April 2026 arXiv papers — page 136

Melih Emin Can, Marcin Kulczycki

Let $(X,T)$ be a compact dynamical system. This article proves that if $(X,T)$ has the partial specification property, then it has the average shadowing property. It is also proven that if $(X,T)$ is surjective and has the partial specification property, then the set of ergodic measures of $(X,T)$ is dense in the space of its invariant measures. An example o

Cheyanne Shariat

Adding citations while drafting in LaTeX often requires leaving the editor, searching for a paper in mind, copying its BibTeX entry into the project bibliography, renaming the cite key, and then returning to the sentence. \texttt{OverCite} is an open-source, lightweight tool that lets authors find, select, and insert citations without leaving the writing env

Alberto Neri, Veronica Penza, Nazim Haouchine, Leonardo S. Mattos

Unsafe surgical care is a critical health concern, often linked to limitations in surgeon experience, skills, and situational awareness. Integrating patient-specific 3D models into the surgical field can enhance visualization, provide real-time anatomical guidance, and reduce intraoperative complications. However, reliably registering these models in general

Francisco J. Peña, Rafael García-Zamora, Gabriele De Chiara, Jorge Flores

We demonstrate that flat-band engineering provides a direct route to control and optimize the thermodynamic performance of quantum heat engines. We consider noninteracting bosons on a rhombic chain lattice described by a Bose-Hubbard model in the noninteracting limit, where a magnetic flux serves as a tunable parameter that continuously reshapes the single-p

Pablo Rosillo-Rodes, Julia Witte Zimmerman, Laurent Hébert-Dufresne, Peter Sheridan Dodds

Many complex systems are composed of disparate, interacting types of varying sizes: Species abundances in ecosystems, firm sizes in markets, city populations in countries, word counts in language, etc. A longstanding mystery of complex systems is Zipf's law, which is the empirical observation that component size decreases as the inverse of component rank --

Hongyang Zhang, Yinhao Liu, Haitao Zhang, Zhongyi Wen

Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe semantic inconsistency caused by viewpoint variation and poor generalization under domain shift. Existing methods mainly rely on 2D correspondence, but they are easily distracted b

Marica Minucci, Rodrigo Panosso Macedo, Christiana Pantelidou, Laura Sberna

We explore the properties of bilinear products for black-hole quasinormal modes (QNMs) formulated on hyperboloidal foliations. We find that, although QNM solutions are smooth and finite on future-directed hyperboloids, the integrand of the bilinear form with respect to which the modes are orthogonal is still divergent. This is a result of the reflection (equ

Sakthi Prabhu Gunasekar, Prasanna Kumar Rangarajan

Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models operate in complex Hilbert spaces. This similarity suggests that using both the magnitude and phase of SAR data in quantum encoding should help automatic target recognition in SAR images. In this study, we test this assumption by comparing five encod

Tanmay Kumar Poddar, Michael Spannowsky

Quantum phase measurements offer a complementary route to axion searches. We show that axion-photon interactions can imprint both Aharonov-Bohm (AB) and Berry phases in experimentally motivated quantum setups. For a coherently oscillating axion dark matter background, the induced effective current generates a time dependent magnetic flux in an rf-SQUID, lead

Qibin Liu, Julia Gonski

Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliable deployment in real-world scientific research. In this work, we present a safe, lightweight, and user-friendly agentic framework for the autonomous execution of well-defined scientific tasks. The framework

Trinh Tran, Binh Nguyen, Truong X. Nghiem

This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipliers (ADMM) into a trainable neural network for solving constrained convex optimization problems. Existing end-to-end learning methods operate as black-box mappings from parameters to solutions, often lacking ex

Sridhar Prabhu, Saeed A. Khan, Xingrui Song, Mathieu Ouellet

Quantum computational sensing (QCS) combines quantum sensing with quantum computing to extract task-relevant information from the physical world. QCS can in principle achieve an accuracy advantage for specific tasks versus the alternative of raw-signal estimation using conventional quantum sensing followed by task-specific classical postprocessing. Here we r

