April 2026 arXiv papers — page 87

Tanmay Singh, Sanchayeeta Borthakur, Dylan Nelson, Romeel Davé

We compare OVI absorption in synthetic spectra from galaxy groups in the SIMBA and TNG50 cosmological hydrodynamic simulations against those observed from the COS-IGrM survey. We select 14 galaxy groups from each simulation with $12.89 \le \log(M_{\rm halo}/M_\odot) \le 13.61$, closely matching COS-IGrM, and create 90,000 synthetic spectra per group. We demo

Yetao He, Wenhan Guo, Deliang Wei, Evan Bel

Three-dimensional (3D) wide-field fluorescence microscopy is a widely used modality for volumetric imaging, but suffers from characteristic out-of-focus blur. Existing reconstruction methods either struggle to operate on high-dimensional volumes or fail to provide credibility characterization of the reconstruction. In this work, we introduce Volumetric Trans

Shang Wang, Shuai Liu, Owen Randall, Matthew E. Taylor

3D-IC netlist partitioning is commonly optimized using proxy objectives, while final PPA is treated as a costly evaluation rather than an optimization signal. This proxy-driven paradigm makes it difficult to reliably translate additional PPA evaluations into better PPA outcomes. To bridge this gap, we present DOPP (D-Optimal PPA-driven partitioning selection

Martiño Ríos-García, Nawaf Alampara, Chandan Gupta, Indrajeet Mandal

Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we evaluate LLM-based scientific agents across eight domains, spanning workflow execution to hypothesis-driven inqui

Yuanbang Liang, Zhengwen Chen, Yu-Kun Lai

Latent Diffusion Models (LDMs) achieve high-fidelity synthesis but suffer from latent space brittleness, causing discontinuous semantic jumps during editing. We introduce a Riemannian framework to diagnose this instability by analyzing the generative Jacobian, decomposing geometry into \textit{Local Scaling} (capacity) and \textit{Local Complexity} (curvatur

Zhiyuan Jiang, Weihao Hong, Xinlei Guan, Tejaswi Dhandu

Vision-Language Models (VLMs) are increasingly deployed in settings where reliable visual grounding carries operational consequences, yet their behavior under progressively coercive prompt phrasing remains undercharacterized. Existing hallucination benchmarks predominantly rely on neutral prompts and binary detection, leaving open how both the incidence and

Majid Bahraminasr, Anand Yethiraj

We present a tunable, non-equilibrium oil-in-oil emulsion that serves as a model system for investigating the transition from controlled droplet deformation to multiscale flows reminiscent of turbulence. By utilizing a miscible mixture of silicone and motor oils as the continuous phase and the immiscible castor oil as the droplet phase, we isolate electrical

Stephen Bruehl, Brian Doherty, Alina Streblyanska, Beatriz Villarroel

Transient, star-like point sources that appear and vanish over short timescales are described in astronomical images prior to launch of Sputnik. We have reported that transient numbers diminish significantly in Earth's shadow (shadow deficit) and are more likely within (plus/minus) one day of nuclear testing (nuclear window). These findings remain debated wi

Evangelos G. Filothodoros

We study the BCS--BEC crossover of the large $N$ attractive Fermi-Hubbard model on a one-dimensional lattice using the mean field approximation in the presence of an imaginary chemical potential. We show that the crossover is governed by three parameters. The imaginary chemical potential $i\theta$, the temperature via a thermal kernel $g(\beta E_k,\beta\thet

Linkai Peng, Cuiling Sun, Zheyuan Zhang, Wanying Dou

Automatic pancreas segmentation is fundamental to abdominal MRI analysis, yet deep learning models trained on one MRI sequence often fail catastrophically when applied to another-a challenge that has received little systematic investigation. We introduce CrossPan, a multi-institutional benchmark comprising 1,386 3D scans across three routinely acquired seque

Marcos M. Alexandrino, Benigno O. Alves, Patricia Marcal

We investigate singular Finsler foliations (SFFs) on a manifold equipped with an $(\alpha,\beta)$-metric. To be precise, we verify that any SFF of an $(\alpha,\beta)$-space is, under some hypotheses on the metric, a singular Riemannian foliation (SRF). This gives a partial answer to the general question "under which conditions a SFF is a SRF with respect to

