October 2025 arXiv papers — page 207

Michael A. Bender, Alex Conway, Martín Farach-Colton, Rob Johnson

Filters such as Bloom, quotient, and cuckoo filters are fundamental building blocks providing space-efficient approximate set membership testing. However, many applications need to associate small values with keys-functionality that filters do not provide. This mismatch forces complex workarounds that degrade performance. We argue that maplets-space-efficien

Mario Carcamo, Sebastian Franco, Dongwook Ghim, Georgios P. Goulas

We extend the study of relevant deformations connecting 2d (0,2) gauge theories on D1-branes probing toric Calabi-Yau 4-folds beyond pure mass deformations. The underlying geometry provides powerful insights when field-theoretic tools are still lacking. We observe that the volume of the Sasaki-Einstein base of the Calabi-Yau 4-fold grows towards the IR, sign

Wei-Ting Tang, Akshay Kudva, Joel A. Paulson

Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targets a (modified) Newton step by jointly learning gradient and Hessian information with Gaussian process (GP) surrogates, and selecting evaluations via a one-step lookahead bound on

Garima Rajguru, Lea Marcotulli, Marco Ajello, Mattia Di Mauro

We have utilized the largest sample of $\gamma$-ray selected Fermi flat-spectrum radio quasars (FSRQs) ever used (519 sources) to construct the luminosity function and its evolution through the cosmic history. In addition to spanning large redshift ($0<z\lesssim 4$) and luminosity ranges ($2.9\times10^{43}$ erg s$^{-1}$ - $7.3\times10^{48}$ erg s$^{-1}$), th

Tobias Johnson, Jacob Richey

We consider the activated random walk particle system, a model of self-organized criticality, on $\mathbb{Z}$ with i.i.d.-Bernoulli initial configuration. We show that at subcritical density, the system's odometer function, which counts the number of actions taken at each site, has a stretched exponential tail. It follows that the expected odometer at each s

Munehito Shoda, Kyogo Tokoro, Daikou Shiota, Shinsuke Imada

The potential field source surface (PFSS) method is a widely used magnetic field extrapolation technique in the space weather community. The only free parameter in the PFSS method is the source-surface height ($R_{\rm SS}$), beyond which all field lines are open. Although $R_{\rm SS}$ is known to vary with solar activity, there is no consensus on how to dete

Xun-Jiang Luo, Jin-Xin Hu, Meng-Li Hu, K. T. Law

Odd-parity magnets (OPMs) have recently emerged as a new magnetic class, but their general symmetry criteria remain elusive. In this Letter, we establish these criteria through a comprehensive spin group symmetry analysis. Concretely, we identify eight distinct symmetry-driven cases that support OPMs with collinear, coplanar, or noncoplanar magnetic order. T

Aavid Mathrawala, Dhruv Kurup, Josie Lau

Current pain assessment within hospitals often relies on self-reporting or non-specific EKG vital signs. This system leaves critically ill, sedated, and cognitively impaired patients vulnerable to undertreated pain and opioid overuse. Electroencephalography (EEG) offers a noninvasive method of measuring brain activity. This technology could potentially be ap

Shunpei Norihama, Yuka Iwane, Jo Takezawa, Simo Hosio

Writing about personal experiences can improve well-being, but for family caregivers, fixed or user-initiated schedules often miss the right moments. Drawing on Construal Level Theory, we conducted a three-week field study with 47 caregivers using a chatbot that delivered daily reflective writing prompts and captured temporal, spatial, and social contexts. W

Sanjay Deshpande, Jakub Szefer

Quantum computing is rapidly emerging as one of the most transformative technologies of our time. With the potential to tackle problems that remain intractable for even the most powerful classical supercomputers, quantum hardware has advanced at an extraordinary pace. Today, major platforms such as IBM Quantum, Amazon Braket, and Microsoft Azure provide clou

Bao-An Li

Nuclear symmetry energy $E_{\mathrm{sym}}(\rho)$ encoding the cost to make nuclear matter more neutron rich has been the most uncertain component of the EOS of dense neutron-rich nucleonic matter. It affects significantly the radii, tidal deformations, cooling rates and frequencies of various oscillation modes of isolated neutron stars as well as the strain

