April 2026 arXiv papers — page 34

Ruslan Tepelyan, Daniel Lam

For a sequence of binary bets, the Kelly criterion provides a closed-form solution that maximizes the expected growth rate of wealth. In contrast, when multiple bets are placed simultaneously (e.g., in portfolio allocation or prediction markets), the optimal Kelly strategy generally requires numerical optimization over a joint outcome space. A naive formulat

D. Ronco, F. Arrouas, N. Ombredane, E. Flament

Quantum simulation with ultracold atoms provides a versatile platform to emulate condensed-matter models. In particular, momentum-space lattices enable the realization of programmable tight-binding Hamiltonians. Here, we generalize this approach by exploiting quantum resonances of a periodically driven (shaken) rotor within the Floquet framework. Using first

Shu Wang, Jong-Hak Woo, Lizvette Villafaña, Tommaso Treu

We present the results of broad-line region (BLR) dynamical modeling for eight high-mass active galactic nuclei (AGNs) from the Seoul National University AGN Monitoring Project, by constraining BLR geometry and kinematics as well as black hole (BH) mass ($M_{\rm BH}$). We find that the H$\beta$-emitting BLRs are best described as thick disks viewed at interm

Hermawan Manurung, Ibrahim Al-Kahfi, Ahmad Rizqi, Martin Clinton Tosima Manullang

Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment tools unreliable in practice. This paper describes a two-track classification pipeline applied to the PRDECT-ID dataset, which contains 5,400 product reviews from 29 Indonesian e-commerce categories, each labele

Tal Grossman, Noa Cahan, Lev Ayzenberg, Hayit Greenspan

Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transfer to medical data. Consequently, accurate segmentation typically requires extensive fine-tuning and expert-designed prompts. We propose DiffuSAM, a diffusion-based adaptation of S

Vandita Shukla, Fabio Remondino, Blair Costelloe, Benjamin Risse

Monocular RGB cameras mounted on drones are widely used for wildlife monitoring, yet most analytical pipelines remain confined to two-dimensional image space, leaving geometric information in video underexploited. We present WildLIFT, a computational framework that integrates three-dimensional scene geometry from monocular drone video with open-vocabulary 2D

Hailing Cheng, Daqi Sun, Xinyu Lu

Every Transformer architecture dedicates enormous capacity to learning rich representations in semantic embedding space -- yet the rotation manifold acted upon by Rotary Positional Embeddings (RoPE) has been treated as a fixed, hand-crafted structure, populated only by discrete ordinal indices. We argue that this rotation space is a largely overlooked second

Utkan Güngördü, Rusko Ruskov, Silas Hoffman, Kyle Serniak

Gatemon qubits are based on a superconductor-quantum dot-superconductor (S-QD-S) junction which enables in situ electrostatic tuning via a gate electrode. For a single-channel QD this structure gives rise to two subgap Andreev bound states (ABSs), and generally leads to a richer quantum phase dynamics as compared to conventional transmons. In a recent work [

Parsa Ashrafi Fashi, Utkarsh Saxena, Mehdi Rezagholizadeh, Aref Jafari

Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretrained from scratch and therefore fail to reuse existing Transformer checkpoints. We study upcycling as a practical path to convert pretrained Transformer LLMs into hybrid architect

Donato Quiccione, Mariam Pirashvili, Nathan Broomhead, Sean J. Fallon

Understanding the structure of the brain, and how it changes with time and disease, is a core goal of structural neuroimaging. Contemporary approaches to structural brain analysis are dominated by voxel-wise, mass-univariate methods such as voxel-based morphometry (VBM). However, these techniques require images to be normalized to a standard template, which

Johannes Girsch, Elad Zelingher

Piatetski-Shapiro--Rallis discovered an integral representation construction, known as the doubling method, for the tensor product $L$-function of a cuspidal automorphic representation of $G \times \mathrm{GL}_1$, where $G$ is a classical group. Lapid--Rallis defined and studied the counterpart local factors. In this article, following Lapid--Rallis, we defi

Srita Padmanabhuni, Bhargavi Karuturi, Jerusha Karen Indupalli, Santhan Reddy Chilla

