October 2025 arXiv papers — page 177

Matteo Pistillo, Charlotte Stix

This memorandum presents four recommendations aimed at strengthening the principles of AI model reliability and AI model governability, as DoW, ODNI, NIST, and CAISI refine AI assurance frameworks under the AI Action Plan. Our focus concerns the open scientific problem of misalignment and its implications on AI model behavior. Specifically, misalignment and

Yuanhao Zou, Zhaozheng Yin

Medical Visual Question Answering (Med-VQA) is a challenging task that requires a deep understanding of both medical images and textual questions. Although recent works leveraging Medical Vision-Language Pre-training (Med-VLP) have shown strong performance on the Med-VQA task, there is still no unified solution for modality alignment, and the issue of hard n

Guangya Wan, Mingyang Ling, Xiaoqi Ren, Rujun Han

Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents: small errors compound across steps, and even state-of-the-art models often hallucinate or lose coherence. We identify context management as the central bottleneck -- extended histories cause agents to overlook critical evidence or become dist

Sara Behnamian, Fatemeh Fogh

European grapevine (\textit{Vitis vinifera} L.) is a climate-sensitive perennial whose flowering and ripening govern yield and quality. Phenological records from monitoring programs are typically collected at irregular intervals, so true transition dates are interval-censored, and many site-years are right-censored. We develop a reproducible workflow that tr

Shuo Xing, Soumik Dey, Mingyang Wu, Ashirbad Mishra

Video quality assessment (VQA) is a fundamental computer vision task that aims to predict the perceptual quality of a given video in alignment with human judgments. Existing performant VQA models trained with direct score supervision suffer from (1) poor generalization across diverse content and tasks, ranging from user-generated content (UGC), short-form vi

Noorbakhsh Amiri Golilarz, Hassan S. Al Khatib, Shahram Rahimi

Artificial intelligence has advanced significantly through deep learning, reinforcement learning, and large language and vision models. However, these systems often remain task specific, struggle to adapt to changing conditions, and cannot generalize in ways similar to human cognition. Additionally, they mainly focus on mimicking brain structures, which ofte

Andrey Pudovikov, Alexandra Khirianova, Ekaterina Solodneva, Gleb Molodtsov

Managing millions of digital auctions is an essential task for modern advertising auction systems. The main approach to managing digital auctions is an autobidding approach, which depends on the Click-Through Rate and Conversion Rate values. While these quantities are estimated with ML models, their prediction uncertainty directly impacts advertisers' revenu

Yiming Li, Nael Darwiche, Amirreza Razmjoo, Sichao Liu

We propose a Geometry-aware Policy Imitation (GPI) approach that rethinks imitation learning by treating demonstrations as geometric curves rather than collections of state-action samples. From these curves, GPI derives distance fields that give rise to two complementary control primitives: a progression flow that advances along expert trajectories and an at

José Felipe Voloch

In a paper of Tate and the author, we conjectured a uniform bound for the p-adic distance of torsion points on a semiabelian variety, not lying in a subvariety, to that subvariety. We survey the progress made on that conjecture and on similar statements in analogous situations.

Joan Vendrell Gallart, Russell Bent, Solmaz Kia

This paper considers an optimal radial reconfiguration problem in multi-source distribution networks, where the goal is to find a radial configuration that minimizes quadratic distribution costs while ensuring all sink demands are met. This problem arises in critical infrastructure systems such as power distribution, water networks, and gas distribution, whe

Marta Fuentes Zamoro, Benjamín Grinstein, Pablo Quílez

Perturbative partial-wave amplitudes diverge in cases with a massless exchanged particle in the $t$-channel. We argue that the divergence is an artifact of perturbation theory and give a prescription for the all-orders correction factor that renders the partial waves finite. As an example, we apply this to longitudinal $W^+W^-$ elastic scattering, for which

Reuben A. Luera, Ryan Rossi, Franck Dernoncourt, Samyadeep Basu

In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration. Recent advances in multimodal large language models (MLLMs) offer a promising opportunity to act as early evaluators, helping designers narrow options before formal testing. Unli

Yunhui He, Andreas Mang

In this work, we propose a generalized alternating nonlinear generalized minimal residual method (GA-NGMRES) to accelerate first-order optimization schemes for PDE-constrained optimization problems governed by transport equations. We apply GA-NGMRES to a preconditioned first-order optimization scheme by interpreting the update rule as a fixed-point (FP) iter

