October 2025 arXiv papers — page 210

Yang Ge, Huan Jiang, Hong Yao, Shao-Kai Jian

The presence of a boundary enriches the nature of quantum phase transitions. However, the boundary critical phenomena in topological superconductors remain underexplored so far. Here, we investigate the boundary criticality in a two-dimensional correlated time-reversal-invariant topological superconductor tuned through a quantum phase transition into a trivi

Joshua N. Winn, Guðmundur Stefánsson

The fate of hot Jupiters is thought to be engulfment by their host stars, the outcome of tidal orbital decay. Transit timing has revealed a few systems with apparently shrinking orbital periods, but such signals can be mimicked by light travel-time effects (LTTE) of a distant companion. By combining transit timings with precise radial-velocity data, includin

Haining Pan, James V. Roggeveen, Erez Berg, Juan Carrasquilla

Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce. To fill this gap, we present CMT-Benchmark, a dataset of 50 problems covering condensed matter theory (CMT) at the level of an expert researcher. Topics span analytical and computa

Simon H. Hille, Attila Szabó

We introduce TeMFpy, a Python library for converting fermionic mean-field states to finite or infinite matrix product state (MPS) form. TeMFpy includes new, efficient, and easy-to-understand algorithms for both Slater determinants and Pfaffian states. Together with Gutzwiller projection, these also allow the user to build variational wave functions for vario

Darren Pereira, Erich J. Mueller

We study the hard-core Fermi-Hubbard model in the crossover between square and triangular lattices near half-filling. As was recognized by Nagaoka in the 1960s, on the square lattice the presence of a single hole leads to ferromagnetic spin ordering. On the triangular lattice, geometric frustration instead leads to a spin-singlet ground state, which can be a

Dominic J. Williamson, Bence Hetényi

We introduce several dynamical schemes that take advantage of mid-circuit measurement and nearest-neighbor gates on a lattice with maximum vertex degree three to implement topological codes and perform logic gates between them. We first review examples of Floquet codes and their implementation with nearest-neighbor gates and ancillary qubits. Next, we descri

Kryštof Kolář, Tero T. Heikkilä, Päivi Törmä

According to Anderson's theorem, the gap of a time-reversal symmetric weak-coupling superconductor is unaffected by non-magnetic disorder. However, the superfluid weight (stiffness) is reduced in the disordered limit by a factor of $Δτ$, a product of the scattering time $τ$ and the superconducting order parameter $Δ$. Here we show that the opposite holds

James W. Johnson

Disk galaxies like the Milky Way are expected to experience gas flows carrying matter toward their centers. This paper investigates the role of these radial gas flows in models of Galactic chemical evolution (GCE). We follow five different parameterizations of the Galactocentric radial velocity, $v_{r,g}$, of the interstellar medium (ISM). Relative to the $v

Frederik K. Marqversen, Gefen Baranes, Maxim Sirotin, Johannes Borregaard

Modular architectures offer a scalable path toward fault-tolerant quantum computing by interconnecting smaller quantum processing units (QPUs) provided that high-rate, fault-tolerant interfaces can be realized across modules. We present a comprehensive analysis and comparison of known and new methods for establishing such interfaces, including lattice surger

Jack Higginson, Rongmon Bordoloi, Robert A. Simcoe, Jorryt Matthee

We investigate the association between galaxies and neutral OI absorption systems at z~6, which trace metal-enriched gas during the epoch of reionization. We identify 40 galaxies across six quasar fields, residing in 15 overdensities within 300 kpc of the background sightlines. Five OI absorption systems are associated with five of these overdensities, yield

Ajit Bhand, Ashoke Sen, Ranveer Kumar Singh

We introduce manifestly duality invariant generating function of the index of single centered black holes in the heterotic string theory compactified on a six dimensional torus. This function is obtained by subtracting, from the inverse of the Igusa cusp form, the generating function of the index of two centered black holes constructed from the Dedekind eta

Edward Hirst, Sanjaye Ramgoolam

Ensembles of neural network weight matrices are studied through the training process for the MNIST classification problem, testing the efficacy of matrix models for representing their distributions, under assumptions of Gaussianity and permutation-symmetry. The general 13-parameter permutation invariant Gaussian matrix models are found to be effective models