Deep Learning methods are becoming prominent in automated software bug detection; however, they lack the global understanding of the given code. Consequently, their performance tends to degrade, especially when they are applied to large interconnected code bases or complex modular programs. Recently, Large Language Models (LLMs) have proven to be effective a

Amal AKLI, Mike PAPADAKIS, Maxime CORDY, Yves Le TRAON

Large language models are increasingly used for code generation, yet the correctness of their outputs depends not only on model capability but also on how tasks are specified. Prior studies demonstrate that small changes in natural language prompts, particularly under-specification can substantially reduce code correctness; however, these findings are largel

Miriam Eleazer, Alexandra Pope, Anna Sajina, Lin Yan

We present a kinematic study of six infrared-luminous galaxies observed with the Mid-InfraRed Instrument Medium-Resolution Spectrometer (MIRI/MRS) onboard JWST. These galaxies lie at $z = 0.5$--$0.6$, midway between the present day and the peak of cosmic star formation. Our sample spans a range of star formation (SF) and active galactic nucleus (AGN) contrib

Aaryan Shah, Andrew Hines, Alexia Downs, Denis Bajet

Objective. Clinical AI documentation systems require evaluation methodologies that are clinically valid, economically viable, and sensitive to iterative changes. Methods requiring expert review per scoring instance are too slow and expensive for safe, iterative deployment. We present a case-specific, clinician-authored rubric methodology for clinical AI eval

Florian Naef, Robin Stoll

In Part I, we proved that a rational model for the fiberwise THH transfer of a map $f$ of fibrations over a base space is given by the Hochschild homology transfer of a cdga model of $f$. In this paper, we provide an explicit description of this Hochschild homology transfer in terms of $A_\infty$-algebras, generalizing work of Bouc. Using a result of Lind-Ma

Hailing Cheng, Tao Huang, Chen Zhu, Antonio Alonso

Training large neural networks with data-parallel stochastic gradient descent allocates N GPU replicas to compute effectively identical updates -- a practice that leaves the rich space of learning rate configurations entirely unexplored during training. We propose Hyperparameter-Divergent Ensemble Training (HDET), a method that repurposes these replicas for

Ali Tourani, Miguel Fernandez-Cortizas, Saad Ejaz, David Pérez Saura

Doorways and passages are critical structural elements for indoor robot navigation, yet they remain underexplored in modern Visual SLAM (VSLAM) frameworks. This paper presents a passage-aware structural mapping approach for RGB-D VSLAM that detects doors and traversable openings by jointly fusing geometric, semantic, and topological cues. Doors are modeled a

Tobias A. Farger, Adam W. Hall, Angela P. Schoellig

Learning-based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning-based controllers are often computationally inefficient, limiting their practicality. To address this limitation, we propose a learning-based controller that exploits differential flatness, a property of many robotic systems. Recent

Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser, Wolf Fichtner

Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated under study-specific datasets, time periods, information sets, and scoring setups, while widely used benchmarks and competition datasets are t

Vivek Maradia, Martin Bues

Proton therapy exploits the finite range of charged particles in tissue to achieve dose distributions no photon based modality can replicate. Yet the modality reaches fewer than 1 percent of patients who might benefit a gap rooted in cost and complexity rather than clinical evidence. This Review reframes proton therapy adoption as a physics problem. Two fund

Amal Akli, Mike Papadakis, Maxime Cordy, Yves Le Traon

Large language models are widely used for code generation, yet they rely on an implicit assumption that the task descriptions are sufficiently detailed and well-formed. However, in practice, users may provide defective descriptions, which can have a strong effect on code correctness. To address this issue, we develop SpecValidator, a lightweight classifier b

Lauren Smyth

This work investigates the role of the $U(N) \times U(\tilde{N})$ global symmetry in tree-level scattering amplitudes of the bi-adjoint $\phi^3$ theory from three perspectives: combinatorics, correlation functions, and a massive extension of the CHY formalism. We derive a planar scattering potential whose extrema reproduce Dolan and Goddard's massive scatter

Elie Bursztein, Michael Gruber, Karel Král, Jean-Michel Picod

Side Channel Analysis (SCA) relaxes the black-box assumption of conventional cryptanalysis by incorporating physical measurements acquired during cryptographic operations. Electro-magnetic (EM) emissions of a chip during computations often provide a very valuable source of side channel leakage. During the evaluation of a chip for electro-magnetic side channe

