November 2025 arXiv papers — page 155

Lukas Rapp, Muriel Médard, Ken R. Duffy

Binary turbo product codes (TPCs) are powerful error-correcting codes constructed from short component codes. Traditionally, turbo product decoding passes log likelihood ratios (LLRs) between the component decoders, inherently losing information when bit correlation exists. Such correlation can arise exogenously from sources like intersymbol interference and

Bill Chunyuan Zheng, Vivek Myers, Benjamin Eysenbach, Sergey Levine

Learning how to reach goals in an environment is a longstanding challenge in AI, yet reasoning over long horizons remains a challenge for modern methods. The key question is how to estimate the temporal distance between pairs of observations. While temporal difference methods leverage local updates to provide optimality guarantees, they often perform worse t

Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar

Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results hig

B. A. Sanjoyo, M. Yunus, N. Hidayat

The Durand-Kerner algorithm is a widely used iterative technique for simultaneously finding all the roots of a polynomial. However, its convergence heavily depends on the choice of initial approximations. This paper introduces two novel approaches for determining the initial values: New bound 1 and the lambda maximal bound, aimed at improving the stability a

Xiaohan Zhang, Yan Ding, Yohei Hayamizu, Zainab Altaweel

Task planning and motion planning are two of the most important problems in robotics, where task planning methods help robots achieve high-level goals and motion planning methods maintain low-level feasibility. Task and motion planning (TAMP) methods interleave the two processes of task planning and motion planning to ensure goal achievement and motion feasi

Matthias R. Gaberdiel, Bin Guo

The CFT dual of string theory on $({\rm AdS}_3 \times {\rm S}^3)/\mathbb{Z}_k\times \mathbb{T}^4$ is believed to be described by the subspace of the symmetric orbifold of $\mathbb{T}^4$ that comprises the low-lying excitations on top of a certain reference state. (This `non-perturbative' reference state lies in the twisted sector associated to the conjugacy

Piotr Szwed, Paweł Skrzynski, Jarosław Wąs

The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the service area into zones that group regions with similar temporal patterns of vehicle presence and service dema

Nasir Kenarangui, Arthur Powalka, Laszlo Kish

Instantaneous Noise-Based Logic (INBL) presents a classical noise-based computing framework as an alternative to quantum computation, though some logic gates remain unimplemented for achieving universality over superpositions. INBL encodes M noise-bits using 2M orthogonal stochastic reference noises to construct a 2^M-dimensional product-based Hilbert space

Albin Cakaj, Markus Schmid, Alexander Hofmann, Wolfgang Brütting

Spontaneous orientation polarization (SOP) occurs when molecules with a finite permanent dipole moment are grown as thin films by physical vapor deposition and their alignment is such that a net non-zero polarization remains. We discuss how SOP in organic semiconductors can be controlled by the design of molecules as well as the film growth conditions and di

Peiqi Sui, Eamon Duede, Hoyt Long, Richard Jean So

LLMs hallucinate, yet some confabulations can have social affordances if carefully bounded. We propose critical confabulation (inspired by critical fabulation from literary and social theory), the use of LLM hallucinations to "fill-in-the-gap" for omissions in archives due to social and political inequality, and reconstruct divergent yet evidence-bound narra

Terence Tao

For any fixed dimension $d \geq 3$ we construct a Nikodym set in $F_q^d$ of cardinality $q^d - (\frac{d-2}{\log 2} +1+o(1)) q^{d-1} \log q$ in the limit $q \to \infty$, when $q$ is an odd prime power. This improves upon the naive random construction, which gives a set of cardinality $q^d - (d-1+o(1)) q^{d-1} \log q$, and is new in the regime where $F_q$ has

Vít Růžička, Gonzalo Mateo-García, Itziar Irakulis-Loitxate, Juan Emmanuel Johnson

