October 2025 arXiv papers — page 190

José Antonio Marín Guzmán, Yu-Xin Wang, Tom Manovitz, Paul Erker

Autonomous quantum machines (AQMs) execute tasks without requiring time-dependent external control. Motivations for AQMs include the restrictions imposed by classical control on quantum machines' coherence times and geometries. Most AQM work is theoretical and abstract; yet an experiment recently demonstrated AQMs' usefulness in qubit reset, crucial

A. Lola Danhaive, Sandro Tacchella

Wide-field slitless spectroscopy (WFSS) is a powerful tool for studying large samples of galaxies across cosmic times. With the arrival of JWST, and its NIRCAM grism mode, slitless spectroscopy can reach a medium spectral resolution of $(R\sim 1600)$, allowing it to spatially resolve the ionised-gas kinematics out to $z\sim 9$. However, the kinematic informa

Yunwei Deng, Hui Li, Federico Marinacci, Yang Ni

Major mergers of galaxies are likely to trigger bursty star formation activities. The accumulation of dense gas and the boost of star formation efficiency (SFE) are considered to be the two main drivers of starbursts. However, it remains unclear how each process operates on the scale of individual star-forming clouds. Here, we present a high-resolution (2 Ms

Matthew C. Nixon, R. Sander Somers, Arjun B. Savel, Jegug Ih

Sub-Neptunes with substantial atmospheres may possess magma oceans in contact with the overlying gas, with chemical interactions between the atmosphere and magma playing an important role in shaping atmospheric composition. Early JWST observations have found high abundances of carbon- and oxygen-bearing molecules in a number of sub-Neptune atmospheres, which

Jed McKinney, Miriam Eleazer, Alexandra Pope, Anna Sajina

We present a large spectroscopic survey with \textit{JWST}'s Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS) targeting $37$ infrared-bright galaxies between $z=0.65-2.46$ with infrared luminosities $\log L_{\rm IR}/L_\odot>11.5$ and $\log M_*/M_\odot=10-11.5$. Targets were taken from a \textit{Spitzer} $24\,\mu$m-selected sample with archiva

Ci-Siang Lin, Min-Hung Chen, I-Jieh Liu, Chien-Yi Wang

Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense mask annotations, which could be computation-consuming and less scalable. In this work, we rethink the RVOS problem and aim to investigate the key to this task. Based on existing fo

Yunhao Fang, Weihao Yu, Shu Zhong, Qinghao Ye

Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in attention-based Transformers. Inspired by the Multi-Store Model in cognitive science, we introduce a memory framework of artificial neural networks. Our method maintains a sliding windo

Natacha Kuete Meli, Shuteng Wang, Marcel Seelbach Benkner, Michele Sasdelli

Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing that leverages quantum-mechanical effects in computations inaccessible to cl

Gangwei Xu, Haotong Lin, Hongcheng Luo, Xianqi Wang

This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps

Zezhong Qian, Xiaowei Chi, Yuming Li, Shizun Wang

Wrist-view observations are crucial for VLA models as they capture fine-grained hand-object interactions that directly enhance manipulation performance. Yet large-scale datasets rarely include such recordings, resulting in a substantial gap between abundant anchor views and scarce wrist views. Existing world models cannot bridge this gap, as they require a w

Sumeet Ramesh Motwani, Alesia Ivanova, Ziyang Cai, Philip Torr

Large language models excel at short-horizon reasoning tasks, but performance drops as reasoning horizon lengths increase. Existing approaches to combat this rely on inference-time scaffolding or costly step-level supervision, neither of which scales easily. In this work, we introduce a scalable method to bootstrap long-horizon reasoning capabilities using o

Markus Ackermann, Denys Malyshev, Dmitry V. Malyshev

Future gamma-ray survey instruments, such as newASTROGAM and AMEGO-X, will significantly improve previous and current all-sky surveys at MeV energies. In this paper we discuss the continuum emission from the Milky Way, two prominent large extended sources, the Fermi bubbles and Loop I, and the extragalactic gamma-ray background. We highlight the importance o

