March 2026 arXiv papers — page 118

Duc Toan Nguyen, César A. Uribe

Decentralized optimization on Riemannian manifolds is foundational for many modern machine learning and signal processing applications in which data are non-Euclidean and generated and processed in a distributed manner. Although intrinsic Riemannian methods exploit manifold geometry without relying on Euclidean embeddings, existing decentralized Riemannian o

Farinaldo S. Queiroz, Clarissa Siqueira, Carlos E. Yaguna

The Galactic Center Excess (GCE) is a compelling signature of dark matter annihilation, but its spectral morphology is difficult to reconcile with the traditional paradigm of a single particle species. In this work, we perform a systematic investigation of multi-component dark matter sectors, exploring scenarios with two ($N=2$) and three ($N=3$) distinct pa

Ryo Kamoi, Ameya Godbole, Binglin Zhou, Xiaoxin Lu

Simulating human conversations using large language models (LLMs) has emerged as a scalable methodology for modeling human social interaction. This paper reconsiders the evaluation of simulated conversations by explicitly recognizing that human conversations inherently involve inconsistent and uncollaborative behaviors, such as misunderstandings and interrup

A. Aleshko, E . Boos, V. Bunichev, L. Dudko

Nowadays, the Standard Model Effective Field Theory (SMEFT) provides a standard framework to parameterize potential deviations from the Standard Model and to combine information from multiple processes in global analyses. This review summarizes dedicated studies that constrain dimension-six Wilson coefficients using three top-quark and four top-quark product

Elham Daneshmand, Shafeef Omar, Glen Berseth, Majid Khadiv

Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is problematic, as it poses risks of mechanical failure and suffers from high sample inefficiency. In this paper, we address the challenge of safely and efficiently fine-tuning reinf

Amin Taghieh, SangWoo Park

Data-driven model predictive control based on Willems' fundamental lemma has proven effective for linear systems, but extending stability guarantees to nonlinear systems remains an open challenge. In this paper, we establish conditions under which data-driven MPC, applied directly to input-output data from a nonlinear system, yields practical exponential sta

Ido Aharon, Jonathan Shaki, Sarit Kraus

We present an ensemble-driven self-training framework for unsupervised neural machine translation (UNMT). Starting from a primary language pair, we train multiple UNMT models that share the same translation task but differ in an auxiliary language, inducing structured diversity across models. We then generate pseudo-translations for the primary pair using to

Yuan Wang, Jian Yin, Nicholas Riccardi, Drik-Bart Den Ouden

Persistent homology (PH) characterizes the shape of brain networks through persistence features. Group comparison of persistence features from brain networks can be challenging as they are inherently heterogeneous. A recent scale-space representation of persistence diagrams (PDs) through heat diffusion reparameterizes them using a finite number of Fourier co

Elizaveta Popova, Elad Tzalik

We present a simple greedy procedure to compute an $(\alpha,\beta)$-spanner for a graph $G$. We then show that this procedure is useful for building fault-tolerant spanners, as well as spanners for weighted graphs. Our first main result is an algorithm that, given a multigraph $G$, outputs an $f$ edge fault-tolerant $(k,k-1)$-spanner $H$ of size $O(fn^{1+\fr

Zahra Mohammadi Khangheshlaghi, Katrin Tent

We begin the investigation of the free factor complex of a free group of finite rank. For the case of rank 2 we axiomatize its theory and show that it is $\omega$-stable with prime model $AF_2$.

J. F. Aguilar, P. Cruz, E. Solano, P. R. T. Coelho

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Two Micron All-Sky Survey (2MASS). Methods. Our method consists of a semi-supervised machine learning approach based on the k-means method combined with a modifie

Xue Zhang, Abla Kammoun, Mohamed-Slim Alouini

In this work, we study the design of receivers for uplink multi-user systems, aiming to estimate both the channel and the transmitted symbols. We consider two estimation strategies: (i) a joint estimation approach, where the channel and symbols are estimated simultaneously, and (ii) a sequential estimation approach, where the channel is first estimated and t

W. U. Boeglin, P. Ambrozewicz, K. Aniol, J. Arrington

The d(e,e'p) cross section was measured at momentum transfers $Q^2 = $ 0.8, 2.1 and 3.5 $(GeV/c)^2$ covering a wide range of proton kinematics at each $Q^2$ setting that made it possible to study this reaction as a function of missing momentum as well as a function of the neutron laboratory recoil angle $\theta_{nq}$. Missing momentum distributions were dete

