arXiv papers from June 2026

Ulisses Franceschi Eliano

In his 1996 doctoral thesis, Maurice Pagnucco created the first AGM-like abductive expansion operation. Taking his operation as a basis, as well as a taxonomy -- inspired by Atocha Aliseda -- responsible for highlighting and formalizing the main components of abductive reasoning, the main aim of this paper is to present a new paraconsistent AGM-like abductiv

Fabrizio Cleri, Ralf Blossey, Stefano Giordano

Intramolecular electron transport in biological systems is typically described as a diffusive hopping process, according to the semi-classical rate theories of Marcus and Hopfield combined with classical Pauli-type master equations. However, the possibility that non-trivial quantum mechanical effects could play a functional role in the transport dynamics in

Jiayi Chen, Weiting Ou, Guangxu Zhu

WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks. The primary bottleneck is the Heterogeneity Gap: the disparity in signal dim

Giovanni Gava

We review the classical theory of principal bundles, with particular emphasis on frame bundles and $G$-structures. We then develop the noncommutative framework by introducing the necessary notions of differential calculi, Hopf algebras, quantum principal bundles, and associated quantum vector bundles. Within this setting, we review Majid's notion of a qu

Gábor Timár, Jonathan A. Ward, Péter L. Simon

Mean-field approximations for dynamical processes on networks are widely used, but existing derivations often rely either on moment closures or on idealised assumptions about network structure, leaving the nature of the underlying averaging unclear. Here we present a mathematically principled framework for deriving edge-based mean-field approximations for a

Taufiq Wirahman

The transmission chains (sanad) of Islamic Hadith literature encode relationships among tens of thousands of historical narrators whose biographical records are dispersed across independently maintained digital databases that share no common identifier. We present a two-phase entity resolution pipeline that links narrator names from the Sanadset 650K corpus

Sinh Vu Trong, Dung Nguyen Manh, Hieu Hoang Minh, Hieu Pham Trung

Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world dataset of classroom videos collected at the Banking Academy of Vietnam (BAV-Classroom dataset), annotated with nine distinctive beha

Yijiashun Qi, Xiang Xu, Yuxuan Li

Long-lived language agents increasingly write reusable memories from their own execution traces. The key safety question is not only what agents should remember, but when they should refuse to write memory at all. Repeated observations across agents are not necessarily independent evidence: the same claim may be copied from a shared source, induced by a shar

Glenn Bruda

Let $p$ be a binary word of length $\ell$ with $r\geq2$ runs. Previously known only for $k\leq4$, we show for $n$ sufficiently large that the number of binary words of length $n$ with exactly $k$ subsequences equal to $p$ is polynomial in $n$ of degree at most $\ell-r+1$ for any positive integer $k$. We also prove a sharp upper bound on the number of subsequ

Vishvesh Bhat, Jay Vaghasiya, Muhammad Ahmed Mohsin, Asad Aali

Tool-calling benchmarks are increasingly used to rank language-model agents, yet their scores are often treated as ground truth without validating the evaluators themselves. We present a systematic validity and reproducibility audit of four major tool-calling benchmark families: BFCL v4, τ2-Bench, LiveMCPBench, and MCP-Atlas. Across 496 expert-reviewed bench

Pengcheng Wang, Ziran Liu, Wei Wang, Wei Jiang

Parameter-Efficient Fine-Tuning (PEFT) commonly adapts pretrained weights through low-rank updates, and recent methods further exploit the singular value decomposition (SVD) of the base weight for initialization or subspace selection. However, these methods do not explicitly preserve the coupled geometry between the pretrained left and right singular bases.

