October 2025 arXiv papers — page 65

Jiyu Cui, Fang Wu, Haokai Zhao, Minggao Feng

Large language models have demonstrated remarkable reasoning capabilities across diverse natural language tasks. However, comparable breakthroughs in scientific discovery are more limited, because understanding complex physical phenomena demands multifaceted representations far beyond language alone. A compelling example is the design of functional materials

Tiago Mendonça dos Santos, Rafael Izbicki, Luís Gustavo Esteves

Random Forest ensembles are a strong baseline for tabular prediction tasks, but their reliance on hundreds of deep trees often results in high inference latency and memory demands, limiting deployment in latency-sensitive or resource-constrained environments. We introduce DiNo (Distance with Nodes) and RanBu (Random Bushes), two shallow-forest methods that c

Darrin Lea, James Ghawaly, Golden Richard, Aisha Ali-Gombe

Reverse engineering (RE) of x86 binaries is indispensable for malware and firmware analysis, but remains slow due to stripped metadata and adversarial obfuscation. Large Language Models (LLMs) offer potential for improving RE efficiency through automated comprehension and commenting, but cloud-hosted, closed-weight models pose privacy and security risks and

Michael Bezick, Vittorio Giammarino, Ahmed H. Qureshi

Reinforcement Learning (RL) from raw visual input has achieved impressive successes in recent years, yet it remains fragile to out-of-distribution variations such as changes in lighting, color, and viewpoint. Point Cloud Reinforcement Learning (PC-RL) offers a promising alternative by mitigating appearance-based brittleness, but its sensitivity to camera pos

Yiming Ren, Guowei Wei

Recent years have witnessed a fast growth in mathematical artificial intelligence (AI). One of the most successful mathematical AI approaches is topological data analysis (TDA) via persistent homology (PH) that provides explainable AI (xAI) by extracting multiscale structural features from complex datasets. This work investigates the interpretability and rep

Takahiro Kushida, Kenichiro Tanaka

This paper introduces a novel method for detailed 3D shape reconstruction utilizing thermal polarization cues. Unlike state-of-the-art methods, the proposed approach is independent of illumination and material properties. In this paper, we formulate a general theory of polarization observation and show that long-wave infrared (LWIR) polarimetric imaging is f

Kaustav Chatterjee, Ulrik Lund Andersen

Gaussian states are of fundamental importance in the physics of continuous-variable quantum systems. They are appealing for the experimental ease with which they can be produced, and for their compact and an elegant mathematical description. Nevertheless, many proposed quantum technologies require us to go beyond the realm of Gaussian states and introduce no

Jubilee Lee, Daniele E. Schiavazzi

Implicit neural representations (INRs, also known as neural fields) have recently emerged as a powerful framework for knowledge representation, synthesis, and compression. By encoding fields as continuous functions within the weights and biases of deep neural networks-rather than relying on voxel- or mesh-based structured or unstructured representations-INRs

S. Palafox, M. Salado-Mejía, M. Santiago-García, R. Román-Ancheyta

In the deep-strong coupling (DSC) regime, the interaction between light and matter exceeds their bare frequencies, leading to an effective decoupling. Theoretical and experimental evidence for this behavior has relied solely on measurements of local observables at equilibrium. However, such a local approach is insufficient to accurately describe energy fluxe

Yanzhi Chen, Zijing Ou, Adrian Weller, Michael U. Gutmann

Estimating mutual information (MI) is a fundamental task in data science and machine learning. Existing estimators mainly rely on either highly flexible models (e.g., neural networks), which require large amounts of data, or overly simplified models (e.g., Gaussian copula), which fail to capture complex distributions. Drawing upon recent vector copula theory

Sraavya Sambara, Sung Eun Kim, Xiaoman Zhang, Luyang Luo

Current Vision-Language Models (VLMs) struggle to ground anatomical regions in 3D medical images and reason about them in a step-by-step manner, a key requirement of real-world diagnostic assessment. This ability is essential for aligning model outputs with the diagnostic workflows clinicians use in practice, enabling trustworthy clinician-AI collaboration.

