October 2025 arXiv papers — page 74

Jiazheng Li, Yawei Wang, David Yan, Yijun Tian

Large Language Models (LLMs) have demonstrated remarkable capabilities, enabling language agents to excel at single-turn tasks. However, their application to complex, multi-step, and long-horizon tasks remains challenging. While reinforcement learning (RL) offers a promising avenue for addressing these challenges, mainstream approaches typically rely solely

Samir Kusmic, Kristian Finlator, Ezra Huscher, Maya Steen

We compare the \texttt{Technicolor Dawn} cosmological simulations with recent observations of galactic nebular line emission during the Epoch of Reionization, providing stringent tests of the predicted ionization and metal enrichment levels. We validate the simulated population with the UVLF and $M_{\mathrm{UV}}-M_*$ relation and see that the simulated resul

Luise Ge, Gregory Kehne, Yevgeniy Vorobeychik

Social choice theory offers a wealth of approaches for selecting a candidate on behalf of voters based on their reported preference rankings over options. When voters have underlying utilities for these options, however, using preference rankings may lead to suboptimal outcomes vis-\`a-vis utilitarian social welfare. Distortion is a measure of this suboptima

Curtis Lee Shull, Merrick Green

Radio Frequency Identification (RFID) tracking may be a viable solution for defense assets that must be stored in accordance with security guidelines. However, poor sensor specificity (vulnerabilities include long range detection, spoofing, and counterfeiting) can lead to erroneous detection and operational security events. We present a supervised learning s

Ryan Kavanagh, Chuta Sano, Brigitte Pientka

The Proto-Quipper family of programming languages aims to provide a formal foundation for the Quipper quantum programming language. Unfortunately, Proto-Quipper languages have complex operational semantics: they are inherently effectful, and they rely on set-theoretic operations and fresh name generation to manipulate quantum circuits. This makes them diffic

Dena Firoozi, Anastasis Kratsios, Xuwei Yang

Traditional mean-field game (MFG) solvers operate on an instance-by-instance basis, which becomes infeasible when many related problems must be solved (e.g., for seeking a robust description of the solution under perturbations of the dynamics or utilities, or in settings involving continuum-parameterized agents.). We overcome this by training neural operator

Hojin Kim, Romit Maulik

We present a framework that leverages the Discrete Empirical Interpolation Method (DEIM) for interpretable deep learning and dynamical system analysis. Although DEIM efficiently approximates nonlinear terms in projection-based reduced-order models (POD-ROM), its fixed interpolation points are repurposed for identifying dynamically representative spatial stru

Neema Jakisa Owor, Joshua Kofi Asamoah, Tanner Wambui Muturi, Anneliese Jakisa Owor

Fisheye cameras offer an efficient solution for wide-area traffic surveillance by capturing large fields of view from a single vantage point. However, the strong radial distortion and nonuniform resolution inherent in fisheye imagery introduce substantial challenges for standard object detectors, particularly near image boundaries where object appearance is

Taner Karateke, Abdullah Mevlut Mutluel

This study comprehensively investigates the mechanical stability of intraocular lenses (IOLs) - critical components in cataract surgery - by analyzing their haptic designs. Three commercial models (ALSEE, GF3, and UD613) and five geometric variations (V1 to V5) derived from the GF3 model were comparatively evaluated in both dry and saline environments. The m

Carlos A. Bertulani

We investigate aspects of low-energy nuclear reactions that could be explored at the forthcoming Electron-Ion Collider (EIC) at Brookhaven National Laboratory and compare them with analogous measurements performed in ultraperipheral collisions (UPCs) at the Large Hadron Collider (LHC) at CERN. The estimated fragmentation cross sections at the EIC are roughly

Paata Ivanisvili, Xinyuan Xie

We asked GPT-5 Pro to look for counterexamples among a public list of open problems (the Simons ``Real Analysis in Computer Science'' collection). After several numerical experiments, it suggested a counterexample for the Non-Interactive Correlation Distillation (NICD) with erasures question: namely, a Boolean function on 5 bits that achieves a strictly larg

Adam Diamant

This study develops an AI-based pose estimation pipeline for quantifying movement kinematics in resistance training. Using videos from Wolf et al. (2025), comprising 303 recordings of 26 participants performing eight upper-body exercises under full (fROM) and lengthened partial (pROM) conditions, we extract joint-angle trajectories using five distinct deep-l