Kaiyun Yang, Ruilin Yang, Zhimin Yao, J. Wang

Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the support set for task induction. In the standard ICL setting, once the task is induced, its decision criterion remains fixed. However, in real-world applications, many tasks exhibit a stable high-level intent, while t

Jie Li, Tongyang Wang, Yong Chen

The key-value (KV) cache has become a first-order memory object in LLM serving rather than a temporary per-request tensor. This survey classifies more than thirty KV-management systems and frameworks using four axes: locality, lifetime, ownership, and substrate. The axes reveal five architectural archetypes -- local-paged, disaggregated-pipeline, shared-stor

Hassan Ugail, Irfan Mehmood

Islamic geometric patterns are governed by exact rotational symmetry and strict construction rules. This paper treats these rules as formal geometric knowledge and embeds them in a neural completion framework, rather than leaving them to be learned statistically from data. Given sparse control geometry and a target symmetry order, the system completes the pa

Zhizhong Fu, Wei Zhou, Zhaoyang Jiang, Yulong Lin

In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-

Ying Chen, Jinyue Li, Kun Wang, Qiankun Li

The Segment Anything Model with Concepts (SAM3) heralds a new paradigm for open-vocabulary segmentation through natural language interaction, offering significant potential for medical image analysis. However, effectively adapting such a powerful vision-language model to the diverse and nuanced domain of medical imaging remains a key challenge. Naive fine-tu

Jakob Garbe, Jan W. Kantelhardt, Katja Seeliger, Thomas Schmid

In this document, we describe characteristics and technical details of the multimodal biosignal dataset DOSE-I of procedural sedation for endoscopy published on zenodo. The DOSE-I dataset includes 78.5 hours of recording in 171 records ranging from 6.7 to 70.8 minutes (mean: 27.5, SD: 11.6) of 281 endoscopic procedures. 1129 (median: 6 per record) transition

Karim Mardhani

This paper presents a safety-centered empirical evaluation of uncertainty-aware last-layer adaptation for referable diabetic retinopathy screening using RETFound, a self-supervised vision-transformer retinal foundation model used here as a frozen feature encoder, and the public APTOS 2019 and DDR diabetic retinopathy fundus image datasets. We compare a cache

Weize Quan, Zhengwei Wu, Kai Wang, Dong-Ming Yan

View-based point cloud completion aims to recover a complete 3D shape from a partial point cloud, guided by a single-view image. However, existing approaches often suffer from limited performance due to weak modality alignment and limited self-geometry enhancement. To overcome these challenges, we propose a unified geometry-aware framework that integrates ef

Qijun Chen, Shaofan Li

Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets. This mismatch can limit reconstruction accuracy, reduce sample quality, and constrain the expressive power of the learned latent space. We pr

Raghvendra Singh, Sergey Bondarenko

We present a covariant mechanism in which a smooth change of metric signature, from a Euclidean to a Lorentzian regime, drives a finite interval of accelerated expansion. The transition, encoded by a scalar interpolator along a timelike congruence, occurs on a codimension-one hypersurface where the continued metric is degenerate but curvature invariants rema

Aizierjiang Aiersilan

Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must run on limited edge hardware. We present a multi-seed benchmark jointly evaluating federated learning (FL) and knowledge distillation (KD) for 3D point cloud classification. It spans 13 FL algorithms and 10 KD

Alex Citkin

We study structural completeness in the infinitary sense (strong structural completeness) in an algebraic setting. A variety is structurally complete (SCpl) if it is generated, as a quasivariety, by its free algebras, and it is strongly structurally complete (SSCpl) if it is generated, as a prevariety, by its free algebras. A quasivariety is SSCpl if it is g

Artatrana Suna, Prasanta Kumar Ray

The sequence of Mersenne numbers $\{M_n\}_{n\geq 0}$ is defined as $M_n = 2^n-1.$ In this study we introduce the Mersenne-Bernoulli and Mersenne-Euler polynomials. Using the generating functions and $M$-calculus we find some identities associated with them. Moreover, we define the corresponding matrices with these polynomials, factorise them and find their i

Artatrana Suna, Prasanta Kumar Ray

In this work, we study a normalized remainder $T_{n,λ}[\e_λ]$ for the degenerate exponential $\e_λ(u)=(1+λu)^{1/λ}$ ($λ>0$). We establish an integral representation, an exact monotonicity threshold at $λ=1/(n+1)$, and rigorous conditions for the local failure of logarithmic convexity at the origin. We then prove a sharp asymptotic result: for every $λ$ in th

Roman D. Oleinik

We reformulate the bounded-length-distortion condition for maps between metric spaces in a certain relaxed form that requires the presence of a reference measure on the source space, which makes the new approach more natural from the perspective of maps from metric measure spaces to metric spaces. In terms of the introduced notion, we establish some mapping