Grigor Tukharyan, William Reed Kendrick, Areg Danagoulian, Benoit Forget

Long lived fission products represent a major challenge in nuclear waste management due to persistent radiotoxicity over very long timescales. This study focuses on six of these fission products: Se-79, Zr-93, Tc-99, Sn-126, I-127, Cs-135. This study investigates the feasibility of spallation driven transmutation, in which a high energy proton beam strikes a

James Dickens

Recent research into human action recognition (HAR) has focused predominantly on skeletal action recognition and video-based methods. With the increasing availability of consumer-grade depth sensors and Lidar instruments, there is a growing opportunity to leverage dense 3D data for action recognition, to develop a third way. This paper presents a novel appro

Hokuto Konno, Abhishek Mallick, Masaki Taniguchi

We prove that there exist infinitely many contractible compact smooth $4$-manifolds $C$ that admit absolutely exotic diffeomorphisms of infinite order in $\pi_0(\mathrm{Diff}(C))$. By ``absolutely", we mean that isotopies are not required to be relative to the boundary. This follows from a theorem that produces absolutely exotic diffeomorphisms from relative

Jinho Cha, Justin Yu, Eunchan Daniel Cha, Emily Yoo

Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly

Na Wang, Yueling Yang, Junfeng Sun

Inspired by the promising prospect of the $B_{c}(2S)$ meson at the coming HL-LHC experiments, the nonleptonic $B_{c}(2S)$ meson weak decays induced by both the $b$ and $c$ decays are investigated with the QCD factorization approach. It is found that branching ratios for the color- and CKM-favored $B_{c}(2S)$ ${\to}$ $B_{s}{\rho}$, $B_{s}{\pi}$ decays can rea

Tianyi Xiao, Junjie Liu

Electron-photon hybrid systems serve as ideal light-matter interfaces with broad applications in quantum technologies. These systems are typically operated dynamically under nonequilibrium conditions, giving rise to coupled electronic and photonic currents. Understanding the joint fluctuation behavior of these currents is essential for assessing the performa

Andreas-Stephan Elsenhans, John Voight

We describe the computation of class groups and unit groups of number fields as implemented in Magma (V2.29). After quickly reviewing the main algorithms based on factor bases, relation collection, and analytic class number evaluation, we distinguish their behavior across formalizable, rigorous, GRH-conditional, and heuristic regimes.

Giordano Cotti

This work investigates the vertical quantum cohomology and quantum spectra of flag bundles, uncovering new links between the Gromov-Witten theory of homogeneous fibrations and analytic number theory. Building on previous constructions by Astashkevich and Sadov (arXiv:hep-th/9401103) and by Biswas, Das, Oh, and Paul (arXiv:2408.06616), we establish functorial

Ceyhun Efe Kayan, Li Zhang

Despite impressive breadth, LLMs still rely on explicit reasoning instructions or static, one-fits-all steering methods, leaving a gap for adaptive, instruction-free reasoning amplification. We present Prototype-Based Dynamic Steering (PDS), a test-time method that amplifies large language model (LLM) reasoning without adding or altering instructions. We int

Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou

Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open-weight models, achieving remarkable model capability similar to proprietary ones. But their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit LLM serving systems. To understa

Tadashi Wadayama

We present a numerical method to evaluate mutual information (MI) in nonlinear Gaussian noise channels by using denoising score matching (DSM) learning for estimating the score function of channel output. Via de Bruijn's identity, Fisher information estimated from the learned score function yields accurate estimates of MI through a Fisher integral representa

Michal Shavit, Fabio Pusateri, Zhou Zhang, Yulin Pan

In weakly nonlinear dispersive wave systems, long-time dynamics are typically governed by time resonances, where wave phases evolve coherently due to exact frequency matching. Recent advances in spatio-temporal spectrum measurements, however, reveal prominent features that go beyond the predictions of time resonance theory. In this work, we develop a theoret