Iosif Bena, Angèle Lochet

We identify singularity-free Running-Kerr-Taub-Bolt solutions of eleven-dimensional supergravity that descend to four-dimensional rotating solutions with flat-space asymptotics. We compute their spin-induced quadrupole moment and find that for a certain range of charges this quadrupole moment is positive. This behavior differs from the Kerr black hole and fr

Yurii Kvasiuk, Anderson Lai, Moritz Münchmeyer, Kendrick M. Smith

We present a novel implementation for the quadratic maximum likelihood (QML) power spectrum estimator for multiple correlated scalar fields on the sphere. Our estimator supports arbitrary binning in redshift and multipoles $\ell$ and includes cross-correlations of redshift bins. It implements a fully optimal analysis with a pixel-wise covariance model. We im

Jiayi Sun, Yu-Hsuan Teng, I-Da Chiang, Adam K. Leroy

We present radial profiles of surface brightness in UV and IR bands, estimate stellar mass surface density ($\Sigma_\star$) and star formation rate surface density ($\Sigma_\mathrm{SFR}$), and predict the CO-to-H$_2$ conversion factor ($\alpha_\mathrm{CO}$) for over 5,000 local galaxies with stellar mass $M_\star\,{\geq}\,10^{9.3}\rm\,M_\odot$. We build thes

Yicheng Zhang, Kangning Yang, Benjamin L. Lev, Marcos Rigol

We study the effects of the intertube dipole-dipole interactions (DDI) in recent experiments with arrays of nearly integrable one-dimensional (1D) dipolar Bose gases of $^{162}$Dy atoms. An earlier theoretical modeling ignored those interactions, which we include here via a modification of the 1D confining potentials. We investigate the effects of the intert

Zijian Liang, Yu-An Chen

Bivariate bicycle codes are promising candidates for high-threshold, low-overhead fault-tolerant quantum memories. Meanwhile, color codes are the most prominent self-dual CSS codes, supporting transversal Clifford gates that have been demonstrated experimentally. In this work, we combine these advantages and introduce a broad family of self-dual bivariate bi

Michael Zemcov, James J. Bock, Asantha Cooray, Shuji Matsuura

The background light produced by emission from all sources over cosmic history is a powerful diagnostic of structure formation and evolution. At near-infrared wavelengths, this extragalactic background light (EBL) is comprised of emission from galaxies stretching all the way back to the first-light objects present during the Epoch of Reionization. The Cosmic

Ethan Huecker, Yuxuan Wang

We analyze the leading vestigial instability due to the melting of a bidirectional pair-density-wave state in two dimensions. In a previous work by one of the authors, it was found that the interplay between pair-density-wave fluctuations with ordering momenta along the $x$ and $y$ directions can provide a strong attractive interaction for charge-$4e$ superc

Kelvin Ka-Ho Lam, Adrian Ka-Wai Chung, Nicolás Yunes

A key obstacle for theory-specific tests of general relativity is the lack of accurate black-hole solutions in beyond-Einstein theories, especially for moderate to high spins. We address this by developing a general framework--based on spectral and pseudospectral methods--to obtain analytic, closed-form spacetimes representing stationary, axisymmetric black

Christopher Eur, Alex Fink, Matt Larson

We establish strong vanishing theorems for line bundles on wonderful varieties of hyperplane arrangements, and we show that the resulting positivity properties of Euler characteristics extend to all matroids. We achieve this by showing that every degeneration of a wonderful variety within the permutohedral toric variety is reduced and Cohen--Macaulay. The sa

Sebastian Wagner-Carena, Aizhan Akhmetzhanova, Sydney Erickson

A common challenge in the natural sciences is to disentangle distinct, unknown sources from observations. Examples of this source separation task include deblending galaxies in a crowded field, distinguishing the activity of individual neurons from overlapping signals, and separating seismic events from an ambient background. Traditional analyses often rely

Shuntaro Aoki, Alessandro Strumia

Normal particles carry a microscopic arrow of causality. Lee-Wick ghosts carry the reversed arrow, mediating characteristic collider signals in flat space: opposite-sign scattering amplitudes that violate positivity bounds; acausality on time scales set by their negative decay rate. During inflation, the corresponding cosmo-collider ghost signals are: opposi