Arina Medvedeva, Edoardo Balzani, Alex H Williams, Stephen L Keeley

The Poisson Generalized Linear Model (GLM) is a foundational tool for analyzing neural spike train data. However, standard implementations rely on discretizing spike times into binned count data, limiting temporal resolution and scalability. Here, we develop Monte Carlo (MC) methods and polynomial approximations (PA) to the continuous-time analog of these mo

Jesse Haworth, Juo-Tung Chen, Nigel Nelson, Ji Woong Kim

Robotic suturing is a prototypical long-horizon dexterous manipulation task, requiring coordinated needle grasping, precise tissue penetration, and secure knot tying. Despite numerous efforts toward end-to-end autonomy, a fully autonomous suturing pipeline has yet to be demonstrated on physical hardware. We introduce SutureBot: an autonomous suturing benchma

José Antonio Nájera, Indranil Banik, Harry Desmond, Vasileios Kalaitzidis

We test whether $f(Q)$ symmetric teleparallel gravity theories can solve the Hubble tension consistently with DESI DR2 BAO. We consider three $f(Q)$ functional forms: logarithmic, exponential, and hyperbolic tangent. We extend these models by allowing a cosmological constant, and compare to phenomenological models with a flexible exponential, hyperbolic seca

Arnab Ghosh, Patrick Brosseau, Dmitry N. Dirin, Rui Tao

Since Landau's theory, polarons have been understood as quasiparticles in which charges are dressed by the lattice field, yet decades of transport and spectroscopic studies have yielded only static indirect renormalizations. Whether such dressing can dynamically reorganize electronic spectra to generate new quantum-coherent states has remained unresolved. He

Alexey V. Gavrilov

We give a formula for the Euler characteristic of a triangulated manifold of even dimension in terms of the numbers of even-dimensional faces only. The coefficients in this formula are universal (they do not depend on the dimension of the manifold).

Christo Guguschev, Michael Schulze, Andrea Dittmar, Detlef Klimm

MgO single crystals with diameters between 3.5 and 5 mm and lengths up to 40 mm were grown by the optical floating zone technique (OFZ). Despite challenging material properties such as the high melting point of 2825 {\deg}C, very high evaporation rate and perfect {100} cleaving characteristics, crack-free crystals were grown at high growth rates exceeding 40

Sizhe Rao, Runqiu Zhang, Sajal Saha, Liang Chen

Falls among elderly and disabled individuals remain a leading cause of injury and mortality worldwide, necessitating robust, accurate, and privacy-aware fall detection systems. Traditional fall detection approaches, whether centralized or point-wise, often struggle with key challenges such as limited generalizability, data privacy concerns, and variability i

Sam Hughes, Wolfgang Lueck

We develop the theory of $L^2$-torsion of an automorphism of a group and compute it for every automorphism of a group which is hyperbolic and one-ended relative to a finite collection of virtually polycyclic groups. We also prove a combination formula for the $L^2$-torsion of a group in terms of the $L^2$-torsion of its stabilisers of a sufficiently nice act

Asif Islam, Farhan Ishtiaque, Md. Muhyminul Haque, Farhana Sarker

The prevalence of online learning poses a vital challenge in real-time monitoring of students' concentration. Traditional methods such as questionnaire assessments require manual intervention, and webcam-based monitoring fails to provide accurate insights about learners' mental focus as it is deceived by mere screen fixation without cognitive engagement. Exi

Josh McGiff, Khanh-Tung Tran, William Mulcahy, Dáibhidh Ó Luinín

We present Irish-BLiMP (Irish Benchmark of Linguistic Minimal Pairs), the first dataset and framework designed for fine-grained evaluation of linguistic competence in the Irish language, an endangered language. Drawing on a variety of linguistic literature and grammar reference works, we manually constructed and reviewed 1020 minimal pairs across a taxonomy

Zheng-Xin Yong, Stephen H. Bach

We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaking. Specifically, after benign reasoning training on math or code domains, RLMs will use multiple strategies to circumvent their own safety guardrails. One strategy is to introduce benign assumptions about users and sc

Francisco J. Cruz-Zamorano, Konstantinos Zarvalis

This paper deals with semigroups of holomorphic self-maps of the upper half-plane that exhibit an extremal (i.e. the slowest possible) rate of convergence to their Denjoy--Wolff point. The main novelty lies in the parabolic case of zero hyperbolic step. We provide several characterizations for such semigroups in terms of the Herglotz representation of their

Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan

Forecasting in the real world requires integrating structured time-series data with unstructured textual information, but existing methods are architecturally limited by fixed input/output horizons and are unable to model or quantify uncertainty. We address this challenge by introducing LLM-integrated Bayesian State space models (LBS), a novel probabilistic

