October 2025 arXiv papers — page 162

Aashiq Muhamed, Leonardo F. R. Ribeiro, Markus Dreyer, Virginia Smith

The ability of language models in RAG systems to selectively refuse to answer based on flawed context is critical for safety, yet remains a significant failure point. Our large-scale study reveals that even frontier models struggle in this setting, with refusal accuracy dropping below 50% on multi-document tasks, while exhibiting either dangerous overconfide

Marcela Hanzer

Let $F$ be a non-archimedean local field of characteristic zero. We study theta correspondence for (complex) representations of symplectic--even orthogonal dual reductive pairs over $F;$ more specifically, the big theta lifts. We prove that, starting from a discrete series representation $\pi$ of a symplectic (even orthogonal group) over $F,$ its big theta l

Ivan Molodyk

This paper concerns the geometry of bicycle tracks. We model bicycle as an oriented segment of a fixed length that is moving in the Euclidean plane so that the trajectory of the rear point is tangent to the segment at all times. The trajectories of front and back points of the segment are called bicycle tracks, and one asks if it is possible that the front t

Chenlanhui Dai, Wenyan Wang, Yusi Fan, Yueying Wang

Predicting stock returns remains a central challenge in quantitative finance, transitioning from traditional statistical methods to contemporary deep learning techniques. However, many current models struggle with effectively capturing spatio-temporal dynamics and integrating multiple relational data sources. This study proposes GrifFinNet, a Graph-Relation

Zhaoran Liu, Tadayuki Kodama, Brian C. Lemaux, Mariko Kubo

We present results from a dual narrow-band imaging survey targeting the CL1604 supercluster at z = 0.9 using the Subaru Telescope. By combining the NB921 filter on HSC and the NB1244 filter on SWIMS, we can detect redshifted H$\alpha$ and H$\beta$ emission lines from the supercluster. This unique technique allows us to measure both star formation rates and d

M. Nizovkina, S. S. Larsen, A. G. A. Brown, A. Helmi

The precision of cluster parameter determination has significantly improved with the availability of homogeneous photometric Gaia data, however, challenges such as age-metallicity degeneracy and lack of spectroscopic observations remain. In this paper we investigate whether metallicities derived from low-resolution Gaia XP spectra can be effectively used to

Sazan Mahbub, Souvik Kundu, Eric P. Xing

Designing protein sequences that fold into a target 3-D structure, termed as the inverse folding problem, is central to protein engineering. However, it remains challenging due to the vast sequence space and the importance of local structural constraints. Existing deep learning approaches achieve strong recovery rates, however, lack explicit mechanisms to re

Hakyung Sung, Kristopher Kyle

Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 i

Sai Teja Erukude

CNNs are now prevalent as the primary choice for most machine vision problems due to their superior rate of classification and the availability of user-friendly libraries. These networks effortlessly identify and select features in a non-intuitive data-driven manner, making it difficult to determine which features were most influential. That leads to a ``bla

Emi Tanaka

The tidyverse is a popular meta-package comprising several core R packages to aid in various data science tasks, including data import, manipulation and visualisation. Although functionalities offered by the tidyverse can generally be replicated using other packages, its widespread adoption in both teaching and practice indicates there are factors contributi

Sebastián Rodríguez-Falcón, Luciano Stucchi

Emergent behaviors are a defining feature of complex systems, yet their quantitative characterization remains an open challenge, as traditional classifications rely mainly on visual inspection of spatio-temporal patterns. In this Letter, we propose using the Mean Information Gain (MIG) as a metric to quantify emergence in Agent-Based Models. The MIG is a con

Shouxu Lin, Zimeng Pan, Yuhang Yao, Haeyoung Noh

Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing the models' accuracies and training times in the MMFL setting requires adapting to data and system heterogeneity across clients as in single-model FL; these challenges are amplified

Rohan Gupta, Trevor Asbery, Zain Merchant, Abrar Anwar

Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multi-robot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet provides abstractions for planning, schedul

Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Andrews Danyo

Pavement crack detection has long depended on costly and time-intensive pixel-level annotations, which limit its scalability for large-scale infrastructure monitoring. To overcome this barrier, this paper examines the feasibility of achieving effective pixel-level crack segmentation entirely without manual annotations. Building on this objective, a fully sel