Martin Sandfuchs, Carla Ferradini, Renato Renner

We consider a pair of causally independent processes, modelled as the tensor product of two channels, acting on a possibly correlated input to produce random outputs X and Y. We show that, assuming the processes produce a sufficient amount of randomness, one can extract uniform randomness from X and Y. This generalizes prior results, which assumed that X and

I. Chousein-Basia, A. Zezas, I. Leonidaki, M. Kopsacheili

Supernova remnants (SNRs) are the aftermath of massive stellar explosions or of a white dwarf in a binary system, representing critical phases in the life cycle of stars and playing an important role in galactic evolution. Physical properties of SNRs such as their shock velocity, density and age are important elements for constraining models for their evolut

Harley Katz, Martin P. Rey, Corentin Cadiou, Oscar Agertz

We present the MEGATRON suite of cosmological radiation hydrodynamics simulations following the formation of Milky Way-mass galaxies from the earliest cosmic epochs when Population III stars form to Cosmic Noon. The suite represents the first set of cosmological simulations that couples a vast non-equilibrium thermochemistry network of primordial species, me

Pierre Heidmann, Paolo Pani, Jorge E. Santos

We construct a new class of smooth, horizonless, non-supersymmetric solutions in five-dimensional minimal supergravity, which we call rotating topological stars. Built from a Kerr-Taub-bolt geometry embedded in five dimensions, they constitute the first rotating generalization of the topological star compatible with both smoothness in the interior and standa

Wout M. Goesaert, Konrad R. W. Tristram, C. M. Violette Impellizzeri, Alexander P. S. Hygate

Context: Most active galactic nuclei (AGN) are believed to be surrounded by a dusty molecular torus on the parsec scale which is often embedded within a larger circumnuclear disk (CND). AGN are fuelled by the inward transport of material through these structures and can launch multi-phase outflows that influence the host galaxy through AGN feedback. Aims: We

Yunjia Bao, Dhong Yeon Cheong, Nicholas L. Rodd, Joey Takach

Is the usual treatment of axion dark matter as a classical field reliable? We show that the answer is subtle: the axion field could well be in a quantum state that has no complete classical description, but realistic detectors cannot tell the difference. To see this, we solve a fully quantum model of axion detection using quantum optics techniques. We show t

Cheng Xin, Fan Xu, Xin Ding, Jie Gao

Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields, yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently, intrinsically interpretable GNNs have been studied to provide insights into model predictions by identifying rationale substructures in graphs. However, existing

M. R. A. Wells, R. Pillsworth, H. Beuther, R. E. Pudritz

Here we use data from multi-scale galactic MHD simulations to observe filaments and star forming clumps on 10's of pc scales and investigate flow rate relationships along, and onto filaments as well as flows towards the clumps. Using the FilFinderPPV identification technique, we identify the prominent filamentary structures in each data cube. Each filament a

Stefan Roiser, Robert Schöfbeck, Zenny Wettersten

We present Rex and teaRex, C++17 libraries for efficient management of parton-level hard scattering event information and completely generic reweighting of such events, respectively. Rex is primarily an interfacing and I/O library for Les Houches Event format files and provides an internal event format designed with data parallelism in mind, and teaRex exten

Nathan Constantinides, Jeffery Yu, Dhruv Devulapalli, Ali Fahimniya

Routing is the task of permuting qubits in such a way that quantum operations can be parallelized maximally, given constraints on the hardware geometry. When simulating fermions in the Jordan-Wigner encoding with qubits, a one-dimensional nearest-neighbor-connected geometry is effectively imposed on the system, independently of the underlying hardware, which

Carina Curto

Graphical domination was first introduced in [1] in the context of combinatorial threshold-linear networks (CTLNs). There it was shown that when a domination relationship exists between a pair of vertices in a graph, certain fixed points in the corresponding CTLN can be ruled out. Here we prove two new theorems about graphical domination, and show that they