Shadikur Rahman, Hasibul Karim Shanto, Umme Ayman Koana, Syed Muhammad Danish

In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a l

Yang Cao, Zhao Song, Jiahao Zhang, Jiale Zhao

Graph neural networks (GNNs) have become a core paradigm for learning on relational data. In materials science, equivariant GNNs (EGNNs) have emerged as a compelling backbone for crystalline-structure prediction, owing to their ability to respect Euclidean symmetries and periodic boundary conditions. Despite strong empirical performance, their expressive pow

L. Li, C. A. Morales, B. Shin

We prove that every dynamically coherent plaque expansive partially hyperbolic diffeomorphism is topologically stable with respect to the central foliation (in short, {\em plaque topologically stable}). Next, we study partially hyperbolic diffeomorphisms that are both expansive and topologically stable with respect to a central foliation. We show that the ce

Zhuoyi Huang, Nutan Sahoo, Anamika Kumari, Girish Kumar

The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offers a promising solution, the real-world use of existing model-synthesized ECGs is limited by persistent gaps in trustworthiness and clinical utility. In this work, we address two ma

Zichong Li, Liming Liu, Chen Liang, Weizhu Chen

The choice of optimizer significantly impacts the training efficiency and computational costs of large language models (LLMs). Recently, the Muon optimizer has demonstrated promising results by orthogonalizing parameter updates, improving optimization geometry through better conditioning. Despite Muon's emergence as a candidate successor to Adam, the potenti

Zhoutong Fu, Yihan Cao, Yi-Lin Chen, Aman Lunia

Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking platforms, introduces distinct challenges. At LinkedIn, the job person fit task requires analyzing a candidate's public

Reza T. Batley, Sourav Saha

Training - the optimisation of complex models - is traditionally performed through small, local, iterative updates [D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature 323, 533-536 (1986)]. Approximating solutions through truncated gradients is a paradigm dating back to Cauchy [A.-L. Cauchy, Comptes Rendus Math\'ematique 25, 536-538 (1847)] and Newton [I.

Peizhi Yan, Rabab Ward, Qiang Tang, Shan Du

3D Gaussian Splatting (3DGS) has enabled photorealistic and real-time rendering of 3D head avatars. Existing 3DGS-based avatars typically rely on tens of thousands of 3D Gaussian points (Gaussians), with the number of Gaussians fixed after training. However, many practical applications require adjustable levels of detail (LOD) to balance rendering efficiency

Jinho Cha, Sahng-Min Han, Long Pham

Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian logistics. This study develops an optimization framework com

Cassie Huang, Stuti Mohan, Ziyi Yang, Stefanie Tellex

LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning domain and problem in a formal language that can derive plans deterministically. However, both lines of work rely on standard benchmarks that include only generic and simplistic environmental specifications, leading

Adam Filipek

Modern natural language processing models have achieved unprecedented scale, yet the tools for their evaluation often remain a computational bottleneck, limiting the pace of research. This is particularly acute for in-training evaluation metrics, such as per-sentence reward signals in Reinforcement Learning, which must operate efficiently on batches of token

Abhejay Murali, Saleh Afroogh, Kevin Chen, David Atkinson

Current safety alignment for Large Language Models (LLMs) implicitly optimizes for a "modal adult user," leaving models vulnerable to distributional shifts in user cognition. We present ChildSafe, a benchmark that quantifies alignment robustness under cognitive shifts corresponding to four developmental stages. Unlike static persona-based evaluations, we int

Michael S. Turner

The current cosmological paradigm, $\Lambda$CDM, is characterized its expansive description of the history of the Universe, its deep connections to particle physics and the large amounts of data that support it. Nonetheless, $\Lambda$CDM's critics argue that it has been falsified or must be discarded for various reasons. Critics and boosters alike do agree o

Luke Thompson, Davy Guan, Dai Shi, Slade Matthews

Molecular dynamics (MD) simulations underpin modern computational drug discovery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predictions without the need to repeatedly solve quantum mechanical forces, enabling significant speedups over conventional pipelines. Yet many such methods typically enforce strict equ

Vivek Kumar, JSM Quintero, Aleksey Baldygin, Paul Molina

Dynamic surface tension measurements play a critical role in interfacial activities for liquids with varying viscosities. Understanding the rate at which the interface attains the equilibrium, for surface tension measurements, after the formation of a new interface is of significant interest. Although surface tension is independent of viscosity, the time req