Audrey Dong, Claire Xu, Samuel R. Guo, Kevin Yang

Caregivers of individuals with autism spectrum disorder (ASD) often find the 77-item Autism Treatment Evaluation Checklist (ATEC) burdensome, limiting its use for routine monitoring. This study introduces a generalizable machine learning framework that seeks to shorten assessments while maintaining evaluative accuracy. Using longitudinal ATEC data from 60 au

Kushan Choudhury, Shubhrodeep Roy, Ankur Chanda, Shubhajit Biswas

Deep learning models, especially convolutional neural networks, have achieved impressive results in medical image classification. However, these models often produce overconfident predictions, which can undermine their reliability in critical healthcare settings. While traditional label smoothing offers a simple way to reduce such overconfidence, it fails to

Asad Hussain, Maximiliano Isi, Aaron Zimmerman

Many astrophysical population studies involve parameters that exist on a bounded domain, such as the dimensionless spins of black holes or the eccentricities of planetary orbits, both of which are confined to $[0, 1]$. In such scenarios, we often wish to test for distributions clustered near a boundary, e.g., vanishing spin or orbital eccentricity. Conventio

Chongyang Shi, Wesley A. Suttle, Michael Dorothy, Jie Fu

We study the problem of jointly selecting sensing agents and synthesizing decentralized active perception policies for the chosen subset of agents within a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework. Our approach employs a two-layer optimization structure. In the inner layer, we introduce information-theoretic metrics, d

Swati Dantu, Robert Pěnička, Martin Saska

This paper tackles the challenge of learning a generalizable minimum-time flight policy for UAVs, capable of navigating between arbitrary start and goal states while balancing agile flight and stable hovering. Traditional approaches, particularly in autonomous drone racing, achieve impressive speeds and agility but are constrained to predefined track layouts

To-Wen Liu, Matthew Green

Digital transactions currently exceed trillions of dollars annually, yet traditional paper-based agreements remain a bottleneck for automation, enforceability, and dispute resolution. Natural language contracts introduce ambiguity, require manual processing, and lack computational verifiability, all of which hinder efficient digital commerce. Computable lega

P. Mróz, A. Udalski, M. K. Szymański, I. Soszyński

In a recent arXiv post, Hawkins & Garcia-Bellido raised doubts on the results of 20-yr long OGLE photometric monitoring, which did not find a large number of gravitational microlensing events in the direction of the Magellanic Clouds. These results implied that primordial black holes and other compact objects with masses from 10^{-8} to 10^3 M_solar cannot c

C. J. Dyrseth, K. V. Samokhin

We explore quantum localization phenomena in a system of two coupled tight-binding chains with incommensurate periods. Employing the inverse participation ratio as a measure of localization, we investigate the effects of geometric incommensurability and external magnetic fields. Numerical results reveal the existence of a mobility edge in the spectrum charac

Federico Bonetto, Alberto Mario Maiocchi

We study the long time evolution of the position-position correlation function $C_{\alpha,N}(s,t)$ for a harmonic oscillator (the {\it probe}) interacting via a coupling $\alpha$ with a large chain of $N$ coupled oscillators (the {\it heat bath}). At $t=0$ the probe and the bath are in equilibrium at temperature $T_P$ and $T_B$, respectively. We show that fo

Alexandra Apostolopoulou, Konstantinos Kanaris, Athanasios Koursaris, Dimitris Tsakalidis

The advancement of natural language processing for morphologically rich and moderately-resourced languages like Modern Greek has been hindered by architectural stagnation, data scarcity, and limited context processing capabilities, particularly in specialized domains such as law. In this work, we propose the Greek Embedding Models (GEMs), a new family of tra

Yunpeng Xiao, Carl Yang, Mark Mai, Xiao Hu

Large language models (LLMs) show promise for clinical use. They are often evaluated using datasets such as MedQA. However, Many medical datasets, such as MedQA, rely on simplified Question-Answering (Q\A) that underrepresents real-world clinical decision-making. Based on this, we propose a unifying paradigm that characterizes clinical decision-making tasks

Minjun Kim, Seokhwan Moon, Jinsu Kim

Continuous-time Markov chains on non-negative integers can be used for modeling biological systems, population dynamics, and queueing models. Qualitative behaviors of birth-and-death models, typical examples of such one-dimensional continuous-time Markov chains, have been substantially studied. For one-dimensional Markov chains with polynomial transition rat