E. Celi, R. Linehan, P. M. Harrington, M. Li

Quasiparticle poisoning following particle impacts poses a significant challenge to the development of fault-tolerant superconducting quantum computers, as a sudden excess of quasiparticles can simultaneously degrade the coherence of multiple qubits across large device arrays. In this work, we present a statistical analysis that models the time evolution of

Aadyot Bhatnagar, Peter Mørch Groth, Ali Madani

Large language models can be aligned with human preferences through offline reinforcement learning (RL) on small labeled datasets. While single-objective alignment is well-studied, many real-world applications demand the simultaneous optimization of multiple conflicting rewards, e.g. optimizing both catalytic activity and specificity in protein engineering,

Tian-Hua Yang, Sarang Gopalakrishnan, Dmitry A. Abanin

We establish a rigorous relation between the thermalization of typical initial states and the dynamics of local operators. We introduce a concept of simple slow operators (SSOs), defined as operators that have a small commutator with the Hamiltonian and have significant small-sized components. We show that if typical initial states (drawn from a low-complexi

Jalees Nehvi, Timo Bolkart, Thabo Beeler, Justus Thies

The human face is central to communication. For immersive applications, the digital presence of a person should mirror the physical reality, capturing the users idiosyncrasies and detailed facial expressions. However, current 3D head avatar methods often struggle to faithfully reproduce the identity and facial expressions, despite having multi-view data or l

M. Mezcua, B. Laloux, M. Scialpi, M. Siudek

Dual active galactic nuclei (AGNs) are expected in hierarchical galaxy evolution models, in which low-mass galaxies merge to build more massive ones. While observational evidence for dual AGNs is growing in massive galaxies, no clear detection has yet been found in the low-mass regime. We used photometry and spectroscopy from the first \Euclid Quick Data Rel

Albert Rico, Jofre Abellanet-Vidal, Naga Bhavya Teja Kothakonda, Anna Sanpera

Motivated to understand how entanglement resources can be distributed in quantum networks, we introduce threshold entanglement (TE) states. These are multipartite quantum states whose entanglement across bipartitions forces all marginals of half or less local systems to be (absolutely) separable. First, in contrast to states used for quantum secret sharing,

Anastasia Triantafillides, Thomas G. Beatty, Matthew C. Nixon, Taylor J. Bell

Transmission spectroscopy with the James Webb Space Telescope (JWST) is revealing growing chemical complexity in giant exoplanet atmospheres. Of particular interest is sulfur, which had essentially no observational constraints before JWST. Recent work has shown that a planet's atmospheric sulfur content traces its refractory budget and is therefore a sen

Luca Buonocore, Maximilian Delto, Kirill Melnikov, Pier Francesco Monni

We present a new method to compute the soft function for the $N$-Jettiness variable for arbitrary $N$ at high perturbative orders in QCD. It is based on the observation that the most singular part of the soft function, the dipole contribution, can be represented by a sum of an analytically calculable inclusive soft function and a remainder. The latter is abs

Giovanni Scala, Gniewomir Sarbicki

Randomized measurements access nonlinear functionals without full tomography, yet turning third-order local single-copy data into a strong entanglement test remains difficult. We convert the reduction criterion into an experimentally measurable separability criterion by testing it on squared affine combinations of the identity, the local marginals, and the s

Harald Schmid, Andrea Pizzi, Johannes Knolle

Unstable periodic orbits act as organizing structures for classical chaotic systems and underpin quantum scarring. Long known in single-particle systems, genuine quantum scars based on unstable periodic orbits have been recently extended to isolated many-body systems for time-independent Hamiltonians. Their fate under periodic driving, however, remains large

Pratik K. Das, Sarthak Duary, Sourav Maji

We study noncommutativity in open strings using the covariant phase space formalism. For tensile open strings in a constant Kalb-Ramond background, we show that the (pre)-symplectic current splits into a bulk kinetic term plus an exact boundary term, recovering the Seiberg-Witten noncommutativity parameter. We then extend the analysis to intrinsically tensio