Thomas Ayral, Eric Cancès, Fabian M. Faulstich, Lin Lin

This article presents a mathematical analysis and numerical strategies for solving the optimization problem of minimizing the quadratic function $J(P) = \text{Tr}(BP)- \frac{1}{2} \text{Tr}(A P A P)$, where $A,B \in \mathbb R^{M \times M}_{\rm sym}$, with $A \succeq 0$, over the Grassmann manifold ${\rm Gr}(m,\mathbb R^M)$. While this problem is non-convex a

M. Arda Aydın, Melih B. Yilmaz, Aykut Koç, Tolga Çukur

The success of CLIP-like vision-language models (VLMs) on natural images has inspired medical counterparts, yet existing approaches largely fall into two extremes: specialist models trained on single-domain data, which capture domain-specific details but generalize poorly, and generalist medical VLMs trained on multi-domain data, which retain broad semantics

Joan Alba, Laura Ares, Jan Sperling, Julien Pinske

The role of quantum entanglement in thermodynamical systems remains elusive. Does entanglement result in thermodynamic advantages or does it impose fundamental limitations? Here, we unambiguously quantify the amount of heat and work in a quantum system that is due to the presence of entanglement. This is achieved by constraining the system's non-equilibrium

Ross Silver, Nuria Torres-Alba, Stefano Marchesi, Vittoria Gianolli

This work discusses the broadband X-ray spectral analysis of 11 candidate heavily-obscured active galactic nuclei (AGN) selected based on their infrared and X-ray properties by a recently published machine learning algorithm. This paper is part of a larger work to identify and characterize all AGN in the local universe (z < 0.1) with the largest line-of-sigh

Shubham Agarwal

Breakloose friction, the transient force peak at the onset of sliding, is often pronounced in nanoscale contacts but weak or absent in macroscopic systems. Although this behavior is commonly associated with rupture fronts and process-zone effects, how the stiction peak is controlled by system size, temperature, driving rate, and loading geometry, and what me

Weikun K. Zhang, Rohan Pandey, Bhaumik Mehta, Kaijie Jin

Motivated by auto-proof generation and Valiant's VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and multiplication gates. We formulate this problem as a single-player game, where an RL agent attempts to build the circuit within a fixed number of operations. We implement an Alpha

Bharat Runwal, Ashish Agrawal, Anurag Roy, Rameswar Panda

We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and attention-Mamba hybrid), and scales from 3B to 24B parameters, we show that mid-training

Karl Bergson Hallberg, Guillermo Nava Antonio, Chiara Ciccarelli, Jacob Linder

Quenching quantum order via laser pulses has proven a useful tool to access exotic physical effects in systems that are strongly perturbed out of equilibrium. However, theoretical modelling of experimental measurements is typically done phenomenologically or by assuming translational invariance due to the complexity of the problem. Here, we solve a microscop

Lorenzo Speri, Francisco Duque, Susanna Barsanti, Alessandro Santini

The Laser Interferometer Space Antenna (LISA) will enable precision studies of Extreme and Intermediate Mass Ratio Inspirals (EMRIs/IMRIs), providing unique probes of astrophysical environments of galactic nuclei and strong-field gravity. Using a fully relativistic pipeline across primary masses $m_1 \in [5\times10^4, 10^7]\,M_\odot$ and secondary masses $m_

Lucas Bandarkar, Alan Ansell, Trevor Cohn

In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in knowledge transfer is primarily a script barrier. First, we conduct an observational data analysis on the performance of thinking models on two datasets with local knowledge from around the world, ECLeKTic and Mu

Haitian Wang, Yiren Wang, Xinyu Wang, Sheldon Fung

Falls in wet bathroom environments are a major safety risk for seniors living alone. Recent work has shown that mmWave-only, vibration-only, and existing multimodal schemes, such as vibration-triggered radar activation, early feature concatenation, and decision-level score fusion, can support privacy-preserving, non-intrusive fall detection. However, these d

Netanel Eliav

This paper documents and theorises a self-reinforcing dynamic between two measurable trends: the exponential expansion of large language model (LLM) context windows and the secular contraction of human sustained-attention capacity. We term the resulting asymmetry the Cognitive Divergence. AI context windows have grown from 512 tokens in 2017 to 2,000,000 tok