Yixiao Wang, Zishan Shao, Ting Jiang, Aditya Devarakonda

We present a novel enhanced cyclic coordinate descent (ECCD) framework for solving generalized linear models with elastic net constraints that reduces training time in comparison to existing state-of-the-art methods. We redesign the CD method by performing a Taylor expansion around the current iterate to avoid nonlinear operations arising in the gradient com

Bernadette Lessel

For a Polish space $X$, we define the Shape space $\mathcal{S}_p(X)$ to be the Wasserstein space $W_p(X)$ modulo the action of a subgroup $G$ of the isometry group $ISO(X)$ of $X$, where the action is given by the pushforward of measures. The Wasserstein distance can then naturally be transformed into a \emph{Shape distance} on Shape space if $X$ and the act

Abraham Itzhak Weinberg

Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Ex

Michael A. Covington

This paper presents a fundamental algorithm for parsing natural language sentences into dependency trees. Unlike phrase-structure (constituency) parsers, this algorithm operates one word at a time, attaching each word as soon as it can be attached, corresponding to properties claimed for the parser in the human brain. Like phrase-structure parsing, its worst

Yiming Lu, Xun Wang, Simin Ma, Shujian Liu

Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost. We propose a scalable framework implemented via Communication to Completion (C2C), which explicitly models communi

Baibhav Srivastava, André Izidoro

The accretion ages of the first planetesimals-the parent bodies of magmatic iron meteorites-suggest they formed within the first 0.5-1 Myr of Solar System history. Yet, planetesimal formation appears to have occurred in at least two distinct phases. A temporal offset separates early-forming bodies from later-forming chondrite parent bodies, which accreted 2-

F. Moreno-Insertis, V. H. Hansteen, D. Nóbrega-Siverio

We present a radiation-magnetohydrodynamics (RMHD) simulation of a magnetic cancellation event. The model is calculated with the Bifrost code and spans from the uppermost convection zone to the corona. The cancellation occurs between the positive polarity of an emerged magnetic bipole and a preexisting negative polarity. We try both to understand the RMHD as

Blas Durá-Azorín, Antonio I. Fernández-Domínguez, Alejandro Manjavacas

We theoretically investigate the emergence of quantum nonlinearities in the optical response of lattices of two-level quantum emitters coherently driven by a laser. For subwavelength lattice periods, where the system behaves as a quantum metasurface, we find that a resonant incident plane wave can populate excitonic Bloch states with parallel wavevectors dif

Taeyoung Lee, Gregory S. Chirikjian

This paper presents a unified geometric framework for Brownian motion on manifolds, encompassing intrinsic Riemannian manifolds, embedded submanifolds, and Lie groups. The approach constructs the stochastic differential equation by injecting noise along each axis of an orthonormal frame and designing the drift term so that the resulting generator coincides w

Zachary Horvitz, Raghav Singhal, Hao Zou, Carles Domingo-Enrich

The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving. While current tooling for reasoning is built around next-token prediction trained models, recent works introduce an alternative choice: masked diffusion language models (MDLMs). MDLMs are trained to infill positions

Xin Lian, Kenneth D. Forbus

Despite the broad applicability of large language models (LLMs), their reliance on probabilistic inference makes them vulnerable to errors such as hallucination in generated facts and inconsistent output structure in natural language understanding (NLU) tasks. By contrast, symbolic NLU systems provide interpretable understanding grounded in curated lexicons,

M. M. Sadman Shafi, Tasnia Siddiqua Ahona, Ashraful Islam Mridha

Accurate modulation classification is a core challenge in cognitive radio, adaptive communications, spectrum analysis, and related domains, especially under dynamic channels without transmitter knowledge. To address this need, this article presents a labeled synthetic dataset designed for wireless modulation classification under realistic propagation scenari

Konstantinos Kitsios, Marcel Böhme, Alberto Bacchelli

A greybox fuzzer is an automated software testing tool that generates new test inputs by applying randomly chosen mutators (e.g., flipping a bit or deleting a block of bytes) to a seed input in random order and adds all coverage-increasing inputs to the corpus of seeds. We hypothesize that the order in which mutators are applied to a seed input has an impact