Mattie Tesfaldet, Adam W. Harley, Konstantinos G. Derpanis, Derek Nowrouzezahrai

Tracking a point through a video can be a challenging task due to uncertainty arising from visual obfuscations, such as appearance changes and occlusions. Although current state-of-the-art discriminative models excel in regressing long-term point trajectory estimates -- even through occlusions -- they are limited to regressing to a mean (or mode) in the pres

Yuncheng You

In this paper we study robust synchronization of time-fractional Hopfield neural networks with memristive couplings and Hebbian learning rules. This new model of artificial neural networks exhibits strong memory and long-range path-dependence in learning processes. Through scaled group estimates it is proved that under rather general assumptions the solution

Mina Aganagic, Edward Frenkel, Andrei Okounkov

In our paper arXiv:1701.03146 we established, for every simply-laced Lie algebra g, a canonical isomorphism between the spaces of deformed conformal blocks of the deformed W-algebra and the quantum affine algebra corresponding to g, which we view as a q-deformation of the quantum Langlands correspondence. This was done by realizing the deformed conformal blo

Florian D. van Leeuwen, Sara van Erp

Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear. This paper compares MCMC and non-MCMC methods for high-dimensional penalized regression, examinin

Lidiya Ahmed, Michael L Stevens, Kristoff Paulson, Anthony W Case

We describe a novel scheme for analyzing particle detector measurements when a well-calibrated, similarly instrumented spacecraft is present in a similar orbit. To prepare ground truth from measurements provided by a reference spacecraft, the method uses dynamic time warping (DTW)--a technique often used for pattern-matching in time series data. An artificia

Urs Frauenfelder, Joa Weber

In this article, we are considering the Hessian of the area functional in a non-Darboux chart. This does not seem to have been considered before and leads to an interesting new mathematical structure which we introduce in this article and refer to as almost extendable weak Hessian field. Our main result is a Fredholm theorem for Robbin-Salamon operatorsassoc

Max M. Kurzner, Patrick Côté, Laura Ferrarese, Kaixiang Wang

We present a study of morphologies, based on deep $u^{*}g^{\prime}i^{\prime}z^{\prime}$ imaging of the Virgo Cluster from the Next Generation Virgo Cluster Survey (NGVS), for 3689 Virgo cluster members spanning a mass range of $\sim$$10^{11}M_{\odot}$ to $\sim$$10^5~M_{\odot}$. Our analysis introduces a new, two-component visual classification scheme develop

Srivathsan Badrinarayanan, Yue Su, Janghoon Ock, Alan Pham

Protein mutations can have profound effects on biological function, making accurate prediction of property changes critical for drug discovery, protein engineering, and precision medicine. Current approaches rely on fine-tuning protein-specific transformers for individual datasets, but struggle with cross-dataset generalization due to heterogeneous experimen

Heeju Lim, Victor E. Lachos, Victor H. Lachos

The Heckman selection model is widely used in econometric analysis and other social sciences to address sample selection bias in data modeling. A common assumption in Heckman selection models is that the error terms follow an independent bivariate normal distribution. However, real-world data often deviates from this assumption, exhibiting heavy-tailed behav

Li Zhang, Jaromir Savelka, Kevin Ashley

Large language models (LLMs) with extended context windows show promise for complex legal reasoning tasks, yet their ability to understand long legal documents remains insufficiently evaluated. Developing long-context benchmarks that capture realistic, high-stakes tasks remains a significant challenge in the field, as most existing evaluations rely on simpli

Yacin Ameur

In this note we examine the relationship between two-dimensional Coulomb systems and the thermal equilibrium measure, such as defined in the lecture notes [arXiv:2407.21194v1], pointing out some of its discrepancies with respect to the one-particle distribution.

Giovane Ferreira, Vanessa Ramos

We study a broad class of local homeomorphisms and continuous potentials, proving the existence and uniqueness of weak Gibbs measures. From the Gibbs property, we show the uniqueness of equilibrium states and derive a large deviations principle. Furthermore, we extend these results to a class of attractors that are semiconjugate to local homeomorphisms from

A. Amiri, K. Azizi

We investigate the nonleptonic two-body weak decays of the single bottom baryon $\Omega_{b}^{*}$ into $\Omega_{c}^{*}P(V)$ final states within the factorization framework. Employing this framework and incorporating the contributions from the current-current operators, we compute the tree-level decay amplitudes and decay widths of the $\Omega_{b}^{*}\rightarr

Alfonso Garmendia, David Miyamoto, Leonid Ryvkin

The classical Serre-Swan theorem asserts that any finitely generated projective module over the algebra $C^\infty(M)$ of smooth functions of a manifold $M$ can be realized as the sections of a vector bundle over $M$. In this article, we extend this theorem beyond the projective case by introducing a notion of singular vector bundle whose sections can realize