Aaron J. Li, Nicolas Sanchez, Hao Huang, Ruijiang Dong

Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users phrase queries, a gap not well addressed by existing red-teaming efforts. We propose Green Shielding, a user-centric agenda for building evidence-backed deployment guidance by characterizing how benign input var

Thomas Hales

We give a correspondence between simple matroids and a reconstruction of Alfred North Whitehead's theory of dimension, as developed in "On Mathematical Concepts of the Material World" (1906). In brief, if a geometrical system in the generalized sense of Whitehead has finite ground set and is phi-maximal, then it is a simple matroid. Here "generalized" means

Yunze Xiao, Vivienne J. Zhang, Chenghao Yang, Ningshan Ma

Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive failure mode we term \emph{Persona Collapse}: agents each assigned a distinct profile nonetheless converge into a narrow behavioral mode, producing a homogeneous simulated population. To quantify persona coll

Vladislav Gavryusev, Massimo Inguscio

The Varenna school is a hub where generations of physicists, including numerous Nobel laureates, have shaped the field, often through collaborative exchanges across political and cultural boundaries. We examine the scientific legacy of Enrico Fermi and its influence on modern atomic, molecular, and optical physics. Beginning with Fermi's 1954 lectures at the

Asliddin Khudoyberdiev, Dag-Björn Hering, Vanessa Sulaiman, Götz S. Uhrig

The switching of antiferromagnetic order and its efficient control promise to enable ultrafast manipulation of data and large storage capacity. Recently, the time-dependent Schwinger boson mean-field theory has been successfully developed to study the N\'eel vector switching in hypercubic antiferromagnetic lattices. In the present article, we aim at demonstr

Omer Rathore, Alastair Basden, Nicholas Chancellor, Halim Kusumaatmaja

Quantum computing has emerged as a powerful potential accelerator for computational fluid dynamics (CFD), but whether this promise can be realized in practice depends on how fluid information is encoded on quantum hardware. This review provides an architecture-agnostic assessment of encoding strategies for quantum-enhanced fluid simulation, focusing on the t

Brandon Hsu, Daniel Beaglehole, Adityanarayanan Radhakrishnan, Mikhail Belkin

Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited labeled data. While effective, existing methods often apply a fixed steering strength to all tokens, resulting in inconsistent steering quality across diverse input prompts. In this work, we introduce Context

Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. Egorov

We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs a sparse similarity graph on the samples and identifies the Nishimori temperature $\beta_N$ the critical inverse temperature at which the Bethe Hessian becomes singular. The corres

Fengjiao Liu, Yixiao Zhang, Panagiotis Tsiotras

In this paper, we study the reachability of two closely related matrices appearing in the analysis of linear time-varying (LTV) systems over a finite time interval, namely, its closed-loop state transition matrix via a state feedback control and its state covariance matrix starting from some given initial state covariance matrix. Under a mild assumption, we

Lirong Gao, Zeqing Wang, Yuyan Cai, Jiayi Deng

While Large Language Models (LLMs) have increasingly assisted in historical tasks such as text processing, their capacity for professional-level historical reasoning remains underexplored. Existing benchmarks primarily assess basic knowledge breadth or lexical understanding, failing to capture the higher-order skills, such as evidentiary reasoning,that are c

Łukasz Cywiński, Mats Volmer, Tom Struck, Giordano Scappucci

Charge separation from the $(4,0)$ to the $(3,1)$ state in a Si/SiGe double quantum dot is commonly used for initialization of spin qubits and Pauli-spin-blockade readout. It was used in recent experiments involving creation of the $(3,1)$ singlet, and subsequent shuttling of one of the electrons. We present a theoretical description of the process of charge

N. Johnson, D. Marenduzzo, A. Morozov, E. Orlandini

We revisit the classical transfer matrix solution of the one- and two-dimensional Ising model from the perspective of Clifford and conformal geometric algebras. Building on Kaufman's spinor formulation, we show that all elements entering the solution, including the transfer matrix, its eigenvectors, and the quasiparticle excitations, admit a natural and unif

