December 2025 arXiv papers — page 95

Tinglong Feng, Jesse Moes, Tomislav Prokopec

Metastable decay exhibits a familiar exponential regime bracketed by early-time deviations and late-time power-law tails. We adopt the real-time, flux-based definition of the decay rate in the spirit of Andreassen et al.\ direct method and present a complete analysis of one-dimensional quantum-mechanical resonance models. We show that the kernel admits a uni

Behzad Tahmasebzadeh, Monica Valluri, Shashank Dattathri, Tatsuya Akiba

A recent study of the compact elliptical galaxy NGC 4486B using JWST-NIRSpec IFU kinematics confirmed a supermassive black hole (SMBH) of mass $M_{BH}=3.6\pm0.7\times10^8$ (~8% of the stellar mass). In addition to its double nucleus, the nuclear kinematics show pronounced asymmetries: a velocity-dispersion peak displaced by 6 pc from the galaxy center and a

Julian Shapiro

I report the discovery of three faint, semi-resolved quiescent candidate dwarf galaxies, which are potential backsplash galaxies associated with the nearby spiral M101 ($D \sim 6.7 \,\mathrm{Mpc}$). The galaxies are cataloged as Shapiro DG-I/MAGE1412+5650, Shapiro DG-II, and Shapiro DG-III. Sha DG-I is concurrently discovered in ID-MAGE. The backsplash candi

Chandramouli Chowdhury, Arthur Lipstein, Joe Marshall, Alex Jiayi Zhang

In-in correlators are the basic observables in cosmology and are traditionally computed using the Schwinger-Keldysh formalism. In this paper we revisit this formalism for photons, gluons, and gravitons coupled to scalars in four dimensional de Sitter space and provide a novel treatment of boundary gauge-fixing of the underlying path integral. We also derive

Zitian Gao, Lynx Chen, Yihao Xiao, He Xing

Universal transformers (UTs) have been widely used for complex reasoning tasks such as ARC-AGI and Sudoku, yet the specific sources of their performance gains remain underexplored. In this work, we systematically analyze UTs variants and show that improvements on ARC-AGI primarily arise from the recurrent inductive bias and strong nonlinear components of Tra

Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang

Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this chal

Zefan Cai, Haoyi Qiu, Tianyi Ma, Haozhe Zhao

Video foundation models generate visually realistic and temporally coherent content, but their reliability as world simulators depends on whether they capture physical, logical, and spatial constraints. Existing metrics such as Frechet Video Distance (FVD) emphasize perceptual quality and overlook reasoning failures, including violations of causality, physic

Henry T. Klest

The photon is arguably the most universally important particle across all fields of physics. Despite its status as a fundamental particle, at high energies the photon can be seen as a hadronic source of partons. The partonic content of the photon is very poorly constrained compared to that of the proton, with photon PDF uncertainties typically one or two ord

Sirui Chen, Zi-ang Cao, Zhengyi Luo, Fernando Castañeda

Recent progress in humanoid robots has unlocked agile locomotion skills, including backflipping, running, and crawling. Yet it remains challenging for a humanoid robot to perform forceful manipulation tasks such as moving objects, wiping, and pushing a cart. We propose adaptive Compliance Humanoid control through hIsight Perturbation (CHIP), a plug-and-play

P. Concha, N. Merino, L. Ravera, E. Rodríguez

The ultra-relativistic (Carrollian) regime of gravity has recently emerged as a fertile framework for exploring holography, non-Lorentzian symmetries, and geometric limit of General Relativity. In this letter, we establish the presence of a non-vanishing torsion within three-dimensional Carrollian gravity by constructing the Carrollian Mielke-Baekler (C-MB)

Yen-Ju Lu, Kunxiao Gao, Mingrui Liang, Helin Wang

Recent audio language models can follow long conversations. However, research on emotion-aware or spoken dialogue summarization is constrained by the lack of data that links speech, summaries, and paralinguistic cues. We introduce Spoken DialogSum, the first corpus aligning raw conversational audio with factual summaries, emotion-rich summaries, and utteranc

David Wright, Kalista Wayt, Jeffrey S. Hazboun, Xavier Siemens

In 2023, after more than two decades of searching, pulsar timing array (PTA) collaborations around the world announced evidence for a stochastic gravitational wave background. It was quickly followed by work from the International Pulsar Timing Array (IPTA), demonstrating that the results of regional collaborations were consistent with each other. The combin