Xin-Cheng Wen, Zirui Lin, Yijun Yang, Cuiyun Gao

The exponential increase in software vulnerabilities has created an urgent need for automatic vulnerability repair (AVR) solutions. Recent research has formulated AVR as a sequence generation problem and has leveraged large language models (LLMs) to address this problem. Typically, these approaches prompt or fine-tune LLMs to generate repairs for vulnerabili

Kim Bente, Roman Marchant, Fabio Ramos

To reliably project future sea level rise, ice sheet models require inputs that respect physics. Embedding physical principles like mass conservation into models that interpolate Antarctic ice flow vector fields from sparse & noisy measurements not only promotes physical adherence but can also improve accuracy and robustness. While physics-informed neural ne

Brenden Roberts, Jin Ming Koh, Yi Tan, Norman Y. Yao

The existence of self-correcting quantum memories in three dimensions is a long-standing open question at the interface between quantum computing and many-body physics. We take the perspective that large contributions to the entropy arising from fine-tuned spatial symmetries, including the assumption of an underlying regular lattice, are responsible for fund

Masato Takiguchi, Takuro Fujii, Hisashi Sumikura, Akihiko Shinya

We report the demonstration of continuous-wave (CW) lasing at room temperature from a III-V semiconductor nanowire integrated into a Si photonic crystal (PhC) cavity. Conventional hybrid nanowire lasers [M. Takiguchi. et.al., APL Photonics, 2, 046106 (2017)], which typically feature circular nanowire-cross-sections, suffer from a weak optical confinement, pr

Xi Wang, Bin Ma, Jongryool Kim, Byungil Koh

Message Passing Interface (MPI) is a foundational programming model for high-performance computing. MPI libraries traditionally employ network interconnects (e.g., Ethernet and InfiniBand) and network protocols (e.g., TCP and RoCE) with complex software stacks for cross-node communication. We present cMPI, the first work to optimize MPI point-to-point commun

Fabio Bagarello, Francesco Gargano, Polina Khrennikova

We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of machine learning techniques, including reinforcement learning and deep neural networks, in financial statement an

Marios Mertzanidis, Athina Terzoglou

Myerson's seminal characterization of the revenue-optimal auction for a single item \cite{myerson1981optimal} remains a cornerstone of mechanism design. However, generalizing this framework to multi-item settings has proven exceptionally challenging. Even under restrictive assumptions, closed-form characterizations of optimal mechanisms are rare and are larg

Wilson J. Yánez-Parreño, Teun A. J. van Schijndel, Anthony P. McFadden, Kaixuan Ji

We explore the molecular beam epitaxy synthesis of superconducting aluminum thin films grown on c-plane sapphire substrates at cryogenic temperatures of 6 K and compare their behavior with films synthesized at room temperature. We demonstrate that cryogenic growth increases structural disorder, producing crystalline grains that modify the optical, electrical

Xiantao Li, Chunhao Wang

Linear response functions are a cornerstone concept in physics as they enable efficient estimation of many dynamical properties. In addition to predicting dynamics of observables under perturbations without resimulating the system, these response functions lead to electric conductivity, magnetic susceptibility, dielectric constants, etc. Estimating two-time

Katrijn Everaert, Saipriya Satyajit, Jiashen Tang, Zechuan Yin

Magnetic response measurements in the presence of AC drive fields provide critical insight into the properties of magnetic and conductive materials, such as phase transitions in two-dimensional van der Waals magnets, the heating efficiency of magnetic nanoparticles in biological environments, and the integrity of metals in eddy current testing. Nitrogen-vaca

Tsung-Ju Lee, Bong H. Lian, Shing-Tung Yau

We continue our study on the pairs of singular Calabi--Yau varieties arising from double covers over semi-Fano toric manifolds. In this paper, we first investigate singular CY double covers of \(\mathbb{P}^{3}\) branched along (1) a union of eight hyperplanes in general position, and (2) a union of four hyperplanes and a quartic in generation. Our previous c

Gerhard Schindl

We introduce new weighted $L^p$-type spaces defined in terms of weight function matrices and characterize the inclusion relations in terms of the defining matrices. Moreover, we provide a detailed study concerning the coincidence with the common (non-weighted) $L^p$-spaces, the (non-)triviality of such weighted spaces and investigate their translation invari