David Martinez

Polysymmetric functions, introduced by Asvin G and Andrew O'Desky as a generalization of symmetric functions, have natural connections to algebraic geometry and provide a foundation for further developments. In this paper, we study polysymmetric functions using stack partitions and develop combinatorial descriptions of several polysymmetric bases. We introdu

Kenichi Satoh

Non-negative matrix factorization (NMF) is widely used for dimensionality reduction and interpretable analysis, but standard formulations are unsupervised and cannot directly exploit class labels. Existing supervised or semi-supervised extensions usually incorporate labels only via penalties or graph constraints, still requiring an external classifier. We pr

Ziyi Wei, Huaiyang Zhong, Xiaocheng Li

We address the problem of multi-group mean estimation, which seeks to allocate a finite sampling budget across multiple groups to obtain uniformly accurate estimates of their means. Unlike classical multi-armed bandits, whose objective is to minimize regret by identifying and exploiting the best arm, the optimal allocation in this setting requires sampling e

Hector N. Salas

For Banach spaces of analytic functions on the disc for which the polynomials are dense and their pointt evaluations continuous, we prove the following: If they contain a function such that the limit superior of its modulus is infinite almost everywhere on the unit circle, then the same is true for a residual set of functions.

Hongxiang Qiu, Marco Carone, Alex Luedtke, Peter B. Gilbert

It is often of interest to study the association between covariates and the cumulative incidence of a right-censored time-to-event outcome. When time-varying covariates are measured on a fixed discrete time scale, it is desirable to account for these more up-to-date covariates when addressing censoring. For example, in vaccine trials, it is of interest to st

Oleg V. Ponomarev, Sergey V. Kolesov, Michail A. Nosov

A method is presented for determining the vertical direction relative to the axes of seismometers installed in seafloor observatories. The method is based on the linear relationship between the vertical component of seafloor acceleration and pressure variations at the ocean bottom, which follows directly from Newton's second law and holds within the freq

Min Woong Ahn

We develop a topological framework for Engel expansions that treats both directions of the correspondence between points of $(0,1]$ and nondecreasing digit sequences. We endow the sequence space with the product topology to study the evaluation map, and we fix a nonterminating digit algorithm to study the digit coding map. We also record the correspondence b

Mukul Lokhande, Tanushree Dewangan, Mohd Sharik Mansoori, Tejas Chaudhari

This paper introduces Bhasha-Rupantarika, a light and efficient multilingual translation system tailored through algorithm-hardware codesign for resource-limited settings. The method investigates model deployment at sub-octet precision levels (FP8, INT8, INT4, and FP4), with experimental results indicating a 4.1x reduction in model size (FP4) and a 4.2x spee

Emanuele Peschiera, Sangbu Yun, Youngjoo Lee, Liesbet Van der Perre

Increasing attention is given to the upper mid-band or Frequency Range 3 (FR3), from 7 to 24 GHz, in the research towards sixth-generation (6G) networks. Promises of offering large data rates at favorable propagation conditions are leading to novel FR3 base station (BS) architectures, with up to thousands of antenna elements and radio-frequency (RF) chains.

Áron Holló, Dániel Varjas, Cosma Fulga, László Oroszlány

The Chebyshev expansion method is a well-established technique for computing the time evolution of quantum states, particularly in Hermitian systems with a bounded spectrum. Here, we show that the applicability of the Chebyshev expansion method extends well beyond this constraint: It remains valid across the entire complex plane and is thus suitable for arbi

Soikot Sarkar, Ahmed Zubair

We designed an ingenious all-dielectric metasurface, employing cuboid structures patterned with bow-tie-shaped nanoholes, exhibiting multiple Fano resonances induced by quasi-bound states in the continuum (quasi-BICs) through structural asymmetry. Among them, several resonant modes demonstrated high quality factors in the range of 10^3-10^4, along with near-

Jungsoo Hong, Seong Ho Kim, Seung Kyu Min, Joonsuk Huh

Hybrid oscillator-qubit processors have recently demonstrated high-fidelity control of both continuous- and discrete-variable information processing. However, most of the quantum algorithms remain limited to homogeneous quantum architectures. Here, we present a compiler for hybrid oscillator-qubit processors, implementing state preparation and time evolution