Nicholas Franklin, Jacob Richards, Harry Lane

We analyze the ground state properties of the honeycomb Ising antiferromagnet in an external magnetic field. We demonstrate the existence of extensive ground state degeneracy at finite field that maps to a polyhex tiling problem. This state is shown to be a jammed spin liquid, with no local zero modes connecting ground states. Through Monte Carlo simulations

German Marin, Jatin Chaudhary

Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without any code change. We propose the \textbf{Informational Viability Principle}: governing an agent reduces to estimating a bound on unobserved risk $\hat{B}(x) = U(x) + SB(x) + RG(x)$ and allowing an action only when

Jorge L. A. Lima, Filipe R. Cordeiro

Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily dependent on expert specialists, often requiring several days per patient. Although Deep Learning models have achieved high performance in chromosome detection, most proposed solutions remain restricted to research

Zihao He, Souvik Dhara, Debankur Mukherjee

In a seminal work, Chatterjee and Durrett (2009) established that for the SIS epidemic process on random graphs with power-law degree distributions, the infection survives for an exponentially long time (in the network size) for any fixed, positive infection rate. Equivalently, the critical infection rate separating polynomial and exponential survival regime

Jayashree Pati, P. Senthilkumar, Deepak Seth, Riya Gulati

We report the electrochemical investigation and study the diffusion kinetics of boron doped Na$_{0.66}$Mn$_{0.8}$Fe$_{0.2}$O$_{2}$ (B-NMFO) cathode materials for sodium-ion batteries. Notably, the B-NMFO cathode exhibits improved specific capacity of 163 mAh g$^{-1}$ as compared to 133 mAhg$^{-1}$ at 0.1~C for the NMFO cathode. Further, we observe better cap

Andrea Vaccaro

We prove that separable, simple, unital, non-elementary, stably finite C*-algebras that have stable rank one, and that have locally finite nuclear dimension in a tracial sense, have uniform property $\Gamma$. In particular, Villadsen algebras of the first type and crossed products of free minimal actions of FC (in particular, abelian) groups on compact metri

Cosimo C. Rusconi, Eric Sierra, Wai-Keong Mok, Avishi Poddar

Cooperative emission is well understood for idealized symmetric systems, but its limits in spatially extended, free-space ensembles remain an open question. Here, we derive a universal law for the scaling of the maximum photon emission rate with system size that unifies both ordered arrays and disordered atomic clouds in arbitrary dimensions at fixed density

Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson, David A. Clifton

Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across a wide range of downstream tasks. However, systematic comparisons of these models for clinically meaningful prediction problems remain limited, especially in the context of survival prediction under external

Sivajeet Chand, Kevin Nguyen, Peter Kuntz, Alexander Pretschner

Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change generation spanning multiple files and folder structures from a single natural-language (NL) instruction. We report an industrial case study at B

Nicolas Tokka

We prove the existence of the local limit of uniform random d-regular bipartite planar maps, for every $d\geq 3$, as the number of vertices tends to infinity. The proof relies on a bijection between maps and so-called blossoming trees established in a previous work. After proving local convergence of the associated decorated trees, we extend the bijection to

Pete Gautam

We classify all corefree fusion systems on a Sylow $3$-subgroup of the sporadic groups $\mathrm{Fi}_{22}$, $\mathrm{Fi}_{23}$ and $\mathrm{B}$. We show that the $3$-group in each case does not support any exotic fusion systems. This is the first of two papers that will complete the classification of all corefree fusion systems on Sylow $p$-subgroups of spora

Santiago Cabrera, Gonzalo De Polsi, Adam Rançon, Nicolás Wschebor

Conformal symmetry is expected to be realized in many equilibrium statistical mechanical systems at criticality. Although this is certainly true in two-dimensional systems, the three-dimensional case is subtler, and only a few proofs exist, only so in very specific cases. In this work, we give two proofs for the large $N$ limit of the $O(N)$ universality cla

Shaunak Kolhe, Peng Jiang, Maggie Wigness, Philip Osteen

Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. While most prior work has studied radar odometry in urban, largely planar settings, its performance in off-road environments remains less understood. In this paper, we investigate the potential of radar for off-r

