April 2026 arXiv papers — page 148

Lyuxing He, Eric Cai, Shobhit Aggarwal, Jianjun Wang

Recent advances in robotic manipulation have highlighted the effectiveness of learning from demonstration. However, while end-to-end policies excel in expressivity and flexibility, they struggle both in generalizing to novel object geometries and in attaining a high degree of precision. An alternative, object-centric approach frames the task as predicting th

Junhao Chen, Kejun Gao, Yuehan Cui, Mingze Sun

Despite rapid progress in video generation, existing models are incapable of producing vector animation, a dominant and highly expressive form of multimedia on the Internet. Vector animations offer resolution-independence, compactness, semantic structure, and editable parametric motion representations, yet current generative models operate exclusively in ras

Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron, Pablo Samuel Castro

Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an LLM's layers in the latent dimension, resulting in looped reasoning language models. Despite promising results, few works have investigated how their internal dynamics differ from those of standard feedforward

Wei Zhao, Zhe Li, Peixin Zhang, Jun Sun

Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect prompt injection. Adversaries exploit this weakness by embedding malicious instructions within tool-returned content, which agents directly incorporate into their conversation history

Yuqian Yuan, Wenqiao Zhang, Juekai Lin, Yu Zhong

Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks requiring precise object-level grounding, fine-grained spatial reasoning, and controllable visual manipulation. In particular, existing systems often struggle to identify the correct instance, preserve object id

Naomi Ken Korem, Mohamed Oumoumad, Harel Cain, Matan Ben Yosef

High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its mismatch with the bounded, perceptually compressed data on which these models are trained. A natural solution is to learn new representations for HDR, which introduces additional complexity and data requiremen

Martin Spitz, Deng Zhang, Zhenqi Zhao

In this paper, we develop the well-posedness theory and uncover the noise-regularization effect on scattering for the stochastic Zakharov system in dimensions $d \geq 4$ and beyond the energy space. Our focus is particularly directed at the large data regime, where the global existence and long-time dynamics of the deterministic Zakharov system remain largel

Jiayuan Rao, Tianlin Gui, Haoning Wu, Yanfeng Wang

Modeling open-play soccer tactics is a formidable challenge due to the stochastic, multi-agent nature of the game. Existing computational approaches typically produce single, deterministic trajectory forecasts or focus on highly structured set-pieces, fundamentally failing to capture the inherent variance and branching possibilities of real-world match evolu

Md Rakibul Haque, KM Arefeen Sultan, Tushar Kataria, Shireen Elhabian

While medical Vision-Language models (VLMs) achieve strong performance on tasks such as tumor or organ segmentation and diagnosis prediction, their opaque latent representations limit clinical trust and the ability to explain predictions. Interpretability of these multimodal representations are therefore essential for the trustworthy clinical deployment of p

D. Richardson, J. Dee, J. Yaeger, M. Viray

Quantum sensors often consist of packaging, such as dielectric-based vapor cells and metallic electrodes, that reduces and spatially alters the locally observed electromagnetic fields. These effects have been well studied in the optical regime, and even in the RF regime over a few GHz. However, there have been few studies in the electrically small regime bel

Fei Tang, Zhiqiong Lu, Boxuan Zhang, Weiming Lu

GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of a coherent full-stack infrastructure: on

Pau Colomer, Christian Deppe, Holger Boche, Andreas Winter

Deterministic identification (DI) has emerged as a promising paradigm for large-scale and goal-oriented communication systems. Despite significant progress, a fundamental open problem has remained unresolved: a persistent gap between the best known lower and upper bounds on the DI capacity, as well as on the corresponding rate-reliability tradeoff bounds. In

Willie Aboumrad, Claudio Girotto, Joshua Goings, Luning Zhao

As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metrics. In this work, we present a scalable framework for application-level quantum benchmarking that is designed to support internal system evaluation and cross-platform comparison ac

Léna Parc, Julia Venturini, François Bouchy, Ravit Helled

Super-Earths and sub-Neptunes represent the most common class of exoplanets discovered to date in our galaxy, yet they have no direct analogues in the Solar System. Since 2014, researchers within the NCCR PlanetS have made significant contributions to understanding the origin and nature of these small planets. This chapter provides an overview of the progres