Scott Chase Waggener, Sai Karthik Navuluru, Lakshman Tamil

We present Aegis, a joint-embedding predictive architecture for breast cancer detection and density assessment in mammography. We train three Vision Transformer variants (Small/Base/Large) using self-supervised joint-embedding predictive architecture (JEPA) pre-training on 71,103 studies from 14 clinical sites, followed by supervised fine-tuning with progres

Dong Zhang

Current large-language-model (LLM) physics benchmarks are usually scored by answer accuracy, which cannot distinguish genuine reasoning from recall of familiar problem patterns and reveals little about where a model's reasoning breaks down. We introduce an auditable four-stage diagnostic that evaluates whether an LLM can reason inside an unfamiliar physi

Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell

Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data. FL is challenging under data heterogeneity and partial client participation. Learning sparse models is useful for communication and computational efficiency in FL, but it is especially difficult in the small-sample high-dime

Shayan Peyghambari Oskoui, Norah Almousa, Zhaoyi Joey Hou, Carolina Gustafson

Effective writing feedback is among the strongest drivers of student learning, yet producing it at scale is labor-intensive. LLMs offer a natural path to scaling writing support, but two gaps stand in the way: few public corpora capture how instructors actually deliver feedback in real classrooms, and no reliable method measures whether generated feedback al

Thinh Phan, Hao Vo, Khoa Vo, Thanh Ngo

The core challenge in multi-view pedestrian detection (MVPD) lies in effective aggregation of visual features from different viewpoints for robust occlusion reasoning. Recent approaches have addressed this by first projecting image-view features onto a Bird's Eye View (BEV) map, where ground localization is then performed. Despite impressive performance,

Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu

Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures. We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomously writes and refines robot control progr

Fereshte Ildarabadi, Stephen R. Power

Electrostatically defined quantum dots (QDs) with layer-antisymmetric gating in Bernal-stacked bilayer graphene (BLG) open a local gap and generate a mass-like term with opposite sign in the two valleys, producing strongly valley-dependent scattering without magnetic fields, strain, or spin-orbit coupling. Building on this mechanism, we propose a tunable pla

Masen Bachleda, Peter Lalor

Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements. We present a machine learning-based approach to this problem that converts list-mode gamma-ray data

Luis Welbanks, Kylie E. Hall, Julien de Wit, Ana Glidden

Sub-Neptunes are the most common class of planets in the Galaxy, yet they have no Solar System analog and remain poorly understood as a population. JWST observations have revealed atmospheres spanning a wide range of metallicities, compositions, and cloud properties, driving active debates over whether warm sub-Neptunes harbor liquid water oceans beneath H2-

Maxime Méloux, Tiago Pimentel, François Portet, Maxime Peyrard

A central goal of science is to produce valid explanations of complex systems: high-level causal accounts that faithfully reflect the behavior of lower-level mechanisms. Yet no consensus exists on how to measure whether a proposed high-level explanation is actually valid. We introduce a benchmark of ten complex systems spanning both discrete and continuous s

Kunwar Kalra, V. V. Kocharovsky

We study the problem of extracting a quantum complexity resource from a mixed Gaussian state of the multimode light. We present the first complete, certificate-checked solution to this problem in a genuinely coupled sector. We carry this out for the two-mode case, the smallest case in which modes are genuinely coupled. Even in this case the solution is highl

Junna Sugiyama, Kyohei Yamada, Bryce Bixler, Daichi Sasaki

The Simons Observatory (SO) is a ground-based Cosmic Microwave Background (CMB) experiment that is located in the Atacama plateau. The Small Aperture Telescopes (SATs) of SO are optimized for polarimetry on the degree scale. Atmospheric $1/f$ contamination of the CMB signal poses a significant challenge for observations at this angular scale. In order to con

Orlagh L. Creevey, Laia Casamiquela, Yveline Lebreton, Christophe Ordenovic

The masses and ages of stars are key quantities for understanding exoplanetary, stellar, and galactic evolution. In the context of Gaia, these parameters provide insights into the stellar populations, helping to trace the formation and history of the Galaxy. As part of the Gaia Data Processing and Analysis Consortium (DPAC), the Final Luminosity Age Mass Est