Paryag Sharma, Raghunathan Srianand, Hum Chand, Labanya Kumar Guha

We present a statistical measurement of the transverse coherence of Mg II $\lambda\lambda2796,2803$ absorption using a large sample of 9204 absorber-centric quasar sightline pairs from the Sloan Digital Sky Survey. We quantify the probability that an Mg II absorber detected along one sightline is also present along a nearby sightline, and measure how this co

Gustav Romare, Daniel Shaffer, Alex Levchenko, Edwin Huang

The superconducting transition temperature $T_c$ of the two-dimensional attractive Hubbard model is computed in the vicinity of both ordinary (logarithmic) and higher-order (power-law) Van Hove singularities using determinant quantum Monte Carlo simulations. For interaction strengths $|U| \lesssim W/3$, where $W$ is the electronic bandwidth, $T_c$ is enhance

Gabriele Calliari, Charles Fromonteil, Francesco Cesa, Torsten V. Zache

We introduce a framework for realizing universal fermionic quantum processing with globally controlled itinerant fermionic particles. Our approach is tailored to the example of neutral atoms in optical lattices, but transposes to other setups with similar capabilities. We give constructive protocols to realize arbitrary fermionic processes, with time-depende

Yotam Vaknin, Ran Finkelstein, Ofer Firstenberg, Alex Retzker

Measurement time represents a critical bottleneck limiting the operational speed of neutral atom quantum computers, as it cannot be accelerated through parallelization like other quantum operations. We present a protocol for fast measurement of neutral atoms based on a new, fast multi-atom Rydberg gate that significantly reduces the measurement integration t

Ricardo Barrué, Lisa Benato, Ali Kaan Güven, Elie Hammou

The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity LHC. So far, PDFs are determined from global fits to binned low-dimensional data obtained from unfolded hard-scattering cross section measurements. In this work we demonstrate for

Yifan Fei, Peiran Li, Zhen Liu, Maxim Pospelov

We study the radiative decay of the $Z$ boson, $Z \to μ^+μ^-γ$, at the LHC, providing both Standard Model (SM) precision analysis and new physics projections. With detailed analysis of Run-2 and future HL-LHC performances, we demonstrate that this decay mode can be measured with a statistical precision at the sub-percentage level. From existing Run-1 data, w

Prajas Wadekar, Venkata Sai Pranav Bachina, Kunal Bhosikar, Ankit Gangwal

3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this accessibility raises a privacy concern: publicly available images or videos can be exploited to reconstruct detailed 3D models of scenes or objects without the owner's consent. We present PatchPoison, a lightweight

Frederick B. Davies, Eduardo Bañados, Sarah E. I. Bosman, Arpita Ganguly

Here we report the spectroscopic and geometric confirmation of an extremely bright ($i=14.77$) and compact (Einstein radius of $\sim0.45''$) quadruply-lensed quasar at $z=2.22$, J1330$-$0905, which we dub Persephone's Torch. The system had been previously selected as a candidate lensed quasar based on large-area survey data; here we confirm its quasar nature

Jaden Park, Jungtaek Kim, Jongwon Jeong, Robert D. Nowak

Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. However, systematically distinguishing and quantifying exploration and exploitation from observed actions

Tianchang Shen, Sherwin Bahmani, Kai He, Sangeetha Grama Srinivasan

Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative reconstruction approach combines the visual fidelity and creative capacity of video models with 3D outputs ready for real-tim

Kathakoli Sengupta, Kai Ao, Paola Cascante-Bonilla

Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and scene graphs, yet evaluation still relies on LLM or VLM judges that score rendered views, making judgments sensitive to viewpoint, prompt phrasing, and hallucination. When the evaluator is unstable, it becomes

Chenwei Xie, Urjeet Shrestha, Corbin McElhanney, Lukas Lorimer

Snowflake revolutionized data warehousing with an elastic architecture that decouples compute and storage, enabling scalable solutions for diverse data analytics needs. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI/ML workloads using Pytho

