November 2025 arXiv papers — page 27

Bruno Padovese, Fabio Frazao, Michael Dowd, Ruth Joy

Automated detection and classification of marine mammals vocalizations is critical for conservation and management efforts but is hindered by limited annotated datasets and the acoustic complexity of real-world marine environments. Data augmentation has proven to be an effective strategy to address this limitation by increasing dataset diversity and improvin

Yingke Li, Yifan Lin, Enlu Zhou, Fumin Zhang

Model Predictive Control (MPC) is a powerful framework for constrained control, but its performance and safety can be severely degraded when the prediction model is learned online and thus remains uncertain. In this work, we develop a Bayesian risk-averse MPC framework for stochastic, discrete-time, nonlinear systems that provides theoretical guarantees on t

Kyan Louisia, Takato Mori, Herbie Warner

Information-theoretic inequalities often impose nontrivial constraints on holographic states. In this work, we study measurement-based classical and quantum correlations in holography, focusing on the proposed duals of classical correlation $J_W$, quantum discord $D_W$, and one-shot distillable entanglement $E_D$, defined in terms of the entanglement wedge c

Wasikul Islam, Sergei Chekanov, Nicholas Luongo

This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the advantages of the anomaly detection approach, as demonstrated in a recent ATLAS analysis, and introduce ADFilter, a web-based

Lap Chi Lau, Raymond Liu

Classical spectral graph theory characterizes graphs with logarithmic mixing time. In this work, we present a combinatorial characterization of graphs with constant mixing time. The combinatorial characterization is based on the small-set bipartite density condition, which is weaker than having near-optimal spectral radius and is stronger than having near-op

Alexey Uvarov, Artur F. Izmaylov

In electronic structure calculations, the transcorrelated method consists in transforming the Hamiltonian so as to remove the Coulomb cusp in its eigenfunctions. As a result, the wavefunction can be described more accurately without increasing the size of the basis set. However, the transcorrelated Hamiltonian is non-Hermitian and non-normal, which makes man

Diego Vallarino

This paper examines how institutional belonging shapes long-term development by comparing Spain and Uruguay, two small democracies with similar historical endowments whose trajectories diverged sharply after the 1960s. While Spain integrated into dense European institutional architectures, Uruguay remained embedded within the Latin American governance regime

Nicholas Vaiopoulos, Alexander Vavoulas, Harilaos G. Sandalidis, Konstantinos K. Delibasis

This letter presents a unified analytical framework for internodal distance distributions in 2D and 3D wireless networks, with nodes confined to concentric circular or spherical regions. Four deployment scenarios are considered, covering all combinations of static (uniform) and mobile (random waypoint-based) nodes. For each scenario, closed-form expressions

Eric Yeats, Darryl Hannan, Wilson Fearn, Timothy Doster

Score-based generative models require guidance in order to generate plausible, on-manifold samples. The most popular guidance method, Classifier-Free Guidance (CFG), is only applicable in settings with labeled data and requires training an additional unconditional score-based model. More recently, Auto-Guidance adopts a smaller, less capable version of the o

Siyu Wu, Zihan Tang, Yuting Zeng, Hui Chen

Large Language Models (LLMs) are increasingly deployed in both latency-sensitive online services and cost-sensitive offline workloads. Co-locating these workloads on shared serving instances can improve resource utilization, but directly applying this approach to Prefill/Decode (P/D) disaggregated systems introduces severe load imbalance, as fluctuating requ

Eduardo Soares, Emilio Vital Brazil, Victor Shirasuna, Breno W. S. R. de Carvalho

We present PDE-FM, a modular foundation model for physics-informed machine learning that unifies spatial, spectral, and temporal reasoning across heterogeneous partial differential equation (PDE) systems. PDE-FM combines spatial-spectral tokenization, physics-aware conditioning, and a Mamba-based state-space backbone with an operator-theoretic decoder, enabl

Paulo Cavalin, Cassia Sanctos, Marcelo Grave, Claudio Pinhanez

In this work we present the Consistency-Rebalanced Accuracy (CoRA) metric, improving the reliability of Large Language Model (LLM) scores computed on multiple choice (MC) benchmarks. Our metric explores the response consistency of the LLMs, taking advantage of synthetically-generated questions with altered answer choices. With two intermediate scores, i.e. B