Mitigating anthropogenic methane sources is one of the most cost-effective levers to slow down global warming. While satellite-based imaging spectrometers, such as EMIT, PRISMA, and EnMAP, can detect these point sources, current methane retrieval methods based on matched filters produce a high number of false detections requiring manual verification. To addr

Yifan Liu, Fangneng Zhan, Wanhua Li, Haowen Sun

Estimating robot pose from a monocular RGB image is a challenge in robotics and computer vision. Existing methods typically build networks on top of 2D visual backbones and depend heavily on labeled data for training, which is often scarce in real-world scenarios, causing a sim-to-real gap. Moreover, these approaches reduce the 3D-based problem to 2D domain,

Jian-Min Wang, Chen Hu, Yong-Jie Chen, Yu-Yang Songsheng

Delayed reverberations of broad emission lines in response to optical continuum variations have been widely observed in active galactic nuclei (AGNs). They serve as a powerful tool for probing inner structures of AGNs and estimating the masses of supermassive black holes (SMBHs). The delays exhibit a strong correlation with approximately the square root of t

Suyeon Son, Minjin Kim, Luis C. Ho, Ruancun Li

To investigate the physical origin of the Balmer decrement in the broad-line region of active galactic nuclei (AGNs), we measure the temporal variability of the fluxes of the broad H$\beta$ and H$\alpha$ emission lines using multi-epoch spectroscopic data of low-redshift AGNs from the Sloan Digital Sky Survey. The analysis of the mean spectra reveals that th

Siddhesh Pimpale

The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependenc

Jiale Liu, Haoming Zhou, Yishu Liu, Bingzhi Chen

Fine-grained image-text alignment is a pivotal challenge in multimodal learning, underpinning key applications such as visual question answering, image captioning, and vision-language navigation. Unlike global alignment, fine-grained alignment requires precise correspondence between localized visual regions and textual tokens, often hindered by noisy attenti

Lars Olt, Luis Diego Fonseca Flores, Ian Mckinley

Thermal Desktop (TD) is an industry-standard thermal analysis tool used to create and analyze thermal models for landers, rovers, spacecraft, and instrument payloads. Currently, limited software exists to extract and visualize metrics relevant to heat flow within TD, impeding thermal engineers from analyzing their results quickly. This paper discusses a grap

Zeyang Lu, Chan Li, Gang Wang, Zhu Cao

Quantum steering is a fundamental quantum correlation that plays a pivotal role in quantum technologies, but its verification crucially relies on precise measurements -- an assumption often undermined by practical imperfections. Here, we investigate multipartite steering verification under imprecise measurements and develop a quantitative method that effecti

Manonmani Sekar, Nasim Nezamoddini

Reconfigurable manufacturing systems (RMS) are critical for future market adjustment given their rapid adaptation to fluctuations in consumer demands, the introduction of new technological advances, and disruptions in linked supply chain sections. The adjustable hard settings of such systems require a flexible soft planning mechanism that enables realtime pr

Jamila Taaki, Lia Corrales, Alfred Hero

Astrometry measures shifts in a star's photocentre and can be used to detect reflex motion due to orbiting exoplanets. Brightness asymmetries (e.g. starspots) rotating in and out of view can also cause apparent motion of the photocenter, termed astrometric jitter, that has previously been considered a source of noise. Here, we explore whether it can be used

Zhuogen Li, Keyu Feng, Hector Gomez

Intrathecal (IT) injection is an effective way to deliver drugs to the brain bypassing the blood-brain barrier. To evaluate and optimize IT drug delivery, it is necessary to understand the cerebrospinal fluid (CSF) dynamics in the central nervous system (CNS). In combination with experimental measurements, computational modeling plays an important role in re

Yejin Yoon, Yuri Son, Namyoung So, Minseo Kim

Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two. Yet, real-world conversations naturally involve fluid transitions between these modes. To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transition-aware