M. E. Shirokov

We construct for a Schur concave function $f$ on the set of quantum states a tight upper bound on the difference $f(\rho)-f(\sigma)$ for a quantum state $\rho$ with finite $f(\rho)$ and any quantum state $\sigma$ $m$-partially majorized by the state $\rho$ in the sense described in [1]. We also obtain a tight upper bound on this difference under the addition

Mohammad-Ali Miri, Uchenna Chukwu, Nicholas Chancellor

In this work we explore the potential of implementing an optical quantum optimizer using non-linear optics, specifically using sum-frequency generation and/or two photon absorption. This proposal uses Zeno effects to enforce independence constraints and then a linear protocol to find a maximum independent set in a way where the elements of the set can be wei

Miguel A. Montalvo Hernandez, Andy D. Goulding, Jenny E. Greene

Reddened Type-1 quasars offer a unique window into the structure and evolution of active galactic nuclei (AGN), yet their physical origin and the source of their reddening remain uncertain. Optical surveys often miss these dust-obscured objects, resulting in an incomplete view of the quasar population. In this work, we construct a sample of 6,600 Type-1 quas

Ya-Qi Yu, Fangyu Hong, Xiangyang Qu, Hao Wang

The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal tasks. Existing pipelines often rely on off-policy perturbations or coarse outcome-based signals, which are not well suited to fine-grained visual reasoning. We propose rDPO, a preference optimization framework b

Baris Sarper Tezcan, Hrishikesh Viswanath, Rubab Saher, Daniel Aliaga

Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been monitored using Earth-observing satellites and numerical modeling frameworks. For example, land surface temperature derived from Landsat or Sentinel imagery is commonly used to characterize surface heating pat

Tom Ben-Ami, Roderich Moessner, Markus Heyl

Many-body cages have very recently emerged as a general route for nonergodic behaviour in quantum matter. Here, we show that new types of many-body cages can be engineered in Floquet circuits with the potential to realize novel nonequilibrium quantum states. For that purpose, we first identify an explicit, general construction of Floquet circuits capable of

Kunal Marwaha, James Sud

We study the computational complexity of 2-local Hamiltonian problems generated by a positive-weight symmetric interaction term, encompassing many canonical problems in statistical mechanics and optimization. We show these problems belong to one of three complexity phases: QMA-complete, StoqMA-complete, and reducible to a new problem we call EPR*. The phases

Bence Deák, Péter Madarasi

The family of $(k,\ell)$-sparse graphs, introduced by Lorea, plays a central role in combinatorial optimization and has a wide range of applications, particularly in rigidity theory. A key algorithmic problem is to decide whether a given graph is $(k,\ell)$-sparse and, if not, to produce a vertex set certifying the failure of sparsity. While pebble game algo

Benzhao Tang, Shiyu Yang

The explosive growth of system logs makes streaming compression essential, yet existing log anomaly detection (LAD) methods incur severe pre-processing overhead by requiring full decompression and parsing. We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams. CLAD bypasses these bottlenecks by exploiting a k

Luoyi Sun, Xiao Zhou, Zeqian Li, Ya Zhang

Large Audio-Language Models (ALMs) have recently demonstrated remarkable capabilities in holistic audio understanding, yet they remain unreliable for temporal grounding, i.e., the task of pinpointing exactly when an event occurs within long-form audio. This limitation stems from two factors: training data dominated by clip-level supervision lacking precise t

Yihang Sun, Huaijin Wang, Patrick Hayden, Jose Blanchet

The Energy Conserving Descent (ECD) algorithm was recently proposed (De Luca & Silverstein, 2022) as a global non-convex optimization method. Unlike gradient descent, appropriately configured ECD dynamics escape strict local minima and converge to a global minimum, making it appealing for machine learning optimization. We present the first analytical study o

Cristian Minoccheri, Emily Wittrup, Kayvan Najarian, Ryan Stidham

Computed tomography (CT) enterography is a primary imaging modality for assessing inflammatory bowel disease (IBD), yet the representational choices that best support automated analysis of this modality are unknown. We present the first study of vision-language transfer learning on abdominal CT enterography and identify two main findings. First, mean pooling