Clément Dallard, Daniël Paulusma, Erik Jan van Leeuwen

The Chromatic Sum problem asks, given a graph $G$ and an integer $k$, whether $G$ admits a colouring $c$ with sum $\sum_{v\in V}c(v) \leq k$. We study the complexity of Chromatic Sum on graph classes defined by some set of forbidden graphs. First, we show that three known frameworks fully classify the complexity of Chromatic Sum on $HH$-minor-free graphs and

Ananda Shikhara Bhat, Hanna Kokko

Multi-level selection and senescence do not at first sight have much in common. Here, we demonstrate that the emergent mortality patterns generated by demographic senescence can be understood as the product of multi-level selection. We formulate a two-level Moran type process and use its scaling limits to illustrate that a simple mathematical framework that

Woonyoung Chang

This paper establishes finite-sample worst-case maximal inequalities for averages of independent centered heavy-tailed random vectors. The object of interest is the expected top-$k$ Euclidean norm of the sample average, which includes the expected coordinate-wise maximum as the special case $k=1$. Under coordinatewise variance constraints and tail-envelope c

Afshar Shamsi, Xiao-Yu Guo, Hamid Alinejad-Rokny, Arash Mohammadi

Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in the absence of supervision, entropy-based adaptation is fundamentally underconstrained: multiple distinct parameter updates can achieve similarly low entropy while inducing drastically different decision bound

Virginia Morini, Valentina Pansanella, Luca Pappalardo, Dino Pedreschi

Social media algorithms allocate users' visibility by ranking content within their social networks. Yet, how recommendation logic and network structure jointly shape visibility across content and creators remains largely understudied. In this work, we tackle this question through agent-based simulations using YSocial, a social media virtual twin, in whic

Max Kreider, John Harlim, Daning Huang

Accurate prediction of complex dynamical systems from noisy measurements remains a significant challenge in scientific computing. Kernel ridge regression learning strategies are often effective when applied to clean data, but have limited success with noisy data. Recent work has observed that a weak formulation can act to filter noisy data, and different lea

Feibo Jiang, Li Dong, Lei Mao, Kezhi Wang

Unmanned Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight Small Language Mod

Huayi Wang, Jun Xu, Gromit Yeuk-Yin Chan

Many modern AI workflows, ranging from LLM post-training pipelines to agentic reasoning tasks, can be expressed as declarative queries whose expensive predicate is evaluated by a large model or reward function. We propose a query-centric formulation of these workflows and show that classical database techniques, namely approximate query processing (AQP) and

Philipp del Hougne

Dynamic metasurface antennas (DMAs) enable programmable wave-domain signal processing that can be jointly optimized with downstream digital processing in an end-to-end manner. Existing studies, however, typically assume ideal analog-to-digital conversion (ADC) and often rely on simplified electromagnetic models. Here, we study ADC-aware end-to-end optimizati

Naren Sarayu Manoj, Kumar Kshitij Patel

We present an algorithm for the group distributionally robust (GDR) least squares problem. Given $m$ groups, a parameter vector in $\mathbb{R}^d$, and stacked design matrices and responses $\mathbf{A}$ and $\mathbf{b}$, our algorithm obtains a $(1+\varepsilon)$-multiplicative optimal solution using $\widetilde{O}(\min\{\mathsf{rank}(\mathbf{A}),m\}^{1/3}\var

Samira Malek, Haichuan Zhang, Chul Lee, Vishal Monga

While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details. To that end, we first develop novel linear minimum mean squared (LMMSE) estimators of the amplitude and phase of the blurred, noisy image

Geeling Chau, Ran Liu, Juri Minxha, Wenhui Cui

New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they are able to generalize to new layouts effectively. To improve cross-layout transfer, we study how different channel embedding techniques behave when pretraining layouts differ substa

Bytedance Seed

We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' genuine needs and constructing a reliable, forward-looking evaluation system by selecting and abstracting benchmarks grounded in these needs and in realistic, complex scenarios. Guided by this evaluation sy