Morgan K. Wall, Alexander J. Pattison, Edward S. Barnard, Stephanie M. Ribet

Recent improvements in large language models (LLMs) have had a dramatic effect on capabilities and productivity across many disciplines involving critical thinking and writing. The development of the model context protocol (MCP) provides a way to extend the power of LLMs to a specific set of tasks or scientific equipment with help from curated tools and reso

Lucas Babati, Shane Rightley, Nathaniel Shaffer, Scott Baalrud

A model for the collisional stopping of ions on free electrons in warm dense matter is developed and explored. It is based on plasma kinetic theory, but with modifications to address the warm dense matter regime. Specifically, it uses the Boltzmann-Uehling-Uhlenbeck kinetic equation to incorporate effects of Fermi degeneracy of electrons. The cross section i

Sophia R. Vaughan, Jayne L. Birkby, Natasha E. Batalha, Luke T. Parker

LTT-9779 b is an inhabitant of the hot Neptune desert and one of only a few planets with a measured high albedo. Characterising the atmosphere of this world is the key to understanding what processes dominate in creating the hot Neptune desert. We aim to characterise the reflected light of LTT-9779 b at high spectral resolution to break the degeneracy betwee

Longji Bing, Seb Oliver, Mengyuan Xiao, Guilaine Lagache

We present AC-2168, an almost NIRCam-dark, millimetre-bright galaxy in the COSMOS field. The source was identified blindly in ALMA Band-4 continuum data and remains undetected in the COSMOS-Web DR1 NIRCam catalogue. We spectroscopically confirm a redshift of $z_{\rm spec}=6.631$ from [CII] 158 $μ$m and four tentatively detected CO lines in NOEMA and ALMA dat

David Regan, Ozan Aksakal, Athena Zitti, John McLarnon

Stimulated Raman scattering (SRS) microscopy offers great potential to surpass fluorescent-based approaches, owing to the sharp linewidth of Raman vibrations amenable to super-multiplex cell imaging, but currently lacks one crucial component: genetically encodable tags equivalent to fluorescent proteins. Here, we show that infrared fluorescent proteins (IRFP

J. Penuela, H. Ouerdane

Physics-Informed Neural Networks (PINNs) have emerged as a promising framework for solving forward and inverse problems governed by differential equations. However, their reliability when used in ill-posed inverse problems remains poorly understood. In this study, we explore the fundamental limitations of PINNs using a simple illustrative case: RC low-pass f

Neelavro Saha, Rafi Shahriyar, Nafis Ashraf Roudra, Saadman Sakib

Bangla Sign Language (BdSL) translation represents a low-resource NLP task due to the lack of large-scale datasets that address sentence-level translation. Correspondingly, existing research in this field has been limited to word and alphabet level detection. In this work, we introduce Bangla-SGP, a novel parallel dataset consisting of 1,000 human-annotated

Xiao-jing Gao, Yanfeng Ge, Yan Gao

Topological semimetals (TSMs) featuring symmetry-protected band degeneracies have attracted considerable attention due to their exotic quantum properties and potential applications. While nodal line (NL) and nodal surface (NS) semimetals have been extensively studied, the realization of a material where both NL and NS coexist and are intertwined, particularl

Luna Lomonaco, Sabyasachi Mukherjee

We present an overview of the rapidly evolving field of dynamics of algebraic correspondences, with a focus on matings between rational maps and Kleinian groups. These correspondences exhibit rich dynamics, both within the Sullivan dictionary and beyond. We highlight unifying structures in their parameter spaces, showing how moduli spaces of rational maps an

H. Zhu, T. Samizadeh, R. C. Sofia

Autonomous Mobile Robots (AMRs) increasingly adopt containerized micro-services across the Edge-Cloud continuum. While Kubernetes is the de-facto orchestrator for such systems, its assumptions of stable networks, homogeneous resources, and ample compute capacity do not fully hold in mobile, resource-constrained robotic environments. This paper describes a ca