Robin Courant, Xi Wang, David Loiseaux, Marc Christie

Treating human motion and camera trajectory generation separately overlooks a core principle of cinematography: the tight interplay between actor performance and camera work in the screen space. In this paper, we are the first to cast this task as a text-conditioned joint generation, aiming to maintain consistent on-screen framing while producing two heterog

Zeyu Zhu, Kevin Qinghong Lin, Mike Zheng Shou

Academic presentation videos have become an essential medium for research communication, yet producing them remains highly labor-intensive, often requiring hours of slide design, recording, and editing for a short 2 to 10 minutes video. Unlike natural video, presentation video generation involves distinctive challenges: inputs from research papers, dense mul

Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao

Large reasoning models (LRMs) generate intermediate reasoning traces before producing final answers, yielding strong gains on multi-step and mathematical tasks. Yet aligning LRMs with human preferences, a crucial prerequisite for model deployment, remains underexplored. The statistically correct objective for preference alignment requires marginalizing over

Tingting Liao, Chongjian Ge, Guangyi Liu, Hao Li

Imagine Mr. Bean stepping into Tom and Jerry--can we generate videos where characters interact naturally across different worlds? We study inter-character interaction in text-to-video generation, where the key challenge is to preserve each character's identity and behaviors while enabling coherent cross-context interaction. This is difficult because characte

Avichal Goel, Yoon Kim, Nir Shavit, Tony T. Wang

Finetuning (pretrained) language models is a standard approach for updating their internal parametric knowledge and specializing them to new tasks and domains. However, the corresponding model weight changes ("weight diffs") are not generally interpretable. While inspecting the finetuning dataset can give a sense of how the model might have changed, these da

Le Zhuo, Songhao Han, Yuandong Pu, Boxiang Qiu

While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering, and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, system

Runchu Tian, Junxia Cui, Xueqiang Xu, Feng Yao

Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering advantages such as accelerated parallel decoding and bidirectional context modeling. However, the vanilla decoding strategy in discrete dLLMs suffers from a critical limitation: once a token is accepted, it can no longer be revised

Amira Abbas, Yanlin Chen, Tuyen Nguyen, Ronald de Wolf

The technique of combining multiple votes to enhance the quality of a decision is the core of boosting algorithms in machine learning. In particular, boosting provably increases decision quality by combining multiple weak learners-hypotheses that are only slightly better than random guessing-into a single strong learner that classifies data well. There exist

Ola Carlsson, Sambuddha Chattopadhyay, Jonathan B. Curtis, Frieder Lindel

Vacuum cavity control of quantum materials is the engineering of quantum materials systems through electromagnetic zero-point fluctuations. In this work we articulate a generic mechanism for vacuum optical control of correlated electronic order: Casimir control, where the zero-point energy of the electromagnetic continuum, the Casimir energy, depends on the

Janos Perczel, Jin Chow, Dorottya Demszky

The promise of generative AI to revolutionize education is constrained by the pedagogical limits of large language models (LLMs). A major issue is the lack of access to high-quality training data that reflect the learning of actual students. Prompt engineering has emerged as a stopgap, but the ability of prompts to encode complex pedagogical strategies in ru

Fahad Rafique, Saadia Masood, Shabbir Ahmad, Sadaf Amin

In the practical industry, the most commonly used application of statistical analysis for monitoring the process mean is the control chart. Control charts are generated based on the presumption that we have a sample from a stable process. The control chart then provides a graphical display to test this presumption. In the existing estimator \textcolor{red}{$

Ethan Kahn

Electrospray thrusters are emerging as a leading propulsion technology for CubeSats, offering high specific impulse ($I_{sp} > 1000$ s) and low power requirements. However, the divergent ion plumes can impinge on spacecraft surfaces, particularly body-mounted solar arrays, causing contamination and thrust efficiency losses. This study presents a validated pa

Loan Vulliard, Teresa Glauner, Sven Truxa, Miray Cetin

Colorectal cancer (CRC) is highly heterogeneous, with five-year survival rates dropping from $\sim$90% in localized disease to $\sim$15% with distant metastases. Disease progression is shaped not only by tumor-intrinsic alterations but also by the reorganization of the tumor microenvironment (TME). Metabolic, compositional, and spatial changes contribute to