Yi Zhou, Basel Shbita

Many evaluations of Large Language Models (LLMs) target tasks that are inherently ill-defined, with unclear input and output spaces and ambiguous success criteria. We analyze why existing evaluation benchmarks and metrics fail to provide reliable or diagnostic signals of model capability for such tasks. We examine two case studies: Complex Instruction Follow

Samhita Pal, Jared D. Huling, Amir Asiaee

We develop estimators that improve precision of heterogeneous treatment effect estimates that allow borrowing information from observational studies when the available covariates in each data source do not perfectly match. Standard data-borrowing methods often assume perfectly matched covariates. We propose MR-OSCAR, an RCT-calibrated, two-stage estimation a

Omar Rayyan, Maximilian Gilles, Yuchen Cui

Despite increasing dataset scale and model capacity, robot manipulation policies still struggle to generalize beyond their training distributions. As a result, deploying state-of-the-art policies in new environments, tasks, or robot embodiments often requires collecting additional demonstrations. Enabling this in real-world deployment settings requires tools

M. Reshma, C. S. Stalin, Amit Kumar Mandal, S. B. Gudennavar

Radio-loud active galactic nuclei (AGN) are among the most luminous objects in the Universe, emitting radiation from low-energy radio waves to high energy $\gamma$-rays. They are well known to exhibit flux variations at nearly all accessible wavelengths. However, their variability properties in the ultraviolet (UV) band remain relatively less explored compar

Gregory Coppola

Transformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways. First, we prove that every sigmoid transformer with any weights implements weighted loopy belief propagation on its implicit factor graph. One layer is one roun

Minchen Qiao, Zi-Ming Li, Yu-xi Liu

The most widely used approach for simulating the dynamics of time-dependent Hamiltonians via quantum computation depends on the quantum-classical hybrid variational quantum time evolution algorithm, in which ordinary differential equations of the variational coefficients for determining time evolution are solved via classical simulations with a time discreti

Ronnie de Souza Santos, Italo Santos, Mariana Bento, Giuseppe Destefanis

Background: Cheating in university education is commonly described as context dependent and influenced by assessment design, institutional norms, and student interpretation. In software engineering education, programming oriented coursework has historically involved ambiguity around collaboration, reuse, and external assistance. Recently, large language mode

Jyoti Rani

In this paper, the $q$-numerical radius of operators in semi-Hilbertian spaces is studied. New characterizations are established, and sharp upper and lower bounds for the $q$-numerical radius are derived. Moreover, several inequalities involving operator monotone functions and matrix means for the $q$-numerical radius of sectorial matrices are obtained.

Mahdis Rabbani, Navid Mojahed, Shima Nazari

Nash equilibria provide a principled framework for modeling interactions in multi-agent decision-making and control. However, many equilibrium-seeking methods implicitly assume that each agent has access to the other agents' objectives and constraints, an assumption that is often unrealistic in practice. This letter studies a class of asymmetric-information

Yasaswini Chebolu

Reliable terrain perception is a fundamental requirement for autonomous navigation in unstructured, off-road environments. Desert landscapes present unique challenges due to low chromatic contrast between terrain categories, extreme lighting variability, and sparse vegetation that defy the assumptions of standard road-scene segmentation models. We present De

Yijian Wang, Qingsen Yan, Jiantao Zhou, Duwei Dai

Image Restoration (IR) agents, leveraging multimodal large language models to perceive degradation and invoke restoration tools, have shown promise in automating IR tasks. However, existing IR agents typically lack an insight summarization mechanism for past interactions, which results in an exhaustive search for the optimal IR tool. To address this limitati

Bilal Karaman, Ilhan Basturk, Engin Zeydan, Ferdi Kara

Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivit

Wenhao Zhao, Qiran Zou, Rushi Shah, Yudi Wu

Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data representations for large language models, diffusion models, and other generative models. Despite its prevalence, the characteristics and behaviors of vector quantization in generative mod

Songchun Zhang, Zeyue Xue, Siming Fu, Jie Huang

Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences. Existing reinforcement learning (RL) frameworks are not naturally suited to these architectures, typically requiring either expensive re-distillation or solver-coupled reverse-process optimization that introduces considerabl