Chuan He

Stochastic optimization is fundamental to modern machine learning. Recent research has extended the study of stochastic first-order methods (SFOMs) from light-tailed to heavy-tailed noise, which frequently arises in practice, with clipping emerging as a key technique for controlling heavy-tailed gradients. Extensive theoretical advances have further shown th

Audrey Cheng, Shu Liu, Melissa Pan, Zhifei Li

Artificial Intelligence (AI) is beginning to transform the research process by automating the discovery of new solutions. This shift depends on the availability of reliable verifiers, which AI-driven approaches require to validate candidate solutions. Research focused on improving systems performance is especially well-suited to this paradigm because system

Trung Chau

We obtain a recursive formula for the Gotzmann threshold of a power of a variable. Consequently, we give an affirmative answer to a conjecture of Bonanzinga and Eliahou.

Dimitris Bertsimas, Yu Ma, Kimberly Villalobos Carballo, Gagan Singh

Hospitals lack automated systems to harness the growing volume of heterogeneous clinical and operational data to effectively forecast critical events. Early identification of patients at risk for deterioration is essential not only for patient care quality monitoring but also for physician care management. However, translating varied data streams into accura

David O. Williams Rogers, Hang Woon Lee

Orbital debris poses an escalating threat to space missions and the long-term sustainability of Earth's orbital environment. The literature proposes various approaches for orbital debris remediation, including the use of multiple space-based lasers that collaboratively engage debris targets. While the proof of concept for this laser-based approach has been d

Lanxiang Hu, Siqi Kou, Yichao Fu, Samyam Rajbhandari

Multi-token generation has emerged as a promising paradigm for accelerating transformer-based large model inference. Recent efforts primarily explore diffusion Large Language Models (dLLMs) for parallel decoding to reduce inference latency. To achieve AR-level generation quality, many techniques adapt AR models into dLLMs to enable parallel decoding. However

Sicheng Xu, Guojun Chen, Jiaolong Yang, Yizhong Zhang

We propose VASA-3D, an audio-driven, single-shot 3D head avatar generator. This research tackles two major challenges: capturing the subtle expression details present in real human faces, and reconstructing an intricate 3D head avatar from a single portrait image. To accurately model expression details, VASA-3D leverages the motion latent of VASA-1, a method

Ricardo Martínez von Dossow, Eduardo Barredo-Alamilla, Maxim A. Gorlach, Luis Fernando Urrutia

We investigate Cherenkov radiation in isotropic chiral matter using Carroll-Field-Jackiw electrodynamics, with an axion angle linear in time, to describe a charge moving at constant velocity. By solving the modified Maxwell's equations in cylindrical coordinates and in the space-frequency domain, we derive closed expressions for the circularly polarized elec

Ellie Y. Cheng, Logan Weber, Tian Jin, Michael Carbin

The rise of large language models (LLMs) has introduced a new type of programming: natural language programming. Users write prompts, which are instructions in natural language, to direct LLMs to perform tasks such as natural language processing, code generation, reasoning, etc. An emerging area of research enables interoperability between prompts and progra

Janka Lengyel, Stéphane G. Roux, Patrice Abry, Norman Wildmann

Wind turbine wakes play a central role in determining wind farm performance, yet their spatial organization remains only partially understood. Here, we apply a spatially localized multifractal analysis to quantify the strength of dependencies (local roughness) and extreme velocity fluctuations (local intermittency) in turbine wakes, and relate these properti

Rae Chipera, Jenny Du, Irene Tsapara

Contemporary reservoir computing relies heavily on globally Lipschitz, well-behaved activation functions, limiting applications in defense, disaster response, and pharmaceutical modeling where robust operation under extreme conditions is critical. We systematically investigate non-smooth activation functions, including chaotic, stochastic, and fractal varian

Valeri P. Frolov

We propose a new approach to the quasitopological theory of gravity based on a modified classical double--copy construction. Focusing on static, spherically symmetric configurations, we show that all vacuum solutions of $D$--dimensional quasitopological gravity can be obtained from an auxiliary non--linear electrodynamics defined in a flat $(D+1)$--dimension

Ronnie de Souza Santos, Cleyton Magalhães, Italo Santos

LLM based chatbots have become central interfaces in technical, educational, and analytical domains, supporting tasks such as code reasoning, problem solving, and information exploration. As these systems scale, sustainability concerns have intensified, with most assessments focusing on model architecture, hardware efficiency, and deployment infrastructure.