Hagit Attiya, Armando Castañeda, Dhrubajyoti Ghosh, Thomas Nowak

We revisit the relationship between two fundamental models of distributed computation: the asynchronous message-passing model with up to $f$ crash failures ($\operatorname{AMP}_f$) and the Heard-Of model with up to $f$ message omissions ($\operatorname{HO}_f$). We show that for $n > 2f$, the two models are equivalent with respect to the solvability of colorl

Md. Sad Abdullah Sami, Mushfiquzzaman Abid

This study investigates the effectiveness and efficiency of two variants of the XGBoost regression model, the full-capacity and lightweight (tiny) versions, for predicting the concentrations of carbon monoxide (CO) and nitrogen dioxide (NO2). Using the AirQualityUCI dataset collected over one year in an urban environment, we conducted a comprehensive evaluat

Md Meftahul Ferdaus, Nathan Alton Cooper, Austin B. Schmidt, Pujan Pokhrel

Predicting ocean wave behavior is challenging due to the difficulty in choosing suitable numerical models among many with varying capabilities. This review examines the development and performance of numerical wave models in coastal engineering and oceanography, focusing on the difference between phase-averaged spectral models and phase-resolving models. We

Masoud Soleimani

We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses thr

Reihaneh Jahedan, Satya Peddada, Mark Jennings, Sunil Katragadda

As the automotive industry moves towards vehicle electrification, designing and optimizing thermal management systems (TMSs) for Battery Electric Vehicles (BEVs) has become a critical focus in recent years. The dependence of battery performance on operating temperature, the lack of waste combustion heat, and the significant effect of TMS energy consumption o

Soumya Bandyopadhyay, Sourav Chatterjee, Dallas R. Trinkle, Richard G. Hennig

Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe-C system that couples a CALPHAD-based chemical free energy

J. Sebastian Pineda, Stefano Bellotti, Jackie Villadsen, Aline Vidotto

The recent detections of radio emission from the nearby exoplanet host, YZ Ceti, suggest that the star is possibly interacting with its rocky innermost planet. These radio emissions are characterized by strong circular polarization, and appear to repeat within consistent orbital phase windows dictated by the orbital position of YZ Ceti b. If confirmed, this

Victor A. Rodriguez, Mykhaylo Usatyuk, Zi-Yue Wang

We revisit ADE minimal string theories, focusing on the D- and E-series minimal models coupled to Liouville theory. Unlike the A-series, whose duals are solvable two-matrix models, these theories are conjectured to correspond to unsolvable four-matrix integrals. We compute sphere four-point and torus one-point amplitudes in the AMS, DMS, and EMS via direct n

Aviv Alpern, Svetlozar Rachev

We introduce a simple portfolio optimization strategy using ESG data with the Black-Litterman allocation framework. ESG scores are used as a bias for Stein shrinkage estimation of equilibrium risk premiums used in assigning Black-Litterman asset weights. Assets are modeled as multivariate affine normal-inverse Gaussian variables using CVaR as a risk measure.

Majid Saberi, Samin Aref

Network centralization, driven by hub nodes, impacts communication efficiency, structural integration, and dynamic processes such as diffusion and synchronization. Although numerous centralization measures exist, a major challenge lies in determining measures that are both theoretically sound and empirically reliable across different network contexts. To res

Eric Leonardis, Akira Nagamori, Ayesha Thanawalla, Yuanjia Yang

The brain has evolved to effectively control the body, and in order to understand the relationship we need to model the sensorimotor transformations underlying embodied control. As part of a coordinated effort, we are developing a general-purpose platform for behavior-driven simulation modeling high fidelity behavioral dynamics, biomechanics, and neural circ

Amit Jena, Na Li, Le Xie

System identification in control theory aims to approximate dynamical systems from trajectory data. While neural networks have demonstrated strong predictive accuracy, they often fail to preserve critical physical properties such as stability and typically assume stationary dynamics, limiting their applicability under distribution shifts. Existing approaches

Teppei Yonetsu, Ryohei Kawabe, Yuki Yoshimura, Kotomi Taniguchi

We present the results of mapping and single-point spectral scans toward Orion-KL/OMC-1 performed as science demonstrations of a 2 mm SIS receiver, the Band 4 Receiver (B4R), installed on the 50 m Large Millimeter Telescope (LMT). To prove the capabilities of mapping and spectral scans with the B4R on the LMT, commissioning observations were conducted employ