Yurun Song, Zhuoyi Yang, Ian G. Harris, Sangeetha Abdu Jyothi

Large Language Models (LLMs) are scaling rapidly, creating significant challenges for collaborative server client distributed training, particularly in terms of communication efficiency and computational overheads. To address these challenges, we implement Parameter-efficient Split Learning, which effectively balances efficiency and performance for collabora

A. Mućka, A. Romanowska

Dyadic rationals are rationals whose denominator is a power of 2. A dyadic n-dimensional convex set is defined as the intersection with n-dimensional dyadic space of an n-dimensional real convex set. Such a dyadic convex set is said to be a dyadic n-dimensional polytope if the real convex set is a polytope whose vertices lie in the dyadic space. Dyadic conve

Diego A. Martínez-Valera

In this work, we prove that the classical Schwarzschild-de Sitter spacetime is an exact solution of a class of weakly non-local, UV finite conformal quantum gravity theories, without the necessity of including a cosmological constant term in the action, thus associating the effective cosmological constant $\Lambda$ appearing in the metric with the coupling c

Osman Tugay Basaran, Falko Dressler

Artificial intelligence (AI)-native radio access networks (RANs) will serve vertical industries with stringent requirements: smart grids, autonomous vehicles, remote healthcare, industrial automation, etc. To achieve these requirements, modern 5G/6G design increasingly leverage AI for network optimization, but the opacity of AI decisions poses risks in missi

Roman Ya. Kezerashvili

Propellantless propulsion refers to methods of space travel that do not require onboard propellant, instead relying on natural forces or external energy sources. In this paper, I review different approaches that have been explored and discuss the pros and cons of each method for interstellar space exploration. Gravitational assist uses planetary gravity to c

J. J. González-Avilés

In this paper, we present a numerical simulation of an impulsively driven chromospheric jet in the solar atmosphere using the non-ideal magnetohydrodynamic (MHD) equations coupled with frequency- and angle-averaged radiation transport equations. These include the dynamics of the radiation energy density and radiation flux. The jet is initiated by a localized

Elena Corbae, Rong Zhang, Cong Li, Kunihiro Kihou

While the superconducting transition temperature of hole-doped Ba_{1-x}K_{x}Fe_{2}As_{2} decreases past optimal doping, superconductivity does not completely disappear even for the fully doped KFe_{2}As_{2} compound. In fact, superconductivity is robust through a Lifshitz transition where electron bands become hole-like around the zone corner at around x=0.7

G. P. Nikopoulos, D. Watson, A. Sneppen, V. Rusakov

Little Red Dots (LRDs) are a new class of compact extragalactic objects, with a v-shaped optical spectral energy distribution breaking close to the Balmer break wavelength, and broad, typically exponentially-shaped lines. They are believed to be supermassive black holes surrounded by very dense, ionized gas, leading us to explore for any departures from Case

Veronika C. Stangier, Mathias S. Scheurer, Daniel E. Sheehy, Jörg Schmalian

We demonstrate that superconductivity driven by strong quantum-critical fluctuations can emerge near relativistic Mott transitions in twisted two-dimensional materials, taking on a remarkably rich character. In twisted double-bilayer WSe$_2$, all time-reversal-even, gap-opening collective modes promote pairing, whereas time-reversal-odd modes do not. In a Di

Tiago de Conto, John Armston, Ralph Dubayah

Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution m

Aleksandra Szczupak, Grzegorz Cios, Benedykt R. Jany

Controlling optical and tribological properties of metal surfaces, like color and wear rate, without altering their chemical composition is a highly desirable process across numerous fields of science and industry. It represents a cost-effective alternative to traditional chemical methods, particularly for copper, one of the most important metals widely used

Z. Zhang, K. Shukla, Z. Wang, A. Morales

Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions of both mean flow and turbulence statistics. Here, we consider flow past a circular cylinder over a range of Reynolds numbers (3,900-100,000) and Mach numbers (0-0.3), encompassin