Junlin Liu, Shengnan An, Shuang Zhou, Dan Ma

Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics and physics. However, their ability to generalize these reasoning skills to more general and broader contexts--often termed general reasoning--remains under-explored. Unlike domain-specific reasoning, general rea

Roberto de A. Capistrano-Filho, Ailton C. Nascimento

We develop a framework for the fifth-order Kadomtsev--Petviashvili equation on $\mathbb{T}_x \times \mathbb{R}_y$ within a mean-zero KP-adapted Sobolev scale. A localized high-order feedback acting on the periodic variable yields a $5/2$--derivative gain in suitable space--time norms, leading to propagation of regularity and a unique continuation property fo

B. Kaoui, A. Bou Orm, P. Navet, J. Baish

Bicuspid valves with crescent-shaped leaflets are found in lymphatic vessels and veins, where their primary function is to prevent reflux and ensure unidirectional flow toward the heart. These valves are passive, and their functionality emerges spontaneously from a complex interplay between the properties of the valve leaflets and the flow patterns developin

Ricardo Coimbra Brioso, Giulio Sichili, Damiano Dei, Nicola Lambri

Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to the large number of coalition evaluations and the high cost of sliding-window inference. We present an efficient KernelSHAP framework for volumetric CT segmentation that restricts

J. Oppliger, M. Stifter, A. Rüegg, I. Biało

Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain challenging. For example, crystal alignment strongly relies on humans with the ability to comprehend diffraction patterns. Here we introduce an autonomous system that aligns single crystals without a

Ricardo Bessa, Rui Claro, João Trindade, João Lourenço

Large Language Models (LLMs) are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human intervention. While attackers take advantage of dark LLMs such as XXXGPT and WolfGPT to produce malicious code, ethical hackers can follow similar approaches to automate traditional pentesting workflows. In this work, we p

Hari Sadasivan

We set out to train behavioral dispositions (self-verification, uncertainty acknowledgment, feedback integration) into small language models (0.6B to 2.3B effective parameters) through a four-stage all-MIT distillation pipeline, with follow-on experiments on inference-time attention-head interventions and a frozen-base confidence-gated sidecar. An internal d

Qin Liu

Existing LLM agent frameworks lack formal semantics: there is no principled way to determine whether an agent configuration is well-formed or will terminate. We present $\lambda_A$, a typed lambda calculus for agent composition that extends the simply-typed lambda calculus with oracle calls, bounded fixpoints (the ReAct loop), probabilistic choice, and mutab

Osama Farooqui

We introduce the strong $q$-timelike Brunn-Minkowski condition $\mathsf{sTBM}_q(K,N)$ on synthetic Lorentzian spaces, for $0<q<1$. We show that, in the timelike $q$-essentially non-branching setting, the $q$-timelike curvature dimension condition $\mathsf{TCD}_q(K,N)$ is equivalent to $\mathsf{TBM}_q(K,N^+)$, and that the entropic $q$-timelike curvature dime

Kengo Hashimoto

A combinatorial game is a two-player game without hidden information or chance elements. The disjunctive sum $G + H$ of games $G$ and $H$ is the game in which $G$ and $H$ are played in parallel, and a player makes a move on exactly one of $G$ and $H$ in a turn. The ordinal sum $G \colon H$ is similar to the disjunctive sum, but once the left game $G$ is play

Zengqi Chen, Wu Zhou, Hao Chen, Kaihang Lu

Arbitrary manipulation of light across multiple physical dimensions is essential for harnessing its parallelism in fundamental research and advanced applications, such as optical interconnects, computing, imaging, sensing, and quantum networks. However, creating a universal device capable of arbitrary operations of multidimensional optical beams has been cha

Paula Arguello, Berk Tinaz, Mohammad Shahab Sepehri, Maryam Soltanolkotabi

Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven largely by public datasets focused on brain and knee imaging, shaping how models are trained and evaluated. As a result, careful studies of the reliability of these models across diverse anatomical setti