L. J. R. Bezerra, W. S. Dias

We investigate the modulational instability of uniform wave packets governed by a discrete third-order nonlinear Schr\"odinger equation in finite square lattices, modeling light propagation in two-dimensional nonlinear waveguide arrays. We analyze how initially stable uniform distributions evolve into self-trapped (localized) regimes and the influence of a r

Xinwen Hu, Yunqing Huang, Nianyu Yi, Peimeng Yin

Neural network-based function approximation plays a pivotal role in the advancement of scientific computing and machine learning. Yet, training such models faces several challenges: (i) each target function often requires training a new model from scratch; (ii) performance is highly sensitive to architectural and hyperparameter choices; and (iii) models freq

Vaibhav Jain, Gerrit Grossmann

Reinforcement Learning (RL) agents often struggle in sparse-reward environments where traditional exploration strategies fail to discover effective action sequences. Large Language Models (LLMs) possess procedural knowledge and reasoning capabilities from text pretraining that could guide RL exploration, but existing approaches create rigid dependencies wher

Lucas Fernández Sarmiento, Irvin Martínez Rodríguez

We develop a symmetry-based construction of gravity from a phase in which Weyl invariance is spontaneously broken. Using the coset formalism, the dilaton acts as a Stuckelberg field for the gauged scale symmetry, giving the Weyl gauge field a mass of order $\mathcal{O}(M_{\rm Pl})$. The resulting gauge-gravity interactions fall within the ghost-free generali

Zekun Wu, Anna Maria Feit

Monitoring interfaces are crucial for dynamic, highstakes tasks where effective user attention is essential. Visual highlights can guide attention effectively but may also introduce unintended disruptions. To investigate this, we examined how visual highlights affect users' gaze behavior in a drone monitoring task, focusing on when, how long, and how much at

Kimaya Basu, Savi Kolari, Allison Yu

With LLM usage becoming widespread across countries, languages, and humanity more broadly, the need to understand and guardrail their multilingual responses increases. Large-scale datasets for testing and benchmarking have been created to evaluate and facilitate LLM responses across multiple dimensions. In this study, we evaluate the responses of frontier an

Siyuan Guo, Taylor Jones

Hausdorff measure and Hausdorff dimension are useful tools to describe fractals. This paper investigates the bounds on the $d\log_32$-dimensional Hausdorff measure of the $d$-fold Cartesian product of the $1/3$ Cantor set, $\mathcal C^d$. By applying known theorems on the Hausdorff measure of fractals satisfying the strong open set condition and generalizing

Paula Maddigan, Andrew Lensen, Rachael C. Shaw

Accurate recognition and re-identification of individual animals is essential for successful wildlife population monitoring. Traditional methods, such as leg banding of birds, are time consuming and invasive. Recent progress in artificial intelligence, particularly computer vision, offers encouraging solutions for smart conservation and efficient automation.

Shikun Liu, Haoyu Wang, Mufei Li, Pan Li

Text embeddings from Large Language Models (LLMs) have become foundational for numerous applications. However, these models typically operate on raw text, overlooking the rich structural information, such as hyperlinks or citations, that provides crucial context in many real-world datasets. This paper introduces and systematically evaluates a new paradigm fo

Ignacio Fushimi, Marta Reboiro

We study a hybrid system formed by an ensemble of colour nitrogen-vacancy centres in diamond interacting with a superconducting flux-qubit at finite temperature. The presence of impurities in the system is modelled through pseudo-hermitian Hamiltonian, by introducing an asymmetry parameter in the interaction between the superconducting flux qubit and the ens

Yukiya Saito, Ante Ravlić, Pranav Nalamwar, Rebecca Surman

$\beta$-decay is known to play an essential role in the rapid neutron capture process ($r$-process) during $(n, \gamma) \leftrightarrow (\gamma, n)$ equilibrium and freeze-out when the neutron-rich nuclei decay back to stability. Recent systematic theoretical studies on $\beta$-decay at finite temperature indicated that under hot conditions ($T\sim10$~GK), a