Jason Kumar, Pearl Sandick, Shuting Xu

We consider the effect of Light (but Massive) Relics (LiMRs) on the clustering of matter in the early Universe. We account for the fact that LiMRs which are massive enough may cluster on large length scales at early times, and may thus impact weak lensing of the cosmic microwave background (CMB) even on small angular scales. In particular, we find that LiMRs

Martin Teuscher, Ruth Durrer, Killian Martineau, Aurélien Barrau

We consider the stochastic gravitational wave background induced by arbitrary source fields that are amplified during cosmological inflation. The associated tensor spectral index is shown to be given, under minimal assumptions, by a simple formula easy to apply in most situations of accelerated expansion. For slow-roll inflation, the induced spectrum is near

Feng Qi

Since 2023, through the detailed examination of numerous concrete examples, the author and his collaborators have identified a recurring pattern. Building upon this observation, they introduced the concept of the normalized remainder. They deliberately chose this term and subsequently explored its historical background and mathematical significance. In 2026,

Mark Booth, Patricia Luppe, Sebastian Marino, Joshua B. Lovell

Debris discs reveal the architectures and dynamical histories of planetary systems. Sub-millimetre observations trace large dust grains within debris discs, revealing their bulk properties. Debris discs have so far only been detected around ~20% of stars, representing the bright end of the population. A new facility is required to reach fainter discs, overco

Max Heller, Fabian R. N. Schneider, Jan Henneco, Vincent A. Bronner

Stellar multiple systems are the norm, not the exception, with many systems undergoing interaction phases during their lifetimes. A subset of these interactions can lead to stellar mergers, where the two components of a stellar binary system come close enough to coalesce into a single star. Accurately modeling stellar mergers requires computationally expensi

Gabriele Perna, Nicola Bellomo, Vincenzo Roatti, Daniele Bertacca

The presence of a gravitational wave background (GWB) can be established not only via exquisitely precise pulsar timing array (PTA) measurements, but also via astrometric observations. Indeed, the very same background responsible for the delay in the arrival time of pulse causes an apparent displacement of galactic objects as stars and asteroids. In this wor

Rahul Lall, Youngho Seo, Ali M. Niknejad, Mekhail Anwar

Surgical tumor resection aims to remove all cancer cells in the tumor margin and at centimeter-scale depths below the tissue surface. During surgery, microscopic clusters of disease are intraoperatively difficult to visualize and are often left behind, significantly increasing the risk of cancer recurrence. Radioguided surgery (RGS) has shown the ability to

Zechen Bai, Chen Gao, Mike Zheng Shou

Achieving truly adaptive embodied intelligence requires agents that learn not just by imitating static demonstrations, but by continuously improving through environmental interaction, which is akin to how humans master skills through practice. Vision-Language-Action (VLA) models have advanced robotic manipulation by leveraging large language models, yet rema

Marco Blanchini, Giovanna Maria Dimitri, Benedetta Tondi, Tarcisio Lancioni

Visual Sentiment Analysis (VSA) is a challenging task due to the vast diversity of emotionally salient images and the inherent difficulty of acquiring sufficient data to capture this variability comprehensively. Key obstacles include building large-scale VSA datasets and developing effective methodologies that enable algorithms to identify emotionally signif

Elif Uskuplu, Lawrence S. Moss, Valeria de Paiva

Mathematical knowledge exists in many forms, ranging from informal textbooks and lecture notes to large formal proof libraries, yet moving between these representations remains difficult. Informal texts hide dependencies, while formal systems expose every detail in ways that are not always human-readable. Dependency graphs offer a middle ground by making vis

Paolo Pani, Massimiliano Maria Riva, Luca Santoni, Nikola Savić

We investigate the nonlinear tidal response of relativistic neutron stars by computing the fully relativistic, static, quadratic Love numbers. Using both the worldline effective field theory for extended gravitating bodies and second-order perturbations of relativistic stellar models, we derive the nonlinear tidal deformation induced by an external gravito-e

Damir Filipović

This paper develops a model-free framework for static fixed-income pricing and the replication of liability cash flows. We show that the absence of static arbitrage across a universe of fixed-income instruments is equivalent to the existence of a strictly positive discount curve that reproduces all observed market prices. We then study the replication and su

Chiyue Wei, Cong Guo, Junyao Zhang, Haoxuan Shan

Vision-Language Models (VLMs) have demonstrated strong performance on tasks such as video captioning and visual question answering. However, their growing scale and video-level inputs lead to significant computational and memory overhead, posing challenges for real-time deployment on hardware accelerators. While prior work attempts to reduce redundancy via t