Vittorio De Falco, Marco Gallo

This work is based on the letter Phys. Lett. B, 865, 139484 (2025), where we developed the analytical expression of the coordinate time in terms of the eccentric anomaly at the second post-Newtonian order in General Relativity for a compact binary system moving on eccentric orbits. The aim of this paper is to provide more details about the performed calculat

Felipe Gonçalves, João Paulo Ferreira

We prove a sharp Fourier extension inequality on the circle for the Tomas-Stein exponent for functions whose spectrum $\{\pm \lambda_n\}$ satisfies $\lambda_{n+1}>3 \lambda_{n}$.

Moin Safdar, Shahzaib Iqbal, Mubeen Ghafoor, Tariq M. Khan

Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monitoring because it provides precise morphological and spatial information on anatomical structures that directly influence treatment decisions. Convolutional neural networks significantly impact image segmentation; howev

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally

In the past year, generative AI (GenAI) models have received a tremendous amount of attention, which in turn has increased attention to computing systems for training and inference for GenAI. Hence, an update to this survey is due. This paper is an update of the survey of AI accelerators and processors from past seven years, which is called the Lincoln AI Co

Soham Hans, Stacy Marsella, Sophia Hirschmann, Nikolos Gurney

Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack chains. However, real-time defense requires the ability to infer attacker intent and cognitive strategy directly from low-level system telemetry such as intrusion detection system (IDS) logs. In this paper, we pr

Ming Du, Hanna Ruth, Steven Henke, Yi Jiang

Ptychography has become an indispensable tool for high-resolution, non-destructive imaging using coherent light sources. The processing of ptychographic data critically depends on robust, efficient, and flexible computational reconstruction software. We introduce Pty-Chi, an open-source ptychographic reconstruction package built on PyTorch that unifies state

Zhenghao Zeng, David Arbour, Avi Feller, Ishita Dasgupta

Human annotations play a crucial role in evaluating the performance of GenAI models. Two common challenges in practice, however, are missing annotations (the response variable of interest) and cluster dependence among human-AI interactions (e.g., questions asked by the same user may be highly correlated). Reliable inference must address both these issues to

Tom Reed, Tegan McCaslin, Luca Righetti

Most frontier AI developers publicly document their safety evaluations of new AI models in model reports, including testing for chemical and biological (ChemBio) misuse risks. This practice provides a window into the methodology of these evaluations, helping to build public trust in AI systems, and enabling third party review in the still-emerging science of

Natasha Johnson, Amanda Bertsch, Maria-Emil Deal, Emma Strubell

As language models become capable of processing increasingly long and complex texts, there has been growing interest in their application within computational literary studies. However, evaluating the usefulness of these models for such tasks remains challenging due to the cost of fine-grained annotation for long-form texts and the data contamination concern

Rattana Pukdee, Ziqi Ke, Chirag Gupta

We study the problem of regression with interval targets, where only upper and lower bounds on target values are available in the form of intervals. This problem arises when the exact target label is expensive or impossible to obtain, due to inherent uncertainties. In the absence of exact targets, traditional regression loss functions cannot be used. First,

Noah DAmico, Sandeep Puri, Ian Jones, Andrew Gillespie

The neutron detection efficiency of Columbia Resin 39 (CR-39) nuclear track detectors was assessed for AmBe, 252Cf, and D-T (14 MeV) neutron source spectra. A boron nitride (BN) coating for CR-39 was developed to enhance the neutron detection efficiency by converting neutrons into energetic alpha particles through the well-known 10B(n,a)7Li reaction. Separat

André Carvalho, Ana-Catarina C. Monteiro

In this paper, we explore conjugacy languages when the base problem is the generalized conjugacy problem (with constraints): given $g\in G$ and $U\subset G$, does $g$ have a conjugate in $U$ (with conjugators in a certain subset)? To do so, for subsets $U,V\subseteq G$, we define the corresponding languages $\text{ConjGeo(U,V)}$, $\text{CycGeo(U)}$, $\text{C

Luigi D. C. Soares, Mário S. Alvim, Natasha Fernandes

Quantitative information flow (QIF) is concerned with assessing the leakage of information in computational systems. In QIF there are two main perspectives for the quantification of leakage. On one hand, the static perspective considers all possible runs of the system in the computation of information flow, and is usually employed when preemptively deciding