Siyoon Jin, Seongchan Kim, Dahyun Chung, Jaeho Lee

Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a sys

Yue Li, Ran Tao, Derek Hommel, Yusuf Denizay Dönder

Text-to-SQL benchmarks have traditionally only tested simple data access as a translation task of natural language to SQL queries. But in reality, users tend to ask diverse questions that require more complex responses including data-driven predictions or recommendations. Using the business domain as a motivating example, we introduce CORGI, a new benchmark

Baiyang Song, Huan Ping, Wenli Chen, Yong Cao

This paper investigates the flow past a flexible splitter plate attached to the rear of a fixed circular cylinder at a low Reynolds number of 150. A systematic exploration of the plate length ($L/D$), flexibility coefficient ($S^{*}$), and mass ratio ($m^{*}$) reveals new laws and phenomena. The large-amplitude vibration of the structure is attributed to a r

Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang

While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly constrained. Current MLE benchmarks suffer from low scalability and limited applicability because they rely on static, manually curated tasks, demanding extensive time and manual effor

Dhruv S. Bal, Aditya Narendra, Maria Giovanna Dainotti, Nikita S. Khatiya

Gamma-ray Bursts (GRBs) are valuable probes of cosmic star formation reaching back into the epoch of reionization, and a large dataset with known redshifts ($z$) is an important ingredient for these studies. Usually, $z$ is measured using spectroscopy or photometry, but $\sim80\%$ of GRBs lack such data. Prompt and afterglow correlations can provide estimate

Filippo Girardi, Freek Witteveen, Francesco Anna Mele, Lennart Bittel

Gaussian states are widely regarded as one of the most relevant classes of continuous-variable (CV) quantum states, as they naturally arise in physical systems and play a key role in quantum technologies. This motivates a fundamental question: given copies of an unknown CV state, how can we efficiently test whether it is Gaussian? We address this problem fro

Donghwan Kim, Xin Gu, Jinho Baek, Timothy Lo

Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differential privacy (DP), such as DP-SGD, have been gaining attention as a solution. However, DP-SGD adds a noise at each training iteration, which degrades the accuracy of the trained model. To improve accuracy, a new family

George H. Rieke, Yang Sun, Jianwei Lyu, Christopher N. A. Willmer

We evaluate the underlying assumptions for the identification of Active Galactic Nuclei (AGNs) through near- and mid-infrared photometry and spectral energy distribution (SED) fitting out to z ~ 3. For massive galaxies, log(M) > 9.5, our high resolution spectra of the rest optical range generally confirm the results of SED fitting, which relies primarily on

Inzamamul Alam, Md Tanvir Islam, Khan Muhammad, Simon S. Woo

Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust

Yi-Ping Ma, Ivan Sudakow, P. L. Krapivsky, Sergey A. Vakulenko

We study the dynamics of a mean-field Ising model whose coupling depends on the magnetization via a linear feedback function. A key feature of this linear feedback Ising model (FIM) is the possibility of temperature-induced bistability, where a temperature increase can favor bistability between two phases. We show that the linear FIM provides a minimal model

Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang

Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the ``think-then-answer'' paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations when processing non-English languages: (1) They often struggle to maintain input-output language consistency; (2

Peter Plantinga, Roozbeh Sattari, Karine Marcotte, Carla Di Gironimo

The speech of people with Parkinson's Disease (PD) has been shown to hold important clues about the presence and progression of the disease. We investigate the factors based on which humans experts make judgments of the presence of disease in speech samples over five different speech tasks: phonations, sentence repetition, reading, recall, and picture descri

Quentin Buzet, André Chailloux

Unambiguous measurements play an important role in quantum information, with applications ranging from quantum key distribution to quantum state reconstruction. Recently, such measurements have also been used in quantum algorithms based on Regev's reduction. The key problem for these algorithms is the S-$|LWE>$ problem in the lattice setting and the Quantum

Henry Wang, Md Sirajus Salekin, Jake Lee, Ross Claytor

Generative AI has unlocked new possibilities in content discovery and management. Through collaboration with the National Football League (NFL), we demonstrate how a generative-AI based workflow enables media researchers and analysts to query relevant historical plays using natural language rather than traditional filter-and-click interfaces. The agentic wor

Vladimir Medvedev

This paper studies three-dimensional compact static manifolds with boundary and positive scalar curvature. We prove that, under a suitable bound on the Ricci curvature, the orientable quotient of the Nariai static manifold with boundary $Nar_{-1,1}(\mathbb S^2)$ is the only such manifold with connected boundary, provided that the zero-level set of the potent