Kabir Tomer, Mark Zhandry

In this work, we study the hardness required to achieve proofs of quantumness (PoQ), which in turn capture (potentially interactive) quantum advantage. A ``trivial'' PoQ is to simply assume an average-case hard problem for classical computers that is easy for quantum computers. However, there is much interest in ``non-trivial'' PoQ that actually rely on quan

Ronen Kamenetsky, Sara Dorfman, Daniel Garibi, Roni Paiss

Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two properties are especially desirable: disentanglement, where changing one attribute does not unintentionally alter others, and continuous control, where the strength of an edit can be s

Yangyang Wang, Tayo Fabusuyi

This study presents a novel small-area estimation framework to enhance urban transportation planning through detailed characterization of travel behavior. Our approach improves on the four-step travel model by employing publicly available microdata files and machine learning methods to predict travel behavior for a representative, synthetic population at sma

Tanjona R. Rabemananjara

We present NeoPDF, an interpolation library that supports both collinear and transverse momentum-dependent parton distribution functions. NeoPDF is designed to be fast and reliable, with modern functionalities that target both current and future hadron collider experiments. It aims to address the shortcomings of existing interpolation libraries while providi

William Fleurat

We prove sandwich theorems and a Tauberian theorem in the space of compact metric measure spaces, endowed with the Gromov-Hausdorff-Prokhorov (GHP) topology. These results hold with respect to a close relative of Gromov's Lipschitz order. As a proof-of-concept of a general method to prove metric scaling limits of random discrete structures, we give an applic

Chenyu Wang, Zishen Wan, Hao Kang, Emma Chen

With the rapid development of language models, the number of small language models (SLMs) has grown significantly. Although they do not achieve state-of-the-art accuracy, they are more efficient and often excel at specific tasks. This raises a natural question: can multiple SLMs be orchestrated into a system where each contributes effectively, achieving high

Praise Adeyemo, Dominic Bunnett, Fabián Levicán-Santibáñez

Let $X$ be a projective toric variety of dimension $n$ and let $L$ be a ample line bundle on $X$. For $k \geq 0$, it is in general difficult to determine whether $L^{\otimes k}$ is very ample and whether it additionally gives a projectively normal embedding. These two properties are equivalent to the very ampleness, respectively normality, of the correspondi

Huai-Dong Cao, Junming Xie

In this paper, we investigate curvature pinching phenomena in complete non-compact asymptotically conical gradient expanding Ricci solitons and establish several Hamilton-Ivey type curvature pinching estimates. These results are parallel to those known for shrinking and steady Ricci solitons. In particular, we prove a three-dimensional Hamilton-Ivey type cur

Farhan T. Chowdhury, Luke D. Smith, Daniel R. Kattnig

The quantum control of spin-correlated radical pairs (SCRPs) holds promise for the targeted manipulation of magnetic field effects, with potential applications ranging from the design of noise-resilient quantum information processors to genetically encodable quantum sensors. However, achieving precise handles over the intricate interplay between coherent ele

N. Watwood, C. R. Hoffman, B. P. Kay, I. A. Tolstukhin

The $^{32}$Si($^3$He,$d$)$^{33}$P reaction was studied in inverse kinematics at 6.3~MeV/$u$. States in $^{33}$P corresponding to the proton $1s-0d$ single-particle orbitals were identified up to $\sim$4.5 MeV in excitation energy. The ($^{3}$He,$d$) spectroscopic factors were determined from Distorted Wave Born Approximation calculations. When combined with

Álvaro Tejero

The asymmetry between heating and cooling in open quantum systems is a hallmark of nonequilibrium dynamics, yet its thermodynamic origin has remained unclear. Here, we investigate the thermalization of a quantum system weakly coupled to a thermal bath, focusing on the entropy production rate and the quantum thermokinetic uncertainty relation (TKUR). We deriv

Debojyoti Ghosh, Soumya K Ghosh, Adrijit Goswami

Efficient and accurate classification of waste and industrial surface defects is essential for ensuring sustainable waste management and maintaining high standards in quality control. This paper introduces the Neuroplastic Modular Classifier, a novel hybrid architecture designed for robust and adaptive image classification in dynamic environments. The model