Vanesa Ramirez, Yamila Miguel, Saburo Howard

Although Uranus and Neptune are commonly classified as ice giants, their exact compositions remain poorly constrained. Recent studies of outer Solar System bodies challenge the traditional view that these planets are primarily ice-dominated, suggesting that refractory material plays a more significant role. Determining the proportions of ice and rock within

Megha Chakraborty, Darssan L. Eswaramoorthi, Madhur Thareja, Het Riteshkumar Shah

AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation--predefined quizzes, uniform pacing, or generic feedback--limiting their ability to respond to learners' evolving understanding. This shortfall highlights the need for systems that are both context-aware and adaptive in real time. We int

Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang

On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a systematic investigation of OPD dynamics and mechanisms. We first identify that two conditions govern whether OPD succeeds or fails: (i) the student and teacher should share compatib

Yaru Niu, Zhenlong Fang, Binghong Chen, Shuai Zhou

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity, and contact-aware interaction under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based lower-body con

Zahra Asghari Varzaneh, Niclas Wölner-Hanssen, Reza Khoshkangini, Thomas Ebner

The selection of the optimal embryo for transfer is a critical yet challenging step in in vitro fertilization (IVF), primarily due to its reliance on the manual inspection of extensive time-lapse imaging data. A key obstacle in this process is predicting blastocyst formation from the limited number of daily images available. Many clinics also lack complete t

Stefano Fronzoni

We study a nonlocal diffusion equation of porous medium type featuring a generalised fractional pressure with spatial anisotropy. We construct a finite element method for the numerical solution of the equation on a bounded open Lipschitz polytopal domain $\Omega \subset \mathbb{R}^{d}$, where $d = 2$ or $3$. The pressure in the model is defined as the soluti

Yinghao Qin, Mosab Bazargani, Edmund K. Burke, Carlos A. Coello Coello

This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging decisions separately or jointly depending on the search stage. By analyzing their interaction, we introduce a surrogate objective at the upper level to guide the search and accelerate convergence. A bilevel L

William Iania, Angelo Ricciardone

Scalar-induced gravitational waves (SIGWs) have recently attracted considerable interest, both as a possible explanation for the nanohertz signal reported by the Pulsar Timing Array (PTA) collaboration and for their connection with primordial black hole (PBH) physics. In addition to SIGWs, scalar-tensor-induced gravitational waves (STGWs) have emerged as a p

Rodrigo Maier

We investigate the stability of ultra-compact stellar configurations in the context of an interacting vacuum component. By extending the Tolman-Oppenheimer-Volkoff equations to include a covariant energy exchange between the fluid and vacuum sectors, we examine how the classical Buchdahl stability limit is modified. We analyze two phenomenological interactio

Nicholas Pather, Joshua Fouché, Sitwala Mundia, Karl-Günter Technau

Manual digitisation of structured handwritten documents is slow and costly. We benchmark 17 leading frontier multi-modal large language models and open-source models against a very challenging real-world medical form that mixes dates; structured, printed text; hand-written responses and significant variability challenges. None of the smaller or older models

Yecheng Wu, Song Han, Hai Cai

On-policy distillation (OPD) is an effective post-training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infrastructure overhead. We investigate whether OPD can be performed offline by precomputing teacher log-probabilities once over SFT rollouts and reusing them during training. We find t

Katharina Boguslawski, Paweł Tecmer

We introduce a new equation-of-motion coupled-cluster method based on a pair coupled-cluster doubles (pCCD) reference, termed frozen-pair EOM-CCSD (EOM-fpCCSD). This approach combines the computational efficiency of the pCCD ansatz with a dynamical correlation correction, enabling a reliable description of electronically excited states within the EOM framewo

Chao Cheng, Fan Li

Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. We study network causal effects on outcome quantiles in the presence of p

Daniel Cui, Parthiban Santhanam, Aaswath P. Raman

Solid-state cooling via electroluminescent emission from light-emitting diodes is a promising alternative to thermoelectric and vapor-compression refrigeration, but practical performance remains limited by nonradiative losses and unfavorable tradeoffs between efficiency and cooling power. Thermophotonic (TPX) architectures partially address this by recycling