Tobin A. Driscoll, Yuxing Zhou

We describe an adaptive greedy algorithm for Thiele continued-fraction (TCF) approximation of a function defined on a continuum domain in the complex plane. The algorithm iteratively selects interpolation nodes from an adaptively refined set of sample points on the domain boundary. We also present new algorithms for evaluating Thiele continued fractions and

Alessandro Lenci, Yves Méheust, Maria Klepikova, Vittorio Di Federico

Heat transfer in fractured media is governed by the interplay between advective transport along rough-walled fractures and conductive transport, both within the fractures and in the surrounding low-permeability matrix. Flow localization induced by aperture heterogeneity, combined with matrix conduction, gives rise to anomalous thermal behavior. To capture th

Peize He, Zichen Wen, Yubo Wang, Yuxuan Wang

Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention ($O(N^2)$) and with modeling long-range temporal dependencies. Existing audio benchmarks are built mostly from short clips and do not evaluate models in realistic long context settings. To address this gap, we in

Tiago Silva, António Grilo

In recent years, Unmanned Aerial Vehicles (UAVs) have brought a new true revolution to military tactics. While UAVs already constitute an advantage when operating alone, multi-UAV swarms expand the available possibilities, allowing the UAVs to collaborate and support each other as a team to carry out a given task. This entails the capability to exchange info

Zherui Chen, Joao Basso, Zhiyan Ding, Lin Lin

The presence of energy barriers in the state space of a physical system can lead to exponentially slow convergence for sampling algorithms like Markov chain Monte Carlo (MCMC). In the classical setting, replica exchange (or parallel tempering) is a powerful heuristic to accelerate mixing in these scenarios. In the quantum realm, preparing Gibbs states of Ham

Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Jun Wang

The increasing integration of Industrial IoT (IIoT) exposes critical cyber-physical systems to sophisticated, multi-stage attacks that elude traditional defenses lacking contextual awareness. This paper introduces L2M-AID, a novel framework for Autonomous Industrial Defense using LLM-empowered, Multi-agent reinforcement learning. L2M-AID orchestrates a team

Guangliang Liu, Haitao Mao, Bochuan Cao, Zhiyu Xue

Large Language Models (LLMs) are able to improve their responses when instructed to do so, a capability known as self-correction. When instructions provide only a general and abstract goal without specific details about potential issues in the response, LLMs must rely on their internal knowledge to improve response quality, a process referred to as intrinsic

Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang

Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge associated with submolecular instances (atoms and bonds). While molecular pre-training approaches incorporate such knowledge into their pre-trai

Fernando De Terán, Froilán M. Dopico

We present two new canonical forms for real congruence of a real square matrix $A$. The first one is a direct sum of canonical matrices of four different types and is obtained from the canonical form under $^*$congruence of complex matrices provided by Horn and Sergeichuk in [Linear Algebra Appl. 416 (2006) 1010-1032]. The second one is a direct sum of canon

Jigang Fan, Xiaoran Jiao, Shengdong Lin, Zhanming Liang

Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction; we provide a unifying view by interpreting natural evolution as implicit reward maximizat

Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Qi Hu

The escalating complexity of network threats and the inherent class imbalance in traffic data present formidable challenges for modern Intrusion Detection Systems (IDS). While Graph Neural Networks (GNNs) excel in modeling topological structures and Temporal Convolutional Networks (TCNs) are proficient in capturing time-series dependencies, a framework that

MohammadHossein Rezaei, Robert Vacareanu, Zihao Wang, Clinton Wang

Rubrics provide a flexible way to train LLMs on open-ended long-form answers where verifiable rewards are not applicable and human preferences provide coarse signals. Prior work shows that reinforcement learning with rubric-based rewards leads to consistent gains in LLM post-training. Most existing approaches rely on rubrics that remain static over the cours

Ahmet Bilican, M. Akın Yılmaz, A. Murat Tekalp

While the BD-rate performance of recent learned video codec models in both low-delay and random-access modes exceed that of respective modes of traditional codecs on average over common benchmarks, the performance improvements for individual videos with complex/large motions is much smaller compared to scenes with simple motion. This is related to the inabil

Ximena S. Ramos, Pablo Benitez-Llambay

The migration of planets within their nascent protoplanetary disks is a fundamental process that shapes the final architecture of planetary systems. However, studying this phenomenon through direct hydrodynamical simulations is computationally demanding, with traditional methods on fixed grids being ill-suited for tracking planet migration over long timescal

Michael I. Eides

Uncertainty of the theoretical prediction for the hyperfine splitting in the ground state of muonium is considered. It is compared with the respective discussion in the two most recent CODATA adjustments of the fundamental physical constants.