Nathan Schofield, Richard White, Ruth Baker, Helen Byrne

Melanoma is an aggressive form of skin cancer. Survival rates are excellent if it is detected early but fall markedly if it metastasises. A key step in early tumour progression is the formation of cell clusters, which can promote metastasis. However, the mechanisms driving cell clustering, and the role of phenotypic heterogeneity in the dynamics of these clu

Sun Woo Park, Yun Young Choi, U Jin Choi, Youngho Woo

We provide a mathematical interpretation of convolutional (or message passing) neural networks by using presheaves and copresheaves of the set of continuous functions over a topological space. Based on this interpretation, we formulate a theoretical heuristic which elaborates a number of empirical limitations of these neural networks by using obstructions on

Michele Capra, Christian S. Kern, Mira S. Arndt, Karl J. Schiller

Stacked van der Waals heterostructures based on transition metal dichalcogenides (TMDs) exhibit a rich variety of exotic interfacial phenomena. Substituting one component with an organic semiconductor (OSC) enables the design of hybrid heterostructures with tunable functionalities for optoelectronic, photovoltaic, and spintronic applications. In this work, e

Ray Iskander, Khaled Kirah

Barrett reduction is the nonlinear core of every practical NTT-based post-quantum cryptography implementation. Existing composition frameworks (ISW, t-SNI, PINI, DOM) address Boolean masking over GF(2); none provides a machine-checked characterization of Barrett's leakage under first-order arithmetic masking and the first-order probing model over prime field

Carina van der Linde, Iason Sideris, Léa Deillon, Mohamadreza Afrasiabi

The microstructure of Ti-6Al-4V has a decisive impact on its mechanical performance; however, controlling phase composition during Laser Powder Bed Fusion (LPBF) remains difficult because of the inherent localized and cyclic thermal history. To fully leverage the design flexibility of LPBF while maintaining an efficient process, it is desirable to tailor the

Saiei-Jaeyeong Matsubara-Heo, Simon Telen

We develop a theory of principal determinants and hypergeometric systems for realizable matroids. Our framework parallels the toric theory of Gel'fand, Kapranov, and Zelevinsky (GKZ), but with the combinatorics of matroids and their flats replacing the usual role of polytopes and their faces. In this analogy, the toric variety is replaced by a reciprocal lin

Chih-Chen Kao, Grzegorz Makowski, Shin Fujieda, Takahiro Harada

We extend the Locally-Subdivided Neural Intersection Function (LSNIF) to support parameterized deformable and animated geometry. Our approach introduces a rest-space and deformed-space formulation inspired by meshless rendering, allowing ray samples to be mapped back to a canonical space where a single neural network represents geometry consistently across p

Sercan Karakaş, Yusuf Şimşek

This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensitivity. We study the past-domain contrast between -DI and -mIs in controlled cloze contexts where the information source is overtly external, while only its perceived reliability is manipulated (High-Trust vs. L

Petr Čoupek, Tyrone E. Duncan, Bozenna Pasik-Duncan, Jakub Slavík

In the article, we address the problem of absolute continuity of translated Rosenblatt measures on the path space. In [\v{C}oupek, P., K\v{r}\'i\v{z}, P., Maslowski, B., Stoch. Proc. Appl. 179 (2025) art. no. 104499], it is shown that there is no probability measure that would be equivalent to the original probability measure and under which a Rosenblatt pat

Daniel Cao, Beixi Du, Andrew Lowitt, Sunmook Choi

We study finite-horizon quadratic control of linear systems with bilinear observations, in which the control input affects not only the state dynamics but also the partial observations of the state. In this setting, the separation principle can fail because control inputs influence the future quality of state estimates. State estimation requires an input-dep

Abdelmalek Boumali

The single-ionization rate coefficient of a plasma neutral depends both on the microscopic electron-impact cross section and on the macroscopic shape of the electron energy distribution function (EEDF). We present a reproducible benchmark and sensitivity study -- not a new theory -- of these two effects for the three lightest neutrals He, Li, and Be, combini

Hao Wang, Limeng Qiao, Chi Zhang, Lin Ma

Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception across both images and videos remains limited. Foundation segmentation models such as the SAM series produce high-quality masks, but they rely on low-level visual prompts and cannot natively interpret complex conv