Kexin Yu

Let $M_n$ be an $n\times n$ signed random combinatorial matrix whose rows are independent and uniformly distributed over the set of $\{-1,0,1\}$-vectors with exactly $n/2$ zero coordinates. Despite the dependence induced by the row constraints, we prove that there exist constants $C,c > 0$ such that for any $\varepsilon\ge0$, \begin{align*} \textbf{P}\left(s

Moslem Rashidi

Item nonresponse to financial questions is a persistent source of survey error, especially in interviewer-administered surveys. We examine whether interviewers' expectations about respondents' willingness to report income are associated with actual item responses to income and asset questions in Wave 6 of the Survey of Health, Ageing and Retirement in Europe

Jinhui Ye, Ning Gao, Senqiao Yang, Jinliang Zheng

Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark-specific engineering. In this work, we introduce St

Tom van der Steen, Simon Maenaut, Stef J. B. Husken, Pedro G. S. Fernandes

Within the effective field theory approach to gravity, deviations from general relativity can be systematically described by higher-curvature operators. However, computing the resulting corrections to black hole quasinormal mode spectra remains challenging in the rapidly rotating regime, where perturbative expansions in the spin break down. We use recently c

Abbas Arghavani

Covert wireless communication aims to establish a reliable link while hiding the transmission from an adversary. In wireless settings, uncertainty plays a central role in this tradeoff: it can help mask the signal from a warden, but it also complicates robust system design. This raises a basic question: under bounded uncertainty, are reliability and covertne

J. Francisco Presenza, Leonardo J. Colombo, Juan I. Giribet, Ignacio Mas

In this work, we study angle-based localization and rigidity maintenance control for multi-robot networks. First, we establish the relationship between angle rigidity and bearing rigidity considering \textit{directed} sensing graphs and \textit{body-frame} bearing measurements in both $2$ and $3$-\textit{dimensional space}. In particular, we demonstrate that

Yoonsang Lee, Howard Yen, Xi Ye, Danqi Chen

We study parallel test-time scaling for long-horizon agentic tasks such as agentic search and deep research, where multiple rollouts are generated in parallel and aggregated into a final response. While such scaling has proven effective for chain-of-thought reasoning, agentic tasks pose unique challenges: trajectories are long, multi-turn, and tool-augmented

Carl Lochstampfor, Ayan Roy

Smishing (SMS phishing) has become a serious cybersecurity threat, especially for elderly and cyber-unaware individuals, causing financial loss and undermining user trust. Although prior work has focused on detecting smishing at the level of individual messages, real-world attackers often rely on multi-stage social engineering, gradually manipulating victims

Quanyi Li, Lan Feng, Haonan Zhang, Wuyang Li

In Model Predictive Control (MPC), world models predict the future outcomes of various action proposals, which are then scored to guide the selection of the optimal action. For visuomotor MPC, the score function is a distance metric between a predicted image and a goal image, measured in the latent space of a pretrained vision encoder like DINO and JEPA. How

T. D. Gorelkina, I. E. Kalantaevskii, A. N. Abramov, K. A. Gasnikova

Layered van der Waals antiferromagnet CrSBr supports strong light--matter coupling and formation of magnetically tunable exciton-polaritons, yet active magnetic control over polariton propagation direction has remained elusive. Here, we investigate self-hybridized exciton-polaritons in photonic crystal slabs fabricated from CrSBr flakes and their evolution a

Yi Jing, Weiyun Qiu, Yihang Peng, Zhifang Sui

Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical and social transformation. Despite recent advances in diachronic semantics and discourse analysis, existing computational approaches often (i) concentrate on a single concept or a single corpus, making findi

Yuxin Chen, Chumeng Liang, Hangke Sui, Ruihan Guo

Continuous diffusion has been the foundation of high-fidelity, controllable, and few-step generation of many data modalities such as images. However, in language modeling, prior continuous diffusion language models (DLMs) lag behind discrete counterparts due to the sparse data space and the underexplored design space. In this work, we close this gap with Lan

Gabriel Arpino, Ramji Venkataramanan

We study the problem of identifying change points in high-dimensional generalized linear models, and propose an approach based on sample-weighted empirical risk minimization. Our method, Weighted ERM, encodes priors on the change points via weights assigned to each sample, to obtain weighted versions of standard estimators such as M-estimators and maximum-li