C. G. L. Bøttcher, E. Önder, T. Connolly, J. Zhao

Superconducting qubits are among the most promising platforms for realizing practical quantum computers. One requirement to create a quantum processor is nonlinearity, which in superconducting circuits is typically achieved by sandwiching a layer of aluminum oxide between two aluminum electrodes to form a Josephson junction. These junctions, however, face se

Vipin Rathi, Lakshya Chopra, Rudraksh Rawal, Nitin Rajput

Quantum computing is reshaping the security landscape of modern telecommunications. The cryptographic foundations that secure todays 5G systems, including RSA, Elliptic Curve Cryptography (ECC), and Diffie-Hellman (DH), are all susceptible to attacks enabled by Shors algorithm. Protecting 5G networks against future quantum adversaries has therefore become an

Martha Teiko Teye, Ori Maoz, Matthias Rottmann

We propose FutrTrack, a modular camera-LiDAR multi-object tracking framework that builds on existing 3D detectors by introducing a transformer-based smoother and a fusion-driven tracker. Inspired by query-based tracking frameworks, FutrTrack employs a multimodal two-stage transformer refinement and tracking pipeline. Our fusion tracker integrates bounding bo

Renzhao Liang, Sizhe Xu, Chenggang Xie, Jingru Chen

Time series forecasting plays a pivotal role in critical domains such as energy management and financial markets. Although deep learning-based approaches (e.g., MLP, RNN, Transformer) have achieved remarkable progress, the prevailing "long-sequence information gain hypothesis" exhibits inherent limitations. Through systematic experimentation, this study reve

Tushar Nayan, Ziqi Zhang, Ruimin Sun

With the increasing deployment of Large Language Models (LLMs) on mobile and edge platforms, securing them against model extraction attacks has become a pressing concern. However, protecting model privacy without sacrificing the performance benefits of untrusted AI accelerators, such as GPUs, presents a challenging trade-off. In this paper, we initiate the s

Luke Postle

We discuss the recently developed method of refined absorption and how it is used to provide a new proof of the Existence Conjecture for combinatorial designs. This method can also be applied to resolve open problems in extremal and probabilistic design theory while providing a unified framework for these problems. Crucially, the main absorption theorem can

Hanbin Hong, Ashish Kundu, Ali Payani, Binghui Wang

Randomized smoothing has become essential for achieving certified adversarial robustness in machine learning models. However, current methods primarily use isotropic noise distributions that are uniform across all data dimensions, such as image pixels, limiting the effectiveness of robustness certification by ignoring the heterogeneity of inputs and data dim

Babaniyi Olaniyi, Ina Kalisa, Ana Fernández del Río, Jean Marie Vianney Hakizayezu

As part of Rwanda's transition toward universal health coverage, the national Community-Based Health Insurance (CBHI) scheme is moving from retrospective fee-for-service reimbursements to prospective capitation payments for public primary healthcare providers. This work outlines a data-driven approach to designing, calibrating, and monitoring the capitation

Suyash Bajpai, Aviva Lucas-DeMott, Nirosha J Murugan, Michael Levin

While computational capacity limits of the universe and carbon-based life have been estimated, a stricter bound for aneural organisms has not been established. Physarum polycephalum, a unicellular, multinucleated amoeba, is capable of complex problem-solving despite lacking neurons. By analyzing growth dynamics in two distinct Physarum strains under diverse

Shaocong Ma, Heng Huang

In this paper, we explore the two-point zeroth-order gradient estimator and identify the distribution of random perturbations that minimizes the estimator's asymptotic variance as the perturbation stepsize tends to zero. We formulate it as a constrained functional optimization problem over the space of perturbation distributions. Our findings reveal that suc

Hien Bui, Yufeiyang Gao, Haoran Yang, Eric Cui

Non-prehensile manipulation of diverse objects remains a core challenge in robotics, driven by unknown physical properties and the complexity of contact-rich interactions. Recent advances in contact-implicit model predictive control (CI-MPC), with contact reasoning embedded directly in the trajectory optimization, have shown promise in tackling the task effi

Marc Amorós-Trepat, Luis Medrano-Navarro, Qiang Liu, Luca Guastoni

The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popularity for solving this problem due to their ability to learn complex patterns from data and to generalize across diverse conditions. Among these, diffusion models have emerged as be

Leonardo D. Secchin, Wagner da Rocha, Mariana da Rosa, Leo Liberti

The Molecular Distance Geometry Problem (MDGP) is essential in structural biology, as it seeks to determine three-dimensional protein structures from partial interatomic distances. Its discretizable subclass (DMDGP) admits an exact combinatorial formulation that enables efficient exploration of the search space. However, in practical settings such as Nuclear

Chiara Marletto, Vlatko Vedral

We rebut a recent paper that claims that classical gravity can entangle two massive superpositions by local means. We refute the misconceptions appearing in this paper and confirm that the quantum features are necessary in the gravitational field if it can lead to entanglement by local propagation between distant masses.