Xiaohui Li, Shaobin Zhuang, Shuo Cao, Yang Yang

Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity (O(N^2)) creates a major computational bottleneck. Linear Attention offers an O(N) solution, but its promise for photorealistic SR has remained largely untapped, historically hindered by a cascade of interrelated and previo

Gregory Yeghiyan, Jurius Azar, Devson Butani, Chan-Jin Chung

This paper presents a real-time spill detection system that utilizes pretrained deep learning models with RGB and thermal imaging to classify spill vs. no-spill scenarios across varied environments. Using a balanced binary dataset (4,000 images), our experiments demonstrate the advantages of thermal imaging in inference speed, accuracy, and model size. We ac

Connor G. McMahan, Chia-Ming Chang, Raymond Nguyen, Souren Soukiazian

This study demonstrates the first realization of wireless strain, temperature and crack growth sensing within 3D-printed metallic structures using standard electromagnetic inspection hardware. This establishes a path toward need-based maintenance for parts operating in harsh environments driven by accurate, real-time damage assessments instead of relying on

Proggya Chakraborty, Aaquib Asrar, Jayasree Sengupta, Sipra Das Bit

5G networks enable diverse services such as eMBB, URLLC, and mMTC through network slicing, necessitating intelligent admission control and resource allocation to meet stringent QoS requirements while maximizing Network Service Provider (NSP) profits. However, existing Deep Reinforcement Learning (DRL) frameworks focus primarily on profit optimization without

Francisco Pascoa, Ian Lalonde, Alexandre Girard

Reinforcement learning (RL) policies often fail to generalize to new robots, tasks, or environments with different physical parameters, a challenge that limits their real-world applicability. This paper presents a simple, zero-shot transfer method based on Buckingham's Pi Theorem to address this limitation. The method adapts a pre-trained policy to new syste

Marius O. Eji, Md. Sakauat Hasan Sakib, Joseph P. Corbett

Indium selenide is a phase-change chalcogenide whose polymorphism enables a variety of physical properties to be tuned. Here we directly quantify the evolution of the surface work function across the amorphous-to-crystalline transition in RF-sputtered In2Se3 thin films grown on c-plane Al2O3 (001). By varying deposition temperature (100-500 {\deg}C) and film

Vasuda Trehan, Kevin H. Knuth, M. J. Way

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amo

Mojtaba Amiri, Rouhollah Amiri

This letter proposes an algebraic solution for the problem of 3-D source localization utilizing the minimum number of measurements, i.e., one Time Difference of Arrival (TDOA) and one Angle of Arrival (AOA) pair. The proposed method employs a closed-form weighted least squares estimator and enables the positioning using a single ground station and a cooperat

David Fenwick, Navid NaderiAlizadeh, Vahid Tarokh, Nicholas Felice

Protocol optimization is critical in Computed Tomography (CT) to achieve high diagnostic image quality while minimizing radiation dose. However, due to the complex interdependencies among CT acquisition and reconstruction parameters, traditional optimization methods rely on exhaustive testing of combinations of these parameters, which is often impractical. T

Jiayang Liu, Daniel Tso, Yiming Bu, Qinru Qiu

Adversarial attacks significantly challenge the safe deployment of deep learning models, particularly in real-world applications. Traditional defenses often rely on computationally intensive optimization (e.g., adversarial training or data augmentation) to improve robustness, whereas the human visual system achieves inherent robustness to adversarial perturb

Yuan Fa, Milan Buttberg, Ke Ran, Rana Walied Ahmad

Memristors based on two-dimensional materials (2DMs) have garnered significant attention due to their fast resistive switching (RS) behavior and atomic-level thickness, which enables low power consumption, making them promising candidates for neuromorphic computing. Among these, memristors based on molybdenum disulfide (MoS${_2}$) have been extensively studi

Suman Mondal

We explore the connection between cyclotomic mapping permutation polynomials and permutation polynomials of the form $x^rf(x^{\frac{q-1}{l}})$ over finite fields. We present a new necessary and a new sufficient condition to verify permutation behavior of such polynomials over finite field. As its application, for particular values of $r$, we point out some p

Graham Tierney, Srikar Katta, Christopher Bail, Sunshine Hillygus

Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry reviews use profane language), we must control for possible latent confounding to isolate causal effects. Recent literature proposes adapting large language models (LLMs) to learn latent