Pratik Poudel

The Key-Value (KV) cache is integral to efficient autoregressive inference in large language models (LLMs), yet its unbounded growth in stateful multi-turn scenarios presents major challenges. This paper examines the interplay between KV cache management strategies, the architectural context limits of models like meta-llama/Meta-Llama-3-8b-instruct, and the

Alejandro Francetich, Burkhard C. Schipper

We consider a principal who wishes to screen an agent with \emph{discrete} types by offering a menu of \emph{discrete} quantities and \emph{discrete} transfers. We assume that the principal's valuation is discrete strictly concave and use a discrete first-order approach. We model the agent's cost types as non-integer, with integer types as a limit case. Our

Howard Baer, Vernon Barger, Jessica Bolich, Dibyashree Sengupta

There is great interest within the particle physics community for building a $\mu^+\mu^-$ collider with center-of-mass (CoM) energies ranging from $\sqrt{s}\sim$ 1-14 TeV. For Beyond-the-Standard-Model (BSM) physics, natural supersymmetry seems perhaps the most motivated, plausible extension of the Standard Model. Here, we examine what can be accomplished by

Christiaan Boersma, Alexandros Maragkoudakis, Louis J. Allamandola, Jesse D. Bregman

Observations by the SpectroPhotometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) are combined with Spitzer spectral map data to study the aromatic, aliphatic, and PAH size evolution across the northwest photo-dissociation region (PDR) of the Iris Nebula (NGC7023). The 3.3-3.4 $\mu$m complex (I$_{3.3}$) and 11.2 $\mu$m

Alejandro Francetich, Burkhard C. Schipper

We analyze a principal-agent procurement problem in which the principal (she) is unaware some of the marginal cost types of the agent (he). Communication arises naturally as some types of the agent may have an incentive to raise the principal's awareness (totally or partially) before a contract menu is offered. The resulting menu must not only reflect the pr

Russell Gabrys, Stefan Sremac

In this paper we investigate the discrete version of the classical hanging chain problem. We generalize the problem, by allowing for arbitrary mass and length of each link. We show that the shape of the chain can be obtained by solving a convex optimization problem. Then we use optimality conditions to show that the problem can be further reduced to solving

Sydney M. Katz, Robert J. Moss, Dylan M. Asmar, Wesley A. Olson

Aircraft collision avoidance systems is critical to modern aviation. These systems are designed to predict potential collisions between aircraft and recommend appropriate avoidance actions. Creating effective collision avoidance systems requires solutions to a variety of technical challenges related to surveillance, decision making, and validation. These cha

Lennart Becker, Stefan Teufel, Marius Wesle

We prove an adiabatic theorem for infinitely extended lattice fermion systems with gapped ground states, allowing perturbations that may close the gap. The Heisenberg dynamics on the CAR-algebra is generated by a time dependent two-parameter family of Hamiltonians $H^{\varepsilon,\eta}_t=\eta^{-1}(H_t+\varepsilon(H^1_t+V_t))$, where $H_t$ is assumed to have

Poonam Chandra

The interaction of post-explosion supernova ejecta with the surrounding circumstellar medium creates emission across the electromagnetic spectrum. Since the circumstellar medium is created by the mass lost from the progenitor star, it carries tell-tale signatures of the progenitor. Consequently, observations and modeling of radiation produced by the interact

Sydney A. Willis, Serge A. Krasnokutski, Nathaniel J. Morin, Robin T. Garrod

Icy interstellar dust grains are a source of complex organic molecule (COM) production, although their formation mechanisms are debated. Laboratory experiments show that atomic C deposited onto interstellar ice analogs can react with solid-phase NH3 to form a CHNH2 radical, a possible precursor to COMs, including aminoketene (NH2CHCO). We used astrochemical

Adriel G B Mocellin, J. Caraça-Valente, C. Covault, E. Dalcan

The Pierre Auger Observatory (Auger) and the Telescope Array (TA) are the world's two largest ultra-high-energy cosmic ray (UHECR) observatories. They operate in the Southern and Northern hemispheres, respectively, at similar latitudes but with distinct surface detector (SD) designs. A significant challenge in studying UHECR physics across the full sky is th

Cedegao E. Zhang, Cédric Colas, Gabriel Poesia, Joshua B. Tenenbaum

Reasoning models (RMs), language models (LMs) trained with reinforcement learning to produce long-form natural language reasoning, have been remarkably successful, but they still require large amounts of computation and data to train, and can be slow and expensive to run. In this paper, we show that standard instruct LMs can already be elicited to be strong