Youngjae Kim, Koichiro Yawata, Hiroya Nakao, Kunihiko Taira

The complex dynamics of high-dimensional oscillatory flows can be simplified using phase-reduction analysis, providing a deeper understanding of the flow response to external perturbations. Although phase-based modeling and analysis have been utilized in recent studies on oscillatory fluid flows, their usages are still limited to single-frequency flows due t

Andrés Muñoz, Rodrigo Ramele

Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. In the context of Variational Autoencoders (VAEs), it serves as a central regularization term, imposing structure on the latent space and thereby enabling the model to exhibit generative capabilities. In this

Victor Marcenac, Tommy Nguyen, Julie Chen, Weitao He

Dynamical decoupling techniques are widely used to characterize and control the environments of solid-state quantum defects, enabling solid-state quantum memories and nanoscale quantum sensors. However, resolution is often limited by the timing granularity of control hardware, which can undersample narrow spectral features and distort extracted parameters. H

Hongli Zhan, Emma S. Gueorguieva, Javier Hernandez, Jina Suh

Large language models (LLMs) produce responses rated as highly empathic in single-turn settings (Ayers et al., 2023; Lee et al., 2024), yet they are also known to be formulaic generators that reuse the same lexical patterns, syntactic templates, and discourse structures across tasks (Jiang et al., 2025; Shaib et al., 2024; Namuduri et al., 2025). Less attent

Keyang Zhong, Junlin Xie, Hefeng Wu, Haofeng Li

Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer game settings with imperfect and deceptive information. In this paper, we study a representative multiplayer task, Murder Mystery Games, which require inferring hidden truths based on partial clues provided b

Nitin Kumar

This article aims to introduce the broad field of soft active matter physics and its relevance to the life sciences in simple, accessible language. Although this area of research is relatively new, it has already demonstrated significant potential in providing a physical understanding of many biological processes. While several review articles by leading res

Andreas Gleis, Kevin Lucht, Po-Jui Chen, Daniele Guerci

We demonstrate that interacting electrons in AB-stacked $\mathrm{MoTe}_2/\mathrm{WSe}_2$ realize a topological Kondo insulator at hole filling $\nu=2$ per moir\'e unit cell. In the presence of only local correlations, a symmetry of the moir\'e-scale bandstructure enforces a compensated topological semimetal by tying band inversion to band overlap. We show th

Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Angel Bautista

Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures through full video synthesis remains prohibitively inefficient. We model scene dynamics orders of magnitude more efficiently by directly operating on a long-ter

Alexey Sharapov, David Shcherbatov

We investigate Maxwell-Chern-Simons theory on a three-dimensional noncommutative spacetime endowed with a constant spacelike Poisson structure. By exploiting the residual rotational symmetry, we construct exact classical solutions corresponding to pointlike electric and magnetic charges. We demonstrate that noncommutativity acts as a natural regulator, ensur

Alexander Braverman, David Kazhdan

Let $F$ be a local non-archimedian field of odd residue characteristic and let $G=PGL(2)$. In this paper we study an analog of irreducible cuspidal representations of the group $G(F)$ when $F$ is replaced by the field $K=F((t))$. The story turns out to be similar to the classical case, but also with some differences. We present a construction of such represe

Haojie Bai, Aimin Li, Ruoyu Yao, Xiongwei Zhao

Cooperative driving is a safety- and efficiency-critical task that requires the coordination of diverse, interaction-realistic multi-agent trajectories. Although existing diffusion-based methods can capture multimodal behaviors from demonstrations, they often exhibit weak scene consistency and poor alignment with closed-loop cooperative objectives. This make

Saad Alqithami

We study routing games in which travelers optimize over routes that are remembered or surfaced, rather than over a fixed exogenous action set. The paper develops a tractable design theory for endogenous recall and then connects it back to an explicit finite-memory micro model. At the micro level, each traveler carries a finite memory state, receives surfaced