Mujin Kwun, Depen Morwani, Chloe Huangyuan Su, Stephanie Gil

Optimizing neural networks for quantized objectives is fundamentally challenging because the quantizer is piece-wise constant, yielding zero gradients everywhere except at quantization thresholds where the derivative is undefined. Most existing methods deal with this issue by relaxing gradient computations with techniques like Straight Through Estimators (ST

Oliver Pitt, Timothy Clifton

Gravitational waves from compact binary inspirals offer a new opportunity to constrain the cosmological time dependence of gravitational coupling parameters, due to the high precision of the observations themselves as well as the significant cosmological redshifts at which such systems exist. We calculate theory-independent equations of motion for compact ob

Pantea Karimi, Dany Rouhana, Pooria Namyar, Siva Kesava Reddy Kakarla

We posit that we can generate more robust and performant heuristics if we augment approaches using LLMs for heuristic design with tools that explain why heuristics underperform and suggestions about how to fix them. We find even simple ideas that (1) expose the LLM to instances where the heuristic underperforms; (2) explain why they occur; and (3) specialize

Dixon Vimalajeewa, Ursula U. Muller, Brani Vidakovic

Stochastic resonance (SR), a phenomenon originally introduced in climate modeling, enhances signal detection by leveraging optimal noise levels within non-linear systems. Traditional SR techniques, mainly based on single-threshold detectors, are limited to signals whose behavior does not depend on time. Often large amounts of noise are needed to detect weak

David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins

Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agil

A. Wang, C. Jiang, M. Przystupa, J. Valentine

Operating high degree of freedom robots can be difficult for users of wheelchair mounted robotic manipulators. Mode switching in Cartesian space has several drawbacks such as unintuitive control reference frames, separate translation and orientation control, and limited movement capabilities that hinder performance. We propose Point and Go mode switching, wh

Amir Hossein Fahim Raouf, Donggu Lee, Mushfiqur Rahman, Saad Masrur

This Review consolidates publicly available aerial wireless measurement datasets collected using AERPAW. We organize signal-level, power-level, and KPI-level datasets under a unified taxonomy, harmonize metadata, and provide verified access with reproducible post-processing scripts. The curated catalog supports propagation modeling, machine learning, localiz

Adriano Da Silva, Okan Duman, Anderson F. P. Rojas

In this paper, we investigate the control sets of linear control systems on the Heisenberg group associated with singular derivations. Under the Lie algebra rank condition, we provide a complete characterization of these sets by analyzing the trace and determinant of an associated 2 \times 2 submatrix.

Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee, Sarana Nutanong

Federated learning (FL) enables collaborative training without raw data sharing, but still risks training data memorization. Existing FL memorization detection techniques focus on one sample at a time, underestimating more subtle risks of cross-sample memorization. In contrast, recent work on centralized learning (CL) has introduced fine-grained methods to a

Swapnaneel Bhattacharyya, Aaditya Ramdas

Changepoint localization aims to provide confidence sets for a changepoint (if one exists). Existing methods either relying on strong parametric assumptions or providing only asymptotic guarantees or focusing on a particular kind of change(e.g., change in the mean) rather than the entire distributional change. A method (possibly the first) to achieve distrib

Christopher Yeh, Nicolas Christianson, Adam Wierman, Yisong Yue

While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical for deployment in high-stakes applications. The framework of conformal risk control (CRC) provides a distribution-free, finite-sample method for controlling the expected value of an

Yihao Ang, Peicheng Yao, Yifan Bao, Yushuo Feng

Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalies can cause serious operational and economic impacts. Despite advances in both data mining and deep learning, many existing methods struggle with mixed-type tabular data, often rel

Sonu Kumar, Gaël Bastien, Maxim Savinov, Petr Proschek

We report dielectric and magnetoelectric studies of single-crystalline \ce{CeMgAl11O19}, a Kramers triangular magnet embedded in a polarizable hexaaluminate lattice. In zero magnetic field, the permittivity $\varepsilon'(T)$ follows the Barrett law of a quantum paraelectric down to 25 K, below which a broad minimum develops near 3 K without evidence of s