Aaron Isidore Grace, Zhouyuan Huo, Weiran Wang

Large audio-language models (LALMs) frequently hallucinate by overriding acoustic evidence with language priors. While contrastive decoding (CD) offers training-free mitigation, existing methods rely on blunt perturbations like masking or noise, leaving structured audio transformations unexplored. We explore this design space by evaluating a diverse library

Weiyue Zhou, Hooman Gholamzadeh, Lei Ding, Kevin Daub

Dealloying has been extensively studied both as a corrosion degradation mechanism in structural materials, including those used in nuclear, aerospace, or marine environments, and as a versatile method to fabricate porous materials for catalysts and other functional applications. Classical dealloying theory in aqueous environments predicts a critical reactive

Hui Gong

Autonomous AI agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financial markets: if softw

Édouard Bonnet, Maël Dumas, Julien Duron

For every $\varepsilon > 0$, Max Independent Set admits a polynomial-time $n^\varepsilon$-approximation algorithm on $n$-vertex graphs of effectively bounded twin-width [Bergé et al., STACS '23]. The approximation factor actually obtained is more precisely $n^{O(1/ \log \log n)}$. Prior to the current paper, no approximation hardness was known for this p

Alexander Belyaev, Manimala Chakraborti, Shu Chen, Atri Dey

We explore the potential of the Large Hadron Collider to probe a two-component scalar dark matter scenario in the opposite-sign dimuon plus missing transverse energy final state, accompanied by a hard jet. The signal features a soft dimuon system with an invariant mass well below $m_Z$. We consider a 3-Higgs Doublet Model with one active and two inert scalar

Giovanna Rodríguez-García, María Elizabeth Mesa-Pineda, José R. Nicolás-Carlock

Corruption is embedded in networks of access, coordination, and protection, yet little is known about how gender shapes actors' positions within them. This article examines whether corruption networks in Colombia's territorial press reproduce gendered patterns of exclusion. Drawing on an access-to-power perspective, we argue that women's lower pr

Dinh-Liem Nguyen, Nhung H. Nguyen, Thi-Phong Nguyen

This paper focuses on identifying defective units in unbounded periodic arrays of point sources using boundary data. The study is motivated by the noninvasive evaluation of large-scale periodic source systems. Unlike classical inverse source problems in free space, the key challenge here lies in the disruption of periodicity caused by defective sources in th

Rubens Costa, R. C. Anjos

We present a broadband spectral analysis of the $γ$-ray emission from the pulsar wind nebula HESS~J1825$-$137, combining observations from Fermi Large Area Telescope (\textit{Fermi}-LAT), High Energy Stereoscopic System (H.E.S.S.), High-Altitude Water Cherenkov Observatory (HAWC), and Very Energetic Radiation Imaging Telescope Array System (VERITAS) across t

Kalina Borkiewicz, Jixian Li, Joshua A. Levine, Katherine E. Isaacs

In 3D visualizations of natural phenomena, improving aesthetics can provide measurable benefits, but often involves transformations that affect how the data is perceived. As a growing range of tools - including AI-based methods - make visual design and modification more accessible, it is increasingly important to understand trade offs and concerns when makin

Nicolas Folinsbee, Joel Friedman

Let $f\colon{\mathbb Z}^2\to{\mathbb Z}$ be a Riemann function whose weight $W$ is a perfect matching. Then there is a family of sheaves of $k$-vector spaces $\{{M}_{W,{\bf d}}\}_{{\bf d}\in{\mathbb Z}^2}$ on a five-point topological that models $f$ in that $f({\bf d})=b^0({M}_{W,{\bf d}})$ and that $$ b^1({M}_{W,{\bf d}})= f^\wedge_{\bf K}({\bf d}-{\bf K})

Carla Valencia-Negrete, Cristhian Garay-Lopez, Marco Favela-Rodriguez, Alonso Andapia-Viveros

Nonlinear algebraic (polynomial) differential equations that govern fluid-structure interactions, such as those modeling vortex-induced vibrations, and shock waves, often lack analytical solutions, creating significant challenges to efficient prediction and control. While Physics-Informed Neural Networks (PINNs) offer a mesh-free numerical alternative, they