Alex Lynham, Geoff Goodell

Immutability is a core design goal of permissionless public blockchain systems. However, rewrites are more common than is normally understood, and the risk of rewrite, cyberattack, exploit, or black swan event is also high. Taking the position that strict immutability is neither possible on these networks nor the observed reality, this paper uses thematic an

Pietro Bernardelle, Leon Fröhling, Stefano Civelli, Gianluca Demartini

As increasingly capable large language models (LLMs) emerge, researchers have begun exploring their potential for subjective tasks. While recent work demonstrates that LLMs can be aligned with diverse human perspectives, evaluating this alignment on downstream tasks (e.g., hate speech detection) remains challenging due to the use of inconsistent datasets acr

Christoph Aymanns, Jakob Foerster, Co-Pierre Georg, Matthias Weber

We propose multi-agent reinforcement learning as a new method for modeling fake news in social networks. This method allows us to model human behavior in social networks both in unaccustomed populations and in populations that have adapted to the presence of fake news. In particular the latter is challenging for existing methods. We find that a fake-news att

Criscent Birungi, Cody Hyndman

We consider the problem of optimal annuitization with labour income, where an agent aims to maximize utility from consumption and labour income under age-dependent force of mortality. Using a dynamic programming approach, we derive closed-form solutions for the value function and the optimal consumption, portfolio, and labor supply strategies. Our results sh

Fabian Wenz, Omar Bouattour, Devin Yang, Justin Choi

Large language models (LLMs) have been successfully applied to many tasks, including text-to-SQL generation. However, much of this work has focused on publicly available datasets, such as Fiben, Spider, and Bird. Our earlier work showed that LLMs are much less effective in querying large private enterprise data warehouses and released Beaver, the first priva

Arifa Hoque, Sanjukta Bhanja

Domain wall memories have undergone several changes over the years for faster shift, read, and write operations; however, fundamental issues persist due to creating pinning sites topographically along the nanowire. The deformity in notches creates non-uniform pinning strength, leading to multiple faults during shift operation. This study proposes a novel app

Peter Banyas, Shristi Sharma, Alistair Simmons, Atharva Vispute

Is an LLM telling you different facts than it's telling me? This paper introduces ConsistencyAI, an independent benchmark for measuring the factual consistency of large language models (LLMs) for different personas. ConsistencyAI tests whether, when users of different demographics ask identical questions, the model responds with factually inconsistent an

Marcus Lin, Peng Zhang, Aaron D. Ratschow, Oscar Li

Charged water drops are more widespread than commonly acknowledged. For example, raindrops typically carry charges of order Q ~ 1 pC, while routine pipetting in the laboratory produces drops with Q ~ 50 pC. Here, we show that such modest charging can spontaneously generate periodic Coulomb fissions for evaporating water drops on lubricated surfaces, with mor

Aditya De Saha

We introduce a coarse analog of the classical Lusternik-Schnirelmann category which we denote by $\text{c-cat}$, defined for metric spaces in the coarse homotopy category. This provides a new tool for studying large-scale topological properties of groups and spaces. We establish that $\text{c-cat}$ is a coarse homotopy invariant and prove a lower-bound $\tex

Saurabh Kataria, Ayca Ermis, Lovely Yeswanth Panchumarthi, Minxiao Wang

Photoplethysmography (PPG) sensor in wearable and clinical devices provides valuable physiological insights in a non-invasive and real-time fashion. Specialized Foundation Models (FM) or repurposed time-series FMs are used to benchmark physiological tasks. Our experiments with fine-tuning FMs reveal that Vision FM (VFM) can also be utilized for this purpose

Linlian Jiang, Rui Ma, Li Gu, Ziqiang Wang

Point cloud completion is essential for robust 3D perception in safety-critical applications such as robotics and augmented reality. However, existing models perform static inference and rely heavily on inductive biases learned during training, limiting their ability to adapt to novel structural patterns and sensor-induced distortions at test time. To addres