Timir Karmakar, Amrita Mandal

This study examines Couette-Poiseuille flow of variable viscosity within a channel that is partially filled with a porous medium. To enhance its practical relevance, we assume that the porous medium is anisotropic with permeability varying in all directions, making it a positive semidefinite matrix in the momentum equation. We assume the Navier-Stokes equati

Gang Liu, Jie Chen, Yihan Zhu, Michael Sun

In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL contain molecular properties spanning millions of biological assays, yet labeled data for each property remain scarce. To address this limitation, we introduce demonstration-condition

Tianyu Bell Pan, Mengdi Zhu, Alexa Jordyn Cole, Ronald Wilson

Multimodal learning systems often struggle in non-stationary environments due to concept drift, where changing data distributions can degrade performance. Modality-specific drifts and the lack of mechanisms for continuous, stable adaptation compound this challenge. This paper introduces LS-OGD, a novel adaptive control framework for robust multimodal learnin

Isa G. Cox, Jeyhan S. Kartaltepe, Micaela B. Bagley, Steven L. Finkelstein

We present the Cosmic Evolution Early Release Science Survey (CEERS) catalog, including space-based photometry, photometric redshifts, and physical parameters for more than 80,000 galaxies. The imaging used for this catalog comes from the CEERS survey, which has NIRCam coverage over ~100 sq. arcmin of the Extended Groth Strip (EGS) in seven filters from 1.15

Amir Ban

Sequential auctions for identical items with unit-demand, private-value buyers are common and often occur periodically without end, as new bidders replace departing ones. We model bidder uncertainty by introducing a probability that a bidder must exit the auction in each period. Treating the sequential auction as a Markov process, we demonstrate the existenc

Tessa Masis, Brendan O'Connor

Geocoding is the task of linking a location reference to an actual geographic location and is essential for many downstream analyses of unstructured text. In this paper, we explore the challenging setting of geocoding compositional location references. Building on recent work demonstrating LLMs' abilities to reason over geospatial data, we evaluate LLMs' geo

Arpit Raj, Swati Chaudhary, Martin Rodriguez-Vega, Maia G. Vergniory

In this work, we show that suitably designed spatially varying linearly polarized light provides a versatile route to generate and control pseudo-magnetic fields in Weyl semimetals through Floquet engineering. Within a high-frequency expansion, we derive an effective axial gauge potential $\mathbf{A}_5(\mathbf{r})$ whose curl gives the pseudo-magnetic field

Yikai Zhao, Jiekai Ma

Modern time series forecasting increasingly relies on complex ensemble models generated by AutoML systems like AutoGluon, delivering superior accuracy but with significant costs to transparency and interpretability. This paper introduces a comprehensive, dual-approach framework that addresses both the explainability and forecastability challenges in complex

Caden Ginter, Kamal Choudhary, Subhasish Mandal

We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line

Justin Lin, Julia Fukuyama

In this growing age of data and technology, large black-box models are becoming the norm due to their ability to handle vast amounts of data and learn incredibly complex input-output relationships. The deficiency of these methods, however, is their inability to explain the prediction process, making them untrustworthy and their use precarious in high-stakes

Francois Stockart, Alexis Robin, Hal Blumenfeld, Milan Brazdil

Despite many years of research, the quest to identify neural correlates of perceptual consciousness (NCC) remains unresolved. One major obstacle lies in methodological limitations: most studies rely on non-invasive neural measures with limited spatial or temporal resolution making it difficult to disentangle proper NCCs from concurrent cognitive processes. A

Florian Kranzl, Adria Rospars, Johannes Franke, Manoj K. Joshi

Characterization of noise of a quantum harmonic oscillator is important for many experimental platforms. We experimentally demonstrate motional spin-locking spectroscopy, a method that allows us to directly measure the motional noise spectrum of a quantum harmonic oscillator. We measure motional noise of a single trapped ion in a frequency range from 200 Hz

Chen Wang, Xunzhuo Liu, Yuhan Liu, Yue Zhu

Large Language Models (LLMs) demonstrate substantial accuracy gains when augmented with reasoning modes such as chain-of-thought and inference-time scaling. However, reasoning also incurs significant costs in inference latency and token usage, with environmental and financial impacts, which are unnecessary for many simple prompts. We present a semantic route