Shamminuj Aktar, Rishabh Bhardwaj, Tanmoy Bhattacharya, Stephan Eidenbenz

Predicting the overlap of quantum states with specified low-energy subspaces is a key diagnostic for quantum many-body dynamics, with direct applications in state preparation, subspace-based algorithms, and the study of thermalization. We study the supervised prediction of subspace overlaps O_K between time-evolved states and K-dimensional low-energy eigensp

Hao Jiang, Chongjun Ouyang, Yuanwei Liu, Arumugam Nallanathan

The sensing capability of the pinching-antenna system (PASS) is analyzed from a Ziv-Zakai bound (ZZB) perspective, motivated by the sensing ambiguity arising from the multimodal observation model inherent to PASS. In comparison to other Bayesian sensing bounds, the ZZB provides a lower bound on the mean-squared error (MSE) across a broad range of signal-to-n

Yashar Talebirad, Eden Redman, Ali Parsaee, Osmar R. Zaiane

How do two agents invent a shared language from scratch? In a Lewis signaling game, a sender and receiver must coordinate on a code using only their interaction history. We study five memory architectures across varying channel configurations with LLM agents and find that memory architecture matters more than channel capacity. Agents with a persistent privat

Frank Lu

In this paper, we prove that the étale fundamental group of the Néron model of an abelian variety over a number field $K$ is the semidirect product of a finite group with the étale fundamental group of the ring of integers of $K.$ We prove this by studying how the Faltings height of an abelian variety changes under covers that spread out to finite étale cove

Roozbeh Gharakhloo, Nicholas S. Witte

In previous work \cite{GW}, we developed a theory of modulated \(2j-k\) bi-orthogonal polynomial systems \(\{P_n(z;r),Q_n(z;r)\}\) and \(j-2k\) bi-orthogonal polynomial systems \(\{R_n(z;r),S_n(z;r)\}\), which generalize the classical \(j-k\) Toeplitz systems. In the present paper, we further develop this theory in several directions. We derive simplified an

M. Abdullah Khokhar, Malgorzata M. O'Reilly, Richard Turner

In many service systems, an estimation of customers' waiting times for the service can assist in decision making focused on enhancing the operational efficiency, improving the customers' experience, and ensuring efficient resource allocation. In this paper, we study the customers' waiting times in a finite-capacity service system with a finite nu

Carlos Alvarado, Alfredo Aranda, César Bonilla, Yahir Lua

The production of magic states is studied in two settings. The first is the electroweak (EW) sector of the Standard Model (SM). The second is an extension featuring a new broken $U(1)$ gauge symmetry and a Dirac fermion charged under it. This setup resembles a dark $U(1)$ scenario, with the additional fermion playing the role of a dark matter candidate that

Ved G. Shah, Nabeel Rehemtulla, Adam A. Miller, Sushant Sharma Chaudhary

Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy. Robust early classification is crucial for making informed decisions, but is hindered by sparse light curves and degeneracies between cl

Alexander Omelchenko

We model an adaptive contest in which two antagonistically coupled populations continually reallocate effort among competing methods, but decisions are not fielded instantly. Each side has an intended portfolio and a deployed portfolio: intended reallocations follow delayed observations of the opponent, while deployment follows intent through a first-order i

Zuobin Zhang, Rob Fender, James H. Matthews, Jiachen Jiang

We present soft X-ray spectroscopy of the black-hole X-ray binary V4641~Sgr with the \textit{XMM-Newton} Reflection Grating Spectrometer (RGS). The RGS spectrum shows narrow emission features from N\,\textsc{vi--vii} and O\,\textsc{vii--viii} superimposed on a partially covered disk blackbody continuum. A blind Gaussian search confirms the presence of signif

Ruimeng Hu, Quyuan Lin, Qirui Peng

We study weak solutions of electrodiffusion systems coupling the Nernst--Planck equations with fluid models. First, for the three-dimensional Nernst--Planck--Euler system, we establish an Onsager-type criterion for the validity of the coupled kinetic-electrostatic energy balance. The energy equality is shown to hold for weak solutions whose velocity satisfie

Xin Li, Wenhui Zhu, Xuanzhao Dong, Xiwen Chen

Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks through self-attention, enabling modeling of long-range dependencies. Early ViT-based segmentation methods typically retained U-Net-style decoders because pretrained ViT representation