Fanjun Xu, Ralf Feyerherm, Cecilie Glittum, Thomas J. Hicken

$\gamma$-${\rm Ba}_{3}{\rm CoNb}_{2}{\rm O}_{9}$ realizes a disordered simple-cubic spin-$1/2$ lattice in which Co$^{2+}$ ions randomly occupy one third of the sites, placing the system close to the site-percolation threshold for magnetic order. Specific-heat, susceptibility, neutron spin-echo, and muon spin-rotation measurements reveal a broad thermodynamic

Andy Jiang, Greg Stevenson

We observe that for a quasi-compact and quasi-separated scheme the structure sheaf generates the perfect complexes if and only if the lattice of thick subcategories is distributive if and only if the affinization map is 0-affine. Examples are discussed, including an example of a quasi-projective scheme which is not quasi-affine but for which these equivalent

Levi Lucio

Model transformations are central to MDE, but formal verification is difficult because mainstream transformation languages are undecidable. DSLTrans was designed to be Turing-incomplete to improve verifiability, yet earlier verification based on path-condition enumeration still suffered exponential blow-up and did not scale to realistic cases. We present a t

Zijian Zeng, Fei Ding, Huiming Yang, Xianwei Li

Vision-Language-Action (VLA) models fail systematically on long-horizon manipulation tasks despite strong short-horizon performance. We show that this failure is not resolved by extending context length alone in the current reactive execution setting; instead, it stems from three recurring execution-loop deficiencies: the memory gap, the verification gap, an

Johny J. Lopez, Md Meftahul Ferdaus, Mahdi Abdelguerfi, Anton Netchaev

Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing methods achieve high accuracy on standard benchmarks but rely on heavy backbone architectures that preclude real-time deployment on embedded hardware. We present EfficientPENet, a two-branch depth completion n

Jiacheng Liang, Yao Ma, Tharindu Kumarage, Satyapriya Krishna

Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches primarily target policy-level weaknesses, they overlook what we

Afsara Benazir, Felix Xiaozhu Lin

Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inference efficiency via sparse activation but are challenging for NPUs in three ways: expert routing is unpredictable and introduces dynamic tensor shapes that conflict with the shape-specific constraints of NPUs;

Yi-Min Huang, Naoki Bessho, Li-Jen Chen, Judith T. Karpen

It is a widely accepted paradigm that collisionless magnetic reconnection proceeds at a universal fast rate of $\sim0.1$ when normalized to a properly defined reconnecting magnetic field and Alfv\'en speed, effectively independent of the macroscopic system size. This conclusion, derived primarily from kinetic simulations of classical Harris current sheets wi

Seyedali Mohammadi, Manas Gaur, Francis Ferraro

Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or refute it. We frame feasibility assessment as a diagnostic reasoning task in which, given a hypothesis, a model predicts feasible or infeasible and justifies its decision. We evaluate large language models (LLMs)

Marianne Akian, Stephane Gaubert, Shanqing Liu, Yang Qi

We study the approximation of the value function of deterministic optimal control problems with fixed initial state, motivated by \(N\)-body systems. In this setting, the action functional consists of local kinetic and potential terms, along with an interaction potential. We exploit this structure to approximate the value function using a tropical tensor of

Jamie A. Lopez, Amir Erez

Mathematical models are increasingly a part of microbiological research. Here, we share our perspective on how modeling advances the discipline by: (i) enforcing logical consistency, (ii) enabling quantitative prediction, (iii) extracting hidden parameters from data, and (iv) generating intuitive understanding. We map a spectrum of modeling frameworks, from

Moad Abudia, Opeyemi Owolabi, Joel A. Rosenfeld, Rushikesh Kamalapurkar

This paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems. Parameter estimation is achieved by training a data-driven predictive model using state-action measurements and various known values at the parameters of interest. The predictive model is then used in conjunction wi

Ciro Ciliberto, Andreas Leopold Knutsen, Sara Torelli

We give an alternative proof of the Hurwitz existence problem for branched covers of $\mathbb{P}^1$ in the case where the number of ramification points equals the number of branch points, that is, where all the ramification profiles are of the form $[e,1,\ldots,1]$ with $e \geq 2$.

