematic differences observed between chemical abundances derived from collisionally excited lines (CELs) and recombination lines (RLs) of heavy ions. It remains a major unsolved problem in the study of ionized nebulae and is quantified by the abundance discrepancy factor (ADF). In this work, we present a dee

Yudai Sato, Maialen Ortego Larrazabal, Jian-Feng Ge, Ingmar Swart

Charge transport in superconducting junctions at finite voltages is governed by Andreev reflections, including multiple Andreev reflections, which are processes that enable multiple charge transfer, a hallmark that shot noise can directly quantify. Since the effective charge extracted from shot noise measurements varies with the transparency of the junction,

Audrey Ngambia, Anastasiia Gavrilova, Haitao Huang, Zhuodong Lyu

Manganese(II) mobilised by mining activity poses a persistent water-quality challenge, yet the mechanisms by which low-cost sorbents, such as biochar, sequester Mn(II) remain poorly resolved. This study identifies the specific chemical drivers of Mn(II) sequestration by combining fixed-bed column and batch experiments with atomistic molecular dynamics simula

Ethan Ackelsberg, Vitaly Bergelson

This paper addresses the topic of equidistribution and recurrence for polynomial sequences over function fields. The main focus is to note and correct two small errors in [V. Bergelson and A. Leibman, A Weyl-type equidistribution theorem in finite characteristic, Adv. Math. 289 (2016) 928-950], contextualized within the broader developing literature on numbe

Rodrigo M. Sanz, Andreu Angles-Castillo, Eduard Alarcon, Carmen G Almudever

Ansatz selection is a key factor in the performance of variational quantum algorithms (VQAs). While much of the state-of-the-art still relies on heuristic choices, an inadequate circuit structure can compromise both the expressive power and the trainability of the resulting model. Recent results have also established theoretical connections between expressib

Guillermo González-García, Filippo Maria Gambetta, Raul A. Santos

Quantum simulations before fault tolerance suffer from the intrinsic noise present in quantum computers. In this regime, extracting meaningful results greatly benefits from stability against that noise. This stability, defined as an error in observables that is independent of the system's size, is expected in local systems under local noise. In fermionic sys

Ziwei Chai, Sandra Luber

Here we present a density matrix based KS inversion method formulated entirely within a Gaussian basis representation to optimize a KS potential matrix that reproduces a target electron density. Inverse Kohn-Sham (KS) density functional theory (DFT) aims to determine the effective local KS potential that reproduces a target electron density, and is important

Ethan A. Vo, Hung T. Vuong, Zachary K. Goldsmith, Hong-Zhou Ye

For ethylene carbonate on the (100) surface of lithium, we calculate the adsorption energy in two binding motifs as well as the barrier height for a ring-opening decomposition reaction. We validate a scheme for producing results in the thermodynamic limit by correcting results obtained on finite lithium clusters containing only 40-100 atoms, which enables th

Yizhao Yang, Yu Yu, Pengjie Zhang

We propose a new marked two-point correlation function weighted by the global gravitational potential as a probe for testing gravity models. Using the LCDM model based on general relativity (GR) as a reference, we investigate two representative modified gravity (MG) scenarios: f(R) gravity and nDGP. The mark used in this work, the global gravitational potent

Lars Vogt

Semantic data and knowledge infrastructures must reconcile two fundamentally different forms of representation: natural language, in which most knowledge is created and communicated, and formal semantic models, which enable machine-actionable integration, interoperability, and reasoning. Bridging this gap remains a central challenge, particularly when full s

Gary J. Melnick, Joseph L. Hora, Matthew L. N. Ashby, Volker Tolls

SPHEREx is a NASA mission designed to perform an all-sky spectroscopic survey in the 0.75 - 5 $\mu$m wavelength range. Its primary science objectives are to investigate: (1) inflationary cosmology, (2) the history of galaxy formation, and (3) the abundance of molecular ices - critical for prebiotic chemistry - found on the surfaces of interstellar dust grain

Filippa Lo Biundo, Francesca Tripaldi

In this paper, we prove the commutativity between the Pansu pullback of a smooth contact map between Carnot groups and the differentials appearing in the spectral complexes. As a direct application, we also present a way of "lifting" a Pansu derivative (viewed as a Lie algebra homomorphism) from Carnot groups to their central extensions.

Salvador Barranco Carceles, Veronika Zadin, Steve Wells, Aquila Mavalankar

The effective design and operation of electron emitters is the core of critical technologies such as high-resolution electron imaging and spectroscopy or X-ray production for medical imaging. Despite 100 years of theoretical development in thermo- and field-electron emission models, the analysis of experimental data and design of electron emitters remains an