Vipin Rathi, Lakshya Chopra, Madhav Agarwal, Nitin Rajput

The telecommunications industry faces a dual transformation: the architectural shift toward Open Radio Access Networks (O-RAN) and the emerging threat from quantum computing. O-RAN disaggregated, multi-vendor architecture creates a larger attack surface vulnerable to crypt-analytically relevant quantum computers(CRQCs) that will break current public key cryp

Le Ren, Xiangjian Zeng, Qingqiang Wu, Ruoxuan Liang

Lyric translation is a challenging task that requires balancing multiple musical constraints. Existing methods often rely on hand-crafted rules and sentence-level modeling, which restrict their ability to internalize musical-linguistic patterns and to generalize effectively at the paragraph level, where cross-line coherence and global rhyme are crucial. In t

Antonio Amariti, Fabio Mantegazza, Simone Rota, Andrea Zanetti

In this paper we propose 4d and 3d dualities among special unitary gauge theories with fundamentals and antisymmetric flavors and symplectic or orthogonal gauge theories with fundamentals and two index tensor matter. The various dualities originate from a conjectured 4d self-duality for $SU(N)$ with an antisymmetric and four fundamental flavors. While we pro

Harim Jin, Norbert Langer, Andrea Ercolino, Selma E. de Mink

Massive stars often evolve in binary systems, in which binary interactions significantly affect their evolution. Massive stars in the Galaxy serve as valuable testbeds for this due to their proximity. We computed the evolution of more than 38000 galactic binary systems with initial primary star masses of 5...100 Msun. In this paper, we aim to investigate the

Nitsa J Herzog, Rejwan Bin Sulaiman, David J Herzog, Rose Fong

The study explores the potential of AI technologies in personalized learning, suggesting the prediction of academic success through leadership personality traits and machine learning modelling. The primary data were obtained from 129 master's students in the Environmental Engineering Department, who underwent five leadership personality tests with 23 charact

Ricardo Manuel García-Vázquez, Lisan David Cabrera-González, Otoniel Denis-Alpizar, Philippe Halvick

The present study presents a comprehensive theoretical investigation of atom and asymmetric top molecule inelastic scattering based on the R-matrix formalism. The proposed methodology establishes a rigorous framework for treating inelastic collisions in the space-fixed coordinate system. The excellent numerical performance of the method is demonstrated throu

Chen-Lung Lu, Honglu He, Agung Julius, John T. Wen

Accurate robot kinematics is essential for precise tool placement in articulated robots, but non-geometric factors can introduce configuration-dependent model discrepancies. This paper presents a configuration-dependent kinematic calibration framework for improving accuracy across the entire workspace. Local Product-of-Exponential (POE) models, selected for

Michael Kinyon, J. D. Phillips

Although little can be gleaned about a loop with the property that its squares are, say, left nuclear ($xx\cdot yz = (xx\cdot y)z$), if its squares are also, say, middle nuclear ($(x\cdot yy)z = x(yy\cdot z)$), then the loop exhibits more structure than one might initially guess. Loops with squares in (at least) two nuclei include many well known classes of

Nicholas Tenkorang, Kwesi Appau Ohene-Obeng, Xiaogang Su

Kernel Density Estimation (KDE) is a cornerstone of nonparametric statistics, yet it remains sensitive to bandwidth choice, boundary bias, and computational inefficiency. This study revisits KDE through a principled convolutional framework, providing an intuitive model-based derivation that naturally extends to constrained domains, such as positive-valued ra

Gauri Kotiwale, Jorryt Matthee, Daichi Kashino, Aswin P. Vijayan

We aim to characterise the mass-metallicity relation (MZR) and the 3D correlation between stellar mass, metallicity and star-formation rate (SFR) known as the fundamental metallicity relation (FMR) for galaxies at $5<z<7$. Using $\sim800$ [O III] selected galaxies from deep NIRCam grism surveys, we present our stacked measurements of direct-$T\rm_e$ metallic