Ruixuan Zhao, Mats Stensrud, Linbo Wang

In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent events such as survival. Existing strategies face a trade-off: causally interpretable estimands, such as survivor average causal effects, target a latent subgroup, whereas while-alive and composite summar

Ahmad Kamaludeen, Somnath Kundu, Yeganeh Bahoo

We study the symmetric bouncing of a point robot within orthogonally-joined rectangles with equal width, which we refer to as pipes. We provide an exhaustive case analysis of every trajectory pattern inside a single rectangular pipe segment, identifying the conditions under which the robot exits. We then extend the analysis to L-shaped pipes and, more genera

Andrea Ferrario

Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformations raise a metaphysical question: under what conditions does an AI system remain the same system over time or across deployments? Earlier work formulates synchronic and diachronic identity propositionally, by r

Julien Chhor, Xavier D'Haultfœuille, Jérémy L'Hour, Martin Mugnier

We consider inference for parameters of the form $θ_0 = E[F_Y^{-1}\circ F_Z(X)]$ for some variables $X$, $Y$ and $Z$. Such parameters appear, in particular, in the ``changes-in-changes'' model of \cite{AtheyImbens2006}. We first establish that $\widehatθ$, a plug-in estimator of $θ_0$, is root-$n$ consistent and asymptotically normal under weaker con

Siddhant Panpatil, Arth Singh, Mijin Koo, Chaeyun Kim

Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situations while avoiding unnecessary intervention on routine but superficially alarming activity, a distinction that binary safety benchmarks obscure. We introduce EgoSafetyBench, an egocentric vi

Sean Patrick MacBride, R. Lynne Jones, Peter Yoachim, Tiago Ribeiro

The NSF/DOE Vera C. Rubin Observatory is a discovery machine, with unprecedented survey speed, which can be used to identify exotic astrophysical transients. In its prime mission, the ten year Legacy Survey of Space and Time will use 3% of its total time for Target of Opportunity observations, which includes response to gravitational wave events, high energy

Gabriel Covarrubias Maureira, Balarko Chaudhuri, Mark O'Malley

Analysis of sub-synchronous oscillations (SSO) in IBR-dominated grids relies on frequency scan-based estimation of black-box IBR models at selected operating points. Since IBRs may operate over a wide range of operating conditions, frequency responses obtained at a limited number of operating points may not adequately represent the dynamics required for syst

Yixiao Li, Tifanny Portela, Jordis Herrmann, René Zurbrügg

Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computationally prohibitive. We propose ELMP, a framework for data-efficient adaptation via self-supervised fine-tuning. Rather than generating additional expert trajectories with expensiv

Romie Banerjee

This short review examines the primary approaches for estimating the predictive distribution of Laplace-approximated Bayesian neural networks, with particular focus on the Generalized Linear Model (GLM) formulation. We survey the landscape of estimation strategies, from exact GLM computations requiring full Jacobian evaluations to Monte Carlo approximations

Ramón Soto C., Liz Soto

This paper introduces the Federated Sovereign Transport Protocol (FSTP), a synchronization boundary and transport layer for federated networks in which nodes have heterogeneous privacy requirements. Existing federation protocols leave data confinement to operator policy: they define message formats and delivery semantics but impose no structural constraint o

Sylwia Antoniuk, Magdalena Prorok, Nika Salia

A strong majority edge-coloring of a graph is an edge-coloring in which, for every edge $e$ and every color $i$, at most half of the edges adjacent to $e$ have color $i$. Such a coloring exists only for graphs with no pendant path of length two, which, following Kalinowski, Kamyczura, Pilśniak, and Woźniak, we call admissible. They proved that every admissib

Mengqian Wu

Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the conceptual framework of Epistemic AI Li

M. Signorini, V. N. Bennert, E. Dalla Bontà, F. Ricci

The persistent tension between early- and late-Universe measurements of the Hubble constant (H0) remains on of the most significant challenges in modern cosmology. The Spectroastrometry and Reverberation Mapping (SARM) method offers a promising, calibration-independent approach to address this issue by combining time-delay measurements of the Broad-Line Regi