Leonhard Hölscher, Oliver Ahrend, Lukas Karch, Carlotta L'Estocq

Topology optimization is a key methodology in engineering design for finding efficient and robust structures. Due to the enormous size of the design space, evaluating all possible configurations is typically infeasible. In this work, we present an end-to-end, fault-tolerant quantum algorithm for topology optimization that operates on the exponential Hilbert

Oleksandra Kovalska, Jan von Delft, Andreas Gleis

We present a tangent-space Krylov (TaSK) method for efficient computation of zero-temperature real-frequency spectral functions on top of ground state (GS) matrix product states (MPS) obtained from the Density Matrix Renormalization Group. It relies on projecting resolvents to the tangent space of the GS-MPS, where they can be efficiently represented using K

Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong

In this paper we introduce Hierarchical Diffusion Language Models (HDLM) -- a novel family of discrete diffusion models for language modeling. HDLM builds on a hierarchical vocabulary where low-level tokens with detailed semantics are surjectively mapped to high-level tokens with coarse-grained meanings. In the forward process, each token is independently pe

Franco Javier Arellano, José Ignacio Orlando

Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative s

Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian

Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi-objective multi-agent path finding (MO-MAPF) algorithms typically produce conflict-free plans by computing Pareto frontiers. They do not explicitly optimize for user-defined prefe

Sahil, Sohail

Generalized quantum measurements, described by positive operator-valued measures (POVMs), are essential for modeling realistic processes in open quantum systems. While quantum process tomography can fully characterize a POVM, it is resource-intensive and impractical when only specific POVM elements or matrix elements of a particular POVM element are of inter

Tianyu Huang

Walk on stars (WoSt) is currently one of the most advanced Monte Carlo solvers for PDEs. Unfortunately, the lack of reliable geometric query approaches has hindered its applicability to boundaries defined by implicit surfaces. This work proposes a geometric query framework over closed implicit surfaces for WoSt, under the scope of walkin' Robin. Our key obse

Ady Cambraia Junior, Alessandro Gaio Chimenton, Marco Antônio do Couto Fernandes, Mostafa Salarinoghabi

Let $M_c$ be a $2$-dimensional space form of constant curvature $c=-1,0,1$ and $\gamma$ a smooth, closed, convex curve in $M_c$. We explicitly parametrize the \textit{$\alpha$-evolutoid} of $\gamma$, i.e.\ the closed curve $\gamma_\alpha$ describing the envelope of all geodesics $\sigma_s=\sigma_s(t)$ such that $\sigma_s(0)=\gamma(s)$ and $\sphericalangle(\s

Enrico Fontana, Sivaprasad Omanakuttan, Junhyung Lyle Kim, Joseph Sullivan

We present a comprehensive end-to-end quantum algorithm for tensor problems, including tensor PCA and planted kXOR, that achieves potential superquadratic quantum speedups over classical methods. We build upon prior works by Hastings~(\textit{Quantum}, 2020) and Schmidhuber~\textit{et al.}~(\textit{Phys.~Rev.~X.}, 2025), we address key limitations by introdu

Alessandro Lenci, Yves Méheust, Marco Dentz, Vittorio Di Federico

Characterizing hydrodynamic transport in fractured rocks is essential for carbon storage and geothermal energy production. Multiscale heterogeneities lead to anomalous solute transport, featuring breakthrough curve (BTC) tailing and nonlinear growth of plume spatial moments. We focus on purely advective transport within synthetic geological fractures with pr