Murat Yaslioglu

In an age where sustainability is of paramount importance, the significance of both high-performance computing and intelligent algorithms cannot be understated. Yet, these domains often demand hefty computational power, translating to substantial energy usage and potentially sidelining less robust computing systems. It's evident that we need an approach that

Sarina Xi, Vishisht Rao, Justin Payan, Nihar B. Shah

The identification and localization of errors is a core task in peer review, yet the exponential growth of scientific output has made it increasingly difficult for human reviewers to reliably detect errors given the limited pool of experts. Recent advances in Large Language Models (LLMs) have sparked interest in their potential to support such evaluation tas

Md. Sad Abdullah Sami, Mushfiquzzaman Abid

The rapid expansion of Internet of Things (IoT) deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly Detection Systems (ADS) in identifying emerging and zero-day thre

Nassim Bozorgnia, Joseph Bramante, Andrew Buchanan

As the hunt for dark matter progresses, recently there have been advances in the search for heavy dark matter with a mass well above a TeV. We show the importance of properly modeling the local dark matter velocity distribution, beyond the standard Maxwellian halo model, and in particular how the dynamics of the Large Magellanic Cloud and Milky Way may impac

T. Bertin, I. E. Gordon, R. J. Hargreaves, J. Tennyson

Spectroscopic parameters of methane from many different studies were gathered to improve the HITRAN database towards its 2024 version. After a validation process using high-resolution FTS and CRDS spectra, about 80,000 lines of the four most abundant isotopologues were replaced from the dyad to the triacontad regions. These changes amount to 51,000 transitio

Ji Qianga, Yue Hao, Allen Qiang, Jinyu Wan

The auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to various input parameters. It provides an efficient method to study sensitivity of the simulation results with respect to the input parameters. Furthermore, it can be used in gradie

Daniel Carpenter, Carson Ezell, Pratyush Mallick, Alexandria Westray

Estimating catastrophic harms from frontier AI is hindered by deep ambiguity: many of its risks are not only unobserved but unanticipated by analysts. The central limitation of current risk analysis is the inability to populate the $\textit{catastrophic event space}$, or the set of potential large-scale harms to which probabilities might be assigned. This in

Benjamin Bode, Chun-Sheng Hsueh

We show that every canonically fibered link in $S^3$ is the binding of a braided open book in $S^3$, addressing a question of Montesinos and Morton. We introduce mutual arc presentations as our main technical tool, which we consider to be of independent interest. We prove that any fibered link admitting such a presentation is the binding of a braided open bo

Yanbo Fang

We introduce a class of semipositive metrics on ample line bundles in non-Archimedean geometry, called Shilov finite metrics. We calculate the determinant metric distorsion in the exact sequence induced by a global section using non-Archimedean norm reduction techniques. This leads to an analytic proof to the arithmetic Hilbert-Samuel formula over a local pl

Xusheng Zhu, Kai-Kit Wong, Hanjiang Hong, Han Xiao

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. Our contribution lies in establishing a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expr

Lijie Huang, Jingyi Yin, Jingke Zhang, U-Wai Lok

Objective: Hyper-clutter artifacts (HCA), arising from strong tissue reflections or physiological motion, present persistent challenges in ultrafast ultrasound Doppler imaging, often obscuring surrounding small vessel flow signals, especially in fascial regions such as the renal capsule. This study proposes U-profile-based decluttering (UPBD), a robust and c

Ezequiel Alvarez, Leandro Da Rold, Manuel Szewc, Alejandro Szynkman

Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their individual shapes directly in the signal region. The likelihood is a nuanced combination of a one-dimensional kinematic discr

Ashish Kakkar, Samuel Marsh, Yulun Wang, Pranav Mundada

We propose and validate on real quantum computing hardware a new method for extended two-qubit gate set design, replacing iterative, fine calibration with fast characterization of a small number of gate parameters which are then tracked and corrected in circuit compilation. Coherent contributions to the pulse unitary that would traditionally be considered so

Prateek Agrawal, Michael Nee, Mario Reig

We consider the coupling of axions to gauge bosons in higher-dimensional Grand Unified Theories (GUT) inspired by string theory constructions with D-branes on orbifold singularities, the so-called orbifold GUTs. Due to their topological properties, axion couplings to gauge bosons are independent of the gauge symmetry reduction mechanism and the background ge