Gregory Yauney, Shahzaib Saqib Warraich, Swabha Swayamdipta

Micro-benchmarking offers a solution to the often prohibitive time and cost of language model development: evaluate on a very small subset of existing benchmarks. Can these micro-benchmarks, however, rank models as consistently as the full benchmarks they replace? And can they rank models more consistently than selecting a random subset of data points? In ma

Sanchita Pramanik, Timir Karmakar

We provide an analytical solution to examine the impact of steady and pulsatile flow in a symmetric wavy channel filled with an anisotropic porous medium. The flow inside the wavy channel with a porous substrate is governed by the Darcy-Brinkman equation. We used the perturbation approach to ascertain the solution to the problem, assuming that the ratio betw

Nicolas Ewen, Jairo Diaz-Rodriguez, Kelly Ramsay

Traditional transfer learning typically reuses large pre-trained networks by freezing some of their weights and adding task-specific layers. While this approach is computationally efficient, it limits the model's ability to adapt to domain-specific features and can still lead to overfitting with very limited data. To address these limitations, we propose Str

Silvie Illésová, Tomáš Bezděk, Vojtěch Novák, Bruno Senjean

This work investigates the performance of numerical optimization algorithms applied to the State-Averaged Orbital-Optimized Variational Quantum Eigensolver for the H2 molecule under various quantum noise conditions. The goal is to assess the stability, accuracy, and computational efficiency of commonly used gradient-based, gradient-free, and global optimizat

Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve

Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. This paper introduces Neptune, a tensor

Gorjan Alagic, Chen Bai, Christian Majenz, Kaiyan Shi

Block ciphers are versatile cryptographic ingredients that are used in a wide range of applications ranging from secure Internet communications to disk encryption. While post-quantum security of public-key cryptography has received significant attention, the case of symmetric-key cryptography (and block ciphers in particular) remains a largely unexplored top

Ozgur Guldogan, Neeraj Sarna, Yuanyuan Li, Michael Berger

While counterfactual fairness of point predictors is well studied, its extension to prediction sets--central to fair decision-making under uncertainty--remains underexplored. On the other hand, conformal prediction (CP) provides efficient, distribution-free, finite-sample valid prediction sets, yet does not ensure counterfactual fairness. We close this gap b

Joan Feigenbaum, Daniel J. Weitzner

We systematize the intellectual scope of the ACM Computer Science and Law Symposium (CS&Law). In particular, we address the meaning and importance of the word ''and'' in the name of the symposium. We identify previously published papers (from CS&Law and other forums) that exemplify different aspects of the CS&Law scope and note that the scope is expected to

Guillermo Garcia-Segura, Arturo Manchado, Jesus A. Toala, Martin A. Guerrero

Two-dimensional hydrodynamical simulations are presented from the formation up to the late evolution of planetary nebula, for 6 different stellar models from 1 to 5 Mo. The hydrodynamical models use stellar evolution calculations as inner boundary conditions and updated values for the number of ionizing photons. Special emphasis is placed on the formation of

Xianzhen Luo, Jinyang Huang, Wenzhen Zheng, Qingfu Zhu

Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, unstructured collections of wrong codes, suffering from high computational costs and score inflation. Furthermore, they inadvertently reward generators that detect common, trivial b

Debjyoti Biswas, Prabha Mandayam

Quantum error correction (QEC) is an essential tool for quantum computing that enables reliable information processing in the presence of noise. Syndrome measurements play a central role in QEC, making it possible to unambiguously identify the location and type of errors. While syndrome extraction is natural for conventional QEC protocols, where the errors s

Abhijit Chakraborty, Randy Lewis, Christine A. Muschik

Symmetry is fundamental to physical laws across different scales$\unicode{x2014}$from spacetime structure in general relativity to particle interactions in quantum field theory. Local symmetries, described by gauge theories, are central to phenomena such as superconductivity, topological phases, and the Standard Model of particle physics. Emerging simulation

Stamatis Dostoglou, Petros Valettas

We study the local non-extendability of random power series beyond their disk of convergence. We show that random power series formed by independent coefficients which are asymptotically anti-concentrated admit the circle of radius of convergence as strong natural boundary, even in a Nevanlinna sense. Our results extend previous work of Breuer and Simon (201

Stephan Lukasczyk, Gordon Fraser

Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a high demand of resources. Meta-heuristic