Gabor Pataki

Thirty years ago, in a seminal paper Ramana derived an exact dual for Semidefinite Programming (SDP). Ramana's dual has the following remarkable features: i) it is an explicit, polynomial size semidefinite program ii) it does not assume that the primal is strictly feasible, nor does it make any other regularity assumptions iii) yet, it has strong duality wit

Zdenek Masin, Jakub Benda, Martin Crhan, Gregory S. J. Armstrong

Implementation of Effective Core Potentials (ECPs) into the molecular scattering suite UKRmol+ is presented together with a set of calculations for a range of targets relevant for plasma modeling. Continuum description in scattering and photoionization calculations for large targets or high-energy electrons often requires the use of numerical continuum funct

Christine Li, John Preskill, Qian Xu

We introduce transversal dimension jump, a code-switching protocol for lifted product (LP) quantum low-density parity-check (qLDPC) codes across different chain-complex dimensions, enabling universal fault-tolerant quantum computation with low overhead. The construction leverages the product structure of LP codes to implement one-way transversal CNOTs betwee

Pompeu Casanovas

This short essay is a reworking of the answers offered by the author at the Debate Session of the AIHUB (CSIC) and EduCaixa Summer School, organized by Marta Garcia-Matos and Lissette Lemus, and coordinated by Albert Sabater (OEIAC, UG), with the participation of Vanina Martinez-Posse (IIIA-CSIC), Eulalia Soler (Eurecat) and Pompeu Casanovas (IIIA-CSIC) on J

Joao Basso, Shirshendu Ganguly, Alistair Sinclair, Nikhil Srivastava

Despite extensive study, our understanding of quantum Markov chains remains far less complete than that of their classical counterparts. [Temme'13] observed that the Davies Lindbladian, a well-studied model of quantum Markov dynamics, contains an embedded classical Markov generator, raising the natural question of how the convergence properties of the quantu

Yahav Bechavod, Jiuyao Lu, Aaron Roth

We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to generate a sequence of predictions which are broadcast to a collection of downstream decision makers. Each decision maker has their own utility function, as well as a vector of const

Stephan Baehr, Hans Baehr

Aging is a universal consequence of life, yet researchers have identified no universal theme. This manuscript considers aging from the perspective of entropy, wherein things fall apart. We first examine biological information change as a mutational distance, analogous to physical distance. In this model, informational change over time is fitted to an advecti

Varun Upreti, Ulysse Chabaud, Zoë Holmes, Armando Angrisani

Analyzing the impact of noise is of fundamental importance to understand the advantages provided by quantum systems. While the classical simulability of noisy discrete-variable systems is increasingly well understood, noisy bosonic circuits are more challenging to simulate and analyze. Here, we address this gap by introducing the $\textit{displacement propag

Bijan Bagchi, Sauvik Sen

We investigate the implications provided by the modified Painlevé-Gullstrand coordinates in the context of quintessence for the Kiselev black hole. In this regard, we set up a fully static line element in terms of lapse and shift functions, apart from including the deformation parameter signaling deviation from the standard Painlevé-Gullstrand metric. We add

Dario A. Leon, Claudia Cardoso, Kristian Berland

The spectral properties, momentum dispersion, and broadening of bulk plasmonic excitations of 26 elemental metals are studied from first principles calculations in the random-phase approximation. Spectral band structures are constructed from the resulting momentum- and frequency-dependent inverse dielectric function. We develop an effective analytical repres

Monika Singh, Jitendra Kumar

We define the grand amalgam Lebesgue function space $l^{q), \theta}(L^p),$ and study the fundamental structural properties of the space, including completeness. Then we define the small Lebesgue sequence space and study its function space properties. Furthermore, we prove a version of the H\"{o}lders inequality on the frame work of these spaces. Finally, we

Andrew M. Mironov

This paper presents a new, significantly simpler proof of one of the main results of applied pi-calculus: the theorem that the concepts of observational and labeled equivalence of extended processes in applied pi-calculus coincide.