Ronaldo Nombakuse, Nils Messerschmidt, Pitso Tsibolane, Muhammad Irfan Khalid

Our study explores how intelligent assistive technologies (IATs) can enable visually impaired people (VIPs) to overcome barriers to inclusion in a digital society to ultimately improve their quality of life. Drawing on the Social Model of Disability (SMD), which frames disability as a consequence of social and institutional barriers rather than individual im

Sven Koehler, Sarah Kaye Mueller, Jonathan Kiekenap, Gerald Greil

Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase analysis. Accurate cardiac keyframe detection can eliminate this problem. However, automatic methods solely derive end-systole (ES) and end-diastole (ED) frames from left ventricular

Kyu-Young Kim, Heewoo Kim, Dong Hyun Park, Jinhyuk Bea

Hybrid quantum systems play a crucial role in advancing scalable and versatile quantum networks as they combine the strengths of different quantum platforms. An important challenge for the development of hybrid quantum networks lies in interfacing heterogeneous quantum nodes and distributing entanglement among them. Single photons emitted from these dissimil

Martin Costabel, Monique Dauge

It is well known that derivatives of solutions to elliptic boundary value problems may become unbounded near the corner of a domain with a conical singularity, even if the data are smooth. When the corner domain is approximated by more regular domains, then higher order Sobolev norms of the solutions on these domains can blow up in the limit. We study this b

Luca Luceri, Tanishq Vijay Salkar, Ashwin Balasubramanian, Gabriela Pinto

Detecting coordinated inauthentic behavior (CIB) is central to the study of online influence operations. However, most methods focus on text-centric platforms, leaving video-first ecosystems like TikTok largely unexplored. To address this gap, we develop and evaluate a computational framework for detecting CIB on TikTok, leveraging a network-based approach a

Patara Trirat, Jae-Gil Lee

The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality prediction. Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however

Elizabeth Pratt

The Segre determinant is a polynomial which encodes the condition for points to lie on a bilinear hypersurface in the product of projective spaces. We study Segre determinants and compute them in various coordinate systems. We show that the Segre determinant represents the Chow-Lam form of a generic torus orbit in the Grassmannian. These Chow-Lam forms were

Jan A. Bergstra, Alban Ponse

Partial algebras and datatypes are discussed with the use of signatures that allow partial functions, and a three-valued short-circuit (sequential) first order logic with a Tarski semantics. The propositional part of this logic is also known as McCarthy calculus and has been studied extensively. Axioms for the fracterm calculus of partial meadows are given.

Aman Gupta, Denny O'Shea, Fazl Barez

Large language models (LLMs) are increasingly being used for tasks where outputs shape human decisions, so it is critical to verify that their responses consistently reflect desired human values. Humans, as individuals or groups, don't agree on a universal set of values, which makes evaluating value alignment difficult. Existing benchmarks often use hypothet

Patrick King, Mikhail Kotchetov

Self-dual binary linear codes have been extensively studied and classified for length n <= 40. However, little attention has been paid to linear codes that coincide with their orthogonal complement when the underlying inner product is not the dot product. In this paper, we introduce an alternating form defined on F_2^n and study codes that are maximal totall

Ivan Guo, Jan Obłój

We consider the robust pricing and hedging of American options in a continuous time setting. We assume asset prices are continuous semimartingales, but we allow for general model uncertainty specification via adapted closed convex constraints on the volatility. We prove the robust pricing-hedging duality. When European options with given prices are available

Attila Bérczes, Subham Bhakta, Lajos Hajdu, Alina Ostafe

Let $E_1, \ldots, E_s $ be $s$, not necessary distinct, elliptic curves over $\mathbb{Q}$. We give upper bounds on the frequency of $s$-tuples of points in $E_1(\mathbb{Q})\times \ldots \times E_s(\mathbb{Q})$ whose denominators or $x$-coordinates are multiplicatively dependent. More precisely, we give such bounds in two scenarios: one in which we fix $s$ no

Nicolaus Kratochwil, Emilie Roncali, Gerard Arino-Estrada

Modeling the timing performance of light-based radiation detectors accurately is essential for optimizing time-of-flight positron emission tomography (TOF-PET). We present an analytic framework that combines existing models to predict the timing behavior of high-aspect ratio crystals, including contributions from prompt photons such as Cherenkov radiation. T