Jue Zhou, Zoha Sherkat-Masoumi, Merve Bodur

Electric car-sharing systems are pivotal for sustainable urban mobility, but their strategic design is complicated by operational constraints, particularly those arising from the charging needs of electric vehicles. The success of these systems hinges on integrating long-term investment decisions (such as station locations, charger capacities, and fleet size

Arhit Chakrabarti, Yang Ni, Yuchao Jiang, Bani K. Mallick

We consider the problem of clustering nested or hierarchical data, where observations are grouped and there are both group-level and observation-level variables. In our motivating OneK1K dataset, observations consist of single-cell RNA-sequencing (scRNA-seq) data from 982 individuals (groups), totaling 1.27 million cells (observations), along with individual

Nicola Gorini, Chris Jones, Dmitriy Kunisky, Lucas Pesenti

General first-order methods (GFOM) are a flexible class of iterative algorithms which update a state vector by matrix-vector multiplications and entrywise nonlinearities. A long line of work has sought to understand the large-n dynamics of GFOM, mostly focusing on "very random" input matrices and the approximate message passing (AMP) special case of GFOM who

C. Di Maio, S. Benatti, D. Locci, R. Spinelli

Precise mass and radius measurements of small, transitional exoplanets, such as super-Earths and sub-Neptunes, are essential to constrain their bulk density and formation history, serving as prerequisites for atmospheric characterization. The ArMS Large Programme, carried out within GAPS using the HARPS-N spectrograph at the Telescopio Nazionale Galileo, aim

Junkyo Oh, Byeongsu Yang, Cheong Heo, Daeun Jung

To address the Reactor Antineutrino Anomaly (RAA) observed in neutrino experiments, the Reactor Experiment for Neutrino and Exotics (RENE) has been initiated using a liquid scintillation detector. In this study, we investigate the characteristics of two 20-inch Hamamatsu R12860 photomultiplier tubes (PMTs) intended for installation in the RENE detector. The

Joel Stevens, Jeremy Coulson

In this work we examine the problem of data-driven prediction. That is, given a LTI system with unknown dynamics, we wish to use data collected from the system to predict the system's output response to a given sequence of known inputs. Current methods for predicting require strong conditions on the data such as persistency of excitation. We examine this pro

Tatsuya Terao

We consider a fast approximation algorithm for the linear matroid intersection problem. In this problem, we are given two $r \times n$ matrices $M_1$ and $M_2$, and the objective is to find a largest set of columns that are linearly independent in both $M_1$ and $M_2$. We design a $(1 - \varepsilon)$-approximation algorithm with time complexity $\tilde{O}_{\

Armin Hoenen

The age of artificial intelligence has brought many new possibilities and pitfalls in many fields and tasks. The devil is in the details, and those come to the fore when building new pipelines and executing small practical experiments. OCR and stemmatology are no exception. The current investigation starts comparing a range of OCR-systems, from classical ove

Arman Bekov, Azamat Nurgali

Online education platforms have experienced explosive growth over the past decade, generating massive volumes of user-generated content in the form of reviews, ratings, and behavioral logs. These heterogeneous signals provide unprecedented opportunities for understanding learner satisfaction, which is a critical determinant of course retention, engagement, a

Angela Riva, Prakritish Gogoi, Nicolas Gheeraert, Serge Florens

The speed and fidelity of dispersive readout of superconducting qubits should improve by increasing the amplitude of the measurement drive. Experiments show, however, that beyond some drive amplitude there is always a saturation or drop in fidelity, often associated with a decrease in qubit energy relaxation time $T_1$. A simple Lindblad master equation does

Ryan Faulkner, Anushka Deshpande, David Guzman Piedrahita, Joel Z. Leibo

Governing common-pool resources requires agents to develop enduring strategies through cooperation and self-governance to avoid collective failure. While foundation models have shown potential for cooperation in these settings, existing multi-agent research provides little insight into whether structured leadership and election mechanisms can improve collect

Andreas Müller, Denis Lukovnikov, Shingo Kodama, Minh Pham

The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images from training data to prevent model collapse. To address this need, watermarking techniques, specifically designed for AR models, embed a subtle signal at generation time, enabling do