Victor Augias, Lina Uhe

We introduce convex function intervals (CFIs): families of convex functions satisfying given level and slope constraints. CFIs naturally arise as constraint sets in economic design, including problems with type-dependent participation constraints and two-sided (weak) majorization constraints. Our main results include: (i) a geometric characterization of the

Deniz Eren Mol, İbrahim Asrın Üzgüç, Ulaş Eyüpoğlu, Kübra Atar

Programmable photonic computers necessitate the integration of electrically-tunable compact components into the photonic devices. In the state-of-the-art photonic quantum computers~(PQCs), phase-shift and displacement gates can be implemented in an electrically-programmable way. An efficient PQC, however, necessitates also the tuning of third or higher order

Xingyu Wang, Chang-Han Rhee

Stochastic gradient descent (SGD) and its variants enable modern artificial intelligence. However, theoretical understanding lags far behind their empirical success. It is widely believed that SGD has a curious ability to avoid sharp local minima in the loss landscape, which are associated with poor generalization. To unravel this mystery and further enhance

Amir Michaelis, Yael Hillman, Hagai B. Perets

We investigate a sub-Chandrasekhar mass double detonation pathway for Type Ia supernovae arising from single degenerate helium accreting carbon-oxygen white dwarfs. Building on our previous one dimensional study of recurrent helium novae (Hillman et al. 2025), we evolve a 0.7 solar mass white dwarf through steady accretion at 10^-8 Msun yr^-1 until it reache

Yirong Shen, Lu Gan, Cong Ling

Generative models that maximize model likelihood have gained traction in many practical settings. Among them, perturbation based approaches underpin many strong likelihood estimation models, yet they often face slow convergence and limited theoretical understanding. In this paper, we derive a tighter likelihood bound for noise driven models to improve both t

Dimitris Saraidaris, Leo Shaposhnik

Recent work on algebraic formulations of holographic dualities in terms of large $N$ algebras has revealed a deep connection between the properties of the associated spectral functions and the emergence of a semiclassical spacetime and causal horizons therein. One of the main lessons is that, for a radial direction to emerge, the spectral function has to exh

Oscar Bouverot-Dupuis, Laura Foini, Alberto Rosso

The generic Mott transition in one-dimensional quantum systems can be described by the sine-Gordon model with a tilt via bosonization. Because the configuration space of the sine-Gordon model separates into distinct topological sectors, standard local Monte Carlo schemes are limited to very small system sizes. To overcome this limitation, we introduce the sm

Mohamed Hany Radwan

We develop a simple framework for implementing a type of path integral "surgery" via correlated averaging over codimension-one defects/extended operators. This technique is used to construct replica manifolds by effectively cutting and gluing the path integral without explicitly modifying the underlying manifold. We argue that restricted forms of this averag

V. Vallucci-Goy, P. Hennebelle, U. Lebreuilly, G. Verrier

Context: Dust grains undergo significant growth in star-forming environments, especially in dense regions prone to gravitational collapse. Although dust is generally assumed to represent $1 \%$ of the gas mass, dust density variations are expected on small scales due to differential dynamics with the gas, leading to enhanced coagulation rates in regions of d

Pau Amaro Seoane

Sagittarius A* exhibits daily energetic flares characterized by non-thermal emission in the infrared and X-ray bands. While the underlying accretion flow is a Radiatively Inefficient Accretion Flow (RIAF) peaking at radio frequencies, the mechanism powering these non-thermal transients remains debated. Stellar dynamics predict a population of faint brown dwa

Zhiwei Shao, Ying Zu, Andrés N. Salcedo, Jiaqi Wang

We report the first direct measurement of galaxy assembly bias, a critical systematic in cosmology, from the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey. We introduce a novel, cosmology-independent method to measure the halo occupation distribution (HOD) by combining a state-of-the-art group catalog with weak gravitational lensing. For g

Will Barker, Dario Francia, Carlo Marzo, Alessandro Santoni

The covariant description of massless particles of arbitrary spin typically employs symmetric tensors of rank $s$ and rests on a local symmetry carried by symmetric tensor parameters of rank $s-1$, suitably generalizing the $U(1)$ transformations of Maxwell's theory and the diffeomorphisms underlying general relativity. Here we show that Fronsdal's action is

Julien Barrat, Deniz N. Bozkurt, Enrico Marchetto, Alessio Miscioscia

We compute thermal holographic correlators by combining their analytic structure with the Kubo-Martin-Schwinger (KMS) condition and multi-stress tensor OPE coefficients determined from the dual AdS description. We focus on two-point functions of identical scalar operators with integer conformal dimensions at zero spatial separation. In the black brane backgr