Julian Heeck, Diana Sokhashvili, Anil Thapa

Baryon number violation is our most sensitive probe of physics beyond the Standard Model. Its realization through heavy new particles can be conveniently encoded in higher-dimensional operators that allow for model-agnostic analyses. The unparalleled sensitivity of nuclear decays to baryon number violation makes it possible to probe effective operators of ve

Ahmed Zeid, Sidney Bender

Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled approach to interpreting black-box predictions by identifying minimal input changes that alter model outputs. However, existing CFE methods often rely on dataset-specific generative mo

Valentin Mauerhofer, Jérémy Blaizot, Thibault Garel, Anne Verhamme

Low-ionisation state (LIS) absorption lines, such as SiII 1526, are widely used to trace the properties of the interstellar medium (ISM) in galaxies. These lines provide crucial insights into galaxy evolution, including feedback mechanisms, metal enrichment, and the escape fraction of ionising photons ($f_{\rm{esc}}$). We expand our understanding of LIS abso

Jack Isen, Per Kraus, Ruben Monten, Richard M. Myers

We extend a previously developed formulation of the S-matrix, based on a path integral with asymptotic boundary conditions, to include gravity. The path integral defines a Carrollian boundary partition function whose invariance under asymptotic symmetries implies Ward identities obeyed by the associated boundary correlators, which are simply related to stand

Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier

The transition from optical identification of 2D quantum materials to practical device fabrication requires dynamic reasoning beyond the detection accuracy. While recent domain-specific Multimodal Large Language Models (MLLMs) successfully ground visual features using physics-informed reasoning, their outputs are optimized for step-by-step cognitive transpar

Hongyi Sun, Yi-Siou Huang, Junyeob Song, Francis Vásquez-Aza

Functional materials with high electrical conductivity and optical transmittance are vital for thermally tunable free-space photonic systems. Conventional transparent conductors such as graphene and indium tin oxide are limited by high contact resistance, poor mechanical stability, or complex fabrication. Ultra-thin metals, such as pure silver, have also bee

Samuel Whitebook, Jim Fuller, Kevin Burdge, Thomas R. Marsh

Several dozen binary ultracool and brown dwarf systems have been identified to date. These systems represent valuable probes of star and planet formation at the lowest mass scales. To date, the study of these ultracool binaries has been constrained to the non-interacting case. In this paper, we investigate the dynamics, stability, and evolution of mass trans

Aaron Householder, Kaitlyn Shin, Kevin B. Burdge, Thomas R. Marsh

Substellar objects such as brown dwarfs and planets are generally expected to remain detached from their main-sequence host stars unless orbital decay or stellar expansion brings them into contact, leading to rapid engulfment and destruction. Such a fate is predicted for the Earth and other rocky planets in our solar system; however, in certain cases, theory

Samuel Whitebook, Antonio C. Rodriguez, Kevin Burdge, Thomas Prince

Mass transfer in stellar binaries has been well studied in most stellar mass ranges, with the notable exception of ultracool stars and substellar brown dwarfs. We report the discovery of ZTF J1239+8347 with the Zwicky Transient Facility (ZTF), a brown dwarf binary currently undergoing stable mass transfer with an orbital period of 57.41 minutes. Optical time

Thomas Halloran, Gicela Saucedo Salas, Sylvia K. Lewin, J. A. Rodriguez-Rivera

Neutron scattering experiments on the spin-triplet superconductor UTe$_2$ have established that the dominant low-energy magnetic response is along Brillouin zone boundaries, resembling the magnetic susceptibility of narrow-gap interband excitations. We report a study of the sensitivity of these excitations to magnetic field along the crystallographic $\hat{a

Linus Behn, Andrea Cianchi, Lars Diening, Fa Peng

The symmetric $p$-Laplace operator enters various models in mathematical physics, such as incompressible materials with power-type hardening and non-Newtonian fluids. In this work, second-order differentiability properties of solutions to the symmetric $p$-Laplace system are established. They are formulated as maximal Sobolev regularity of the nonlinear stre

Stephane Bonhomme, Elena Manresa, Thibaut Lamadon

The AKM model introduced by Abowd, Kramarz and Margolis (1999) has become a workhorse to study worker and firm heterogeneity, and to understand the sources of wage dispersion in the labor market using linked employer-employee data. In this article, we introduce the model and estimator, discuss some best practices for estimation, and review some empirical fin