Yaozhu Li, Szilvia Kalácska, Phil McCausland, Roberta L. Flemming

We present a multiscale microstructural analysis of olivine from the non-poikilitic lithology of the poikilitic shergottite NWA 7721, using dark-field X-ray microscopy (DFXM), electron backscatter diffraction (EBSD), and context in situ 2D micro-XRD. A single olivine crystal contains two distinct subgrain populations. Type 1 subgrains are fine (1-5 micromete

L. Brumm, J. Schürmann, A. Saenz

Adopting explicitly correlated Kolos-Wolniewicz-type basis functions, the Born-Oppenheimer potential curves of a number of excited $Σ$ states of the hydrogen-antihydrogen system ($\bar{\rm H}$) were calculated for both, even and odd, Q symmetries, including also free positronium states. It is demonstrated that the excited leptonic states support ro-vibration

Marios Koniaris, Argyro Tsipi, Panayiotis Tsanakas

Parliamentary speech generation presents specific challenges for large language models beyond standard text generation tasks. Unlike general text generation, parliamentary speeches require not only linguistic quality but also political authenticity and ideological consistency. Current language models lack specialized training for parliamentary contexts, and

R. Wicker, M. De Petris, A. Ferragamo, I. Bartalucci

Galaxy clusters can be used as powerful cosmological probes, provided one can obtain accurate mass estimates, which requires a precise knowledge of the underlying astrophysics of galaxy clusters. For these purposes, spatially resolved measurements of the thermodynamic properties of intra-cluster medium (ICM), such as density and temperature, are necessary. I

Yuxin Li, Guangyue Lu, Yinghui Ye, Liqin Shi

Ambient backscatter communication (AmBC) offers low-cost and low-power connectivity for Internet of Things (IoT), where a backscatter tag (BT) modulates incident signals transmitted by an ambient radio frequency (RF) source and reflects them to its associated AmBC receiver. In multi-channel multi-tag AmBC, one of major challenges from the aspect of symbol de

Johannes Schmuck, Björn Sinz, Nina Pettinger, Sergey Zherebtsov

We demonstrate carrier-envelope-phase (CEP)-controlled photocurrents in mono-, bi-, and tri-layer MoS$_2$ driven by few-cycle laser pulses. The photocurrent in the two-terminal devices scales quadratically with the field amplitude, indicating perturbative carrier dynamics in the weak-field regime distinct from strong-field tunnelling. Our results extend ligh

Ruichen Ma, Liwei Meng, Guanchao Qiao, Ning Ning

Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This work introduces I2E, an algorithmic framework that resolves this bottleneck by converting static images into high-fidelity event streams. By simulating microsaccadic eye movements with a highly parallelize

Hengyu Wu, Yun Chen, Tonghua Liu, Xiaoyue Cao

Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100,000 galaxy-scale strong lenses, a huge increase over current samples. To harness the statistical power of this vast dataset, we forecast its cosmological

Jorge Mastache, Raúl Henriquez-Ortiz

We study the QCD--DM scenario by analyzing the imprint of energy injection from decaying dark-sector particles on the spectral distortions (SDs) of the Cosmic Microwave Background (CMB). We adopt a unified framework capable of describing both relativistic and non-relativistic particles, as well as fast and slow decay regimes. Within this approach, we model e

Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko

We introduce LOREN, a curvature-aware zeroth-order (ZO) optimization method for fine-tuning large language models (LLMs). Existing ZO methods, which estimate gradients via finite differences using random perturbations, often suffer from high variance and suboptimal search directions. Our approach addresses these challenges by: (i) reformulating the problem o

Connor McShaffrey, Eran Agmon, Randall D. Beer

Nearly all cell models explicitly or implicitly deal with the biophysical constraints that must be respected for life to persist. Despite this, there is almost no systematicity in how these constraints are implemented, and we lack a principled understanding of how cellular dynamics interact with them and how they originate in actual biology. Computational ce