Lei Su

We generalize the gauging of $\mathbb{Z}_2$ symmetries by inserting Majorana fermions, establishing parallel duality correspondences for bosonic and fermionic lattice systems. Using this fermionic gauging, we construct fermionic analogs of $\mathbb{Z}_2$ gauge theories dual to the transverse-field Ising model, interpretable as Majorana stabilizer codes. We d

Lorenzo Speri, Rodrigo Tenorio, Christian Chapman-Bird, Davide Gerosa

Extreme mass-ratio inspirals (EMRIs) are one of the key sources of gravitational waves for space-based detectors such as LISA. However, their detection remains a major data analysis challenge due to the signals' complexity and length. We present a semi-coherent, time-frequency search strategy for detecting EMRI harmonics without relying on full waveform temp

Sheng-Jie Huang, Alison Warman, Sakura Schafer-Nameki, Yanzhu Chen

In universal fault-tolerant quantum computing, implementing logical non-Clifford gates often demands substantial spacetime resources for many error-correcting codes, including the high-threshold surface code. A critical mission for realizing large-scale quantum computing is to develop simple and resource-efficient implementations of logical non-Clifford gate

J. Borowicz, P. Pietrukowicz, J. Skowron, M. J. Mróz

Blue Large-Amplitude Pulsators (BLAPs) are rare short-period ($\lesssim$80 min) pulsating variable stars exhibiting large-amplitude brightness variations (typically between 0.1 and 0.4 mag). As a recently discovered class of radial-mode pulsators, the origin and nature of these variables remain the subject of ongoing investigations. Here, we present a compre

Yihao Meng, Hao Ouyang, Yue Yu, Qiuyu Wang

State-of-the-art text-to-video models excel at generating isolated clips but fall short of creating the coherent, multi-shot narratives, which are the essence of storytelling. We bridge this "narrative gap" with HoloCine, a model that generates entire scenes holistically to ensure global consistency from the first shot to the last. Our architecture achieves

Nanoom Lee, Yacine Ali-Haïmoud, Marc Kamionkowski

We develop a highly accurate analytic approximation for small-scale non-cold relic perturbations by solving the collisionless Boltzmann equation in the quasi-stationary regime. The approximation is implemented in CLASSIER (CLASS Integral Equation Revision), a modified version of the Boltzmann solver CLASS that replaces the traditional truncated Boltzmann hie

Gordon Guocheng Qian, Ruihang Zhang, Tsai-Shien Chen, Yusuf Dalva

Existing personalized image generators still struggle to preserve multiple reference identities in natural and coherent multi-human generations. To address these limitations, we present CanvasComposer, an interactive framework for personalized group photo generation. Inspired by professional image-editing software, CanvasComposer allows users to place refere

Nimrod Berman, Omkar Joglekar, Eitan Kosman, Dotan Di Castro

Recent advances in generative modeling have positioned diffusion models as state-of-the-art tools for sampling from complex data distributions. While these models have shown remarkable success across single-modality domains such as images and audio, extending their capabilities to Modality Translation (MT), translating information across different sensory mo

Yuxuan Bian, Xin Chen, Zenan Li, Tiancheng Zhi

Unified, generalizable semantic control in video generation remains a critical open challenge. Existing methods either introduce artifacts by enforcing inappropriate pixel-wise priors from structure-based controls, or rely on non-generalizable, condition-specific finetuning or task-specific architectures. We introduce Video-As-Prompt (VAP), a new paradigm th

Mateo Guaman Castro, Sidharth Rajagopal, Daniel Gorbatov, Matt Schmittle

A fundamental challenge in robot navigation lies in learning policies that generalize across diverse environments while conforming to the unique physical constraints and capabilities of a specific embodiment (e.g., quadrupeds can walk up stairs, but rovers cannot). We propose VAMOS, a hierarchical VLA that decouples semantic planning from embodiment groundin

Anthony GX-Chen, Jatin Prakash, Jeff Guo, Rob Fergus

It is commonly believed that optimizing the reverse KL divergence results in "mode seeking", while optimizing forward KL results in "mass covering", with the latter being preferred if the goal is to sample from multiple diverse modes. We show -- mathematically and empirically -- that this intuition does not necessarily transfer well to doing reinforcement le