Farzin Ahmadi, Fardin Ganjkhanloo, Kimia Ghobadi

Inverse optimization seeks to recover unknown objective parameters from observed decisions, yet fundamental questions about when recovery is possible have received limited formal treatment. This paper develops a comprehensive theoretical framework for inverse optimization in parametric convex models. We first establish that non-identifiability is the generic

Fenghua Yang, Dae Woong Ham, Stefanus Jasin

Randomized experiments (often known as "A/B tests") are widely used to evaluate product and service innovations. We study how to allocate limited experimentation resources across M concurrent experiments in an experiment-rich regime. Existing work on allocation has predominantly focused on minimizing the worst-case mean squared error (MSE) of estimated treat

Ricardo Faleiro, Flavien Hirsch, Emmanuel Zambrini Cruzeiro, Nicolas Gisin

In this work, we study a particular class of Bell inequalities involving only direct equality-comparisons of outcomes. This arises naturally when outcomes are difficult to characterize. For instance, if measurements yield smells, it may be impractical to process them individually, while still being reasonable to judge whether two smells are identical or not.

Bart De Bruyn, Rhys J. Evans, Sergey Goryainov, Jack Koolen

A Neumaier graph is an edge-regular graph with a regular clique. Such a graph is said to have parameters $(v,k,\lambda;e,s)$ if it is a $k$-regular graph on $v$ vertices having a clique of size $s$ such that every edge is contained in $\lambda$ triangles and every vertex outside $C$ is adjacent with exactly $e$ vertices inside $C$. It was an open problem whe

Ion Santra, Debankur Das

We study stochastic resetting of a probe particle in a viscoelastic environment where only the probe is reset while the medium retains memory of its past dynamics. Using a minimal model with finite correlation time, we analyze the competition between the resetting timescale and the viscoelastic relaxation timescale. This interplay leads to nonequilibrium ste

Giagkos-Ion Chlomoudis, Thomas Fuhrmann-Lieker, Meskerem A. Mebratie, Gokul B. Nair

In this paper we expand the concept of biological speciation by symmetry breaking of Golubitsky and Stewart to the case of three clades in which N populations following the same dynamical laws can separate. The underlying differential equation is based on a fifth order polynomial of a trait variable with first or second order coupling. We present some genera

Shubham Gupta, Adarsh Arigala, B. R. Dilleswari, Sri Rama Murty Kodukula

Human listeners exhibit the remarkable ability to segregate a desired sound from complex acoustic scenes through selective auditory attention, motivating the study of Targeted Sound Detection (TSD). The task requires detecting and localizing a target sound in a mixture when a reference audio of that sound is provided. Prior approaches, rely on generating a s

Shenzhi Wang, Shixuan Liu, Jing Zhou, Chang Gao

Vision-language models (VLMs) show strong multimodal capabilities but still struggle with fine-grained vision-language reasoning. We find that long chain-of-thought (CoT) reasoning exposes diverse failure modes, including perception, reasoning, knowledge, and hallucination errors, which can compound across intermediate steps. However, most existing vision-la

N. Castro Segura, K. Solomons, J. M. Corral-Santana, C. Knigge

Swift J1727.8$-$1613 is a newly discovered transient low-mass X-ray binary harbouring a stellar-mass ($\sim 10M_\odot$) black hole. We present state-resolved VLT/X-Shooter optical spectroscopy of its 2023 outburst, sampling the luminous hard-to-soft and late soft-to-hard transitions. During the onset of the brightest radio flare, He\,\textsc{ii} flux rises r

Kasidit Muenprasitivej, Derya Aksaray

Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for dynamical systems, but solving high-dimensional HJ partial differential equations limits its use in real-time planning. This paper presents a contingency-aware multi-goal navigation framework that integrates learning-based reachability with sampling-based planning in unknown environments

Zhihao Pei, Nir Lipovetzky, Angela M. Rojas-Arevalo, Fjalar J. de Haan

Socio-environmental planning under deep uncertainty requires researchers to identify and conceptualize problems before exploring policies and deploying plans. In practice and model-based planning approaches, this problem conceptualization process often relies on participatory modeling to translate stakeholders' natural-language descriptions into a quantitati