Filippos N. Tzortzoglou, Logan E. Beaver, Andreas A. Malikopoulos

We consider the energy-optimal control problem for double-integrator systems subject to state and control constraints, with fixed terminal time and free terminal speed. When the constraints become active, the optimal trajectory consists of a combination of bang, unconstrained, and coast arcs, whose switching instants must be computed explicitly. In this pape

Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram

Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness under trivial constraints? We show that simple lexical constraints (banning a single punctuation character or common word) cause instruction-tuned LLMs to collapse their responses, losing 14--48\% of comprehensiveness across seven models spanning

Gennifer T. Smith, James M. Sikes, Nicholas Dwork

This work presents an inexpensive optical projection tomography (OPT) system built on a mobile phone platform for three-dimensional optical microscopy. The system uses an iPhone camera together with a low-cost commercial microscope lens attachment, a stepper motor for sample rotation, LED illumination, and custom 3D-printed components, with a total component

Barbara Dietz

Neutrino billiards serve as a model system for the study of aspects of relativistic quantum chaos. These are relativistic quantum billiards consisting of a spin-1/2 particle which is confined to a planar domain by imposing boundary conditions on the spinor components which were proposed in [Berry and Mondragon 1987, {\it Proc. R. Soc.} A {\bf 412} 53) . We r

Emmanuel N. Saridakis

We investigate the cosmological implications of non-polynomial quasi-topological gravity (NPQTG), a novel class of modified gravitational theories in which the background dynamics is encoded in a single function of the Hubble parameter. This framework provides a minimal and theoretically consistent extension of general relativity, incorporating higher-curvat

James Wang, Primo Pu, Zephyr Fung, Alex Wang

The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained by sub-optimal hardware ergonomics, open

Zhaoran Liu, Rohan P. Naidu, Amy Secunda, Jenny E. Greene

Little Red Dots (LRDs) are a population of compact, red sources that have emerged as one of the most puzzling findings of JWST. Variability provides a direct probe of their central engines. Here we present the first joint spectroscopic and photometric time-domain study of LRDs undertaken with the JWST TWINKLE slitless spectroscopy program. Surveying the FRES

Jaywon Koo, Jefferson Hernandez, Ruozhen He, Hanjie Chen

We introduce HypoExplore, an agentic framework that formulates neural architecture discovery for visual recognition as a hypothesis-driven scientific inquiry. Given a human-specified high-level research direction, HypoExplore ideates, implements, evaluates, and improves neural architectures through evolutionary branching. New hypotheses are created using a l

Emre Akusta

This study analyses the impacts of economic complexity on environmental performance in BRICS-T countries. Annual data for the period 1999-2021, Durbin-Hausman cointegration test and Augmented Mean Group (AMG) estimator are used in the analysis. The robustness of the Panel AMG results is tested with CCEMG and CS-ARDL methods. The results indicate that economi

Jonan Richards

Large Language Models (LLMs) have shown much promise in powering a variety of software engineering (SE) tools. Offering natural language as an intuitive interaction mechanism, LLMs have recently been employed as conversational ``programming assistants'' capable of supporting several SE activities simultaneously. As with any SE tool, it is crucial that these

Maxime Nicaise, Yaiza Bermudez, Samir M. Perlaza

This work establishes that an optimal transport~(OT) problem regularized by a given $f$-divergence admits the same solution as another OT problem regularized by a different $g$-divergence, under an appropriate transformation of the cost function. This structural equivalence between OT problems regularized by distinct divergences, in the sense of sharing the

Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan

Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason about policy-related content remains underexplored. To fill this gap, we present \textbf{\textit{PolicyBench}}, the first large-scale cross-system benchmark (US-China) evaluating polic

Syed Md Mukit Rashid, Abdullah Al Ishtiaq, Kai Tu, Yilu Dong

Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although existing automated program repair techniques primarily focus on repairing memory corruption vulnerabilities, they struggle with logical vulnerabilities because of their limited semantic understanding of the vu