Sergio Morell-Ortega, Ángela González-Cebrián, Boris Mansencal, Marien Gadea

Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice. Existing generative super-resolution (SR) methods produce visually compelling isotropic volumes but often introduce anatomical hallucinations, systematic volumetric overestimation, and structural distortions

Benjamin K. Johnson, Thomas Goralski, Ayush Semwal, Hui Shen

Semi-Markov Conditional Random Fields (semi-CRFs) assign labels to segments of a sequence rather than to individual positions, enabling exact inference over segment-level features and principled uncertainty estimates at their boundaries. However, existing implementations must materialize a large edge potential tensor whose size grows with sequence length, ma

Weixi Tong, Yifeng Di, Tianyi Zhang

Existing web agents typically initiate exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures. Without a global view of the website's structure, agents frequently fall into navigation traps, explore irrelevant branches, or fail to reach target information within a limited budget. We propose Mango, a multi-a

Ana Maria Herrera, Elena Pesavento, Alessia Scudiero

We propose a clustered local projection (clustered LP) method to estimate impulse response functions in a class of time-varying models where parameter variation is linked to a low-dimensional matrix of observables. We show that the clustered LP recovers the conditional average response when the driving variables are exogenous and a weighted average of the co

Nei Lopes, Guilherme da Silva do Vale, Daniel G. Barci

We investigate the interplay between superconductivity and nematic order in a two-dimensional electron gas with competing pairing and quadrupolar forward-scattering interactions. The model includes both $s$-wave and $d$-wave superconducting channels. We compute the mean-field free energy density and determine the phase diagrams as functions of interaction st

Aryan Tyagi, Alex de Beer, Tiangang Cui, Jan N. Fuhg

Structural reliability evaluation for composites constitutes a fundamentally high-dimensional multiscale problem, as microscale material uncertainties must propagate to the macroscale and can be quantified as high-dimensional random fields. Conventional approaches are computationally intractable, as they rely on repeatedly solving coupled partial differentia

Hanrui Luo, Shreyank N Gowda

Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarely. In this work, we present an empirical study of output based jailbreak detection under realistic conditions using the JailbreakBench Behaviors dataset and multiple generator models with varying alignment st

Joseph D Consiglio

Popular software packages report four generalizations of the ANOVA F test when conducting a multivariate analysis of variance (MANOVA). The reported operating characteristics of these fours tests vary widely depending on which research article the reader chooses. Some studies report extremely high type I error rates for a particular test even under ideal ass

Toni Scarmato, Abraham Loeb

Building on the jet morphology and periodic wobble analysis of 3I/ATLAS in Scarmato & Loeb (2026), we link observed jet position angles (PAs) and the non-gravitational acceleration components (A1,A2,A3) in the 3D RTN (radial, transverse, normal) frame relative to the Sun. We: (i) compute RTN directions from heliocentric state vectors and project them on the

Y. Lange Simmons, James Greenberg, Brendan M. Heffernan, Antoine Rolland

We demonstrate a low noise, high-gain, resonant optical amplifier that combines injection locking with feed-forward cancellation of residual phase noise. The wavelength-agnostic architecture uses a commercial semiconductor diode laser as a power amplifier while preserving the spectral purity of a weak reference. Although injection locking enforces phase cohe

Ansar Calloo, Matthew Evans, François Madiot, Tristan Pryer

We present a computational study of diffusion synthetic acceleration (DSA) for the monoenergetic, isotropically scattering $S_N$ transport equations, discretised in space by a polytopic discontinuous Galerkin method. Using a discrete ordinates angular discretisation, we construct the DSA correction with an interior-penalty diffusion operator and compare a cl

Abir Trabelsi, Imen Benzarti, Hafedh Mili, Darine Ameyed

Translating business problems into well-specified machine learning solutions is a prerequisite for successful AI systems, yet this upstream translation is still one of the least supported steps in existing methodologies. We conduct a structured narrative literature review of 18 approaches spanning requirements engineering (RE), machine learning (ML) project

Marc Troyanov

We investigate the inner vertex-isoperimetric problem on the $d$-regular tree $T_d$. We first determine the exact value of the inner vertex-isoperimetric profile $I_d(k) = \min\{ |\partial D| \mid D\subset T_d \text{ finite and connected},\ |D|=k \}$, and we then introduce a boundary invariant, called the boundary branching excess $\tau(D)$, and show that it