Edward Schwartz, Hamed Vakili, Alexey A. Kovalev

We develop a symmetry-controlled theory of anisotropic thermomagnonic torques in insulating altermagnets. We identify a spin-splitter magnonic torque linked to thermally generated, sublattice-odd spin currents and an anisotropic entropic torque dictated by crystal symmetry. These torques produce anisotropic magnetic-texture responses to temperature gradients

Jeremy Drew, Shravan Godse, Yuxing Liang, Abhishek Pathak

The thermal transport community is increasingly interested in rigorous uncertainty quantification (UQ) of their measurements. In this work, we argue that Bayesian parameter estimation (BPE) represents a powerful framework for both analysis/fitting and UQ. We provide a detailed walkthrough of the technique (including code to duplicate our results) and example

Alban Puech, Matteo Mazzonelli, Celia Cintas, Tamara R. Govindasamy

We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) lack of realistic stochastic load and topology perturbations, limiting scenario diversity; (2) PF datasets are r

Yiwen Zhao, Jiatong Shi, Jinchuan Tian, Yuxun Tang

Speech Language Models (SLMs) have recently emerged as a unified paradigm for addressing a wide range of speech-related tasks, including text-to-speech (TTS), speech enhancement (SE), and automatic speech recognition (ASR). However, the generalization capability of large-scale pre-trained SLMs remains underexplored. In this work, we adapt a 1.7B parameter TT

Gabriele Accarino, Viviana Acquaviva, Sara Shamekh, Duncan Watson-Parris

We introduce WaveSim, a multi-scale similarity metric for the evaluation of spatial fields in weather and climate applications. WaveSim exploits wavelet transforms to decompose input fields into scale-specific wavelet coefficients. The metric is built by multiplying three orthogonal components derived from these coefficients: Magnitude, which quantifies simi

Iman Ahmadabadi, I-Te Lu, Leonardo A. Cunha, Michael Ruggenthaler

We present results of a photon-free exchange-correlation functional within the local density approximation (pxcLDA) for quantum electrodynamics density functional theory (QEDFT) that efficiently describes the electron density of many-electron systems across weak to strong light-matter coupling. Building on previous work [I-Te. Lu et al., Phys. Rev. A 109, 05

Lihong Wang, Liangqi Li, Weiwei Feng, Jiamin Wu

CoT has significantly enhanced the reasoning ability of LLMs while it faces challenges when extended to multimodal domains, particularly in mathematical tasks. Existing MLLMs typically perform textual reasoning solely from a single static mathematical image, overlooking dynamic visual acquisition during reasoning. In contrast, humans repeatedly examine visua

Yiwen Zhao, Jiatong Shi, Yuxun Tang, William Chen

Singing voice synthesis (SVS) has seen remarkable advancements in recent years. However, compared to speech and general audio data, publicly available singing datasets remain limited. In practice, this data scarcity often leads to performance degradation in long-tail scenarios, such as imbalanced pitch distributions or rare singing styles. To mitigate these

Pawel Swietojanski, Xinwei Li, Mingbin Xu, Takaaki Hori

We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to encode absolute frame positions by exploiting limited acoustic context beyond segment boundaries, but fail to generalize when decoding long-form segments where these cues vanish. The

S. Saoud, M. A. Rbah, R. Sammani, E. H. Saidi

We study a quintessential inflation scenario based on the Inert Doublet Model (IDM) coupled to a quintessence field via an exponential potential $V_0e^{-\beta\phi/M_p}$. Using a conformal transformation from the Jordan frame to the Einstein frame, we derive an effective Starobinsky-type potential modulated by an exponential factor that naturally unifies the

Sharon Mary Tomson, Boris Goncharov, Rutger van Haasteren, Rahul Srinivasan

We perform searches for gravitational wave memory in the data of two major Pulsar Timing Array (PTA) experiments located in Europe and Australia. Supermassive black hole binaries (SMBHBs) are the primary sources of gravitational waves in PTA experiments. We develop and carry out the first search for late inspirals and mergers of these sources based on full n

Charles Wood

Imaging systems are commonly described using resolution, contrast, and signal-to-noise ratio, but these quantities do not provide a general account of how physical transformations affect the flow of information. This paper introduces an operator-based formulation of information theory for imaging. The approach models the imaging chain as a composition of bou

Marco Albán, Dominika Wylezalek, Pranav Kukreti, Rogemar A. Riffel

Increasing evidence shows that AGN with radio detections have more perturbed ionized gas kinematics and higher outflow detection rates, suggesting a link between radio emission and these processes. In galaxies with weak or ambiguous AGN signatures, some studies attribute the radio emission to star formation, while others propose AGN-driven winds or weak, unr

David Schulmeister, Valentin Hartmann, Lars Klein, Robert West

Today, a lot of research on language models is focused on large, general-purpose models. However, many NLP pipelines only require models with a well-defined, small set of capabilities. While large models are capable of performing the tasks of those smaller models, they are simply not fast enough to process large amounts of data or offer real-time responses.