S. F. Sánchez, C. Muñoz-Tuñón, J. Sánchez Almeida, O. González-Martín

Optical emission-line diagnostic diagrams, such as the classical BPT, are widely used to identify ionisation mechanisms in galaxies but often suffer from degeneracies, especially when multiple sources coexist. We critically evaluate the effectiveness of these diagnostics in distinguishing star-forming galaxies, retired galaxies (RGs), and active galactic nuc

M. Sharif, Tayyab Naseer, Hira Shadab

Two distinct non-singular interior models that describe anisotropic spherical configurations are presented in this work. We develop the Einstein field equations and the associated mass function in accordance with a static spherical spacetime. We then discuss certain requirements that must be satisfied for compact models to be physically validated. Two distin

Christopher Talbot, Gideon Bradburd

Ancestral recombination graphs (ARGs) encode the complete genealogical history of a population of recombining lineages. ARGs, and their succinct representation, tree sequences, are increasingly central to modern population genetics methods, yet building an intuition for ARGs remains challenging. This is particularly true when analyzing ancestry in a geograph

Kyprianos-Iason Prodromidis, Allan Sly

We consider the stochastic Ising model on sparse Erdos-Renyi graphs $G(n,d/n)$ with $d>1$ at the critical temperature $\beta_c=\tanh^{-1}(d^{-1})$ and prove that with high probability, the mixing time is at most polynomial in $n$. Our approach combines the recent stochastic localization framework of Chen and Eldan, which yields spectral gap bounds in the wel

Mathilde Mâlin, Kimberly Ward-Duong, Sierra L. Grant, Nicole Arulanantham

Young accreting planetary-mass objects are thought to draw material from a circumplanetary disk (CPD) composed of gas and dust. While the gas within the disk is expected to disperse within the first million years, strong accretion has nonetheless been detected in older systems, including the 30--45 Myr-old planetary-mass companion Delorme 1 AB b. We conducte

Arshad Ali, Yang Lei, Mudassar Sabir

We analytically study the gauge dependence of scalar-induced gravitational waves (SIGWs) sourced by primordial isocurvature perturbations during radiation domination (RD), working across nine gauges. Through analytical integrations of the kernels supported by graphical comparison we identify a clear dichotomy. We find that in some gauges viz. the uniform-den

William James Hughes, Peter Horak

Optical cavities are frequently used in quantum technologies to enhance light matter interactions, with applications including single photon generation and entanglement of distant emitters. The Fabry-P\'{e}rot resonator is a popular choice for its high optical access and large emitter-mirror separation. A typical configuration, particularly for emitters that

Mengqi Lou, Guy Bresler, Ashwin Pananjady

Given a single observation from a Gaussian distribution with unknown mean $\theta$, we design computationally efficient procedures that can approximately generate an observation from a different target distribution $Q_{\theta}$ uniformly for all $\theta$ in a parameter set. We leverage our technique to establish reduction-based computational lower bounds for

Jiaben Chen, Zixin Wang, Ailing Zeng, Yang Fu

In this work, we present TalkCuts, a large-scale dataset designed to facilitate the study of multi-shot human speech video generation. Unlike existing datasets that focus on single-shot, static viewpoints, TalkCuts offers 164k clips totaling over 500 hours of high-quality human speech videos with diverse camera shots, including close-up, half-body, and full-

Jonggeun Lee, Woojung Song, Jongwook Han, Haesung Pyun

Small language models (SLMs) enable scalable tool-augmented multi-agent systems where multiple SLMs handle subtasks orchestrated by a powerful coordinator. However, they struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters. A common failure mode is \textit{schema misalignment}: models hallucinate plausi

Jayant Havare, Srikanth Tamilselvam, Ashish Mittal, Shalaka Thorat

Programming education often assumes English proficiency and text-based interaction, creating barriers for students from multilingual regions such as India. We present CodeVaani, a multilingual speech-driven assistant for understanding code, built into Bodhitree [1], a Learning Management System developed at IIT Bombay. It is a voice-enabled assistant that he

Vikram Ravindranath, Hanchen Liu, Xiao Chen

We introduce a family of hybrid quantum circuits involving unitary gates and projective measurements that display a measurement-induced phase transition. Remarkably, the volume-law phase featuring logarithmic entanglement growth for certain initial states. We attribute this slow entanglement growth to the similarly slow growth of the participation entropy, w

Uma Girish, Alex May, Natalie Parham, Henry Yuen

We establish novel connections between magic in quantum circuits and communication complexity. In particular, we show that functions computable with low magic have low communication cost. Our first result shows that the $\mathsf{D}\|$ (deterministic simultaneous message passing) cost of a Boolean function $f$ is at most the number of single-qubit magic gates