Zhengru Fang, Yu Guo, Fei Liu, Yuang Zhang

Real-world visual systems face time-varying perturbations, including weather, sensor noise, compression artifacts, and background distractions. Existing image restoration methods are typically designed for fixed corruption types and optimized for pixel-level fidelity, leaving open two questions: how restoration behaves under non-stationary corruption switchi

Faizuddin Ahmed, Edilberto O. Silva

In this paper, we investigate the thermodynamic and radiative properties of a regular black hole sourced by a dark matter halo described by the Einasto density profile. The closed-form expressions for the Hawking temperature, specific heat capacity, sparsity parameter of Hawking flux, and the spectral energy emission rate were obtained. All these are examine

Yixiang Zhang, Xinhao Deng, Jiaqing Wu, Yue Xiao

Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileged tools. In such systems, security failures rarely remain confined to a single interface; instead, they can propagate across initialization, input processing, memory, decision-mak

Ali Khalesi, François Baccelli

We establish densification converses for Walker LEO constellations under nearest-visible association in the full-frequency-reuse setting. Performance is evaluated under the invariant (stationary) measure induced by the constellation/Earth dynamics on the user--constellation ``phase state.'' A key Walker-specific feature, absent from unbounded planar

Senthil Rajasekaran, Jean-François Raskin, Moshe Y. Vardi

As part of an effort to apply the rigorous guarantees of formal verification to multi-agent systems, the field of equilibrium analysis, also called rational verification, studies equilibria in multiplayer games to reason about system-level properties such as safety and scalability. While most prior work focuses on deterministic settings, recent probabilistic

Yu-Shiou Willy Lin, Dae Woong Ham, Iavor Bojinov

Multi-armed bandits are widely used for sequential experimentation in clinical trials, recommendation systems, and online platforms. While regret minimization and valid inference from adaptively collected data have each been studied extensively, a basic question remains: when does adaptivity \emph{improve estimation precision} relative to uniform designs, an

Sergey Kovalev, Hassan A. Hafez, Klaas-Jan Tielrooij, Jan-Christoph Deinert

Graphene is conceivably the most nonlinear optoelectronic material. Its nonlinear optical coefficients in the terahertz (THz) frequency range surpass those of other materials by many orders of magnitude. This, in particular, allows one to use graphene for extremely efficient up-conversion of sub-THz electronic input signals into the THz frequency range at ro

Clemens Fuchs, Miriam Schönauer

In this paper, we prove that there are no $k$-th power Diophantine triples of the form $\{a^k,b,c\}$ for $k\geq 3$ and $1<a^k<b<c$.

Milo Liebster, Amin Mohaghegh, Andrew Boutros

Implementing a digital circuit on an FPGA fabric requires clustering technology-mapped netlist primitives into coarser-granularity blocks that can be directly mapped to the physical resources available on the FPGA. As the architecture of FPGA logic blocks (LBs) has grown in complexity, with sophisticated logic elements (LEs) and highly irregular local interc

Arash Adel, Daniel Ruan, Ruxin Xie

Climate change and resource depletion demand a shift from the dominant linear "take-make-use-dispose" paradigm of construction toward circular, low-waste practices. Material reuse offers a promising pathway by reducing raw material extraction, mitigating waste, and extending the service lifespan of carbon-sequestering materials such as timber. Realizing this

Zahra Dehghanighobadi, Asja Fischer

Long-context reasoning is a critical capability of large language models (LLMs), enabling applications such as long-document understanding, summarization, and code generation. However, efficient autoregressive inference relies on the key-value (KV) cache, whose memory footprint grows linearly with sequence length, leading to a major memory bottleneck. To mit

Sriram Narayanan, Daniele Sicoli, Piyush Mehta

Accurate estimation of thermospheric mass density is a prerequisite for orbit prediction and space situational awareness, where the upper atmosphere responds nonlinearly to solar and geomagnetic forcing across several orders of magnitude. Physics-based general circulation models resolve this response but are computationally expensive, while empirical models

Soyeon Kim, Cheongwoong Kang, Myeongjin Lee, Eun-Chul Chang

The development of practical (multimodal) large language model assistants for Korean weather forecasters is hindered by the absence of a multidimensional, expert-level evaluation framework grounded in authoritative sources. To address this, we introduce K-MetBench, a diagnostic benchmark grounded in national qualification exams. It exposes critical gaps acro