Xinyue Sheng, Tuan Dung Pham, Zichi Zhang, Matt Nicholl

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients remains difficult due to extreme class imbalances in training sets, and extracting features from host images is complica

Himadri Singh Raghav, Sachin Maheshwari, Mike Smart, Patrick Foster

Recent advances in artificial intelligence, coupled with increasing data bandwidth requirements, in applications such as video processing and high-resolution sensing, have created a growing demand for high computational performance under stringent energy constraints, especially for battery-powered and edge devices. To address this, we present a mixed-signal

Abhinav Choudhry, Bashab Mazumder, Lauren Alyssa Marks, Roqaya Elmenshawy

We conducted a qualitative co-design study with four adults aged 60+ to gather design insights on a Figma prototype and a generative AI (GenAI) chatbot for an app aimed at providing an AI coach to support older adults' physical activity. The initial design for both incorporates several novel aspects: a curated health knowledge base, personalised responses ba

Rao Muhammad Umer, Daniel Sens, Jonathan Noll, Sohom Dey

Timely and accurate lymphoma diagnosis is essential for guiding cancer treatment. Standard diagnostic practice combines hematoxylin and eosin (HE)-stained whole slide images with immunohistochemistry, flow cytometry, and molecular genetic tests to determine lymphoma subtypes, a process requiring costly equipment, and skilled personnel, causing treatment dela

Fei Wu, Marcel Dreier, Nora Gourmelon, Sebastian Wind

The dynamics of glaciers and ice shelf fronts significantly impact the mass balance of ice sheets and coastal sea levels. To effectively monitor glacier conditions, it is crucial to consistently estimate positional shifts of glacier calving fronts. AMD-HookNet firstly introduces a pure two-branch convolutional neural network (CNN) for glacier segmentation. Y

Gyula O. H. Katona, Yaping Mao, Kenta Ozeki, Zhao Wang

We say that a poset $Q$ contains a copy (resp.~an induced copy) of a poset $P$ if there is an injection $f : P \to Q$ such that for any $x,y \in P$, $f(x)\leq f(y)$ in $Q$ if (resp.~if and only if) $x\leq y$ in $P$. Let $\mathcal{Q}=\{Q_{n} : n\geq 1\}$ be a family of posets such that $Q_n\subseteq Q_{n+1}$ and $|Q_n|<|Q_{n+1}|$ for each $n$. For given $k$ p

Qirui Li, Yizhe Tang, Ran Yi, Guangben Lu

Foreground-conditioned inpainting, which aims at generating a harmonious background for a given foreground subject based on the text prompt, is an important subfield in controllable image generation. A common challenge in current methods, however, is the occurrence of Spatial Relationship Hallucinations between the foreground subject and the generated backgr

Bruno Felipe Costa, Anup Mishra, Israel Leyva-Mayorga, Taufik Abrão

Integrated sensing and communication (ISAC) for next-generation networks targets robust operation under high mobility and high Doppler spread, leading to severe inter-carrier interference (ICI) in systems based on orthogonal frequency-division multiplexing (OFDM) waveforms. Delay--Doppler (DD)-domain ISAC offers a more robust foundation under high mobility,

Syeda Lammim Ahad, Rashaad Reid, Charlie T. Mpetha, James E. Taylor

We investigate how the dynamical state of galaxy clusters influences their galaxy populations and mass distributions. Using photometrically selected clusters from the DESI Legacy Imaging Survey cross-matched with the UNIONS galaxy shear catalogue, we classify clusters as evolved or evolving based on their rest-frame r-band magnitude gaps and stellar mass rat

Vineet Yadav

We introduce a sign-aware, multistate Jaccard/Tanimoto framework that extends overlap-based distances from nonnegative vectors and measures to arbitrary real- and complex-valued signals while retaining bounded metric and positive-semidefinite kernel structure. Formally, the construction is a set- and measure-theoretic geometry: signals are represented as ato

Grigory Belousov, Nivedita Viswanathan

We consider del Pezzo surfaces $X$ with du Val singularities. Assume that $X$ has a $-K_X$-polar cylinder and $\deg X=1$. Let $H$ be an ample divisor. We'll prove that $X$ has a $H$-polar cylinder.