Chenxi Wang, Haining Ding, Michal Gath-Morad

Buildings shape how people feel, yet the mechanisms through which specific facade properties drive affective states remain empirically underspecified. Here we introduce the Cambridge Facade Affect Dataset (CFAD), 86 orthogonally rectified facade images annotated with continuous arousal and valence ratings from 85 participants, and establish a validated pipel

Mohamed Bouka, Moulaye Abdel Kader Moulaye Ismail

Recent disruptions at major maritime chokepoints have exposed the structural fragility of liner shipping networks. Existing indicators measure connectivity, but none quantify its structural vulnerability from a supply-side perspective. We propose the Maritime Connectivity Vulnerability Index (MCVI), capturing three dimensions mapped to distinct UNCTAD source

Marcelo E. Coniglio, Rafael Ongaratto

In this article, the hierarchy of LFIs L$_n^k$, Logics of Controlled Consistency (LCC), is introduced. Inspired by da Costa's original C$_n$ systems, this hierarchy can represent different degrees of paraconsistent commitment and different related notions of consistency, inconsistency, and negation associated with each two-dimensional level of these logics.

Bibek Aryal, Gift Modekwe, Qiugang Lu

Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display non-Euclidean structures where graph neural networks (GNNs) are widely used therein. However, for large-scale systems, local, global, and dynamic relations extensively exist among sensors, and traditional GNNs of

Qihang Wu, Aman Arora, Vidya A. Chhabria

The rapid growth of large language models (LLMs) and AI workloads has pushed monolithic silicon to its reticle and economic limits, accelerating the adoption of 2.5D/3D chiplet systems. However, these systems increase design complexity by requiring co-design across multiple levels of the computing stack, including application, architecture, chip, and package

Piotr Gładysz, Karolina Słowik, Francesco V. Pepe

We investigate spontaneous radiative processes in a driven polar two-level system whose interaction with the laser field is dominated by broken inversion symmetry rather than by the usual transition dipole coupling. Using a polaron transformation, we derive the dressed eigenstates of the atom-laser system and show that their longitudinal coupling reshapes th

Mathew Thomson, Jean-David Therrien, Nikho Hizon, Janet Lin

Wastewater surveillance (WWS) has emerged as a valuable tool for public health surveillance, particularly since the COVID-19 pandemic. Its long-term utility is constrained, however, by fragmented data systems, inconsistent metadata practices, and poor interoperability. The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) was developed

Valentina Kuskova, Dmitry Zaytsev

This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred

Hanshu Rao, Guangzeng Han, Xiaolei Huang

Class imbalance is a widespread challenge in NLP tasks, significantly hindering robust performance across diverse domains and applications. We introduce Hardness-Aware Meta-Resample (HAMR), a unified framework that adaptively addresses both class imbalance and data difficulty. HAMR employs bi-level optimizations to dynamically estimate instance-level weights

Seowung Leem, Lin Gu, Chenyu You, Kuang Gong

The retina provides a unique, noninvasive window into Alzheimer's disease (AD) and dementia, capturing early structural changes through morphometric features, while systemic and lifestyle risk factors reflect well-established contributors to disease susceptibility long before clinical symptom onset. However, current retinal analysis frameworks typically mode

Ahson Saiyed, Sabrina Sadiekh, Chirag Agarwal

Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse Autoencoders (SAEs) are widely used for interpretability, their robustness implications remain underexplored. We present a study of integrating pretrained SAEs into transformer residual streams at inference time, witho

Alina Dubovskaya, David J. P. O'Sullivan, Michael Quayle

Understanding social polarization requires integrating insights from psychology, sociology, and complex systems science. Agent-based modeling provides a natural framework to combine perspectives from different fields and explore how individual cognition shapes collective outcomes. This study introduces a novel agent-based model that integrates two cognitive

Bibhas Adhikari

We develop a unified quantum framework for subgraph counting in graphs. We encode a graph on $N$ vertices into a quantum state on $2\lceil \log_2 N \rceil$ working qubits and $2$ ancilla qubits using its adjacency list, with worst-case gate complexity $O(N^2)$, which we refer to as the graph adjacency state. We design quantum measurement operators that captu