Laura Fredrickson, Rafe Mazzeo, Jan Swoboda, Hartmut Weiss

This is the first of two papers which together prove that the $12$-parameter family of parabolic $SU(2)$-Hitchin moduli spaces on the four-punctured sphere are all ALG gravitational instantons of type D4, and hence are asymptotic to $(\mathbb{C} \times T^2_\tau)/\mathbb{Z}_2$ at infinity. The elliptic modulus $\tau$ is determined by the cross-ratio of the fo

Wenhao Zhao, Qiran Zou, Zhouhan Lin, Dianbo Liu

Vector Quantization (VQ) has become the cornerstone of tokenization for many multimodal Large Language Models and diffusion synthesis. However, existing VQ paradigms suffer from a fundamental conflict: they enforce discretization before the encoder has captured the underlying data manifold. We term this phenomenon Premature Discretization. To resolve this, w

Ysla F. Adans, Marius de Leeuw, Tristan McLoughlin

In this paper we study nearest-neighbour deformations of integrable models. After expanding in the deformation parameter, we identify four possible types of deformations. First there are deformations that simply break or preserve integrability. Then we find two different subtle cases. The first case is where the deformation is only integrable if all orders o

Lifu Tu, Rongguang Wang, Tao Sheng, Sujjith Ravi

Robustness evaluation for Natural Language to SQL (NL2SQL) systems is essential because real-world database environments are dynamic, noisy, and continuously evolving, whereas conventional benchmark evaluations typically assume static schemas and well-formed user inputs. In this work, we introduce a robustness evaluation benchmark containing approximately te

Shuo Sha, Yixuan Wang, Binghao Huang, Antonio Loquercio

Fine-grained, contact-rich teleoperation remains slow, error-prone, and unreliable in real-world manipulation tasks, even for experienced operators. Shared autonomy offers a promising way to improve performance by combining human intent with automated assistance, but learning effective assistance in simulation requires a faithful model of human behavior, whi

Pablo Krupa, Alberto Bemporad

Generalized Nash Equilibrium Problems (GNEPs) arise in many applications, including non-cooperative multi-agent control problems. Although many methods exist for finding generalized Nash equilibria, most of them rely on assuming knowledge of the objective functions or being able to query the best responses of the agents. We present a method for learning solu

Kenny Wiratama, Kenneth Duru, Yunho Kim

In this paper, we propose the first of its kind space-time dual-pairing summation by parts (DP-SBP) numerical framework for forward and adjoint wave propagation problems. This novel approach enables us to achieve spatial and temporal high order accuracy while naturally introducing dissipation in time. Within this framework, initial and boundary conditions ar

Simone Tagliente

The aim of this paper is to produce infinite exotic structures on smooth closed oriented $4-$manifolds with fundamental group isomorphic to the infinite dihedral group, assuming that $b_2^+$ and $b_2^-$ are at least $12$.

Zachary Curtis-Ginsberg, Dan Hooper, Justin Vandenbroucke

Ultrahigh-energy gamma rays ($E_{\gamma}>100 \, {\rm TeV}$) have been detected from a handful of astrophysical sources. Due to the Klein-Nishina suppression of inverse Compton scattering at such high energies, it has sometimes been argued that these sources must be accelerators of PeV-scale protons, making them the long-sought-after Galactic ''PeVatrons.'' H

Idel Waisberg, Boaz Katz

We have recently suggested that Nunki=Sigma Sagittarii is the closest core collapse progenitor candidate to the Sun based on a VLTI/GRAVITY observation that unveiled it as a $6.5+6.3 M_{\odot}$ binary at a projected separation of 0.60 au. Here we combine this observation with three VLTI/PIONIER archival and one previous MAPPIT observation to solve for the or

Charlotte Bäcker, Konstantin Beyer, Walter T. Strunz

Unital quantum channels, defined by their property of leaving the maximally mixed state invariant, form an important class of quantum operations. A distinguished subset of these channels can be represented as a probabilistic mixture of unitary evolutions. Characterizing channels that do not admit such a decomposition is in general a hard problem with signifi

Bowen Hu, Kohei Kamada, Andrey Shkerin

We study false vacuum decay catalyzed by black holes. We consider a scalar field model with unstable potential in the background of a dilaton black hole in two dimensions. The model reproduces many features of the Schwarzschild black hole background in four dimensions, including the centrifugal barrier for linearized field perturbations. We study decays from