Hugo Méndez-Hernández, Ciria Lima-Dias, Antonela Monachesi, Yara L. Jaffé

The evolution of galaxies is shaped by both internal processes and their external environments. Galaxy clusters and their surroundings provide ideal laboratories to study these effects, particularly mechanisms such as quenching and morphological transformation. The Chilean Cluster galaxy Evolution Survey (CHANCES) Low-z sub-survey is part of the CHileAN Clus

Amir Hever, Itai Orr

Generative AI is supercharging insurance fraud by making it easier to falsify accident evidence at scale and in rapid time. Insurance fraud is a pervasive and costly problem, amounting to tens of billions of dollars in losses each year. In the vehicle insurance sector, fraud schemes have traditionally involved staged accidents, exaggerated damage, or forged

Corey Beard, Paul Robertson, Jack Lubin, Eric B. Ford

We present the discovery of GJ 251 c, a candidate super-Earth orbiting in the Habitable Zone (HZ) of its M dwarf host star. Using high-precision Habitable-zone Planet Finder (HPF) and NEID RVs, in conjunction with archival RVs from the Keck I High Resolution Echelle Spectrometer (HIRES), the Calar Alto high-Resolution search for M dwarfs with Exoearths with

Márcus Vinícius Lobo Costa, Sherlon Almeida da Silva, Bárbara Caroline Benato, Leo Sampaio Ferraz Ribeiro

This paper addresses the challenges in representation learning of 3D shape features by investigating state-of-the-art backbones paired with both contrastive supervised and self-supervised learning objectives. Computer vision methods struggle with recognizing 3D objects from 2D images, often requiring extensive labeled data and relying on Convolutional Neural

Joe Meyer, Divyansha Lachi, Mahmoud Mohammadi, Roshan Reddy Upendra

Relational multi-table data is common in domains such as e-commerce, healthcare, and scientific research, and can be naturally represented as heterogeneous temporal graphs with multi-modal node attributes. Existing graph neural networks (GNNs) rely on schema-specific feature encoders, requiring separate modules for each node type and feature column, which hi

Shaocong Ma, Heng Huang

Zeroth-order optimization (ZOO) is an important framework for stochastic optimization when gradients are unavailable or expensive to compute. A potential limitation of existing ZOO methods is the bias inherent in most gradient estimators unless the perturbation stepsize vanishes. In this paper, we overcome this biasedness issue by proposing a novel family of

Guido Ivetta, Laura Moradbakhti, Rafael A. Calvo

Large Language Models are being used in conversational agents that simulate human conversations and generate social studies data. While concerns about the models' biases have been raised and discussed in the literature, much about the data generated is still unknown. In this study we explore the statistical representation of social values across four countri

Marcos Kiwi, Carlos Martinez, Dieter Mitsche

In this paper we study the mixing time of the simple random walk on the giant component of supercritical $d$-dimensional random geometric graphs generated by the unit intensity Poisson Point Process in a $d$-dimensional cube of volume $n$. With $r_g$ denoting the threshold for having a giant component, we show that for every $\epsilon > 0$ and any $r \ge (1+

Shaocong Ma, Heng Huang

In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact. This mismatch between training and deployment environments

Mathieu Andreux, Märt Bakler, Yanael Barbier, Hamza Benchekroun

Building agents that generalize across web, desktop, and mobile environments remains an open challenge, as prior systems rely on environment-specific interfaces that limit cross-platform deployment. We introduce Surfer 2, a unified architecture operating purely from visual observations that achieves state-of-the-art performance across all three environments.

Austin Polanco, M. E. J. Newman

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we pr

Dylan Hebrail, Óscar Jiménez-Arranz, Santi Roca-Fàbrega

Stellar radial migration has predominantly been examined in isolated disc galaxies where non-axisymmetric structures drive the process. By contrast, while tidal interactions are known for having an influence, their contribution remains comparatively under explored. The LMC, the nearest disc galaxy to the Milky Way (MW) and currently interacting with the SMC,

Jiashi Feng, Xiu Li, Jing Lin, Jiahang Liu

Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations: video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics b

Denis Sedov, Mathias S. Scheurer

Analyzing the consequences of the quantum geometry induced by the momentum dependence of Bloch states has emerged as a very rich and active field in condensed matter physics. For instance, for the superfluid stiffness or the pairing mechanism, these geometric aspects can play an important role. We here demonstrate that quantum geometry can also be essential