Siheng Zhao, Yanjie Ze, Yue Wang, C. Karen Liu

Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks. While recent advances in general motion tracking (GMT) have enabled humanoids to reproduce diverse human motions, these policies lack the precision and object awareness required for loco-manipulation. To this end, we introduce ResMimic, a two-sta

Dachuan Shi, Abedelkadir Asi, Keying Li, Xiangchi Yuan

Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two chal

Shreya Meel, Sennur Ulukus

We consider the problem of decentralized constrained optimization with multiple agents $E_1,\ldots,E_N$ who jointly wish to learn the optimal solution set while keeping their feasible sets $\mathcal{P}_1,\ldots,\mathcal{P}_N$ private from each other. We assume that the objective function $f$ is known to all agents and each feasible set is a collection of poi

Roke Cepeda-Arroita, J. A. Rubiño-Martín, R. T. Génova-Santos, C. Dickinson

Anomalous Microwave Emission (AME) is a diffuse microwave component thought to arise from spinning dust grains, yet remains poorly understood. We analyze AME in 144 Galactic clouds by combining low-frequency maps from S-PASS (2.3 GHz), C-BASS (4.76 GHz), and QUIJOTE (10-20 GHz) with 21 ancillary maps. Using aperture photometry and parametric SED fitting via

Dushmanta Sahu

The Barnett effect is a fundamental magnetomechanical phenomenon in which a ferromagnetic material becomes magnetized under rotation. Using a hadron resonance gas (HRG) model under rigid rotation, we compute the Barnett magnetization ($M_{\rm Barnett}$) and show that it produces a magnetic field ($B_{\text{ind}}$) comparable in magnitude to the well-known ex

Kumar Abhinav, Partha Guha, Indranil Mukherjee

Parity and time-reversal (PT ) symmetry is shown as the natural cause of quasi-integrability of deformed integrable models, crucial to represent real physical systems as they posses various irregularities. The condition for asymptotic conservation of quasi-conserved charges appear as a direct consequence of the PT -symmetric phase of the system, ensuring def

Joshua Kazdan, Rylan Schaeffer, Youssef Allouah, Colin Sullivan

Assessing the capabilities and risks of frontier AI systems is a critical area of research, and recent work has shown that repeated sampling from models can dramatically increase both. For instance, repeated sampling has been shown to increase their capabilities, such as solving difficult math and coding problems, but it has also been shown to increase their

Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter, Marco Fumero

Large language models (LLMs) are typically deployed under diverse memory and compute constraints. Existing approaches build model families by training each size independently, which is prohibitively expensive and provides only coarse-grained size options. In this work, we identify a novel phenomenon that we call boomerang distillation: starting from a large

Noah Rhodes

Data visualization is essential for developing an understanding of a complex system. The power grid is one of the most complex systems in the world and effective power grid research visualization software must 1) be easy to use, 2) support unique data that may arise in research, and 3) be capable of creating custom figures for publication and presentation. H

Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi

Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigms are fundamentally limited in these settings. A natural approach to these problems is to combine RL with Linear-time Temporal Logic (LTL), a formal language for specifying complex

Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi

Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive re

Junlin Wang, Jue Wang, Zhen, Xu

Recent advances in large language models (LLMs) opened up new directions for leveraging the collective expertise of multiple LLMs. These methods, such as Mixture-of-Agents, typically employ additional inference steps to generate intermediate outputs, which are then used to produce the final response. While multi-agent inference can enhance response quality,

Trishul Dhalia, Rohit Juneja, Amita Das

In this work, an innovative scheme is proposed that exploits the response of magnetized plasmas to realize a refractive index exceeding unity for right circularly polarized (RCP) waves. Using two- and three-dimensional Particle-in-Cell (PIC) simulations with the OSIRIS 4.0 framework, it is shown that a shaped magnetized plasma lens (MPL) can act as a glass/s

Mingyu Liu, Jiuhe Shu, Hui Chen, Zeju Li

A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that

Alexis Ross, Megha Srivastava, Jeremiah Blanchard, Jacob Andreas

As programmers write code, they often edit and retry multiple times, creating rich "interaction traces" that reveal how they approach coding tasks and provide clues about their level of skill development. For novice programmers in particular, these traces reflect the diverse reasoning processes they employ to code, such as exploratory behavior to understand