Anton A. Markov, Andrey M. Nikishin, Nigel R. Cooper, Nathan Goldman

Luttinger's theorem constrains the particle density of interacting fermions through global properties of the single-particle Green's function, and its violation signals a breakdown of the identification between the quantized Hall response and the Green-function-based Ishikawa-Matsuyama invariant. This phenomenon becomes especially compelling in strongly corr

Lukas Schmitt, Filippo Girardi, Laura Burri

We study a doubly minimized variant of the lautum information - a reversed analogue of the mutual information - defined as the minimum relative entropy between any product state and a fixed bipartite quantum state; we refer to this measure as the tumula information. In addition, we introduce the corresponding Petz Renyi version, which we call the doubly mini

Travis S. Metcalfe, Jennifer L. van Saders, Thomas R. Ayres, Derek Buzasi

Recent observations have shown that sufficiently slow rotation disrupts the organization of large-scale magnetic field in older main-sequence stars, leading to weakened magnetic braking (WMB) and a collapse in the efficiency of the global stellar dynamo. Recent simulations predict a shift from solar-like to anti-solar differential rotation (DR) at slower rot

Samuel J. W. Jones, M. Basil Altaie, Benjamin T. H. Varcoe

We present a controllable quantum spin-chain model that reproduces the Page curve (the rise-and-fall of bipartite entanglement expected in black-hole evaporation), using only local interactions and a kinematic reduction of the subsystem size. Two transverse-field Ising chains are coupled to form a pure bipartite state; Hawking-like evaporation is implemented

Matteo Bruno, Eugenia Colafranceschi, Fabio M. Mele, Carlo Rovelli

The Spin Foam approach to quantum gravity aims at providing a covariant path-integral formulation of canonical Loop Quantum Gravity. Since spin foam amplitudes are defined through discretisations of spacetime, understanding the continuum limit of the theory remains a central open problem. In this work, we investigate the structural aspects of this limit in a

W. C. Haxton, Xing Liu, Anupam Ray, Evan Rule

Motivated by the DAMA/LIBRA annual modulation signal, the dark matter community has invested heavily in ultra-clean underground NaI detectors to search for light WIMPs. We point out a new target of opportunity for these detectors -- axions produced by the carbon-burning stars within our galaxy. These stars synthesize large quantities of $^{23}$Na, keeping it

Maxim Trushin

Field emission from metals underpinned early vacuum-tube technology, and recent nanoscale engineering made field-emission devices compatible with modern silicon platforms. However, the limited tunability of electron transport in metals has restricted their applicability. Here, we show that noble metals coated with graphene exhibit clean non-monotonic $I-V$ c

Ankita Budhraja, Isabelle Pels, Wouter J. Waalewijn

N-point energy correlators are powerful observables for studying strong interactions, with applications ranging from extractions of the strong coupling $α_s$ to probes of jet modification in heavy-ion collisions and determination of the top-quark mass. Their practical use has, however, been limited by the complicated phase space for large N. Using a recently

Víctor Jaramillo, Laura Meneses, Héctor R. Olivares Sánchez, Carlos Herdeiro

$Q$-stars are a class of boson stars arising in scalar-field theories with interacting potentials, minimally coupled to gravity. We show that, in certain regions of parameter space, the angular velocity of stable timelike circular geodesics around $Q$-stars can attain a maximum at a nonzero radius. Notably, this behaviour may occur for stable configurations.

Kirill A. Grishin, Igor V. Chilingarian, Gary A. Mamon, Andrea Biviano

Ram-pressure stripping is a key driver of galaxy morphological transformation in clusters, contributing to the formation of quenched, especially dwarf, populations. Ram-pressure compression can also induce a starburst prior to quenching and build up significant stellar mass in an initially gas-rich galaxy. The detailed physics of these processes remains poor

Matthew Molinelli, Joshua C. Wang, Jeronimo G. C. Martinez, Sonny Lowe

Many-body systems with strong interactions often exhibit macroscopic behavior markedly absent in single-particle or noninteracting limits. Such emergent phenomena are well exemplified in lattice Hubbard models, where the interplay between interactions, geometric frustration, and magnetic flux gives rise to rich physics. Superconducting qubits naturally enabl