Amedeo Altavilla, Maurício Corrêa

We study the Donaldson-Friedman semistable twistor degeneration by combining the scheme-theoretic geometry of its Ferrand pushout with the logarithmic topology of its Kato-Nakayama realisation. For the central fibre $ Z_0=\widetilde Z_1\cup_Q\widetilde Z_2,$ the Ferrand description yields an explicit equaliser presentation of the operational Chow ring and a

Marco Michetti, Luigi Provenzano, Alessandro Savo

We consider the first eigenvalue of the magnetic Laplacian with zero magnetic field on simply connected compact surfaces and we establish isoperimetric inequalities and upper bounds in terms of a bound on the gaussian curvature. As a corollary, we prove that among all simply connected spherical domains of fixed area, the first eigenvalue is maximal for a geo

Shuquan Lian, Juncheng Liu, Yazhe Chen, Yuhong Chen

Prior representative ReAct-style approaches in autonomous Software Engineering (SWE) typically lack the explicit System-2 reasoning required for deep analysis and handling complex edge cases. While recent reasoning models demonstrate the potential of extended Chain-of-Thought (CoT), applying them to the multi-turn SWE task creates a fundamental dilemma: reta

Younghwan Cho, Richard Sowers

Koopman operator theory is a key tool in data assimilation of complex dynamical systems, with the potential to be applied to multimodal data. We formulate the problem of learning Koopman eigenfunctions from observations at arbitrary, possibly non-vanishing, time intervals as an optimization problem. Analysis of the formulation reveals aliasing induced by osc

Junwoo Park, Jangho Lee, Sunho Lim

Pretrained detectors perform well on benchmarks but often suffer performance degradation in real-world deployments due to distribution gaps between training data and target environments. COCO-like benchmarks emphasize category diversity rather than instance density, causing detectors trained under per-class sparsity to struggle in dense, single- or few-class

Michael R. Buche, Alvin Chen

It is common to study polymer physics through the use of idealized single-chain models, and the most popular of these is the freely jointed chain model. In certain thermodynamic ensembles, statistical mechanical treatment of this model is analytically tractable or sometimes exactly solvable. This enables useful relations to be ascertained, like the expected

Riccardo Brumali, Guido Carnevale, Sonia Martínez, Giuseppe Notarstefano

In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task as a bilevel optimization problem, where the upper level formalizes the desired macroscopic target behavior through a suitable performance criterion, which is shaped in the lower l

Nhan Ho, Luu Le, Thanh-Huy Nguyen, Thien Nguyen

Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge for foundation segmentation models in clinical endoscopy. We introduce OccSAM-Bench, a benchmark designed to systematically evaluate SAM-family models under controlled, synthesized surgical occlusion. Our fram

Shuaitongze Zhao, Honghui Liu, Menglei Zhou, Swarnim Shashank

We report time-resolved NICER and Swift X-ray spectroscopy of a bright flare from the black hole X-ray binary GRS 1915+105 during its obscured state, which is characterized by heavy line-of-sight absorption by dense material with complex geometry. In April 2023, an unexpected flare was detected, with the observed X-ray flux increasing by nearly an order of m

Wanli Ma, Sivasakthy Selvakumaran, Dain G. Farrimond, Adam A. Dennis

Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, and support recovery. Traditional field inspections, though precise, are limited by accessibility, safety risks, and time constraints, especially after large explosions. Machine learning with remote sensing has

Aditya A. Krishnan, Donghoon Kim, Hokeun Kim

Open-source software for cyber-physical systems (CPS) often lacks robust testing involving robotic platforms, resulting in critical errors that remain undetected. This is especially challenging when multiple modules of CPS software are developed by various open-source contributors. To address this gap, we propose Automated CPS Testing (ACT) that performs aut

Efstathios Karypidis, Spyros Gidaris, Nikos Komodakis

Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a hierarchical video prediction framework that decomposes forecasting into two stages: semantic representation prediction and representation-guided visual synthesis. In

Tzortzis Koulaxizis, Antonia Statt

Polymer crystallization is a process of great interest in both fundamental theory and industrial settings, particularly in polymer processing and applications involving semi-crystalline materials. The effect of processing on the initial stages of crystallization is not fully understood. Our study investigates the influence of pre-shear on monodisperse melts