Xiangying Huang, Renyu Rao

We study the random walk on a finite dihedral group $G$ driven by the uniform measure on $k$ independently and uniformly chosen elements. We show that the walk exhibits cutoff with high probability throughout nearly the entire regime $1 \ll \log k \ll \log |G|$, and determine the precise cutoff time. Interestingly, this mixing time differs from the entropic

Matan Tsipory, Ran Levinstein, Itay Evron, Mark Kong

We analyze task orderings in continual learning for linear regression, assuming joint realizability of training data. We focus on orderings that greedily maximize dissimilarity between consecutive tasks, a concept briefly explored in prior work but still surrounded by open questions. Using tools from the Kaczmarz method literature, we formalize such ordering

Francesca Bonaiti, Cody Balos, Kyle Godbey, Gaute Hagen

We compute nuclear response functions by solving the time-dependent A-body Schr\"odinger equation, recording the time-dependent transition moment and extracting spectral information via Fourier transforms. The solution of the time-dependent many-body problem accounts for correlations on top of the mean field by taking advantage of a time-dependent formulatio

Abhirup Bhattacharya, Onkar Parrikar

The covariant phase space formalism in general relativity is a covariant method for constructing the symplectic two-form, Hamiltonian and other conserved charges on the phase space of solutions to the Einstein equation with classical matter. In this note, we consider a generalization of this formalism to the semi-classical Einstein equation coupled to quantu

Foad Namjoo, Neng Wan, Devan Mallory, Yuyi Chang

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data)

Simon Martin, Marcin Raczkowski, Fakher F. Assaad, Tarun Grover

A multichannel Kondo system, where a single quantum spin couples to multiple channels of an electronic bath, provides one of the simplest examples of a zero-dimensional non-Fermi liquid. It is natural to ask: what happens when an extensive number of such systems are coupled together? A simple renormalization group argument implies that in a chain of SU(N) mu

Ryoichiro Noda

We introduce a new perspective on positive continuous additive functionals (PCAFs) of Markov processes, which we call space--time occupation measures (STOMs). This notion provides a natural generalization of classical occupation times and occupation measures, and offers a unified framework for studying their convergence. We analyze STOMs via so-called smooth

Xiang Li, Buxin Su, Chendi Wang, Qi Long

Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifying the privacy budget of private FL algorithms is challenging due to the co-existence of complex algorithmic components such as decentralized communication and local updates. This p

Egor Shulgin, Sultan AlRashed, Francesco Orabona, Peter Richtárik

The Muon optimizer has rapidly emerged as a powerful, geometry-aware alternative to AdamW, demonstrating strong performance in large-scale training of neural networks. However, a critical theory-practice disconnect exists: Muon's efficiency relies on fast, approximate orthogonalization, yet all prior theoretical work analyzes an idealized, computationally in

Joachim Trosseille, Hugo Bellezza, Olivier Vincent

Liquids in nanoscale hydrophilic pores generate capillary pressures so large that they could theoretically climb kilometers against gravity. However, droplets on thin nanoporous layers form imbibition fronts stopping at millimeters or less due to evaporation competing with capillary flow. Such droplet infiltration dynamics is of growing interest for studying

Jiaming Pan, Dragan Huterer, Camille Avestruz, Damon H. T. Cheung

Gravitational wave (GW) standard sirens have the potential to measure the Hubble constant $H_0$ in the local universe independently of the distance ladder, and thus offer unique new insights into the Hubble tension. A key challenge with standard sirens is detecting their electromagnetic counterparts, and therefore assigning redshifts to the measured distance

Aaron Calderon, Jing Tao

Given two hyperbolic surfaces and a homotopy class of maps between them, Thurston proved that there always exists a representative minimizing the Lipschitz constant. While not unique, these minimizers are rigid along a geodesic lamination. In this paper, we investigate what happens in the complement of that lamination. To do this, we introduce deflations, ce

Yong-Kang Li, Yi-Ning Wang, Jiang-Hao Yu

We develop a constructive heavy particle effective theory (HPET) through the nonlinear realization of the spontaneously broken Poincar\'{e} symmetry $R^{3,1} \rtimes SO(3,1) \rightarrow R^{3,1} \rtimes SO(3)$. Starting from the heavy one-particle state, we find the nonlinear boost transformation indicates the shift symmetry in the coset construction, corresp