Min Wang, Gui-Fa Zhu, Guo-Fei Long, Jianxing Guo

Quantum Secure Direct Communication (QSDC), a paradigm-shifting breakthrough in quantum communication, exploits quantum states for unmediated information transmission. Rooted in the inviolable fundamental laws of quantum mechanics, QSDC enables ultrasensitive detection of even the faintest eavesdropping attempts, guaranteeing true communication security sole

Vijay Pal Singh, Ludwig Mathey, Herwig Ott, Luigi Amico

We propose an ac-driven atomic Josephson junction as a clean and tunable source of three dimensional (3D) solitary waves in quantum fluids. Depending on the height of the junction barrier, the emitted excitations appear as vortex rings at low velocity or vorticity-free rarefaction pulses near the sound velocity, thus spanning the complete Jones-Roberts famil

Antônio Catão, Melvin Poveda, Leonardo Voltarelli, Paulo Orenstein

Accurate short-term precipitation forecasts predominantly rely on dense weather-radar networks, limiting operational value in places most exposed to climate extremes. We present TUPANN (Transferable and Universal Physics-Aligned Nowcasting Network), a satellite-only model trained on GOES-16 RRQPE. Unlike most deep learning models for nowcasting, TUPANN decom

Joaquim Duran

This chapter deals with the notion of the resolvent of a self-adjoint operator. We pay special attention to the convergence of unbounded self-adjoint operators in several resolvent senses, and how they are related to the convergence of their spectra. We also explore the relations that these notions of convergence have with the so-called strong graph limit, $

Wonduk Seo, Taesub Shin, Hyunjin An, Dokyun Kim

Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, especially when explicit identifiers are missing and product names vary widely across platforms. Rule based heuristics and keyword similarity often misclassify products by overlooking subtle distinctions in brand, specification, or bund

Yu-Jing Liu, Wen-Yu Su, Yong-Feng Yang, Nvsen Ma

While phases and phase transitions are conventionally described by local order parameters in real space, we present a unified framework characterizing the phase transition through the geometry of configuration space defined by the statistics of pairwise distances $r_H$ between configurations. Focusing on the concrete example of Ising spins, we establish cruc

Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko

Second-order optimization methods for training neural networks, such as KFAC, exhibit superior convergence by utilizing curvature information of loss landscape. However, it comes at the expense of high computational burden. In this work, we analyze the two components that constitute the layer-wise Fisher information matrix (FIM) used in KFAC: the Kronecker f

Qing Diao, Hongxin Wang, Manjia Liang, He Wang

We systematically investigate the source confusion of massive black hole binaries (MBHBs) for the Taiji space-based gravitational wave mission. Source confusion, arising from the overlap of signals in both time and frequency domains, can degrade parameter recovery accuracy. To assess this effect, we simulate three representative models MBHB populations to es

Aladin Djuhera, Amin Seffo, Vlad C. Andrei, Holger Boche

Path planning under wireless performance constraints is a complex challenge in robot navigation. However, naively incorporating such constraints into classical planning algorithms often incurs prohibitive search costs. In this paper, we propose SCoTT, a wireless-aware path planning framework that leverages vision-language models (VLMs) to co-optimize average

Dongdong Lin, Yue Li, Benedetta Tondi, Kaiqing Lin

The rapid proliferation of Deep Neural Networks (DNNs) is driving a surge in model watermarking technologies, as the trained models themselves constitute valuable intellectual property. Existing watermarking approaches primarily focus on modifying model parameters or altering sampling behaviors. However, with the emergence of increasingly large models, impro

Takeshi Fukao

We study a transmission problem of {N}eumann--{R}obin type involving a parameter $\alpha$ and perform an asymptotic analysis with respect to $\alpha$. The limits $\alpha \to 0$ and $\alpha \to +\infty$ correspond respectively to complete decoupling and full unification of the problem, and we obtain rates of convergence for both regimes. Biologically, the mod