Abraham Itzhak Weinberg

Current cyber attribution approaches typically operate on a per-incident basis, leaving open whether aggregating evidence across campaigns improves adversary identification. We investigate whether cross-campaign attribution reduces ambiguity or whether structural limits persist under longitudinal data. We model adversary fingerprints as multi-dimensional fea

Hailong Jiang

Early childhood educators work in settings characterized by heavy regulation, emotional labor, staffing instability, and low pay. Although these conditions are well documented in survey-based research, less is known about how they manifest in the day-to-day language educators use in peer spaces. This study examines 7,506 posts from r/ECEProfessionals, a larg

Hai Wang, Xiaochen Yang, Mingzhi Dong, Jing-Hao Xue

The dream of instantly creating rich 360-degree panoramic worlds from text is rapidly becoming a reality, yet a crucial gap exists in our ability to reliably evaluate their semantic alignment. Contrastive Language-Image Pre-training (CLIP) models, standard AI evaluators, predominantly trained on perspective image-text pairs, face an open question regarding t

Ananth Dodabalapur, Chankeun Yoon, Xiao Wang

A consistent and widely accepted physical basis for interpretation of charge transport in amorphous oxide semiconductor (AOS) field-effect transistors (FETs), and more generally device physics, has been hampered by uncertainties in crystalline order, dimensionality, and the effects of a significant density of traps. The overarching theme of this paper is to

Tianyi Xu, Qinglong Liu, Maolin Wang, Fei Zhang

Quantum computers could solve problems beyond the reach of classical devices, but this potential depends on quantum error correction (QEC) to protect fragile quantum states from noise. A central challenge in QEC is decoding: inferring likely physical errors from syndrome patterns generated by repeated stabilizer measurements. Existing decoders, including gra

Rakesh Manjappa, Jinghui Wang, Stavros Melemenidis, Vignesh Viswanathan

Background: Clinical linear accelerators are an accessible platform for preclinical research on the biological effects of ultra rapid electron irradiation (FLASH). However, they are not inherently designed for the accurate pulse control required for experiments using a small number of relatively high-dose pulses, and available methods for beam control such a

Josefina Barrionuevo, Paulo Tirao, Sonia Vera

We construct families of $k$-step nilpotent symplectic Lie algebras associated with graphs, extending the construction given in [Pouseele-Tirao, JPAA 213 (2009)] for the 2-step case. We also show that, under mild conditions on the nilpotency type, there exist symplectic Lie algebras of that type.

Kevin McKee, Thomas Hazy, Yicong Zheng, Zacharie Bugaud

Block-sequential continual learning demands that a single model both protect prior solutions from catastrophic forgetting and efficiently infer at inference time which prior solution matches the current input without task labels. We present Functional Task Networks (FTN), a parameter-isolation method inspired by structural and dynamical motifs found in the m

Alexander Povolotsky

For a fixed integer $k \ge 0$, consider representations of positive integers as sums of binomial coefficients of the form $\binom{n}{k}$. While exact minimal bounds for the number of required summands are known only in a few low-dimensional cases, general existence results have received less explicit treatment. This paper provides: $\bullet$ explicit element

William Oliveira

On-device Small Language Models (SLMs) promise fully offline, private AI experiences for mobile users (no cloud dependency, no data leaving the device). But is this promise achievable in practice? This paper presents a longitudinal practitioner case study documenting the engineering challenges of integrating SLMs (Gemma 4 E2B, 2.6B parameters; Qwen3 0.6B, 60

Andrew Renninger, Till Koebe, Ingmar Weber

Extreme heat suppresses urban activity, but its effects need not be uniform across climates or across the day. Using data on activity at points of interest in 20 cities spanning temperate, tropical, and arid environments, we show that hot days reduce activity overall while shifting it away from midday and toward later hours. This rescheduling is substantiall

Simon Axelrod, Miroslav Kašpar, Kristýna Jelínková, Markéta Šmídková

Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inh

Iason Saganas, Matteo Guardiani, Natalia Porqueres, Torsten Enßlin

Cosmological analyses using the latest set of type Ia SNe data weakly favor an evolving dark energy (EDE) model without strongly disfavoring the standard LCDM paradigm. Nonparametric reconstructions of the expansion history may reveal signal features potentially missed by a parametric LCDM model without laying out a specific functional form for the evolution