Ali Mirzazadeh, Simon Cadavid, Kaiwen Zha, Chao Li

Antidepressant nonadherence is pervasive, driving relapse, hospitalization, suicide risk, and billions in avoidable costs. Clinicians need tools that detect adherence lapses promptly, yet current methods are either invasive (serum assays, neuroimaging) or proxy-based and inaccurate (pill counts, pharmacy refills). We present the first noninvasive biomarker t

Till Preuster, Timo Reis, Manuel Schaller

We consider abstract second order systems of the form $\ddot{x}(t) + D \dot{x}(t) + Sx(t)=0$, which are typically analyzed via the operator matrix $\mathcal{A}=\left[\begin{smallmatrix} 0 & I \\ -S & -D \end{smallmatrix}\right]$ governing the free dynamics of the corresponding first-order in time formulation. While previous work (e.g. on spectral properties

Mehrnaz Asadi, Sina Javadzadeh, Rahil Soroushmojdehi, S. Alireza Seyyed Mousavi

Understanding how distributed brain regions coordinate to produce behavior requires models that are both predictive and interpretable. We introduce Behavior-Adaptive Connectivity Estimation (BACE), an end-to-end framework that learns phase-specific, directed inter-regional connectivity directly from multi-region intracranial local field potentials (LFP). BAC

Ehsan Saleh, Saba Ghaffari, Jeffrey H. Curtis, Lekha Patel

Aerosol-cloud--radiation interactions remain among the most uncertain components of the Earth's climate system, in partdue to the high dimensionality of aerosol state representations and the difficulty of obtaining complete \textit{in situ} measurements. Addressing these challenges requires methods that distill complex aerosol properties into compact yet phy

Rugved Katole, Christopher Stewart

AI-driven crop health mapping systems offer substantial advantages over conventional monitoring approaches through accelerated data acquisition and cost reduction. However, widespread farmer adoption remains constrained by technical limitations in orthomosaic generation from sparse aerial imagery datasets. Traditional photogrammetric reconstruction requires

Giuseppe Di Fazio, Rafayel Teymurazyan, José Miguel Urbano

We establish sharp local $C^{1,\alpha}$-regularity for weak solutions to degenerate elliptic equations of $p$-Laplacian type with data in Morrey spaces. The proof relies on the Fefferman-Phong inequality and standard tools from regularity theory for nonlinear PDEs.

Puneet Velidi, Zhengxiao Wei, Shreena Nisha Kalaria, Yimeng Liu

Concerns about the misuse and misinterpretation of p-values and statistical significance have motivated alternatives for quantifying evidence. We define a generalized form of Jeffreys's approximate objective Bayes factor (eJAB), a one-line calculation that is a function of the p-value, sample size, and parameter dimension. We establish conditions under which

Yang Liu, Bruno Da Costa, Aude Billard

Dynamic manipulation, such as robot tossing or throwing objects, has recently gained attention as a novel paradigm to speed up logistic operations. However, the focus has predominantly been on the object's landing location, irrespective of its final orientation. In this work, we present a method enabling a robot to accurately "throw-flip" objects to a desire

Huaizhi Qu, Ruichen Zhang, Shuqing Luo, Luchao Qi

Recent advances in foundation models have driven remarkable progress in image editing, yet their extension to 3D editing remains underexplored. A natural approach is to replace the image editing modules in existing workflows with foundation models. However, their heavy computational demands and the restrictions and costs of closed-source APIs make plugging t

Riju Pal, Kakan Deb, Nitesh Kumar, Bernd Büchner

MgMn$_6$Sn$_6$ is the itinerant ferromagnet on the kagome lattice with high ordering temperature featuring complex electronic properties due to the nontrivial topological electronic band structure, where the spin-orbit coupling (SOC) plays a crucial role. Here, we report a detailed ferromagnetic resonance (FMR) spectroscopic study of MgMn$_6$Sn$_6$ aimed to

Rinka Ito, Yusuke Miyamoto, Naomasa Nakai, Aya Yamauchi

We present the results of very long baseline interferometry (VLBI) observations of water vapor masers in the nucleus of the LINER galaxy NGC 7738. The red- and blue-shifted and newly detected systemic maser features show an almost edge-on disk located at a distance of ${0.031}\mbox{-}{0.222}$ pc from the galactic center and rotating with a velocity of $324\m