E. F. Talantsev, D. A. Komkova

Phase transitions in materials under fast ramp compression are an ongoing research topic, which is part of several global projects like inertial fusion. Currently, X-ray diffraction (XRD) examination of samples under fast ramp compression is limited to the determination of the sample phase state and the unit cell lattice parameters. Here, we propose to exten

Giovanni Di Muccio, Gonçalo Paulo, Lorenzo Iannetti, Adina Sauciuc

Iontronics is a burgeoning paradigm that employs ions in solution as information carriers for sensing and computing, e.g., in neuromorphic devices. The fundamentally different working principle as compared to electronics requires novel approaches and concepts to control the impedance of nanoscale fluidic circuit elements, such as nanopores. For instance, pre

Narges Taghizade, Robert Schwarzl, Frederik Leinenbach, Maximilian Jeindl

Although squaraine dyes are commonly praised as candidates for light-based applications, little is known about their excited state landscape beyond the low-energy visible light region. Our work aims for an improved understanding of the photophysical properties of squaraines at the example of N-isobutyl substituted anilino-squaraine (SQIB) by extending ground

Yash Vishe, Eric Xue, Xunyi Jiang, Zachary Novack

Music editing plays a vital role in modern music production, with applications in film, broadcasting, and game development. Recent advances in music editing systems have enabled diverse editing tasks such as timbre transfer, instrument substitution, and genre transformation. However, many existing works overlook evaluating their ability to preserve musical f

Alireza Olama, Andreas Lundell, Izzat El Hajj, Johan Lilius

Inter-node communication bandwidth increasingly constrains distributed training at scale on multi-node GPU clusters. While compact models are the ultimate deployment target, conventional pruning-aware distributed training systems typically fail to reduce communication overhead because unstructured sparsity cannot be efficiently exploited by highly optimized

Misha Chernobai, Tai-Peng Tsai

We consider a perturbed Stokes system with critical divergence-free drift in a bounded Lipschitz domain in $R^2$, with sufficiently small Lipschitz constant L. It extends our previous work in $\Bbb R^n, n\ge 3$, to two-dimensional case. For large drift in weak $L^2$ space, we prove unique existence of q-weak solutions for force in $L^q$ with q close to 2. Mo

Aidan H. Y. Chong, Y. Q. Liu, C. Liu, Daniel H. C. Ong

Topological photonic systems support edge states that are robust against disorder and perturbation. Depending on the symmetry and dimensionality of the bulk systems, different edge states emulating soliton, quantum integer and quantum spin Hall effects have been realized. A major concern in photonics is how one can shape the strength and polarisation of elec

Nadejda Blagorodnova, Ondřej Pejcha, Tomek Kamiński, Yongzhi Cai

Dynamical binary interactions such as common envelope (CE) evolution or stellar mergers are a critical phase in the formation of a wide variety of binary phenomena, ranging from blue stragglers to type I supernovae (of all flavours, a, b and c), $\gamma$-ray bursts to bipolar planetary nebulae, Thorne-Zytkow objects to X-ray binaries. In 2040s, the urgency o

Lea Harscouet, Kevin Wolz, Amy Wayland, David Alonso

We present a harmonic-space estimator for the cross-correlation between the kinematic Sunyaev-Zel'dovich effect and the reconstructed galaxy momentum field that offers several practical advantages over the traditional stacking approach. The estimator is easy to deploy using relatively modest computational resources and recovers all information available in t

Rebecca M. Lewis, Oliver Y. Feng, Henry W. J. Reeve, Min Xu

Score estimation has recently emerged as a key modern statistical challenge, due to its pivotal role in generative modelling via diffusion models. Moreover, it is an essential ingredient in a new approach to linear regression via convex $M$-estimation, where the corresponding error densities are projected onto the log-concave class. Motivated by these applic

Richard Ackermann, Simeon Emanuilov

OpenAI has recently argued that hallucinations in large language models result primarily from misaligned evaluation incentives that reward confident guessing rather than epistemic humility. On this view, hallucination is a contingent behavioral artifact, remediable through improved benchmarks and reward structures. In this paper, we challenge that interpreta

Ostap Vykhopen, Viktoria Skorik, Maksym Tereshchenko, Veronika Solopova

Large language models can already query databases, yet most existing systems remain reactive: they rely on explicit user prompts and do not actively explore data. We introduce DAR (Data Agnostic Researcher), a multi-agent system that performs end-to-end database research without human-initiated queries. DAR orchestrates specialized AI agents across three lay