Sk Asrap Murshed, Bitan Roy

Motivated by recent experiments, here we identify valley-coherent charge density wave (VC-CDW) in the nondegenerate quarter metal for the entire family of chirally-stacked $n$ layer graphene, encompassing rhombohedral multilayer, Bernal bilayer, and monolayer cousins. Besides the hallmark broken translational symmetry, yielding a modulated charge density ove

Minjie Hong, Zetong Zhou, Zirun Guo, Ziang Zhang

Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning recommendation models (GRRMs), where the intrinsic modeling gap between textual semantics and collaborative filtering signals, combined with the sparsity and stochasticity of user fee

Guangqi Jiang, Haoran Chang, Ri-Zhao Qiu, Yutong Liang

This paper presents GSWorld, a robust, photo-realistic simulator for robotics manipulation that combines 3D Gaussian Splatting with physics engines. Our framework advocates "closing the loop" of developing manipulation policies with reproducible evaluation of policies learned from real-robot data and sim2real policy training without using real robots. To ena

Yuhan Liu, Lianhui Qin, Shengjie Wang

Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle when reasoning over information-intensive images that densely interleave textual annotations with fine-grained graphical elements. The main challenges lie in precisely localizing critical cues in dense layouts and multi-hop reasoning to integr

Anujraaj Argo Goyal, Guocheng Gordon Qian, Huseyin Coskun, Aarush Gupta

Adapter-based training has emerged as a key mechanism for extending the capabilities of powerful foundation image generators, enabling personalized and stylized text-to-image synthesis. These adapters are typically trained to capture a specific target attribute, such as subject identity, using single-image reconstruction objectives. However, because the inpu

Nathan Steinle, Matthew Mould, Sarah Al-Humaikani, Austin MacMaster

SS$\,$433 is one of the most extreme Galactic X-ray binaries, launching semi-relativistic jets and showing clear signs of super-critical accretion onto what is likely a black hole. Yet the properties of the binary system that produced it remain uncertain. To solve the inverse problem of inferring the progenitor properties of binaries that evolve into SS$\,$4

Mingmeng Geng, Thierry Poibeau

With the widespread use of large language models (LLMs), many researchers have turned their attention to detecting text generated by them. However, there is no consistent or precise definition of their target, namely "LLM-generated text". Differences in usage scenarios and the diversity of LLMs further increase the difficulty of detection. What is commonly r

Xueyan Zou, Jianglong Ye, Hao Zhang, Xiaoyu Xiang

With the rapid growth of research in AI and robotics now producing over 10,000 papers annually it has become increasingly difficult for researchers to stay up to date. Fast evolving trends, the rise of interdisciplinary work, and the need to explore domains beyond one's expertise all contribute to this challenge. To address these issues, we propose a general

Elie Aljalbout, Jiaxu Xing, Angel Romero, Iretiayo Akinola

Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driven by the extensive use of simulation as a critical tool for training and testing robotic systems prior to their deployment in real-world environments. However, simulations consist

Dean L Slack, G Thomas Hudson, Thomas Winterbottom, Noura Al Moubayed

Inspired by the performance and scalability of autoregressive large language models (LLMs), transformer-based models have seen recent success in the visual domain. This study investigates a transformer adaptation for video prediction with a simple end-to-end approach, comparing various spatiotemporal self-attention layouts. Focusing on causal modeling of phy

Paulina Alejandra Roco-Avilez, Roger Coziol, Juan Pablo Torres-Papaqui, Karla Alejandra Cutiva-Alvarez

This article presents the first part of a study aimed at testing the unification paradigm for AGN (UPAGN) using the SED reconstruction code X-CIGALE. Our method consists in obtaining a generic SED for a large sample of Seyfert 1 (Sy1; part 1), then applying this SED to Seyfert 2 (Sy2; Part~II), expecting that the only difference will be the line-of-sight (LO

Aron Brenner, Line Roald, Saurabh Amin

Data center electricity use may reach 12% of U.S. demand by 2030, alongside growing ability to shift workloads geographically in response to prices or carbon signals. We examine the system-level implications of such strategic flexibility using a bilevel two-zone model that couples economic dispatch with consumer cost minimization. Two market failures emerge.

Vera Mikheeva, Daniele Guerci, Daniel Kaplan, Elio J. König

Motivated by recent experiments on rhombohedral stacked multilayer graphene and the observation of the anomalous Hall effect in a spontaneous spin-valley polarized quarter metal state, we calculate the anomalous Hall conductivity for this system in the presence of two types of impurities: weak and dense as well as sparse and strong. Our calculation of $\sigm