Ruikang Zhao, Zhenting Wang, Han Gao, Ligong Han

Reinforcement learning for diffusion large language models (dLLMs) has largely moved to trajectory-aware methods. The current state of the art, TraceRL, holds that random masking is mismatched with the model's inference trajectory, and it reconstructs that trajectory during training by slicing each rollout into up to K/s trajectory-aligned training sampl

Fabrizzio Sabelli

We develop a framework for analyzing the learning dynamics of $\ell_2$-adversarial training of single-index models on Gaussian mixtures in the high-dimensional limit under streaming stochastic gradient descent (SGD). We derive deterministic equivalents for a broad class of statistics of the SGD iterates, including the adversarial risk and distance to adversa

A. Della Croce, E. Vesperini, R. Pascale, A. Askar

The James Webb Space Telescope (JWST) detected numerous massive and relatively compact stellar clumps around proto-galaxies at high redshift (z>0.5). Their properties suggest that these systems may represent proto-globular clusters (GCs), but their possible connection to local old GCs is poorly understood. In this Letter, we explore the dynamical evolution o

Hassan Tavakoli

We establish the exact exponential growth rate of the $ρ$-th moment of the constrained guesswork $G_{\mathrm{coset}}$ -- the rank of the true noise vector within its syndrome coset of a random binary linear code under i.i.d.\ Bernoulli$(p)$ noise: \( \lim_{n\to\infty} \frac{1}{n}\log_2\Eb\!\left[G_{\mathrm{coset}}^ρ\right] = ρ\,h_{\frac{1}{1+ρ}}(p)\;+\;ρ(R-1

Hiroto Fujimaru, Gonzalo Navarro, Francisco Olivares, Jakub Radoszewski

A suffixient array is a novel data structure that, when combined with an index providing direct access on a text $T$, allows us to answer a variety of pattern matching queries. In this work, we show how to compute a smallest suffixient array for $T[1\dots n]$ in $O(\frac{n\log σ}{\sqrt{\log n}}+\min(r,\bar{r})\log^εn)$ time for any $ε> 0$, where $σ$ is the a

Dennis Boakye, Chuang Deng

Phase selection in multicomponent alloys is governed by the competition between entropic stabilization of disordered solutions and enthalpic driving forces for chemical ordering. However, widely used parametric criteria reduce it to a single scalar, carrying no explicit free energy for any competing ordered phase. Herein, we develop a thermodynamic framework

Shokhruz Kakharov, Abraham Loeb

We study the feasibility of natural and directed panspermia via interstellar objects (ISOs) like 3I/ATLAS. The paper is organized around two questions. First, could natural panspermia occur if microbes or biomolecules survived inside shielded ice and were later exposed during perihelion and outbound activity? Second, could directed panspermia occur if a tech

Erich Robbi, Daniele Ravanelli, Andrea Passerini

Robust segmentation of intraluminal thrombus is critical for risk assessment in Abdominal Aortic Aneurysm, yet it remains challenging due to heterogeneous thrombus features and low contrast with surrounding non-enhanced tissues. Domain shifts induced by different Computed Tomography Angiography (CTA) protocols further inhibit multi-center generalization of d

Nathan B. Clayburn, Andrew Glassford, Thomas Uelmen, Ashley R. Kyung

Many proposed extensions to the Standard Model of particle physics introduce new bosons that can mediate forces which couple to particle spin. Here we describe a search for such forces coupling spin-polarized neutrons and protons in our magnetometer to spin-polarized electrons within Earth. We measure these interactions by varying the orientation of an optic

W. A. Zúñiga-Galindo

We present a resolution of the Wigner's Friend paradox within a framework of quantum mechanics (QM) on the hybrid space RxQ_{p}, where Q_{p} denotes the field of p-adic numbers, regarded as a model of discrete microscopic space at the Planck-Bronstein scale. In this framework, wavefunction collapse is not an independent postulate but a dynamical conseque

Haroon Gharwi, Yue Dai, Kai Shu

Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation. The Variability-Aware Recursive Neural Network (VARNN) is designed to track such variability by maintaining a residual-memory state driven by one-step prediction errors. However, its original formulation is limited to o

Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar, Charles D. Young

Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise. For such systems, deterministic neural surrogates fail to capture statistical measures and forecast uncertainty. We introduce TRIE, an evaluati