Amineh Mohseni, Cumrun Vafa

We consider 4d $\mathcal{N}=1$ supergravity theories with modular symmetry, where the modulus $\tau$ is the upper half-plane modulo $SL(2,\mathbf{Z})$ action. We focus on enhanced discrete gauge symmetry points $\tau=i, \exp(2\pi i/3)$, and argue that, if there are no new additional massless fields at these points, they will always be critical points of the

Claudio Dalla Vecchia, Ignacio Trujillo

The physically motivated definition of galaxy size proposed recently, linked to the farther location of the in situ star formation, considerably reduces the scatter of the galaxy mass-size relation and provides a viable method to infer the galaxy stellar mass from its size. We provide a similar relation correlating the size of galaxies with the size of their

Philip Kirkeberg, Rixin Li, Martin E. Pessah

The disks of Active Galactic Nuclei (AGN) have in recent years been recognized as possible sites for gravitational wave sources, leading to a series of numerical studies on the evolution of disk-embedded black hole binaries. The majority of these works have been carried out so far using the shearing box, a local Cartesian domain co-rotating with the binary c

Skylar Grayson, Evan Scannapieco, Romeel Davé, Arif Babul

Modern cosmological simulations have now matured to the point of reproducing the evolution of realistic galaxy populations across cosmic time. These simulations rely on feedback from active galactic nuclei (AGN) to quench massive galaxies, yet the details of this process remain poorly understood. To address this issue, we introduce RAFIKI (Refining AGN Feedb

Samuel Bartlett-Tisdall, Sabine Harribey, William Pannell

We search for new defect universality classes by considering localised interactions placed on an RG interface separating two interacting multiscalar CFTs in $4-\varepsilon$ dimensions. Studying interactions spread throughout the entire interface as well as defects restricted to lines and surfaces within the interface, we find that this setup leads to a great

T. Heinzl, B. King, A. Mercuri-Baron

We study the circular birefringence experienced by linearly polarised photons colliding with a circularly polarised background creating a vacuum of definite chirality (handedness). For this scenario the standard Heisenberg-Euler approach fails and must be supplemented by derivative corrections which we match to known Hilbert series. Choosing a plane wave bac

Douglas Stanford, Cynthia Yan

Three dimensional hyperbolic manifolds have accumulation points in the spectrum of their volumes, leading to a divergence in the sum over topologies. The limit points are cusped hyperbolic manifolds, and we propose to renormalize the sum by including the cusped manifold as a counterterm. This gives a reinterpretation of the zeta-function regularization proce

Margot Leemker, John J. Tobin, Stefano Facchini, Pietro Curone

Water is essential to our understanding of the planet-formation process and habitability on Earth. Although trace amounts of water are seen across all phases of star and planet formation, the bulk of the water reservoir often goes undetected, hiding crucial parts of its journey from giant molecular clouds to planets. This raises the question of whether water

Xiaolin Ma, Volodymyr Takhistov, Norikazu Mizuochi, Ernst David Herbschleb

Quantum sensing with qubits has advanced fundamental physics searches, but higher dimensional systems offer untapped potential. We present a universal qutrit framework that yields a sequence-independent fourfold increase in quantum Fisher information and a twofold gain in sensitivity. In ultralight dark matter searches, spin-1 NV-center qutrits can enhance t

Trajan Murphy, Akshunna S. Dogra, Hanfeng Gu, Caleb Meredith

''Noisy'' datasets (regimes with low signal to noise ratios, small sample sizes, faulty data collection, etc) remain a key research frontier for classification methods with both theoretical and practical implications. We introduce FINDER, a rigorous framework for analyzing generic classification problems, with tailored algorithms for noisy datasets. FINDER i

Chen Yuan, Richard Brito

Black hole spectroscopy allows to infer the properties of the remnant of a binary black hole coalescence. Motivated by the recent proposal that a black hole's information load can alter its classical response to small perturbations, an effect known as the swift memory burden, we develop a minimal phenomenological framework to analyze the ringdown of a binary

Yang Ni, Hongping Deng, Xue-Ning Bai

Gravitational instability (GI) has long been considered a viable pathway for giant planet formation in protoplanetary disks (PPDs), especially at wide orbital separations or around low-mass stars where core accretion faces significant challenges. However, a primary drawback is that disk fragmentation from GI was generally found to produce over-massive clumps