Anna F. Pala, Roberto Raddi, Alberto Rebassa-Mansergas, Boris T. Gänsicke

White dwarf binaries are fundamental astrophysical probes. They represent ideal laboratories to test the models of binary evolution, which also apply to the sources of gravitational waves, whose detection led to the award of the 2017 Nobel Prize in Physics. Moreover, their final fate is intimately linked to Type Ia Supernovae (SNe Ia), i.e. the thermonuclear

Zhenghao Zhao, Haoxuan Wang, Kai Wang, Yuzhang Shang

Dataset distillation aims to synthesize compact yet informative datasets that allow models trained on them to achieve performance comparable to training on the full dataset. While this approach has shown promising results for image data, extending dataset distillation methods to video data has proven challenging and often leads to suboptimal performance. In

Atsuyuki Miyai, Shota Onohara, Jeonghun Baek, Kiyoharu Aizawa

This paper introduces JMMMU-Pro, an image-based Japanese Multi-discipline Multimodal Understanding Benchmark, and Vibe Benchmark Construction, a scalable construction method. Following the evolution from MMMU to MMMU-Pro, JMMMU-Pro extends JMMMU by composing the question image and question text into a single image, thereby creating a benchmark that requires

Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu, Kangfei Zhao

Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global attention was initially introduced, eliminating the necessity for using deep GNNs. However, through empirical and theoretical analysis, we

Paul Heslop, Hector Puerta Ramisa

We derive a compact analytic formula for a complete basis of conformally invariant tensor structures for three-point functions of conserved operators in arbitrary 4D Lorentz representations. The construction follows directly from a novel constraint equivalent to applying conservation conditions at each point: the leading terms in all OPE limits appear as sym

Augustine Denteh, Pierre E. Nguimkeu

This paper considers the estimation of binary choice models when survey responses are possibly misclassified but one of the response category can be validated. Partial validation may occur when survey questions about participation include follow-up questions on that particular response category. In this case, we show that the initial two-sided misclassificat

Omid Khormali

We introduce the Overlap-Weighted Hierarchical Normalized Persistence Velocity (OW-HNPV), a novel topological data analysis method for detecting anomalies in time-varying networks. Unlike existing methods that measure cumulative topological presence, we introduce the first velocity-based perspective on persistence diagrams, measuring the rate at which featur

Cristiano Welter, Kleinner Farias

The integration of cloud computing and the Internet of Things (IoT) is essential for scalable, intelligent systems. However, developing cloud-of-things (CoT) applications remains challenging. It requires significant technical expertise and lacks standardized, model-driven methodologies. Current approaches fail to ensure interoperability, automation, and effi

Franziska Böhnlein, Benjamin Bruske, Sven-Ake Wegner

In their 2022 lecture notes on condensed sets, Clausen and Scholze mentioned in a remark that the important subclass of quasiseparated condensed sets is equivalent to the category of so-called compactological spaces defined by Waelbroeck in the 1960s. In this paper we survey the latter category in detail, we give a rigorous proof of Clausen and Scholze's cla

Benedikt Heizenreder, Bas Gerritsen, Katya Fouka, Robert J. C. Spreeuw

We present a general method for engineering qudits through individually addressable transitions between Zeeman sublevels, achieved by combining a large linear Zeeman shift with a state-dependent light shift. This approach lifts the degeneracy between adjacent states while simultaneously tuning their energy splittings into the radio-frequency (RF) domain, ena

Md. Abdul Aziz, Thilo Strauss, Muhammad Mohebujjaman, Taufiquar Khan

We develop a self-adaptive physics-informed neural network (PINN) framework that reliably solves forward Darcy flow and performs accurate permeability inversion in heterogeneous porous media. In the forward setting, the PINN predicts velocity and pressure for discontinuous, piecewise-constant permeability; in the inverse setting, it identifies spatially vary

Marc-Oliver Pohle, Jan-Lukas Wermuth, Christian H. Weiß

Kendall's tau and Spearman's rho are widely used tools for measuring dependence. Surprisingly, when it comes to asymptotic inference for these rank correlations, some fundamental results and methods have not yet been developed, in particular for discrete random variables and in the time series case, and concerning variance estimation in general. Consequently

Cole Dickerson, Sean Kearney, Sultan Manjur, Ismail Guvenc

The rapid growth of unmanned aerial vehicles (UAVs) in civilian and critical-infrastructure airspace has created a need for reliable detection and tracking systems that operate under diverse environmental and sensing conditions. This paper presents a UAV detection and tracking system that fuses measurements from a network of passive Keysight N6841A RF sensor