L. Espinosa, M. V. del Valle

Supersonic winds from massive stars carry great amounts of kinetic power and modify the surrounding interstellar medium. Through this interaction a stellar bubble is formed. Theoretical studies and recent observations suggest that the winds of massive stars could be sources of Galactic cosmic rays. The first detection of synchrotron emission from the bubble

Farbod Alinezhad, Jianfei Cao, Gary J. Young, Brady Post

Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. We introduce the Causal Diffusion Model (CDM), the first denoising diffusion p

Emre Akusta

This study analyzes the impacts of exchange rate and inflation on exports in Turkiye. Annual data for the period 1995-2023 were used in the analysis. The Johansen cointegration analysis and Dynamic Least Squares (DOLS) method were employed in the study. Identifying the cointegration relationship enabled the estimation of the long-run coefficients. The result

Gregor Meehan, Johan Pauwels

Item cold-start is a pervasive challenge for collaborative filtering (CF) recommender systems. Existing methods often train cold-start models by mapping auxiliary item content, such as images or text descriptions, into the embedding space of a CF model. However, such approaches can be limited by the fundamental information gap between CF signals and content

Liran Ringel, Yaniv Romano

Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming

Wenqi Pei, Shizheng Hou, Boyan Li, Han Chen

Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable. It is sensitive to syntactic variation, ignores that questions may admit multiple interpretations, and is easily misled by erroneous ground-truth SQL. To address this, we introduce ROSE, an inten

Özgür Akarsu, Maria Caruana, Konstantinos F. Dialektopoulos, Luis A. Escamilla

We investigate how strongly late-time inferences about DE dynamics depend on the functional prior used to represent the expansion history. Using identical late-time combinations of CC, DESI BAO measurements, the Pantheon+ SN1a sample, and the H0DN prior, we compare a node-based reconstruction of the reduced Hubble function $E(z)$ with a representative family

Joel Fokou

Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real-world actions (reading files, running commands, making network requests, modifying databases), a fundamental security g

Rafael J. Vicente, Jaime Gómez García, Juan P. Brito, Yorlandy Lobaina

The emergence of Cryptographically Relevant Quantum Computers (CRQCs) presents a critical threat to classical cryptographic systems, particularly widely adopted protocols such as RSA, Diffie-Hellman (DH), and Elliptic Curve Cryptography (ECC). Given their extensive use in the financial sector, the advent of quantum adversaries compels banking institutions to

Marton Hablicsek, Leo Herr, Francesca Leonardi

Recently, the authors of this paper introduced logarithmic Hochschild (co)homology of logarithmic spaces in a geometric way using formality of derived intersections. In this paper, the authors extend the decomposition theorem for the logarithmic Hochschild (co)homology of firm orbifolds to general logarithmic orbifolds and consider two applications of the de

Daniel O. Martinez-Rivillas, Arthur F. Ramos, Ruy J. G. B. de Queiroz

Martinez-Rivillas and de Queiroz gave extensional Kan semantics for the untyped lambda-calculus and later constructed the concrete K-infinity homotopy-model. The two main mathematical results of the present paper are these. First, we show that a smaller front-seed coherence package (WL, WR) together with an inner-right-front pentagon contraction already suff

I-J. Shan, L. A. Angeloni, W. Andreas Schroeder

Comparison of the measured spectral emission properties of single-crystal Cu(001) and W(111) photocathodes to established photoemission theories reveal evidence for an additional one photon emission process predominantly affecting electron emission near and below the photoemission threshold. This additional photoemission process is postulated to be due to a

Amir Hossein Kargaran, Nafiseh Nikeghbal, Jana Diesner, François Yvon

Optical character recognition (OCR) has advanced rapidly with the rise of vision-language models, yet evaluation has remained concentrated on a small cluster of high- and mid-resource scripts. We introduce GlotOCR Bench, a comprehensive benchmark evaluating OCR generalization across 100+ Unicode scripts. Our benchmark comprises clean and degraded image varia