M. Scalco, M. Salaris, L. R. Bedin, S. Blouin

We present a detailed analysis of the white dwarf cooling sequence (WD CS) in omega Centauri based on combined Hubble Space Telescope (HST) and JWST observations. Our analysis confirms the previously reported split - based on HST observations in ultraviolet filters - in the upper part of the WD CS, consistent with the presence of two distinct WD populations,

Dmitry Pasechnyuk-Vilensky, Dmitry Kamzolov, Martin Takáč

We study finite-sum non-convex optimization $\min_{x\in\mathbb{R}^d} F(x) \;=\; \frac{1}{n}\sum_{i=1}^n f_i(x)$ and analyze a variance-reduced cubic Newton method based on EMA-smoothed SARAH estimators for both gradient and Hessian information. The method combines a coarse stochastic backbone with a terminal homotopy refinement: once the iterates enter a cer

Yifei Dong, Fengyi Wu, Guangyu Chen, Lingdong Kong

Enabling embodied agents to imagine future states is essential for robust and generalizable visual navigation. Yet, state-of-the-art systems typically rely on modular designs that decouple navigation planning from visual world modeling, which often induces state-action misalignment and weak adaptability in novel or dynamic scenarios. We propose UniWM, a unif

Zheheng Liu, Nicola Vigano, Henry Proudhon, Wolfgang Ludwig

Topo-Tomography (TT) is a synchrotron-based X-ray diffraction imaging technique used to characterize grain shape and crystal orientation in polycrystalline samples. This work aims to provide a decisive and fundamental understanding of 3D grain shape and orientation field reconstruction from TT diffraction data. We derive mathematical expressions for the TT p

Jiachen Jiang, Zhen Qin, Zhihui Zhu

Channel equalization is fundamental for mitigating distortions such as frequency-selective fading and inter-symbol interference. Unlike standard supervised learning approaches that require costly retraining or fine-tuning for each new task, in-context learning (ICL) adapts to new channels at inference time with only a few examples. However, existing ICL-base

Li Zhang, Matthias Grabmair, Morgan Gray, Kevin Ashley

Case-based reasoning is a cornerstone of U.S. legal practice, requiring professionals to argue about a current case by drawing analogies to and distinguishing from past precedents. While Large Language Models (LLMs) have shown remarkable capabilities, their proficiency in this complex, nuanced form of reasoning needs further investigation. We propose a forma

Maxwell Rosenthal

This paper develops a prior-free model of data-driven decision making in which the decision maker observes the entire distribution of signals generated by a known experiment under an unknown distribution of the state variable and evaluates actions according to their worst-case payoff over the set of state distributions consistent with that observation. We sh

Linli Shi

We prove the motivic classes in the motivic cohomology groups of Picard modular surfaces with non-trivial coefficients constructed in a paper of Loeffler\textendash Skinner\textendash Zerbes are in the motivic cohomology groups of the interior motives. Then we establish a relation between the motivic classes and non-critical values of the motivic $L$-functio

Eduardo Baena, Han Yang, Dimitrios Koutsonikolas, Israat Haque

Smart homes are increasingly populated with heterogeneous Internet of Things (IoT) devices that interact continuously with users and the environment. This diversity introduces critical challenges in device identification, authentication, and security, where fingerprinting techniques have emerged as a key approach. In this survey, we provide a comprehensive a

Yonatan Gutman, Qiang Huo, Masaki Tsukamoto

We prove that a continuous action of $\mathbb{R}^n$ on a compact metrizable space equivariantly embeds into the shift action on the space of one-Lipschitz functions from $\mathbb{R}^n$ to $[0,1]$ if and only if the set of fixed points topologically embeds in $[0,1]$. This is a Lipschitz refinement of classical dynamical embedding theorems of Bebutov, Kakutan

Noah Steinkrüger, Nisarga Nilavadi, Wolfram Burgard, Tanja Katharina Kaiser

Object transportation in cluttered environments is a fundamental task in various domains, including domestic service and warehouse logistics. In cooperative object transport, multiple robots must coordinate to move objects that are too large for a single robot. One transport strategy is pushing, which only requires simple robots. However, careful selection o