Frederic Campana, Ljudmila Kamenova, Misha Verbitsky

An abelian fibration is a proper projective surjective map of complex varieties with general fiber an abelian variety. Consider a multiple fiber of an abelian fibration, and let $m_1, ..., m_k$ be the multiplicities of its irreducible components. We prove that the minimum of $m_i$ is equal to their greatest common divisor $gcd(m_1, ..., m_k)$

Loredana Prisinzano, Germano G. Sacco, Francesco Damiani, Amelia Bayo

The Milky Way (MW) is our unique laboratory to test star formation theories at the level of individual stars, serving as the Rosetta Stone to interpret extragalactic observations. The proposed White Paper focuses on the following key questions regarding the structure and evolution of the MW traced by young stellar populations: Q1. How do large-scale dynamica

E. Yu. Bunkova

In this work we give direct proofs of two theorems concerning explicitly defined polynomial vector fields connected to differentiation of hyperelliptic functions of any genus. We prove that the operators determining the fields commute, and we show that each of them annul polynomials defined in terms of generating functions in $\mathbb{C}^\infty$.

Prasanjit Dubey, Aritra Guha, Zhengyi Zhou, Qiong Wu

Sparse longitudinal (SL) textual data arises when individuals generate text repeatedly over time (e.g., customer reviews, occasional social media posts, electronic medical records across visits), but the frequency and timing of observations vary across individuals. These complex textual data sets have immense potential to inform future policy and targeted re

Lukáš Samuel Marták, Patricia Hu, Gerhard Widmer

Automatic Music Transcription (AMT) -- the task of converting music audio into note representations -- has seen rapid progress, driven largely by deep learning systems. Due to the limited availability of richly annotated music datasets, much of the progress in AMT has been concentrated on classical piano music, and even a few very specific datasets. Whether

Zhaolun Li, Jichang Li, Yinqi Cai, Junye Chen

In this paper, we propose FakeRadar, a novel deepfake video detection framework designed to address the challenges of cross-domain generalization in real-world scenarios. Existing detection methods typically rely on manipulation-specific cues, performing well on known forgery types but exhibiting severe limitations against emerging manipulation techniques. T

Yihan Liao, Jacky Keung, Xiaoxue Ma, Jingyu Zhang

The rapid advancement of Large Language Models (LLMs) has been driven by extensive datasets that may contain sensitive information, raising serious privacy concerns. One notable threat is the Membership Inference Attack (MIA), where adversaries infer whether a specific sample was used in model training. However, the true impact of MIA on LLMs remains unclear

Jimi Evan Harrold, Omar Almaini, Frazer R. Pearce, Robert M. Yates

We investigate the quenching of galaxies using a mock observational lightcone generated from the Semi-Analytic Model (SAM) L-Galaxies, closely matched to observations from the UKIDSS Ultra Deep Survey (UDS). The sample is used to study merging, rejuvenation, and visibility times for star-forming, quiescent, and post-starburst (PSB) galaxies, to assess the im

D. H. Dongwi, C. -J. Naïm, L. Rhode, A. Deshpande

We present a machine-learning-based particle-identification study for the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector of the ePIC experiment at the Electron-Ion Collider. Operating in the backward region ($-3.5 \lesssim \eta \lesssim -1.5$), the pfRICH is designed to achieve at least $3\sigma$ separation among pions, kaons, and protons up to

Frédéric Chassot, Aki Pulkkinen, Ján Minár, Gunther Springholz

The ferroelectric semiconductor {\alpha}-GeTe(111) has attracted significant attention in the last decade due to its unique properties, with extensive studies focusing on its occupied electronic bandstructure. In contrast, its unoccupied states - particularly those near the conduction band minimum - remain largely unexplored. In an effort to characterize tho

Mengyu Li, Xingcheng Zhou, Guang Chen, Alois Knoll

In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and high-speed motion conditions. Event cameras, characterized by low latency, high dynamic range and high temporal resolution, have considerable potential to mitigate these issues. Com

J. Maíz Apellániz, S. Simón-Díaz, A. Herrero, S. R. Berlanas

This document discusses the three main lines expected to dominate massive-star research in the 2040s, namely: (1) The role of metallicity in stellar evolution, especially in determining the end products such as gravitational-wave progenitors. (2) The initial mass function from the most massive stars to substellar objects. (3) The role of the environment in t