Andrew Wang, Ellie Pavlick, Ritambhara Singh

An active challenge in developing multimodal machine learning (ML) models for healthcare is handling missing modalities during training and deployment. As clinical datasets are inherently temporal and sparse in terms of modality presence, capturing the underlying predictive signal via diagnostic multimodal ML models while retaining model explainability remai

Seyed Arash Ghoreishi

We investigate discriminability from an operational and contextuality-oriented perspective using a two-copy comparison game based on SWAP-type measurements. The resulting score $D_{\mathrm{op}}$ provides an experimentally accessible notion of distinguishability that does not rely on a minimum-error discrimination task. We first examine whether this discrimin

Andreas Riedo, Salome Gruchola, Nikita J. Boeren, Peter Keresztes Schmidt

The search for signatures of life beyond Earth has been a major goal of space research and astrobiology for decades. The combination of expanded knowledge on Solar System bodies from past missions and advancements in in-situ detection technologies may place humanity on the verge of discovering extraterrestrial life. Here, we highlight the current measurement

Tarun Suman Cousik, Rohit Rangaraj, Nishith Tripathi, Jeffrey H Reed

Hybrid beamforming architectures reduce hardware complexity but restrict access to full array observations, rendering direct implementation of classical covariance based methods such as minimum variance distortionless response (MVDR) and sample matrix inversion (SMI) infeasible. This work introduces a structured covariance completion framework, termed Rock R

Michael Lampis, Manolis Vasilakis

Capacitated Vertex Cover is the hard-capacitated variant of Vertex Cover: given a graph, a capacity for every vertex, and an integer $k$, the task is to select at most $k$ vertices that cover all edges and assign each edge to one of its chosen endpoints so that no chosen vertex receives more incident edges than its capacity. This problem is a classical bench

Enrique Hernandez Noguera, Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi

Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imbalance, and the need for precise boundary delineation. This paper presents DeltaSeg, a U-shaped encoder-decoder architecture with a tiered attention strategy that integrates Squeeze-and-Excitation (SE) channel

E. S. Kokoulina

Multiparticle production in hadron and lepton interactions still attracts our attention. Simulation by using Monte Carlo event generators is performed before planning any experiment. But it often overestimates (or underestimates) experimental data. These generators are based on the theory of strong interactions, quantum chromodynamics (QCD), which is capable

Shri Harini Ramesh, Foroozan Daneshzand, Matteo Sotelo, Mahsa Sinaei

Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and

Jay Jung, Ahmad Arrabi, Jax Luo, Scott Raymond

Purpose: Automated C-arm positioning ensures timely treatment in patients requiring emergent interventions. When a conventional Deep Learning (DL) approach for C-arm control fails, clinicians must revert to manual operation, resulting in additional delays. Consequently, an agentic C-arm control framework based on multimodal large language models (MLLMs) is h

Lin Yao

Diffusion language models (dLLMs) generate text through iterative denoising, filling multiple masked positions at each step. Positions filled in the same step are predicted without conditioning on one another's newly filled values and can therefore be mutually inconsistent; once retained, these inconsistencies become context for later predictions. We int

Cecilia Ka Yuk Chan

As generative artificial intelligence becomes increasingly embedded in educational practice, a central concern is whether students use AI as cognitive support or as a substitute for thinking. Prior research shows that learners recognise this boundary conceptually and acknowledge that "AI should not replace thinking." However, whether such awareness translate

Benedikt Frohn, Tobias Schmitt, Vanessa Serrano, Anne Schmidt

Hybrid superconductor-topological insulator (TI) nanostructures constitute a promising materials platform for exploring proximity-induced superconductivity in systems with topologically protected surface states. A key obstacle has been the realization of clean and well-controlled superconductor-TI interfaces, as TI surfaces rapidly degrade under ambient cond

V. Gustafsson, M. Brüggen, C. Tasse, S. P. O'Sullivan

Magnetic fields in cluster outskirts and the intercluster medium are poorly constrained because diffuse synchrotron emission is hard to detect at low surface brightness. Faraday rotation measures (RMs) of polarized background sources can probe foreground large-scale structure. The nearby interacting Abell 3391-3395 system hosts a well-established X-ray bridg