Pei Jin, Mariano Méndez, Federico García, Diego Altamirano

We present a timing analysis of \textit{Insight}-HXMT observations of the black-hole X-ray binary Swift J1727.8$-$1613 across a bright soft X-ray flare on 2023 September 19 (MJD 60206). At the peak of the flare, the source undergoes a brief transition from the hard-intermediate state (HIMS) into the soft-intermediate state (SIMS), marked by the simultaneous

Amaresh Sahu

In hydrodynamic descriptions of lipid bilayers, the membrane is often approximated as being impermeable to the surrounding, solute-containing fluid. However, biological and in vitro lipid membranes are influenced by their permeability and the resultant osmotic forces -- whose effects remain poorly understood. Here, we study the dynamics of a fluctuating, pla

Toshiaki Kanai, Chuanwei Zhang

Recent experimental advances highlight electron charge qubits floating above solid neon as an emerging promising platform for quantum computing, but the physical origin of single-electron lateral trapping is still not fully understood. While prior theoretical work has mainly examined electrons above bulk solid neon, experimental systems usually feature neon

Raphael Fischer, Youssef Abdelrahim, Katharina Poitz

Generative artificial intelligence (GenAI) presents both challenges and opportunities across all areas of education. Facing the municipal elections in North Rhine-Westphalia, the Young AI Leaders in Dortmund asked themselves: Could GenAI be used to make political programs more accessible, in order to facilitate political education? To explore respective pote

Jens Hemelaer

We introduce the notion of $n$-pure geometric morphism between Grothendieck toposes, over a Grothendieck base topos $\mathcal{T}$. This is a higher-dimensional analogue of the concepts of dense and pure geometric morphism. We extend the construction of the smallest dense subtopos and smallest pure subtopos by constructing a smallest $n$-pure subtopos, for ea

Ling Zhang, Shaleen Deep, Jignesh M. Patel, Karthikeyan Sankaralingam

In this paper, we present the design and architecture of REI, a novel system for indexing log data for regular expression queries. Our main contribution is an $n$-gram-based indexing strategy and an efficient storage mechanism that results in a speedup of up to 14x compared to state-of-the-art regex processing engines that do not use indexing, using only 2.1

Peter Bubenik, Zachariah Ross

Certain classes of multiparameter persistence modules may be encoded as signed barcodes, represented as points in a polyhedral subset of Euclidean space, we refer to as signed persistence diagrams. These signed persistence diagrams exist in the dual space of compactly supported, Lipschitz functionals on a polyhedral pair. In the interest of statistics and ma

Yuxiang Peng, Chuchu Chen, Kejian Wu, Guoquan Huang

In this paper, we develop and open-source, for the first time, a square-root filter (SRF)-based visual-inertial navigation system (VINS), termed sqrtVINS, which is ultra-fast, numerically stable, and capable of dynamic initialization even under extreme conditions (i.e., extremely small time window). Despite recent advancements in VINS, resource constraints a

Elijah Pelofske

This study numerically investigates the thermal sampling properties of QAOA, the Quantum Alternating Operator Ansatz which was generalized from the original Quantum Approximate Optimization Algorithm. Specifically, the ability of QAOA to sample from the Gibbs distribution, equivalently the Boltzmann distribution, defined by a classical Ising model, specifica

Sicheng Lyu, Yu Gu, Xinyu Wang, Jerry Huang

Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modifications without the computational burden of full retraining. Existing approaches are mainly based on a locate-then-edit framework. However, in sequential editing contexts, where m

Antonio Alfonso Arcos Álvarez, Emilio González Abril, María-Jesús Vázquez-Gallo

This study investigates a generalisation of the Pythagorean theorem to the lengths of conic arcs constructed symmetrically on the sides of a right triangle. It is demonstrated that the theorem remains valid whenever the conic eccentricity is fixed and the ratio between the length of each arc sagitta and its corresponding side is constant. We identify the exi

Minju Gwak, Guijin Son, Jaehyung Kim

Large language models (LLMs) often solve problems using step-by-step Chain-of-Thought (CoT) reasoning, yet these intermediate steps are frequently unfaithful or hard to interpret. Inspired by the Uniform Information Density (UID) hypothesis in psycholinguistics -- which posits that humans communicate by maintaining a stable flow of information -- we introduc