Jianzhi Yang

We develop a fully numerical framework to compute and visualize the \emph{hypershadow}\cite{Novo:2024wyn}, the three-dimensional generalization of the black hole shadow in five-dimensional spacetimes. Our method is based on backward ray tracing and allows flexible control over observer position, enabling the reconstruction of the full shadow volume. For visu

Kuldeep Deka, Marta Losada, Yosef Nir

Following a proposal for an experiment with sensitivity to an electric dipole moment (EDM) of the muon $d_\mu$ of order $6\times10^{-23}\ e$ cm, three to four orders of magnitude below the current bound, but still seven orders of magnitude above the current bound on the EDM of the electron $d_e$, we explore the discovery potential of such an experiment. With

Niccolo Marini, Zhaohui Liang, Sivaramakrishnan Rajaraman, Zhiyun Xue

Multimodal (MM) learning is emerging as a promising paradigm in biomedical artificial intelligence (AI) applications, integrating complementary modality, which highlight different aspects of patient health. The scarcity of large heterogeneous biomedical MM data has restrained the development of robust models for medical AI applications. In the dermatology do

Aleksandr Azatov, Takeshi Kobayashi, Nicklas Ramberg

We present a new classical mechanism for nucleation of bubbles of true vacuum. The mechanism arises when dense boson stars form in the false vacuum. As the boson stars collapse due to attractive self-interactions, the field inside the star cores is enhanced beyond the potential barrier. Subsequently the stars explode as true vacuum bubbles, and induce a cosm

Zixi Fang, Chen Fang

We report two types of singularities that arise from fluctuations during the formation of charge- or spin-density waves. The first is the exceptional point (EP), corresponding to a higher-order pole of the retarded Green's function. Such EPs lead to algebraic corrections in the decay of quasiparticle occupations and are observable through time-resolved angle

Shaun T. Brown, Azadeh Fattahi, Thales A. Gutcke, Sylvia Ploeckinger

We present a suite of zoom-in cosmological hydrodynamical simulations of dwarf galaxies using the LYRA galaxy formation model with an extremely high mass resolution of $4\, \mathrm{M_{\odot}}$, evolved to $z=0$. The suite contains 65 haloes selected from Local Group like environments, spanning $M_{\mathrm{200c}}=10^7$ to $5\times10^9\, \mathrm{M_{\odot}}$. T

Lalit Singh Bhandari, Vikram Rentala, Arun M. Thalapillil, Himanshu Verma

We present the first comprehensive study of astrometric microlensing by exotic astrophysical dark objects, focusing on two theoretically motivated models -- Q-ball and boson star. We demonstrate that these extended objects generate distinctive signatures that depart markedly from point-mass lenses like primordial black holes. The smoking-gun signature for th

J. A. Fernández-Ontiveros, C. López-Sanjuan, A. Hernán-Caballero, A. Lumbreras-Calle

We present the value-added catalogue JOLINES (J-PAS optical line intensities for nebular emission galaxies), which provides emission-line fluxes in galaxies at from the spectrophotometric catalogues of miniJPAS, J-NEP and the J-PAS early data release (EDR). This catalogue will be updated with future data releases, offering a growing resource for the study of

Anna de Graaff, Raphael E. Hviding, Rohan P. Naidu, Jenny E. Greene

We use the DAWN JWST Archive to construct and characterise a sample of 146 little red dots (LRDs) across 2.0<z<9.3, selecting all sources with v-shaped UV-optical continua from NIRSpec/PRISM spectra and compact morphologies in NIRCam/F444W imaging. We show that LRD continuum spectra are ubiquitously well described by modified blackbodies across ~$0.4-1.0μ$m,

Daniel Kun, Teodor Strömberg, Borivoje Dakić, Philip Walther

Entanglement does not always require one particle per party. It was predicted some thirty years ago that a single photon traversing a beam splitter could violate a Bell inequality. Although initially debated, single-photon nonlocality was eventually demonstrated via homodyne measurements. Here, we present an alternate realisation that avoids the complexity o

Arielle C. Frommer, Jason D. Eastman, David R. Ciardi, Steve B. Howell

Through detailed modeling of all three stars, we show that HIP-44302 is a false positive triple-star system. While the Transiting Exoplanet Survey Satellite initially designated the object as a planetary candidate, observing a 10-day transit and secondary eclipse in Sectors 8 and 35, we definitively exclude this scenario, finding instead that the transit com