Christopher Tong, Liran Shirizly, Edward H. Chen, Derek S. Wang

Dynamic quantum circuits integrate unitary evolution with mid-circuit measurement and feedforward, enabling conditional operations essential for efficient quantum algorithms and foundational for fault-tolerant quantum computation. However, such operations introduce measurement-induced errors and control constraints that are not addressed by conventional erro

Xingyu Zhao, Marcos Netto, Junbo Zhao

Dynamic state estimation (DSE) is becoming increasingly important for monitoring inverter-dominated power systems. Due to their cascading control structures, inverter-based resources (IBRs) exhibit multi-timescale dynamics, leading to stiff system models that pose significant challenges for conventional DSE methods. In particular, explicit discretization sch

Aniket Bhagwat, Tiago Costa, Benedetta Ciardi, Fabrizio Arrigoni Battaia

The bright [C II] 158 micron line is widely used to trace star-forming gas and feedback-driven outflows in high-redshift galaxies. Using the SPICE simulations, we investigate how bursty versus smooth stellar feedback shapes galaxy properties at z > 5 as traced by [C II] emission. All models exhibit a tight correlation between [C II] luminosity (L_[CII]) and

Benedetta Casavecchia, Aniket Bhagwat, Benedetta Ciardi, Celine Peroux

ALMA and JWST have recently detected emission lines from the interstellar medium of star-forming galaxies during the Epoch of Reionization, reaching redshifts up to z = 14. Among these, [OIII] lines provide a powerful diagnostic of metal enrichment, gas ionization, and the impact of stellar feedback in galaxies at z > 6. Modeling this emission in cosmologica

Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka

Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose the social assumptions they have internalized from training data. In this paper, we investigate counterfactual unfairness through humor by observing how the model's responses change

Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris, João Marques-Silva

Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints, for simulating the activations. Recent works convexly relax the integer constraints, improving performance, at the cost of soundness. Convex relaxations consider outputs that are

Niraj Agarwal, Timothy A. Smith, Sergey Frolov, Laura C. Slivinski

Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Rep

Anton Pozharskiy, François Pacaud, Moritz Diehl, Armin Nurkanović

This paper presents the Julia package CCOpt, built on top of the interior-point solver MadNLP. CCOpt implements a suite of algorithms for Mathematical Programs with Complementarity Constraints (MPCCs). The solver additionally comes with interfaces for use in Matlab, Python, and C++. MPCCs have recently gained renewed attention in engineering optimization, as

Megan Mirnalini Sundaram Rajaraman, Fons J. Verbeek, Vincent J. Kalkman, Rita Pucci

The correlation between insect morphological traits and climate has been documented in physiological studies, but such studies remain limited by the time-consuming nature of the data analysis. In particular, the open source datasets often lack annotations of species' morphological traits, making dedicated annotations campaigns necessary; these efforts are ty

Afaq Maqsood, Tanima Duary

We perform a model-independent investigation of the thermodynamic evolution of the Universe by reconstructing the expansion history from observational data using Gaussian Process regression. We consider three independent combinations of datasets, namely CC32+DESI DR2+Pantheon+, CC32+DESI DR2+Union3, and CC32+DESI DR2+DES Y5, allowing us to assess the impact

Thanmay Jayakumar, Deepon Halder, Raj Dabre

Cross-lingual transfer in NLP is often hindered by the ``script barrier'' where differences in writing systems inhibit transfer learning between languages. Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap. This paper provides a comprehensive survey of the application o

Nichula Wasalathilaka, Dineth Perera, Oshadha Samarakoon, Buddhi Wijenayake

Visual state-space models (SSMs) are increasingly promoted as efficient alternatives to Vision Transformers, yet their practical advantages remain unclear under fair comparison because existing studies rarely isolate encoder effects from decoder and training choices. We present a strictly controlled benchmark of representative visual SSM families, including

Varun Upreti, Nicolás Quesada, Ulysse Chabaud

The effective description of a bosonic quantum system identifies the minimum finite dimension required to capture its essential dynamics. This effective dimension plays an important role in the complexity of classical and quantum algorithms for learning and simulating bosonic systems. While generic bosonic states require a dimension scaling as $1/\epsilon^2$