Yuri V. Kovchegov, M. Gabriel Santiago, Huachen Sun

There are two $R$-factors frequently used in the phenomenology of exclusive processes at small values of the Bjorken $x$ variable. One $R$-factor takes into account the effects of non-zero longitudinal momentum transfer, which is assumed to be zero in the dipole scattering amplitude. Another $R$-factor accounts for the real part of the elastic scattering amp

Lorenz Gunreben, Nico Heider, Sebastian Zürner, Martin Schieck

Proprietary design in commercial windrow-detection systems restricts transparency and limits progress in open autonomous forage-harvesting research. We present a multi-modal dataset combining stereo vision and LiDAR from tractor-mounted sensors during real baling operations. The dataset includes synchronized sensor data with GNSS trajectories, partly release

Miles Radford, Ahmed Alsulami, Bertrand Martinez, Pablo Bilbao

We present an extension of the OSIRIS particle-in-cell (PIC) code that introduces an axion macroparticle species and three axion-production channels commonly used in thermal-plasma axion phenomenology: screened Primakoff conversion $(\gamma + Z \leftrightarrow a + Z)$, Compton-like photoproduction on electrons in a blackbody photon bath $(\gamma + e \to a +

Shiyi Zhang, Yiji Cheng, Tiankai Hang, Zijin Yin

Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To addre

Junpei Otsuka

A random geometric graph $G(\mathcal{X}_n, r_n)$ is formed by taking a binomial process $\mathcal{X}_n$ as the set of vertices and joining any two distinct points with an edge if they lie within distance $r_n$ of each other. We investigate the limit distribution of the threshold radius for which the maximum degree of the graph is at least a given value that

Zhuoling Li, Ha Linh Hong Tran Nguyen, Valeria Bladinieres, Maxim Romanovsky

Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a structured, semantically coherent context, yielding more grounded answers. However, GraphRAG reasoning process remains a black-box, limiting our ability to understand how specific pieces of structured knowledge

Fan Du, Feng Yan, Jianxiong Wu, Xinrun Xu

Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency-quality trade-off under real-time constraints. We address this issue by rethinking the role o

Utku Boran Torun, Veli Karakaya, Ali Babar, Eray Tüzün

Large Language Models (LLMs) are increasingly embedded in software engineering (SE) tools, powering applications such as code generation, automated code review, and bug triage. As these LLM-based AI for Software Engineering (AI4SE) systems transition from experimental prototypes to widely deployed tools, the question of what it means to evaluate their behavi

Hugo Sousa, Ricardo Campos, Alípio Jorge

Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification remains a considerable challenge. Early attempts framed this task using a comprehensive set of temporal relations between events and tempor

Danny Calegari, Ino Loukidou

A CaTherine wheel is a surjective continuous map $f:S^1 \to S^2$ such that for every closed interval $I\subset S^1$ the image $f(I)$ is homeomorphic to a disk, and $f(\partial I)$ is contained in the boundary of this disk. CaTherine wheels arise in many areas of low-dimensional geometry and topology, including conformal dynamics (expanding Thurston maps, exp

Robert Kirk, Alexandra Souly, Kai Fronsdal, Abby D'Cruz

We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier AI company. We apply two complementary evaluations to four Claude models (Mythos Preview, Opus 4.7 Preview, Opus 4.6, and Sonnet 4.6): an unprompted sabotage evaluation testing model behaviour with opportuni

Qi Li, Jiu Li, Pingtao Wei, Jianjun Xu

This report presents a comparative evaluation of DKnownAI Guard in AI agent security scenarios, benchmarked against three competing products: AWS Bedrock Guardrails, Azure Content Safety, and Lakera Guard. Using human annotation as the ground truth, we assess each guardrail's ability to detect two categories of risks: threats to the agent itself (e.g., instr

B. Vaia, I. Fornasiero, A. Tiengo, A. Bracco

The details of the spiral structure of the Milky Way are still debated due to large uncertainties in the distance estimates obtained through the most common tracers. X-ray dust scattering rings produced by short extragalactic X-ray transients provide instead a direct method to measure the 3D distribution of interstellar clouds up to the edges of our Galaxy w