Nicolas Rodriguez-Alvarez

Deep Neural Networks are highly susceptible to shortcut learning, frequently memorizing low-dimensional spurious correlations instead of underlying causal mechanisms. This phenomenon not only degrades out-of-distribution robustness but also induces severe demographic biases in sensitive applications. In this paper, we propose a geometric \textit{a priori} me

Javad M Alizadeh, Genhui Zheng, Chiu C Tan, Yuzhou Chen

People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG addresses this through a knowledge graph-augmented conversational system that grounds responses in verified, up-to-date data about Philadelphia organizations, services, locations, and hours. Unlike standard large langua

Zeyu Huang, Zhifan Guo, Xingyu Li, Xiaojuan Ma

Human traces scattered across urban landscapes can signify our everyday lives and societal vibrancy in subtle and poetic forms. In this paper, we explore how designed technology can engage biodata as evocative traces. To this end, we present the design, implementation, and evaluation of HeartSway, an interactive hammock that captures a user's heart rate and

Sara Ketabi, Matthias W. Wagner, Birgit Betina Ertl-Wagner, Greg A. Jamieson

In spite of the strong performance of machine learning (ML) models in radiology, they have not been widely accepted by radiologists, limiting clinical integration. A key reason is the lack of explainability, which ensures that model predictions are understandable and verifiable by clinicians. Several methods and tools have been proposed to improve explainabi

Jieying Xue, Phuong Minh Nguyen, Ha Thanh Nguyen, May Myo Zin

This work aims to improve the generalization of logic-based legal reasoning systems by integrating recent advances in NLP with legal-domain adaptive few-shot learning techniques using LLMs. Existing logic-based legal reasoning pipelines typically rely on fine-tuned models to map natural-language legal cases into logical formulas before forwarding them to a s

Michael Fucilla, Yoshitaka Hatta, Bo-Wen Xiao

We calculate the spin density matrix of a back-to-back quark-antiquark pair inclusively produced in electron-nucleus scattering, taking into account the gluon saturation effect and the linearly polarized gluon distribution. We then investigate concurrence and stabilizer R\'enyi entropy, quantifying entanglement, Bell-nonlocality, and magic. We find that the

Jacques H. Daniel

Background/ Objectives: Resolving the origin of the genetic code is fundamental to understanding how life began its journey out of the chemical world. Since its deciphering some 60 years ago, there is still no general theory of the emergence of the genetic code. My objectives are to bring some unique data that might provide some insight into this particular

Walton Green, Perry Kleinhenz

In this paper we introduce a new dynamical condition, the comb geometric control condition, which is sufficient for observability of the Schr\"odinger equation in Euclidean space. We provide examples which show this condition is strictly weaker than the observation set being open and periodic. We also prove for the fractional Schr\"odinger equation that for

Ryuichiro Hada, Teppei Okumura

Accurate modeling of small-scale redshift-space clustering is crucial for full shape RSD analyses, where satellite galaxies contribute to 1-halo terms and Finger-of-God distortions. We investigate halo reconstruction based on the cylinder grouping (CG) method of Okumura et al. (2017), which selects an effective halo center tracer from the observed galaxy dis

Elżbieta Adamus, Zbigniew Hajto

We compare two classes of polynomial automorphisms, strongly nilpotent and Pascal finite. We conclude that every strongly nilpotent automorphism is a Pascal finite one, but not vice versa. We observe that Nagata's automorphism is Pascal finite, but not strongly nilpotent. Considering Vasyunin example leads us to conclusion that not every quadratic polynomial

Zhibo Ren, Jukka I. Väyrynen

An antidot-pinned vortex in a three-dimensional topological insulator-superconductor platform hosts a Majorana zero mode (MZM). However, numerous Caroli-de Gennes-Matricon (CdGM) states coexist with it. We develop a general theory to study the effects of disorder on the system, emphasizing the difference between Majorana zero mode and CdGM states. Using both

Carolin Forster, Marco Oesting

In this paper, we analyze the asymptotic behavior of the point process of exceedances in a spatio-temporal setting whose points are given by the rescaled occurrence times, the sites and the rescaled values of exceedances. Here, the exceedances over a high threshold are flexibly defined via site-dependent risk functionals. Exploiting the framework of stationa