Isaac David, Arthur Gervais

Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear when additional agents help and when they only add cost. We treat topology choice as an empirical systems question. We introduce a controlled benchmark of 20 interactive targets (10 web/API and 10 binary), each

Ray Iskander, Khaled Kirah

Formal verification of masking in post-quantum cryptographic (PQC) hardware relies on SMT solvers over finite domains. Our prior work established structural dependency analysis at scale [1] and quantified the security margin of partial NTT masking [2]. QANARY, our structural dependency analysis framework, verified 1.17 million cells across 30 modules of the

Jonas Sander, Anja Rabich, Nick Mahling, Felix Maurer

Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine current business models where a model owner sells model access, e.g., via MLaaS APIs. Additionally, stolen models can enable powerful white-box attacks, facilitating privacy attacks on sensitive training data, a

Mashrekur Rahman, Samuel J. Barrett, Christina Last

Earth observation foundation models encode land surface information into dense embedding vectors, yet the geometric structure of these representations and its implications for downstream reasoning remain underexplored. We characterize the manifold geometry of Google AlphaEarth's 64-dimensional embeddings across 12.1 million Continental United States samples

Kun Liu, Takahiro Tsunoda, Sophia H. Xue, Evan McKinney

Quantum low-density parity-check codes are promising candidates towards scalable fault-tolerant quantum computation. Among these, bivariate bicycle (BB) codes offer superior encoding rates and large code distance compared to surface codes. However, their requirement on long-range stabilizer measurements poses significant challenges for implementation on real

Cuiling Sun, Linkai Peng, Adam Murphy, Elif Keles

Automated 3D segmentation of prostate lesions from biparametric MRI (bp-MRI) is essential for reliable algorithmic analysis, but achieving high precision remains challenging. Volumetric methods must combine multiple modalities while ensuring anatomical consistency, but current models struggle to integrate cross-modal information reliably. While vision-langua

Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu

Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. In this work, we probe language model representations for human reading times. Using regularized linear regression on two eye-tracking corpora spanning five languages (English, Gr

Jacob R. Taylor, Sankar Das Sarma

We theoretically demonstrate a practical method for tuning randomly disordered 2D quantum-dot grids underlying spin qubit platforms using vision-based neural networks trained on tensor-network generated charge-stability data. We show that a simulatable local $3\times 3$ window already contains sufficient information to tune the central dot within a much larg

Jessica Metzger, Cory Hargus, Julien Tailleur, Frédéric van Wijland

We explore the edge flows that emerge at boundaries in nonequilibrium passive and active chiral colloidal fluids. We show that these complex interface currents obey an equation of state that relates their fluxes to bulk observables. For confined fluids, the edge flux is given by the average odd stress in the fluid. In phase-separated systems, the flux along

B. Çakmak, K. Sümer, S. Campbell, G. Karpat

We study synchronization in the XX qubit chain subject to local or multi-local amplitude-damping noise. Analyzing the decoherence-free subspace (DFS) structure of the model, we show that it is completely determined by a simple number-theoretic function involving the noise sites and the chain length. We derive a closed-form expression for local qubit observab

Tejaswi K. C., William A. Clark, Taeyoung Lee

This paper develops a transfer operator framework for stochastic hybrid systems with guard-induced resets, encompassing both the Koopman and Frobenius--Perron operators. Exploiting their duality, we derive a unified formulation in which observables and probability densities evolve under adjoint generators corresponding to the backward and forward Kolmogorov

Arjun Dey, Loic Herviou, Christopher Mudry, Slava Rychkov

We study at zero temperature a microscopic quantum spin-1 model on the fuzzy sphere that realizes the $O(2)$ Wilson-Fisher conformal field theory (CFT) in $(2+1)$-dimensional spacetime at a quantum critical point. Here, we use the fuzzy-sphere regularization as it preserves the full spatial $SO(3)$ rotational symmetry of the CFT, enabling the state-operator

Daeyeong Jeong, Doojin Kim, Jong-Chul Park

We propose a novel method to determine the mass scale of ambient dark matter, applicable to (at least effectively) two-dimensional direct detection experiments that allow for directionality observables. Due to the motion of the solar system and Earth relative to the Galactic Center and the Sun, the dark-matter flux exhibits a directional preference. We first