Jabari Hastings, Prasanna Ramakrishnan

We consider the matching problem in the metric distortion framework. There are $n$ agents and $n$ items occupying points in a shared metric space, and the goal is to design a matching mechanism that outputs a low-cost matching between the agents and items, using only agents' ordinal rankings of the candidates by distance. A mechanism has distortion $\alpha$

Erin Bevilacqua, Lewis Bowen

We establish general criteria for a countable group $\Gamma$ to have fixed price 1 depending on a choice of left-invariant proper metric on $\Gamma$. We apply this criterion to show that if $\Gamma_1,\Gamma_2$ are two countable groups satisfying a certain growth condition then $\Gamma_1\times \Gamma_2$ has fixed price 1. For example, $\Gamma\times \Gamma$ ha

Akhil Deo, Kate Sanders, Benjamin Van Durme

Making theory-of-mind inferences from human dialogue is a strong indicator of a model's underlying social abilities, which are fundamental for adept AI assistants. However, large language and reasoning models struggle to understand sophisticated social phenomena in transcript data, such as sarcasm and irony. To assess the weaknesses of current models and to

Mukul Singh, Somya Chatterjee, Arjun Radhakrishna, Sumit Gulwani

As artificial intelligence systems increasingly collaborate with humans in creative and technical domains, questions arise about the cognitive boundaries and biases that shape our shared agency. This paper investigates the Dunning-Kruger Effect (DKE), the tendency for those with limited competence to overestimate their abilities in state-of-the-art LLMs in c

Ming Gao, Zhanglin Shangguan, Shuo Liu, Liang Wu

This paper proposes a cascaded control framework for quadrotor trajectory tracking with formal safety guarantees. First, we design a controller consisting of an outer-loop position model predictive control (MPC) and an inner-loop nonlinear attitude control, enabling decoupling of position safety and yaw orientation. Second, since quadrotor safety constraints

Soonwoo Kwon, Liyang Sun

Specifications that impose constant treatment effects are common and can be biased under heterogeneity, whereas fully flexible alternatives can be imprecise or infeasible. Under a bound on the target-weighted variance of treatment effects, this paper proposes a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence inter

Arpit Kapoor, Rohitash Chandra

Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unravelled the potential for combining hydrological models with deep learning models for better interpretability and improved predictive performance. In our previous work, we introduced

Benjamin Plumridge, Cory Hauck, Steffen Schotthofer

We investigate a data-driven approach for tuning the filtered spherical harmonics method (\fpn) when solving the radiation transport equation (RTE). The \fpn method extends the classical spherical harmonics approach (\pn) by introducing regularization through a filter operator, which mitigates spurious oscillations caused by Gibbs' phenomenon. This filter in

Fadi Al Machot, Fidaa Al Machot

Deep transformer models excel at multi-label text classification but often violate domain logic that experts consider essential, an issue of particular concern in safety-critical applications. We propose a hybrid neuro-symbolic framework that integrates Answer Set Programming (ASP) with transformer-based learning on the Aviation Safety Reporting System (ASRS

Saul Goldman, Hong Yi Lin, Jirat Pasuksmit, Patanamon Thongtanunam

Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes. Understanding what types of generated review comments are likely to trigger code changes is crucial for identifying those that are actionable. In this paper, we set out to investig

Matthew Jörke, Defne Genç, Valentin Teutschbein, Shardul Sapkota

Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of HCI research on effective behavior change interactions, we present Bloom, an application for physical activity promotion that integrates an LLM-based health coaching chatbot with ex

Zhong-Peng Zhou

[This is an older version of the paper, which will be updated soon.] In the present paper, we continue our research on the generalized Fermat equation $x^r + y^s = z^t$ with signature $(r, s, t)$, where $r, s, t \ge 2$ are positive integers such that $\frac{1}{r} + \frac{1}{s} + \frac{1}{t} < 1$. All known positive primitive solutions for the generalized Fer

Ranjan Sapkota, Manoj Karkee

This paper presents a comprehensive overview of the Ultralytics YOLO(You Only Look Once) family of object detectors, focusing the architectural evolution, benchmarking, deployment perspectives, and future challenges. The review begins with the most recent release, YOLO26 (or YOLOv26), which introduces key innovations including Distribution Focal Loss (DFL) r