Iason Saganas, Grant Mayberry, Barbara Ercolano

Submoons, moons orbiting other moons, may be exotic environments capable of hosting extraterrestrial life. We extend previous studies to revise the maximum lifetime of these objects due to planetary, lunar and sublunar tidal migration. Using the Euler-Lagrange equation with a tidal dissipation process as specified by the Constant Geometric Lag model, we deri

Giorgia Rensi, Pietro Rossi, Marco Bianchetti

The SABR model is a cornerstone of interest rate volatility modeling, but its practical application relies heavily on the analytical approximation by Hagan et al., whose accuracy deteriorates for high volatility, long maturities, and out-of-the-money options, admitting arbitrage. While machine learning approaches have been proposed to overcome these limitati

Huw Day, Nina C. Snaith

This paper details an observation that for more primitive organisms, such as some yeasts, the statistical distribution of the origins of replication sometimes looks remarkably like the distribution of eigenvalues from the Circular Orthogonal Ensemble (COE) of random matrices. This does not hold for more complex organisms, but a uniform thinning of the COE ei

Yu-Hsuan Lin

Accurate traffic congestion classification is essential for intelligent transportation systems and real-time urban traffic management. This paper presents a multimodal framework combining open-vocabulary visual-language reasoning (CLIP), object detection (YOLO-World), and motion analysis via MOG2-based background subtraction. The system predicts congestion l

Marco Bianucci, Mauro Bologna, Daniele Lagomarsino-Oneto, Riccardo Mannella

Stochastic processes with renewal properties are powerful tools for modeling systems where memory effects and long-time correlations play a significant role. In this work, we study a broad class of renewal processes where a variable's value changes according to a prescribed Probability Density Function (PDF), $p(\xi)$, after random waiting times $\theta$. Th

Shiyu Chen, Ningyuan Huang, Soledad Villar

Graph Neural Networks (GNNs) typically scale with the number of graph edges, making them well suited for sparse graphs but less efficient on dense graphs, such as point clouds or molecular interactions. A common remedy is to sparsify the graph via similarity thresholding or distance pruning, but this forces an arbitrary choice of a single interaction scale a

O. Podladchikova, A. Warmuth, L. Harra, C. Verbeeck

Small-scale impulsive energy-release events are widely considered a key ingredient of quiet-Sun coronal heating. We quantified the thermal-energy content of extreme-ultraviolet (EUV) campfires, examined their impulsive heating characteristics, and investigated how their properties vary with atmospheric height. We analysed 1,468 campfires from an observing se

Shalaleh Rismani, Renee Shelby, Leah Davis, Negar Rostamzadeh

Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the compu

Balagopal Unnikrishnan, Ariel Guerra Adames, Amin Adibi, Sameer Peesapati

While ethical arguments for fairness in healthcare AI are well-established, the economic and strategic value of inclusive design remains underexplored. This perspective introduces the ``inclusive innovation dividend'' -- the counterintuitive principle that solutions engineered for diverse, constrained use cases generate superior economic returns in broader m

Jihong Zhu, Kefeng Huang, Jonathon Pipe, Chris Horbaczewsky

Chemistry, a long-standing discipline, has historically relied on manual and often time-consuming processes. While some automation exists, the field is now on the cusp of a significant evolution driven by the integration of robotics and artificial intelligence (AI), giving rise to the concept of the robochemist: a new paradigm where autonomous systems assist

Kazimier Smith, Yucheng Lu, Qiaochu Fan

Public funding plays a central role in driving scientific discovery. To better understand the link between research inputs and outputs, we introduce FIND (Funding-Impact NSF Database), an open-access dataset that systematically links NSF grant proposals to their downstream research outputs, including publication metadata and abstracts. The primary contributi

Jugal Garg, Eklavya Sharma, Xiaowei Wu

We consider the problem of allocating $m$ indivisible chores among $n$ agents with possibly different weights, aiming for a solution that is both fair and efficient. Specifically, we focus on the classic fairness notion of proportionality and efficiency notion of Pareto-optimality. Since proportional allocations may not always exist in this setting, we allow