A. Mućka, A. B. Romanowska

This paper is the second part of a two-part paper investigating the structure and properties of dyadic polygons. A dyadic polygon is the intersection of the dyadic subplane $D^2$ of the real plane $R^2$ and a real convex polygon with vertices in the dyadic plane. Such polygons are described as subreducts (subalgebras of reducts) of the affine dyadic plane $D

Joseph Enguehard, Morgane Van Ermengem, Kate Atkinson, Sujeong Cha

Evaluating large language model (LLM) outputs in the legal domain presents unique challenges due to the complex and nuanced nature of legal analysis. Current evaluation approaches either depend on reference data, which is costly to produce, or use standardized assessment methods, both of which have significant limitations for legal applications. Although LLM

Leitian Tao, Ilia Kulikov, Swarnadeep Saha, Tianlu Wang

Post-training for reasoning of large language models (LLMs) increasingly relies on verifiable rewards: deterministic checkers that provide 0-1 correctness signals. While reliable, such binary feedback is brittle--many tasks admit partially correct or alternative answers that verifiers under-credit, and the resulting all-or-nothing supervision limits learning

Narayan S Iyer, Shraddha Sharma

In the present article, we examine the relationship of negative conditional quantum Kaniadakis entropy ($\alpha-$CQKE) with the fully entangled fraction (FEF) which is a substantial yardstick for quantum information processing protocols including teleportation, and quantum steerability, executed over four vital quantum states with maximally mixed marginals,

Hugo Thomas, Ulysse Chabaud, Pierre-Emmanuel Emeriau

Learning quantum state properties is both a fundamental and practical problem in quantum information theory. Classical shadows have emerged as an efficient method for estimating properties of unknown quantum states, with rigorous statistical guarantees, by performing randomized measurement on few copies of the state. With the advent of photonic technologies,

Xunyi Jiang, Dingyi Chang, Julian McAuley, Xin Xu

The rapid evolution of large language models (LLMs) and the real world has outpaced the static nature of widely used evaluation benchmarks, raising concerns about their reliability for evaluating LLM factuality. While substantial works continue to rely on the popular but old benchmarks, their temporal misalignment with real-world facts and modern LLMs, and t

Jiarui Cheng, Steven J. Miller, Sebastian Rodriguez-Labastida, Tianyu Shen

We present a multidimensional generalization of Zeckendorf's Theorem (any positive integer can be written uniquely as a sum of non-adjacent Fibonacci numbers) to a large family of linear recurrences. This extends work of Anderson and Bicknell-Johnson in the multi-dimensional case when the underlying recurrence is the same as the Fibonacci one. Our extension

Zheng Jiang, Bu-Yao Qu, Gui-Jun Ding

The $Z_2$ gauging of $Z_N$ symmetry can enforce certain elements of the fermion Yukawa couplings to vanish. We have performed a systematical study of texture zero patterns of lepton mass matrices in the minimal seesaw model, and we present all the possible patterns of the charged lepton Yukawa coupling $Y_E$, neutrino Yukawa coupling $Y_{\nu}$, right-handed

Khaoula Sghaier, Badis Hammi, Ghada Gharbi, Pierre Merdrignac

Software-Defined Vehicles (SDVs) introduce innovative features that extend the vehicle's lifecycle through the integration of outsourced applications and continuous Over-The-Air (OTA) updates. This shift necessitates robust cybersecurity and system resilience. While research on Connected and Autonomous Vehicles (CAV) has been extensive, there is a lack of cl

P. S. Satunin, S. V. Troitsky

From gamma-ray burst (GRB) 221009A, very high-energy photons were detected: >10 TeV with LHAASO and >100 TeV with Carpet-3. Such energetic photons are expected to be absorbed via electron-positron pair production on their way to the Earth. Their observation might be explained by new physics, including Lorentz invariance violation (LIV) or photon mixing with

Zhivar Sourati, Zheng Wang, Marianne Menglin Liu, Yazhe Hu

Question answering over visually rich documents (VRDs) requires reasoning not only over isolated content but also over documents' structural organization and cross-page dependencies. However, conventional retrieval-augmented generation (RAG) methods encode content in isolated chunks during ingestion, losing structural and cross-page dependencies, and retriev