Anastasia A. Ponomareva, P. E. Mancera Piña, A. A. Vărăşteanu, M. Glowacki

Measuring galaxy rotation curves is critical for inferring the properties of dark-matter haloes in the Lambda Cold Dark Matter ($\Lambda$CDM) paradigm. We present HI rotation curves and mass models for 20 galaxies from the MIGHTEE survey. Using extended HI kinematics, we construct resolved mass models that include stellar, gaseous, and dark-matter components

Jisu Nam, Yicong Hong, Chun-Hao Paul Huang, Feng Liu

Recent advances in video diffusion transformers have enabled interactive gaming world models that allow users to explore generated environments over extended horizons. However, existing approaches struggle with precise action control and long-horizon 3D consistency. Most prior works treat user actions as abstract conditioning signals, overlooking the fundame

Lin Li, Haoran Feng, Zehuan Huang, Haohua Chen

We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D priors into 3D via distillation or multi-view mask aggregation, often suffering from cross-view inconsistency and blurred boundaries, or explore native 3D discriminative segmentation, which typically requires large

Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati, Ivan Laptev

Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimation from a single image. Meanwhile, reconstructing and decomposing common scenes into individual 3D objects remains a hard challenge due to the large variety of obje

Kaixuan Wang, Tianxing Chen, Jiawei Liu, Honghao Su

Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data-generation-ready digital object twins. Our pipeline

Jiongze Yu, Xiangbo Gao, Pooja Verlani, Akshay Gadde

Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed S

David Cerdeño, Patrick Foldenauer, Rafael López Noé, Óscar Zapata

We investigate the freeze-in of MeV-scale fermionic dark matter (DM) that couples to the Standard Model via a new vector mediator to assess the potential that future direct detection experiments have to observe new physics in either the DM or neutrino sectors. We study the minimal kinetic mixing dark photon of a secluded $U(1)_D$ as well as gauge bosons of t

Sahil Sen, Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah

Recent advances in Large Language Models (LLMs) have enabled conversational AI agents to engage in extended multi-turn interactions spanning weeks or months. However, existing memory systems struggle to reason over temporally grounded facts and preferences that evolve across months of interaction and lack effective retrieval strategies for multi-hop, time-se

Abhay Deshpande, Maya Guru, Rose Hendrix, Snehal Jauhri

A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific fine-tuning to bridge the gap between simulated and physical environments. We challenge that assumption. With sufficiently large-scale and diverse simulated syntheti

Emily Yue-Ting Jia, Weiduo Yuan, Tianheng Shi, Vitor Guizilini

Robotic manipulation requires sophisticated commonsense reasoning, a capability naturally possessed by large-scale Vision-Language Models (VLMs). While VLMs show promise as zero-shot planners, their lack of grounded physical understanding often leads to compounding errors and low success rates when deployed in complex real-world environments, particularly fo

Jun Saito, Jiefeng Li, Michael de Ruyter, Miguel Guerrero

Parametric human body models are foundational to human reconstruction, animation, and simulation, yet they remain mutually incompatible: SMPL, SMPL-X, MHR, Anny, and related models each diverge in mesh topology, skeletal structure, shape parameterization, and unit convention, making it impractical to exploit their complementary strengths within a single pipe

Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler, Stephanie Marik

Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the Ohio Department of Transportation (ODOT) traffic count data and corresponding ODOT crash records. This work utilizes a S

Gabriele Tartero, Sora Shiratani, Werner Krauth

Phase transitions appear all over science, and are familiar from everyday life, as water boiling, sugar melting into caramel or as nematic molecules turning smectic in liquid-crystal displays. The dynamics of phase transitions can be extremely slow, as for example when fog in winter does not lift, that is when the coarsening takes much time from many tiny wa

Xiaodan Zhou, Brian J Reich, Shu Yang

Per- and polyfluoroalkyl substances (PFAS) are typically encountered as mixtures of distinct chemicals with distinct effects on multiple health outcomes. Estimating joint causal effects using spatially-dependent observed data is challenging. We propose a spatial causal tensor completion framework that jointly models multiple exposures and outcomes within a l

Haowei Wen, Ruixuan Liu, Weiyi Piao, Siyu Li

Interlocking brick assemblies provide a standardized yet challenging testbed for contact-rich and long-horizon robotic manipulation, but existing rigid-body simulators do not faithfully capture snap-fit mechanics. We present BrickSim, the first real-time physics-based simulator for interlocking brick assemblies. BrickSim introduces a compact force-based mech