Hamza Harraf, Mohamed Amazioug, Amjad Sohail, Rachid Ahl Laamara

The monogamy of quantum correlations is a fundamental principle in quantum information processing, limiting how quantum correlations can be shared among multiple subsystems. Here we propose a theoretical scheme to investigate the monogamy of quantum steering and genuine tripartite entanglement in a hybrid qubit-cavity optomagnonic system with a coherent feed

Hieu Le Duc, Leo Liberti

During 2024 and 2025 the discussion about the theorem-proving capabilities of large language models started reporting interesting success stories, mostly to do with difficult exercises (such as problems from the International Mathematical Olympiad), but also with conjectures [Feldman & Karbasi, arXiv:2509.18383v1] formulated for the purpose of verifying whet

J. D. Franson

The Schrodinger equation is not covariant. Nevertheless, quantum field theory is often formulated using the Schrodinger equation to describe the time evolution of the system, which is equivalent to using Feynman path integrals. It is well known that scattering theory gives covariant results provided that the interaction vanishes in the asymptotic limit of $t

Kohio Deflesselle, Mélodie Daniel, Aly Magassouba, Miguel Aranda

We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike holonomic or simpler non-holonomic robots such as differential-drive robots, DASMRs face strong kinematic constraints that make classical planners brittle in cluttered environments. O

Hanchen Su, Wei Luo, Wei Han, Yu Elaine Liu

We propose a practical approach by integrating Large Language Models (LLMs) with a framework designed to navigate the complexities of Airbnb customer support operations. In this paper, our methodology employs a novel reformatting technique, the Intent, Context, and Action (ICA) format, which transforms policies and workflows into a structure more comprehensi

Georg Linden

We explicitly determine the group of isomorphism classes of equivariant line bundles on the non-archimedean Drinfeld upper half plane for $\mathrm{GL}_2(F)$, for its subgroups of matrices whose determinant has even (respectively trivial) valuation, and for $\mathrm{GL}_2(\mathcal{O}_F)$. Our results extend a recent classification of torsion equivariant line

Kumater Ter, Abolanle Adetifa, Daniel Udekwe

Reinforcement learning (RL) has become a foundational approach for enabling intelligent robotic behavior in dynamic and uncertain environments. This work presents an in-depth review of RL principles, advanced deep reinforcement learning (DRL) algorithms, and their integration into robotic and control systems. Beginning with the formalism of Markov Decision P

Jiani Huang, Amish Sethi, Matthew Kuo, Mayank Keoliya

Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, existing MLLMs do not reliably capture fine-grained links between low-level visual features and high-level textual semantics, leading to weak grounding and inaccurate perception. To overcome this challenge, we propose ESCA, a framework that co

Nam Luu, Ondřej Bojar

Speech Translation (ST) is a machine translation task that involves converting speech signals from one language to the corresponding text in another language; this task has two different approaches, namely the traditional cascade and the more recent end-to-end. This paper explores a combined end-to-end architecture of pre-trained speech encoders and Large La

Shingo Kodama, Haya Diwan, Lucas Rosenblatt, R. Teal Witter

The rapid spread of text generated by large language models (LLMs) makes it increasingly difficult to distinguish authentic human writing from machine output. Watermarking offers a promising solution: model owners can embed an imperceptible signal into generated text, marking its origin. Most leading approaches seed an LLM's next-token sampling with a pseudo

Zhuxuanzi Wang, Mingqiao Mo, Xi Xiao, Chen Liu

Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory budgets. Although previous methods improve efficiency through low-rank updates, quantization, or heuristic budget reallocation, they often decouple the allocation of capacity from the way updates evolve during training.