Sercan Kaya, Bayram Tekin

We initiate an investigation into whether fractional calculus, with its intrinsic long-tailed memory and nonlocal features, can provide a viable model for gravitational-wave memory effects. We consider two toy constructions: ($i$) a fractional modification of the linearized Einstein field equations using a sequential Caputo operator; and ($ii$) a fractionali

Gilles Geeraerts, Frédéric Herbreteau, Jean-François Raskin, Alexis Reynouard

This paper revisits timed games by building upon the semantics introduced in "The Element of Surprise in Timed Games". We introduce some modifications to this semantics for two primary reasons: firstly, we recognize instances where the original semantics appears counterintuitive in the context of controller synthesis; secondly, we present methods to develop

Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Jing Huang

Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level

Keigo Takahashi, Hiroyasu Matsuura, Hideaki Maebashi, Masao Ogata

We study the electrical and thermal transport properties and the violation of the Wiedemann-Franz (WF) law of two-carrier semimetals using exact treatments of the Boltzmann equation with the impurity and electron-electron scatterings in a magnetic field. For comparison, we also study those in the case of Baber scattering: a single-carrier system with an impu

Joao D. S. Marques, Andre V. Duarte, Andre Carvalho, Gil Rocha

Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for these programs often creates significant bottlenecks in evaluation processes, due to limited human capacity. In this work, we detail the real-world deployment of AI-assisted evalu

Anarya Ray, Sharan Banagiri, Eric Thrane, Paul D. Lasky

The recently reported binary black hole merger, GW231123, has unusual properties that make it hard to explain astrophysically. Parameter estimation studies are consistent with maximally spinning black holes and the dimensionless spin of the more massive component is constrained to be $\chi_1\gtrsim 0.8$. Analysis of data also revealed potential systematics t

Arjun Krishnakumar, Rhea Sanjay Sukthanker, Hannan Javed Mahadik, Gabriela Kadlecová

Small Language models (SLMs) offer an efficient and accessible alternative to Large Language Models (LLMs), delivering strong performance while using far fewer resources. We introduce a simple and effective framework for pretraining SLMs that brings together three complementary ideas. First, we identify structurally sparse sub-network initializations that co

Lucio La Cava, Luca Maria Aiello, Andrea Tagarelli

Generative Artificial Intelligence is reshaping online communication by enabling large-scale production of Machine-Generated Text (MGT) at low cost. While its presence is rapidly growing across the Web, little is known about how MGT integrates into social media environments. In this paper, we present the first large-scale characterization of MGT on Reddit. U

Michelle Delcourt, Thomas Lesgourgues, Luke Postle

In 2014, Keevash famously proved the existence of $(n,q,r)$-Steiner systems as part of settling the Existence Conjecture of Combinatorial Designs (dating from the mid-1800s). In 2020, Glock, K\"uhn, and Osthus conjectured a minimum degree generalization: specifically that minimum $(r-1)$-degree at least $(1-\frac{C}{q^{r-1}})n$ suffices to guarantee that eve

Yongjun Li, Minghao Song, Yoshiteru Hidaka, Victor Smaluk

We revisit the nonlinear lattice design approach for the National Synchrotron Light Source II (NSLS-II) storage ring. By suppressing chaos, we identify alternative sextupole configurations to the original design, which relied on the conventional strategy of simultaneously minimizing Resonance Driving Terms (RDT) and Amplitude-Dependent Detuning (ADD). These

David Gosset, Robin Kothari, Chenyi Zhang

Prior work of Beverland et al. has shown that any exact Clifford+$T$ implementation of the $n$-qubit Toffoli gate must use at least $n$ $T$ gates. Here we show how to get away with exponentially fewer $T$ gates, at the cost of incurring a tiny $1/\mathrm{poly}(n)$ error that can be neglected in most practical situations. More precisely, the $n$-qubit Toffoli

Julia Millhouse, Lukas Schembecker

For Van Douwen families, maximal families of eventually different permutations and maximal ideal independent families we show that the existence of a $\Sigma^1_2$ family implies the existence of a $\Pi^1_1$ family of the same size. We also prove a similar, but slightly weaker result for generating sets of cofinitary groups.