Ryohei Kawabe, Takeshi Sakai, Kunihiko Tanaka, Akio Taniguchi

We report on the results of the on-sky test and science demonstration conducted with the 2 mm receiver system, B4R, on the 50 m Large Millimeter Telescope (LMT), located at an altitude of 4600 m in Mexico. The B4R receiver was developed based on the dual-polarization sideband-separating mixer technology of the Atacama Large Millimeter/submillimeter Array, an

Ramanjit Sohal, Ruben Verresen

We argue that the presence of any exact $U(1)$ higher-form symmetry, under mild assumptions, presents a fundamental obstruction to ergodicity under unitary dynamics in lattice systems with local interactions and finite on-site Hilbert space dimension. Focusing on the two-dimensional case, we show that such systems necessarily exhibit Hilbert space fragmentat

Isabel Medlock, Daisuke Nagai, Nir Mandelker, Volker Springel

Cold, dense streams of gas are predicted to penetrate deeply into massive halos (> 10^12 Msun) at cosmic noon (z=4-2), fueling galaxies to sustain high star formation rates. We investigate the prevalence of such cold streams in TNG50 over the range z=4-0, using a novel algorithm to automatically detect cold streams in simulated halos. We qualitatively and qu

Lennart M. Böhm, Arshia M. Jacob, Friedrich Wyrowski, Karl M. Menten

Despite being only the 19th most abundant element in the interstellar medium, chlorine's reactivity and volatility give rise to a unique interstellar chemistry, favouring the formation of several chlorine-bearing hydrides. Further, the $^{35}\text{Cl}/ ^{37}$Cl ratio probes nucleosynthesis across the Galaxy. Yet, studies of Cl-bearing molecules have remained

Jelle Vandersnickt, Vincent Vanlaer, Mathijs Vanrespaille, Conny Aerts

Internal magnetic fields are an elusive component of stellar structure. However, they can play an important role in stellar structure and evolution models through efficient angular momentum transport and their impact on internal mixing. We strive to explain the 9 components of one frequency multiplet, identified as a low-order quadrupole gravity mode detecte

Xiyuan Gao

The localized excess of $B\rightarrow K + \textit{invisible}$ events reported by Belle-II is commonly interpreted as a signal for $B\to Ka$, where $a$ is an axion-like particle (ALP). In these proceedings, we summarize two theoretical updates regarding the $b\to sa$ decay amplitude within a minimal UV-complete model for invisible ALPs, namely the DFSZ model.

Horacio Casini, Javier M. Magan

The Doplicher-Haag-Roberts (DHR) reconstruction theorem shows that standard ($0$-form) internal symmetries are associated to groups in relativistic quantum field theory in spacetime dimension $D>2$. In particular, non-invertible symmetry structures in $D>2$ correspond to the choice of a subtheory of a unique parent one, where the symmetry is a compact group.

Puxin Lin, Alessandro Mininno, Gary Shiu

In this work, we investigate the extension of our recent proposal for the Weak Gravity Conjecture (WGC) in AdS space to more general effective field theories. We first extend the conjecture to set-ups where moduli are present and we demand that a particle, produced during the decay of an extremal black hole via the Schwinger effect, is repelled close to the

Chuiyang Kong, Mattia Di Mauro

The Galactic Center excess (GCE) of GeV $\gamma$ rays may hint at dark matter (DM), yet its origin remains debated. Motivated by this, we survey weakly interacting massive particle (WIMP) models that can fit the GCE while satisfying relic-density, direct-detection (DD), and indirect-detection (ID) bounds. We group candidates into hadronic (Higgs portals; sim

Till Bargheer, Albert Bekov

We compute the complete non-planar integrand for the correlation function of four lightest scalar operators in N=4 super Yang-Mills theory at five-loop order. This is equivalent to the super-correlator of nine stress-tensor multiplets in the self-dual theory. Starting with an ansatz of f-graphs, we impose constraints from light-cone limits, and fix the remai

Robin Schäfer, Paul L. Ebert, Noah Hassan, Johannes Reuther

We study the Heisenberg antiferromagnet on the maple-leaf lattice using several numerical approaches, focusing on the numerical linked-cluster expansion (NLCE), which exhibits an unconventional convergence extending to low and even zero temperatures. We evaluate thermodynamic properties as well as spin-spin correlations through the equal-time structure facto