Roberto Gargiulo, Roberto Menta, Vittorio Giovannetti, Robert Zeier

Global control offers a promising route to scalable quantum computing. A recent conjecture by Hu et al. (arXiv:2508.19075) proposes that any connected qubit graph equipped with global Ising-type interactions and tunable global transverse fields achieves universality if and only if an additional control field breaks every non-trivial automorphism of the under

Upasna, Venkata Kalyan Tavva

Real-world graph applications are generally larger than the size of the cache itself. Due to this reason, the memory hierarchy was identified as a key bottleneck by the earlier works. Undoubtedly, the performance can be achieved by improving cache, there is still a scope for performance gain by improving branch prediction accuracy. In graph processing applic

Ruixuan Liu, David Evans, Li Xiong

Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to show a model is sufficiently protected against broader memorization risks. However, we show that indistinguishability properties are neither sufficient nor necessary for preventing data extraction in LLM APIs.

Lukas J. Furtak, Adi Zitrin, Erik Zackrisson, Vasily Kokorev

We report the discovery of four extremely faint ($m_{\mathrm{F444W}}\gtrsim29$) red point sources in recent ultra-deep JWST/NIRCam images of the strong lensing galaxy cluster Abell S1063. All four sources sit in lensed arcs, on the symmetry points very close to the critical curves for their host-galaxies' redshifts ($z\sim1-4$). Remarkably, these point sourc

Yantao Li, Pavlo Sukhachov

Realization of unconventional odd-parity magnets usually requires noncollinear spin textures of the underlying lattice. We propose a different concept of $p$-wave magnetism that originates from an orbital texture induced by loop currents. The resulting $p$-wave orbital magnetism is protected by the combined translation and time-reversal symmetry, with even-p

Biplob Bhattacherjee, Arnav Chauhan, Swagata Mukherjee, Rhitaja Sengupta

We propose a fundamental shift in the search for beyond the Standard Model long-lived particles (LLPs) at high-luminosity hadron colliders by prioritizing physical background suppression over traditional inner tracking. We introduce $\textsf{DELIGHT-SHIELD}$, a dedicated detector design for a 100 TeV Future Circular Collider at a dedicated interaction point

Arpan Gupta, Gargee Sharma

Quantum interference of electrons in disordered conductors is a sensitive probe of the internal structure of quasiparticles, revealing universal signatures of symmetry through weak localization (WL) and weak antilocalization (WAL). While these phenomena are well understood for the conventional Schr\"odinger and Dirac-Weyl fermions, their fate in the broader

Antonio C. Rodriguez, Kareem El-Badry, Iris de Ruiter, Kaustubh Rajwade

Long period radio transients (LPTs) are the slowest radio-pulsing sources ever found, with the current population spanning periods of seven minutes to over six hours. Two of the thirteen published LPTs, ILT J1101+5521 and GLEAM-X J0704--37, have been associated with an M dwarf closely orbiting a white dwarf (WD) through optical spectroscopy. Here, we present

Poulomi Chakraborty, Brian Skinner, Penghao Zhu

Axis-Dependent Conduction Polarity (ADCP) refers to the phenomenon in which electrical transport within a single material is p-type along one crystallographic direction and n-type along the perpendicular direction. This behavior enables a variety of thermoelectric applications that do not require a heterojunction between two different materials. In this work

Peter Arnold, Joshua Bautista, Omar Elgedawy, Shahin Iqbal

The theory of bremsstrahlung $e \to e\gamma$ by extremely high energy electrons passing through ordinary matter has been qualitatively incomplete. We revisit the suppression of bremsstrahlung by the Landau-Pomeranchuk-Migdal (LPM) effect, here accounting for quantum disruption of that effect from pair production. Our analysis covers the full range of ultra-r

Konstantin Rickelt, Denis Sedov, Mathias S. Scheurer

We introduce a generalized Hatsugai-Kohmoto multi-orbital model and study its phase diagram and physical properties in the additional presence of perturbations that lift any extensive ground-state degeneracies. The unperturbed, exactly solvable model already displays a rich set of spectral functions, including regimes reminiscent of unconventional magnets. W