Alper Cakan, Dakshita Khurana, Tomoyuki Morimae, Yuki Shirakawa

We investigate quantum analogues of collision resistance and obtain separations between quantum ``one-way'' and ``collision-resistant'' primitives. 1. Our first result studies one-wayness versus collision-resistance defined over quantum circuits that output classical strings. We show that there is a classical oracle $\mathcal{O}$ relative to which (sub-expon

Peter Van Katwyk, Karianne J. Bergen

Uncertainty quantification is critical for ensuring robustness in high-stakes machine learning applications. We introduce HybridFlow, a modular hybrid architecture that unifies the modeling of aleatoric and epistemic uncertainty by combining a Conditional Masked Autoregressive normalizing flow for estimating aleatoric uncertainty with a flexible probabilisti

Devleena Das, Rajeev Patwari, Ashish Sirasao

Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task performance. While most efforts on performance recovery for deployment focus on robust quantization techniques, we focus on recovering model accuracies from any sources that degrade

Mohammad-Ali Mahmoudpour, Saeed Mahmoudpour

Contrast change is an important factor that affects the quality of images. During image capturing, unfavorable lighting conditions can cause contrast change and visual quality loss. While various methods have been proposed to assess the quality of images under different distortions such as blur and noise, contrast distortion has been largely overlooked as it

Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi, Zhou Yu

The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving adversarial attacks, including multi-turn jailbreaks that iteratively search for successful queries. Current defenses, which are primarily reactive and static, often fail to handle these iterative attacks. In thi

Rohit Jayanti, Swayam Agrawal, Vansh Garg, Siddharth Tourani

Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike keypoint matching, which focuses on localized features, segment matching captures structured regions, offering greater robustness to occlusions, lighting variations, and viewpoint

Priyanka Boora, Siddhartha Karmakar, Dinesh Kumar, Kavita Lalwani

We present a comprehensive analysis of the decay $\Lambda_c^- \to \Lambda(\to p\pi)\,\mu^- \bar\nu_\mu$ within a model-independent effective field theory framework. Previous studies have been restricted to the three-body decay $\Lambda_c^+ \to \Lambda \mu^+ \nu_\mu$ and considered only left-handed neutrinos within the Low-Energy Effective Theory (LEFT). In t

Ahmed Elhussein, Paul Meddeb, Abigail Newbury, Jeanne Mirone

Machine learning in healthcare requires effective representation of structured medical codes, but current methods face a trade off: knowledge graph based approaches capture formal relationships but miss real world patterns, while data driven methods learn empirical associations but often overlook structured knowledge in medical terminologies. We present KEEP

Ondřej Kubíček, Viliam Lisý

Test-time reasoning significantly enhances pre-trained AI agents' performance. However, it requires an explicit environment model, often unavailable or overly complex in real-world scenarios. While MuZero enables effective model learning for search in perfect information games, extending this paradigm to imperfect information games presents substantial chall

Víctor Blanco, Harshit Kothari, James Luedtke

In this paper, we propose a new mathematical optimization model for multiclass classification based on arrangements of hyperplanes. Our approach preserves the core support vector machine (SVM) paradigm of maximizing class separation while minimizing misclassification errors, and it is computationally more efficient than a previous formulation. We present a k

David Beauchemin, Yan Tremblay, Mohamed Amine Youssef, Richard Khoury

To address the need for a more comprehensive evaluation of French Natural Language Understanding (NLU), we introduce COLE, a new benchmark composed of 23 diverse task covering a broad range of NLU capabilities, including sentiment analysis, paraphrase detection, grammatical judgment, and reasoning, with a particular focus on linguistic phenomena relevant to

Mikhail Volkov

We construct a faithful representation of the semiring of all order-preserving decreasing transformations of a chain with $n+1$ elements by Boolean upper triangular $n\times n$-matrices.