N. Kaiser

In these notes the relativistic $n$-body phase-phase is calculated iteratively in $2+1$ space-time dimensions for all $n$. The obtained result shows a simple power-law behavior $\alpha_n (\mu-M)^{n-2}/\mu$ with a dependence only on the total mass $M=m_1+\dots + m_n$. As a consequence of this feature, the $(n-1)$-loop integrals $J_n(-q^2)$ associated to sunse

Linfeng Wang, Susana Campino, Taane G. Clark, Jody E. Phelan

Positive selection drives the emergence of adaptive mutations in Mycobacterium tuberculosis, shaping drug resistance, transmissibility, and virulence. Phylogenetic trees capture evolutionary relationships among isolates and provide a natural framework for detecting such adaptive signals. We present a phylogeny-guided graph attention network (GAT) approach, i

Sarafina Ford

It is known that automorphisms of finite-dimensional bound quiver algebras decompose into inner automorphisms and automorphisms which permute the vertices. In this paper, we show that for string algebras, automorphisms permuting vertices further decompose into a graded automorphism and a composition of certain types of exponential automorphisms. Moreover, th

Zhuolun Li, Haluk Sonmezler, Faiza Shirazi, Febin Shaji

Ensuring ballot secrecy is critical for fair and trustworthy electronic voting systems, yet achieving strong secrecy guarantees in decentralized, large-scale elections remains challenging. This paper proposes the concept of collectively secure voting, in which voters themselves can opt in as secret holders to protect ballot secrecy. A practical blockchain-ba

Jeremy L. Smallwood, William DeRocco, Zhizhen Qin, Antranik A. Sefilian

The nearby binary star system 99 Herculis (99 Her) is host to the only known polar-aligned circumbinary debris disc. We investigate the hypothesis that the narrow structure of this circumbinary disc is sculpted by the gravitational influence of one or more unseen polar circumbinary planets. We first establish the theoretically viable parameter space for a sc

Terry Yue Zhuo, Xiaolong Jin, Hange Liu, Juyong Jiang

Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain, manually examining the quality of LLM-generated content is extremely challenging, as it requires understanding long chunks of raw code and deliberately simulating code execution. T

Yunzhen Feng, Parag Jain, Anthony Hartshorn, Yaqi Duan

Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models (LLMs) on reasoning tasks, with Group Relative Policy Optimization (GRPO) widely used in practice. Yet GRPO wastes substantial compute on negative groups: groups in which no sampled response is correct yield zero advantage and thus no gradie

C. Evan Davis, Jonathan J. Fortney, Aishwarya Iyer, Sagnick Mukherjee

We extend the Sonora Diamondback brown dwarf evolution models to higher effective temperatures to treat the evolution of younger, higher mass objects. Due to an upper temperature limit of $T_\mathrm{eff}=$2400 K in the original Sonora Diamondback model grid, high mass objects ($M\geq$ 0.05 $M_\mathrm{\odot}=$ 52.4 $M_\mathrm{J}$) were limited to ages of $\gt

Seigo Kikura, Hayato Goto, Fumiya Hanamura, Takao Aoki

We propose a single-shot conditional displacement gate between a trapped atom as the control qubit and a traveling light pulse as the target oscillator, mediated by an optical cavity. Classical driving of the atom synchronized with the light reflection off the cavity realizes the single-shot implementation of the crucial gate for the universal control of hyb

Jessica Fintzen

An explicit understanding of the (category of all smooth, complex) representations of p-adic groups provides an important tool not just within representation theory. It also has applications to number theory and other areas, and, in particular, it enables progress on various different forms of the Langlands program. In this write-up of the author's ECM 2024

J. Kammerer, T. O. Winterhalder, S. Lacour, T. Stolker

Direct observations of exoplanet and brown dwarf companions with near-infrared interferometry, first enabled by the dual-field mode of VLTI/GRAVITY, provide unique measurements of the objects' orbital motions and atmospheric compositions. Here, we compile a homogeneous library of all exoplanet and brown dwarf K-band spectra observed by GRAVITY thus far. We r

Predrag Nikolić

Raman scattering is emerging as a surprising probe of electron topology in quantum materials. It has been used recently to detect and characterize a topological phase transition that accompanies the magnetic transition in Nd$_2$Ir$_2$O$_7$. Here we present a theory of Raman scattering from nodal electrons with Weyl and quadratic band touching spectra, which