Maria Milena Araujo Felix

The sophistication of modern malware, specifically regarding communication with Command and Control (C2) servers, has rendered static blacklist-based defenses obsolete. The use of Domain Generation Algorithms (DGA) allows attackers to generate thousands of dynamic addresses daily, hindering blocking by traditional firewalls. This paper aims to propose and ev

Yusuf Dalva, Guocheng Gordon Qian, Maya Goldenberg, Tsai-Shien Chen

While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references, spatial arrangements, pose constraints, and layout annotations. We introduce Canvas-to-Image, a unified framework that

Seungjae Lee, Yoonkyo Jung, Inkook Chun, Yao-Chih Lee

Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments - humans and different robots - are abundant, differences in embodiment, camera, and environment hinder their direct use. We address the small-data problem by introducing a unifying, symbolic representation

Hongjin Su, Shizhe Diao, Ximing Lu, Mingjie Liu

Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and computationally expensive. We show that small orchestrators managing other models and a variety of tools can both push the upper bound of intelligence and improve efficiency in solving

Wenbo Hu, Jingli Lin, Yilin Long, Yunlong Ran

Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G$^2$VLM, a geometry grounded vision-language model that bridges two

Dong Wang, Yang Li, Ansong Ni, Ching-Feng Yeh

Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation tasks require coordinated multi-agent workflows, where specialized agents collaborate to produce data that is higher quality, more diverse, and structurally richer. However, existing f

SungWon Kwak, Ivan Minchev, Christoph Pfrommer, Matthias Steinmetz

We investigate the formation of multiple spiral modes in Milky Way-like disk-halo systems without explicitly exciting perturbations. We explore how numerical resolution, the level of local disk stability, and the presence of a live halo influence both the initial appearance and the subsequent evolution of these modes. To characterize spiral structure, we com

Akash Vijay, Jong Yeon Lee

We develop a novel holographic framework to study dynamical phases in random quantum circuits with a global symmetry $G$. Viewing the circuit as a tensor network, we decompose it into two parts: a symmetric layer, which defines an emergent gauge wavefunction in one higher dimension, and a non-symmetric layer, composed of random multiplicity tensors. For $G\,

Brian Ransom

Historically, proofs of $\mathrm{BPI}$ in models without choice have relied on a contradiction framework that was introduced by Halpern. We introduce the filter extension property for permutation models and symmetric extensions, which formalizes the na\"ive approach to extend arbitrary filters to ultrafilters by repeatedly extending filters by minimal increm

Yves H. Kwan, Ziwei Wang, Glenn Wagner, Nick Bultinck

We review the theoretical modelling of moir\'e materials, focusing on various aspects of magic-angle twisted bilayer graphene (MA-TBG) viewed through the lens of Hartree-Fock mean-field theory. We first provide an elementary introduction to the continuum modelling of moir\'e bandstructures, and explain how interactions are incorporated to study correlated st

Elad Kosloff, Jake P. Solomon

We define genus zero open Gromov-Witten invariants with boundary and interior constraints for a Lagrangian submanifold of arbitrary even dimension. The definition relies on constructing a canonical family of bounding cochains that satisfy the point-like condition of the second author and Tukachinsky. Since the Lagrangian is even dimensional, the parameter of

Zihui Xue, Kristen Grauman, Dima Damen, Andrew Zisserman

Can one perceive a video's content without seeing its pixels, just from the camera trajectory-the path it carves through space? This paper is the first to systematically investigate this seemingly implausible question. Towards this end, we propose a contrastive learning framework to train CamFormer, a dedicated encoder that projects camera pose trajectories

Ryan Alweiss

We show that there is a set which is not a set of multiple recurrence despite being a set of recurrence for nil-Bohr sets. This answers Huang, Shao, and Ye's \enquote{higher-order} version of Katznelson's Question on Bohr recurrence and topological recurrence in the negative. Equivalently, we construct a set $S$ so that there is a finite coloring of $\mathbb

Badr Elmansouri, Anas Ouknine, Youssef Ouknine

We prove existence and uniqueness for a one-dimensional multivalued backward stochastic differential equation with jumps. The equation involves a time-indexed family of maximal monotone operators $k_t(\cdot)$ associated with increasing functions $k(t,\cdot)$ taking values in $\mathbb{R}_-$ and having domains that are intervals with time-dependent boundaries.