Dujun Nie, Fengjiao Chen, Qi Lv, Jun Kuang

While the shortage of explicit action data limits Vision-Language-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A critical challenge in utilizing large-scale human video datasets lies in transforming visual signals into ontology-independent representations, known as latent actions. However, the capacity of latent action

Sandip Maiti

Geometric frustration, arising from competing interactions that prevent simultaneous energy minimization, presents a fundamental challenge for variational quantum algorithms applied to quantum many-body systems. We investigate the transverse-field Ising model on a square lattice with frustrated diagonal coupling and show that geometric frustration leads to s

Utsav Paneru

AI-generated text has become common in academic and professional writing, prompting research into detection methods. Less studied is the reverse: systematically rewriting AI-generated prose to read as genuinely human-authored. We build a parallel corpus of 25,140 paired AI-input and human-reference text chunks, identify 11 measurable stylistic markers separa

Yixuan Nan, Xixun Lin, Yanmin Shang, Ge Zhang

Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often limited under noisy or sparsely supervised scenarios. Recently,

Yuqin Lu, Yang Zhou, Yihua Dai, Guiqing Li

3D Gaussian Splatting (3DGS) has become a state-of-the-art framework for real-time, high-fidelity novel view synthesis. However, its substantial storage requirements and inherently unstructured representation pose challenges for deployment in streaming and resource-constrained environments. Existing Level-of-Detail (LOD) strategies, particularly those based

Andrew M. Naylor, Victor Pacheco-Peña

The engineering of the optical properties of materials in space and time is opening further directions and possibilities to control wave propagation in four dimensions (x,y,z,t). A key example of such modulations are time interfaces where the permittivity of the medium is changed in time from isotropic to another isotropic value. Recently, isotropic-to-aniso

Thomas Buc-d'Alché, Antti Knowles

We analyse the eigenvectors of the adjacency matrix of a random inhomogeneous graph constructed from a specified degree sequence. We assume that the empirical degree sequence has bounded mean and variance. We show that near the edges of the spectrum, the eigenvectors are semilocalized in the sense that their mass concentrates around a small set of resonant v

João Miguel Guerreiro Fernandes, Samih Eisa, Miguel L. Pardal

From production to consumption, ensuring food quality and traceability depends on reliable monitoring of environmental conditions across the supply chain. Ambient sensing devices can collect relevant data such as temperature and humidity, but ensuring its integrity among stakeholders remains a challenge. This work presents AmBox, a system that enables device

Lan Wei, Zongcai Tan, Kangyi Lu, Jian-Qing Zheng

Optical microrobots actuated by optical tweezers (OT) are important for cell manipulation and microscale assembly, but their autonomous operation depends on accurate 3D perception. Developing such perception systems is challenging because large-scale, high-quality microscopy datasets are scarce, owing to complex fabrication processes and labor-intensive anno

Nicholas King, Gareth H. McKinley

Steady shearing and planar extension are commonly viewed as two distinct types of flow field, especially in the context of probing the rheology of complex fluids. By leveraging the kinematic equivalence between the two flows, we derive an effective extension rate experienced by a material element which removes the rotational component of the shearing flow. T

Jean-Baptiste Billand, David Elbaz, Maximilien Franco, Fabrizio Gentile

JWST observations have identified a class of enigmatic sources known as little red dots (LRDs), interpreted as a distinct class of active galactic nuclei (AGNs) and host galaxies, whose black hole masses, AGN emissivities, stellar masses, and possible quasi-stars or black hole stars (BH*) suggest a previously unidentified class of extragalactic objects. Howe

Yoshihide Nakamiya, Kensuke Homma, Madalin-Mihai Rosu, Liviu Neagu

We have developed and commissioned an experimental system at ELI-NP towards searches for axion-like particles (ALPs) in the worldwide 10~PW-class laser facility. The search principle is based on the Four-Wave Mixing (FWM) process at a focal region of coaxially combined two laser beams. The subsystems to control vacuum pressure, area size, spatiotemporal over