Simon Segert, Nathan Wycoff

Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $\Vert \cdot \Vert_*$. However, despite the assortment of computational methods for such problems, there is a surprising lack of understanding of the underlying probability distributions being referred to. In this article

Runlin Zhou, Chixiang Chen, Elynn Chen

We study meta-reinforcement learning in finite-horizon MDPs where related tasks share similar structures in their optimal action-value functions. Specifically, we posit a linear representation $Q^*_h(s,a)=\Phi_h(s,a)\,\theta^{(k)}_h$ and place a Gaussian meta-prior $ \mathcal{N}(\theta^*_h,\Sigma^*_h)$ over the task-specific parameters $\theta^{(k)}_h$. Buil

Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang

Large language models (LLMs) and agent-based frameworks have advanced rapidly, enabling diverse applications. Yet, with the proliferation of models and agentic strategies, practitioners face substantial uncertainty in selecting the best configuration for a downstream task. Prior studies show that different agents and backbones exhibit complementary strengths

Yao Dou, Michel Galley, Baolin Peng, Chris Kedzie

Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. Since human studies are costly, time-consuming, and hard to reproduce, recent work explores using LLMs to simulate users for automatic assistant evaluation. However, there

Shao-Yi Yu, Jen-Wei Wang, Maya Horii, Vikas Garg

Mobile robots, such as ground vehicles and quadrotors, are becoming increasingly important in various fields, from logistics to agriculture, where they automate processes in environments that are difficult to access for humans. However, to perform effectively in uncertain environments using model-based controllers, these systems require dynamics models capab

Zizhao Wang, Dingcheng Li, Vaishakh Keshava, Phillip Wallis

Large Language Model (LLM) agents can leverage tools such as Google Search to complete complex tasks. However, this tool usage introduces the risk of indirect prompt injections, where malicious instructions hidden in tool outputs can manipulate the agent, posing security risks like data leakage. Current defense strategies typically rely on fine-tuning LLM ag

Yiannis Charalambous, Claudionor N. Coelho, Luis Lamb, Lucas C. Cordeiro

This paper introduces UnitTenX, a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code, enhancing test coverage and critical value testing. UnitTenX leverages a combination of AI agents, formal methods, and Large Language Models (LLMs) to automate test generation, addressing the challenges posed by complex and le

Ran Canetti, Ephraim Linder, Connor Wagaman

We initiate an investigation of learning tasks in a setting where the learner is given access to two competing provers, only one of which is honest. Specifically, we consider the power of such learners in assessing purported properties of opaque models. Following prior work in complexity theory that considers the power of competing provers in various setting

Navdeep K. Panesar, Alphonse C. Sterling, Ronald L. Moore, Sanjiv K. Tiwari

We present detailed analysis of an active region coronal jet accompanying a minifilament eruption that is fully captured and well-resolved in high spatial resolution 174A coronal images from Solar Orbiters Extreme Ultraviolet Imager (EUI). The active region jet is simultaneously observed by the Interface Region Imaging Spectrograph (IRIS) and the Solar Dynam

Alexander James Fernandes, Ioannis Psaromiligkos

A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the number of bits required for transmitting phase shift information from the access point (AP) to the RIS controller. The AP computes the phase shifts and compresses them into a binary control message that is se

Kyung-Bin Kwon, Sayak Mukherjee, Veronica Adetola

This paper investigates the dynamic interactions between large-scale data centers and the power grid, focusing on reliability challenges arising from sudden fluctuations in demand. With the rapid growth of AI-driven workloads, such fluctuations, along with fast ramp patterns, are expected to exacerbate stressed grid conditions and system instabilities. We co

David E. J. van Wijk, Ersin Das, Tamas G. Molnar, Aaron D. Ames

Verifying the safety of controllers is critical for many applications, but is especially challenging for systems with bounded inputs. Backup control barrier functions (bCBFs) offer a structured approach to synthesizing safe controllers that are guaranteed to satisfy input bounds by leveraging the knowledge of a backup controller. While powerful, bCBFs requir