Pål Christie Ryalen, Mats Julius Stensrud, Kjetil Røysland

We define dynamic treatment regimes and associated potential outcomes for data described by marked point processes (MPPs). These definitions motivate MPP analogues of the commonly used consistency, exchangeability, and positivity conditions that are sufficient for identifying effects in MPP data structures. The conditions are formulated based on martingale t

Steven Tomsovic

Through semiclassical methods the subject of quantum chaos motivates and depends on Hamiltonian chaos research. Presented here is a selection of Hamiltonian chaos topics that in this way get directly related to any of a variety of quantum chaos research problems. The chapter begins with a description of various useful theoretical and computational tools of c

Yonatan Lin, Oleg A. Starykh, Anna Keselman

We study the dynamical spin and nematic correlations in the bilinear-biquadratic spin-1 chain in the critical phase hosting deconfined spinons. We demonstrate how spinon interactions can be directly probed in the presence of a magnetic field or a single-ion anisotropy. Our analytical predictions are supported by numerical matrix-product-state (MPS) simulatio

Jonathan Coles, Stefano Schuppli, Lukas Drescher, Fawzi Roberto Mohamed

Large Language Models (LLMs) have surged as a transformative technology for science and society, prompting governments worldwide to pursue sovereign AI capabilities that ensure data compliance and cultural representation. However, the associated capital costs and engineering complexity required to train these models have largely restricted such capabilities

Sohyun An, Hayeon Lee, Shuibenyang Yuan, Chun-cheng Jason Chen

Retrieval-Augmented Generation (RAG) is a key approach to mitigating the temporal staleness of large language models (LLMs) by grounding responses in up-to-date evidence. Within the RAG pipeline, re-rankers play a pivotal role in selecting the most useful documents from retrieved candidates. However, existing benchmarks predominantly evaluate re-rankers in s

Andrew J Chen, Raymond Zhao, Lingjia Liu

Open Radio Access Network (O-RAN) is an important 5G network architecture enabling flexible communication with adaptive strategies for different verticals. However, testing for O-RAN deployments involve massive volumes of time-series data (e.g., key performance indicators), creating critical challenges for scalable, unsupervised monitoring without labels or

Hubert Woszczek, Agnieszka Wylomanska

This article introduces cyclic fractional Gaussian noise (cfGn), a stochastic model that integrates second-order cyclostationarity with long-range dependence property. While classical cyclostationary processes are widely discussed in the literature, they often lack the capacity to account for the persistent, slow-decaying correlations found in complex empiri

Yubraj Bhandari, Lavsen Dahal, Paul Segars, Joseph Y. Lo

Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the \textbf{Volume Control Scalar (VCS)}, a standardi

Tong Zhang, Jiangning Zhang, Zhucun Xue, Juntao Jiang

Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, epitomized by stochastic gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines. However, large-scale model training, stringent differential privacy

Sohyun An, Shuibenyang Yuan, Hayeon Lee, Cho-Jui Hsieh

Reinforcement Learning (RL) has shown strong potential for optimizing search agents in complex information retrieval tasks. However, existing approaches predominantly rely on gold supervision, such as ground-truth answers, which is difficult to scale. To address this limitation, we propose Cycle-Consistent Search (CCS), a gold-supervision-free framework for

Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome

Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual reasoning. Recent evidence suggests that this limitation arises not from weak visual representations, but from under-utilization of visual information during instruction tuning, where many tasks

Dongqi Fu, Kaushik Rangadurai, Haiyu Lu, Yunchen Pu

The increase in data volume, computational resources, and model parameters during training has led to the development of numerous large-scale industrial retrieval models for recommendation tasks. However, effectively and efficiently deploying these large-scale foundational retrieval models remains a critical challenge that has not been fully addressed. Commo

Niels Eckert, Marc Wenskat, Wolfgang Hillert

A screening of oxygen profiles in mid-T treated SRF cavities is crucial, in order to infer physical correlations between the microscopic cavity lattice and cavity performance - a problem concerning acceleration physicists for years. This thesis provides an analysis of oxygen diffusion profiles for three differently treated samples: Two mid-T baked and the th