Lawrence Hollom, Gregory B. Sorkin

We consider the probability that the random signed sum $\xi_1 v_1 + \dotsb + \xi_n v_n$ lies within a given distance $r$ of the origin, where $v_1,\dotsc,v_n \in \mathbb{R}^d$ are fixed unit vectors and $\xi_1,\dotsc,\xi_n$ are independently and uniformly distributed on $\{-1,+1\}$. In particular, our results demonstrate that, for certain values of $r$, the

Jaehyeok Jin, Chen Liu, David R. Reichman

The formulation of a fluctuating hydrodynamic theory for interacting particles is a crucial step in the theoretical description of liquids. The microscopic mappings proposed decades ago by Dean and Kawasaki have played a central role in the analytical treatment of such problems. However, the singular mathematical nature of the density distributions used in t

Thomás Jung Spier

In this work, we prove that the universal and maximal abelian covers of a finite multi-graph have the same eigenvalues. This result strengthens a recent theorem of Li, Magee, Sabri, and Thomas (2025) and answers one of their questions. Our proof builds upon their new characterization of the point spectrum of maximal abelian covers in terms of matching polyno

Jihoon Lee, Hoyeon Moon, Kevin Zhai, Arun Kumar Chithanar

Diffusion-based large language models (dLLMs) are trained flexibly to model extreme dependence in the data distribution; however, how to best utilize this information at inference time remains an open problem. In this work, we uncover an interesting property of these models: dLLMs trained on textual data implicitly learn a mixture of semi-autoregressive expe

Fotis Farakos, Ulf Lindström

We discuss 4D N=2 non-abelian gauge theories where one supersymmetry is preserved while the other one is spontaneously broken and non-linearly realized. The goldstino resides in a Maxwell multiplet of the Bagger-Galperin type. We introduce appropriate constraints that eliminate the chiral N=1 superfield sector of the non-abelian N=2 multiplets and discuss th

Omri Uzan, Asaf Yehudai, Roi pony, Eyal Shnarch

Multimodal encoders have pushed the boundaries of visual document retrieval, matching textual query tokens directly to image patches and achieving state-of-the-art performance on public benchmarks. Recent models relying on this paradigm have massively scaled the sizes of their query and document representations, presenting obstacles to deployment and scalabi

Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia

We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, an

Sergio Rozada, Vimal K. B., Andrea Cavallo, Antonio G. Marques

We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our approach builds on generative diffusion models, which are well established in vision and graph generation but remain underexplored for graph signals. Existing methods lack generality, e

C. Ian Short

We announce V. 2025-08-08 of the Chroma+ suite of stellar atmosphere and spectrum modelling codes for fast, approximate, effectively platform-independent stellar spectrum synthesis, written in a number of free well-supported programming languages. The Chroma+ suite now computes the emergent surface intensity and flux distributions and the hydrostatic pressur

Yolo Y. Tang, Jing Bi, Pinxin Liu, Zhenyu Pan

Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and multimodal evidence. The recent emergence of Video-Large Multimodal Models (Video-LMMs), which integrate visual encoders with powerful decoder-based language models, has demonstrat

Markus Englberger, Devendra Singh Dhami

We present a categorical framework for relating causal models that represent the same system at different levels of abstraction. We define a causal abstraction as natural transformations between appropriate Markov functors, which concisely consolidate desirable properties a causal abstraction should exhibit. Our approach unifies and generalizes previously co

Jan Hendrik Bruinier, Martin Raum

We correct an error in Lemma 4.4 and its application in Theorem 4.5 in our paper ``Kudla's Modularity Conjecture and Formal Fourier-Jacobi Series''.

Craig J. Copi, Amirhossein Samandar, Glenn D. Starkman, Javier Carrón Duque

If the Universe has non-trivial spatial topology, observables depend on both the parameters of the spatial manifold and the position and orientation of the observer. In infinite Euclidean space, most cosmological observables arise from the amplitudes of Fourier modes of primordial scalar curvature perturbations. Topological boundary conditions replace the fu

Rhiannon Udall, Sophie Bini, Katerina Chatziioannou, Derek Davis

Gravitational waves from black hole binary mergers carry information about the component spins, but inference is sensitive to analysis assumptions, which may be broken by terrestrial noise transients known as glitches. Using a variety of simulated glitches and gravitational wave signals, we study the conditions under which glitches can bias spin measurements