Ananya Malik, Nazanin Sabri, Melissa Karnaze, Mai Elsherief

Large Language Models' (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to inves

Junhao Xu, Hui Zeng

Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using

Andrey Goncharov, Nikolai Kondusov, Alexey Zaytsev

Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks. We propose latent-space language steering, a lightweight inference-time method that identifies language directions via Principal Component Analysis (PCA) on parallel translations and steers token embeddings along

Ondrej Bohdal, Konstantinos Theodosiadis, Asterios Mpatziakas, Dimitris Filippidis

Large language models (LLMs) are commonly adapted for diverse downstream tasks via parameter-efficient fine-tuning techniques such as Low-Rank Adapters (LoRA). While adapters can be combined to handle multiple tasks separately, standard approaches struggle when targeting the simultaneous execution of complex tasks, such as generating a translated summary fro

Onurcan Kaya, Qiushi Deng, Thomas Souvignet, Catherine Marichy

Amorphous boron nitride (\textrm{$\alpha$}-BN) is a promising ultrathin barrier for nanoelectronics, yet the atomistic mechanisms governing its chemical stability remain poorly understood. Here, we investigate the structure-property relationship that dictates the oxidation of \textrm{$\alpha$}-BN using a combination of machine-learning molecular dynamics sim

Walid Abdela

The seamless integration of physical and digital environments in Cyber-Physical Systems(CPS), particularly within Industry 4.0, presents significant challenges stemming from system heterogeneity and complexity. Traditional approaches often rely on rigid, data-centric solutions like co-simulation frameworks or brittle point-to-point middleware bridges, which

Mihir Gupta, Pratik Desai, Ross Greer

Agricultural disease management in developing countries such as India, Kenya, and Nigeria faces significant challenges due to limited access to expert plant pathologists, unreliable internet connectivity, and cost constraints that hinder the deployment of large-scale AI systems. This work introduces a cost-effective self-consistency framework to improve visi

Jinbin Zhang, Nasib Ullah, Erik Schultheis, Rohit Babbar

Speculative decoding accelerates LLM inference by letting a small drafter propose multiple tokens which a large target model verifies once per speculation step. As vocabularies scale past 10e5 tokens,verification cost in the target model is largely unchanged, but the drafter can become bottlenecked by its O(|V|d) output projection. Recent approaches (e.g., F

Giuliano Armano

Analysing how information flows along the layers of a multilayer perceptron is a topic of paramount importance in the field of artificial neural networks. After framing the problem from the point of view of information theory, in this position article a specific investigation is conducted on the way information is processed, with particular reference to the

Liang Hong, Noura Raydan Nasreddine

Conformal prediction is a model-free machine learning method for constructing prediction regions at a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity of prediction, (ii) the computation

Mahdi Goldani

Innovation is becoming ever more pivotal to national development strategies but measuring and comparing innovation performance across nations is still a methodological challenges. This research devises a new time-series similarity method that integrates Seasonal-Trend decomposition (STL) with Fast Dynamic Time Warping (DTW) to examine Irans innovation trends

Fatoumata Sanogo

Spatio temporal data consist of measurement for one or more raster fields such as weather, traffic volume, crime rate, or disease incidents. Advances in modern technology have increased the number of available information for this type of data hence the rise of multidimensional data. In this paper we take advantage of the multidimensional structure of the da

Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy

Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework combines these graph representations with light-weight (<4B) local

Gabriel Gustavo Restrepo-Sánchez, José Gregorio Rodríguez-Nieto, Olga Patricia Salazar-Díaz, Andrés Sarrazola-Alzate

The concepts of derivations and right derivations for Leibniz algebras and $K$-B quasi-Jordan algebras naturally arise from the inner derivations determined by their algebraic structures. In this paper we introduce the corresponding analogues for dialgebras, which we call diderivations, and examine their properties in relation to antiderivations and right de

Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stando, Arkadiusz P. Trawinski

Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require frequent retraining to stay relevant as data evolves. Continuo

Reuven Ianconescu, Bin Zhang, Aharon Friedman, Jacob Scheuer

Interactions between many (initially separate) quantum systems raise the question on how to prepare and how to compute the measurable results of their interaction. When one prepares each system individually and let them interact, one has to tensor multiply their density matrices and apply Hamiltonians on the composite system (i.e. the system which includes a

Kevin Coulembier, Nate Harman, Andrew Snowden

Recently, the second and third authors introduced a new symmetric tensor category $\underline{\mathrm{Perm}}(G, \mu)$ associated to an oligomorphic group $G$ with a measure $\mu$. When $G$ is the group of order preserving self-bijections of the real line there are four such measures, and the resulting tensor categories are called the Delannoy categories. The