Gauri Pradhan, Joonas Jälkö, Santiago Zanella-Béguelin, Antti Honkela

Training machine learning models with differential privacy (DP) limits an adversary's ability to infer sensitive information about the training data. It can be interpreted as a bound on adversary's capability to distinguish two adjacent datasets according to chosen adjacency relation. In practice, most DP implementations use the add/remove adjacency relation

Weihao Bo, Shan Zhang, Yanpeng Sun, Jingjing Wu

MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo -- solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in t

Arshya Pooladi-Darvish, Heather Rosehart, Marina R. Everest, Ali R. Khan

Background: Cognitive impairment in multiple sclerosis (MS) is driven by both focal inflammation and compartmentalized neurodegeneration, yet the relative effect of lesion-independent thalamic atrophy on information processing speed (IPS) remains unclear. Methods: This retrospective cohort study included 100 participants with MS. Automatic segmentation techn

Leesa Fleury, Alysa Obertas, Harvey Richer, Jeremy Heyl

We analyse the cooling of white dwarfs in the globular cluster 47 Tucanae to look for evidence of axion emission affecting the rate of white dwarf cooling. If axions exist and couple to electrons, then axions could be produced at an appreciable rate in the electron-degenerate core of a white dwarf through axion bremsstrahlung from electrons. The emission of

Sadegh Shirani, Mohsen Bayati

Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physical or social systems, the interaction pathways driving these interference structures remain largely unobserved. We argue that for identifying population-level causal effects, it is

Daniyal Ganiuly, Nurzhau Bolatbek, Assel Smaiyl

5G Standalone deployments can exhibit uplink misbehavior from user equipment that remains fully compliant with standard control plane procedures. Manipulations such as transmit power inflation, gradual timing drift, and short off grant bursts leave the signaling state intact but distort the expected relationships among the telemetry streams produced by the g

Agnes Korcsak-Gorzo, Jesús A. Espinoza Valverde, Jonas Stapmanns, Hans Ekkehard Plesser

Despite remarkable technological advances, AI systems may still benefit from biological principles, such as recurrent connectivity and energy-efficient mechanisms. Drawing inspiration from the brain, we present a biologically plausible extension of the eligibility propagation (e-prop) learning rule for recurrent spiking networks. By translating the time-driv

Pandiyaraju V, Sreya Mynampati, Abishek Karthik, Poovarasan L

Gliomas are brain tumor types that have a high mortality rate which means early and accurate diagnosis is important for therapeutic intervention for the tumors. To address this difficulty, the proposed research will develop a hybrid deep learning model which integrates U-Net based segmentation and a hybrid DenseNet-VGG classification network with multihead a

Pavel A. Andreev

Different analytical features of the Dzyaloshinskii-Moriya interaction are related to different contributions to the Dzyaloshinskii constant in the microscopic Hamiltonian. Consequences appear in the macroscopic Landau--Lifshitz--Gilbert equation. It leads to various phenomena. Three contributions to the Dzyaloshinskii constant are reviewed and combined in t

Nicholas Davidson, Jonathan R. Kujawa, Robert Muth

Let $\mathbb{k}$ be a characteristic zero domain. We define and study a diagrammatic monoidal $\mathbb{k}$-linear supercategory $\mathbf{Web}^{aff}_{A}$ associated to any locally unital Frobenius $\mathbb{k}$-superalgebra $A$. This category can be viewed variously as an affinization of the finite web category $\mathbf{Web}_{A}$ previously defined by the auth

Titus Lupu, Wendelin Werner

We derive an intensity doubling feature of critical Brownian loop-soups on the cable-graphs of ${\mathbb Z}^d$ for $d \ge 7$ that can be described as follows: In the box $[-N, N]^d$ (and with a probability that goes to $1$ as $N$ goes to infinity), the set of all clusters of Brownian loops that do contain proper self-avoiding cycles of diameter comparable to

Fengze Yu, Leshu Li, Brad McDanel, Sai Qian Zhang

Large language model (LLM) inference often suffers from high decoding latency and limited scalability across heterogeneous edge-cloud environments. Existing speculative decoding (SD) techniques accelerate token generation but remain confined to single-node execution. We propose DSD, a distributed speculative decoding framework that extends SD to multi-device

Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon, Hasan Farooq

The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other

Hugh Brosnahan

Artificial intelligence (AI) scribes, systems that record and summarise patient-clinician interactions, are promoted as solutions to administrative overload. This paper argues that their significance lies not in efficiency gains but in how they reshape medical attention itself. Offering a conceptual analysis, it situates AI scribes within a broader philosoph

Jonas Treplin, Philipp Kleinpaß, Davide Orsucci

Advantage Distillation (AD) is a classical post-processing technique that enhances Quantum Key Distribution (QKD) protocols by increasing the maximum acceptable Quantum Bit Error Rate (QBER) and thus extending the distance at which QKD links can be securely established. AD operates by post-selecting blocks of bits and extracting fewer high-fidelity bits, exh

Renato M. Fonseca, Clara Hernandez-Garcia, Javier M. Lizana, Manuel Perez-Victoria

We revisit the emergence of a Yang-Mills symmetry in theories with massless spin 1 particles from fundamental physical properties of scattering amplitudes. In the standard proofs, some symmetry and reality properties of the coupling constants in three-point amplitudes are assumed. These properties cannot be justified using only three-point amplitudes but we

Naifu Zhang, Wei Tao, Xi Xiao, Qianpu Sun

In recent years, Vision-Language-Action (VLA) models in embodied intelligence have developed rapidly. However, existing adversarial attack methods require costly end-to-end training and often generate noticeable perturbation patches. To address these limitations, we propose ADVLA, a framework that directly applies adversarial perturbations on features projec

Liam Schmidt

Motivated by the need for a more comprehensive algebraic structure to calculate coincidence probabilities of a general decay scheme for gamma ray spectroscopy, we model the decay scheme, rather naturally, as a quiver through which we define a decay quiver. The path algebra of quivers is the underlying, more general, algebra for transition matrices that is ty

Tianyi Xiong, Yi Ge, Ming Li, Zuolong Zhang

Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences. However, their ability to follow diverse, fine-grained evaluation criteria remains underexplored. We develop Multi-Crit, a benchmark for evaluating multimodal judges on their capa

Beth Plale, Neelesh Karthikeyan, Isuru Gamage, Joe Stubbs

AI/ML model cards can contain a benchmarked evaluation of an AI/ML model against intended use but a one time assessment during model training does not get at how and where a model is actually used over its lifetime. Through Patra Model Cards embedded in the ICICLE AI Institute software ecosystem we study model cards as dynamic objects. The study reported her

Satvik Maurya, Thilo Maurer, Markus Bühler, Drew Vandeth

Real-time decoding is crucial for fault-tolerant quantum computing but likely requires specialized hardware such as field-programmable gate arrays (FPGAs), whose parallelism can alter relative algorithmic performance. We analyze FPGA-tailored versions of three decoder classes for quantum low-density parity-check (qLDPC) codes: message passing, ordered statis

Jeet Sarkar, Debabrata Pramanik

In the paper, we investigate the uniqueness problem of entire functions concerning their linear differential polynomial in shift and obtain three results which improve and generalize the recent result due to Qi (Ann. Polon. Math., 102 (2011), 129-142.) in a large extend.

Guy Goldberg, Tom Gur, Sidhant Saraogi

We show a nearly optimal lower bound on the length of linear relaxed locally decodable codes (RLDCs). Specifically, we prove that any $q$-query linear RLDC $C\colon \{0,1\}^k \to \{0,1\}^n$ must satisfy $n = k^{1+\Omega(1/q)}$. This bound closely matches the known upper bound of $n = k^{1+O(1/q)}$ by Ben-Sasson, Goldreich, Harsha, Sudan, and Vadhan (STOC 200

Sriram Tolety

We study whether large language models acting as autonomous bidders can tacitly collude by coordinating when to accept platform posted payouts in repeated Dutch auctions, without any communication. We present a minimal repeated auction model that yields a simple incentive compatibility condition and a closed form threshold for sustainable collusion for subga

Kasra Ghaharian, Simo Dragicevic, Chris Percy, Sarah E. Nelson

Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing concerns around transparency and effectiveness have highlighted the absence of standardized methods for evaluating the quality and impact of these tools. This makes it impossible to gauge true progress; eve