J. T. P. Noel

This study outlines a light gradient boosted model aimed at predicting shot outcomes in the NHL. The model uses the NHL's spatiotemporal data to account for both the skill of shooters and goaltenders. This approach involves isolating and engineering features for different aspects of shooter and goaltender skill. These aspects include the overall skill, the l

Meraj Hassanzadeh, Ehsan Ghaderi, Mohamad Ali Bijarchi, Siamak Kazemzadeh Hannani

Multidimensional optimization has consistently been a critical challenge in engineering. However, traditional simulation-based optimization methods have long been plagued by significant limitations: they are typically capable of optimizing only a single problem at a time and require substantial computational time for meshing and numerical simulation. This pa

Xiaolin Sun, Feidi Liu, Zhengming Ding, ZiZhan Zheng

Reinforcement learning (RL) systems, while achieving remarkable success across various domains, are vulnerable to adversarial attacks. This is especially a concern in vision-based environments where minor manipulations of high-dimensional image inputs can easily mislead the agent's behavior. To this end, various defenses have been proposed recently, with sta

Brandon Dominique, Prudence Lam, Nicholas Kurtansky, Jochen Weber

Artificial Intelligence (AI) models have demonstrated expert-level performance in melanoma detection, yet their clinical adoption is hindered by performance disparities across demographic subgroups such as gender, race, and age. Previous efforts to benchmark the performance of AI models have primarily focused on assessing model performance using group fairne

David Autor, Andrew Caplin, Daniel Martin, Philip Marx

The cost of error in many high-stakes settings is asymmetric: misdiagnosing pneumonia when absent is an inconvenience, but failing to detect it when present can be life-threatening. Because of this, artificial intelligence (AI) models used to assist such decisions are frequently trained with asymmetric loss functions that incorporate human decision-makers' t

Mohammadjavad Mehditabar, Saurabhsingh Rajput, Antonio Mastropaolo, Tushar Sharma

The rapid advancement of AI technologies and their accelerated adoption in software development necessitates a systematic evaluation of their environmental impact alongside functional correctness. While prior studies have examined sustainability in large language models, existing approaches lack systematic frameworks for evaluating accuracy-energy trade-offs

Sebastian Petit, Geertrui Van de Voorde

In this paper, we study and characterise certain blocking sets in generalised polygons. This will allow us to derive new results about the minimum weight and minimum weight code words in the code generated by the rows of the incidence matrix of a generalised polygon over a field F.

Nikita Araslanov, Anna Sonnweber, Daniel Cremers

Dense and versatile image representations underpin the success of virtually all computer vision applications. However, state-of-the-art networks, such as transformers, produce low-resolution feature grids, which are suboptimal for dense prediction tasks. To address this limitation, we present FlowFeat, a high-resolution and multi-task feature representation.

Lachlan Nguyen, Aidan Cousins, Arcot Sowmya, Hugh Dixson

Cardiovascular disease causes high rates of mortality worldwide. Coronary artery calcium (CAC) scoring is a powerful tool to stratify the risk of atherosclerotic cardiovascular disease. Current scoring practices require time-intensive semiautomatic analysis of cardiac computed tomography by radiologists and trained radiographers. The purpose of this study is

Manh Nguyen, Sunil Gupta, Hung Le

Large Language Models (LLMs) exhibit strong performance across various natural language processing (NLP) tasks but remain vulnerable to hallucinations, generating factually incorrect or misleading outputs. Uncertainty estimation, often using predictive entropy estimation, is key to addressing this issue. However, existing methods often require multiple sampl

Fiona Lopez, Daniel R. Wik, Cicely Potter, Randall A. Rojas Bolivar

The use of galaxy clusters to constrain cosmology is limited in part due to uncertainties in derived cluster masses, which often depend on the gas temperature. Unfortunately, there exists a longstanding discrepancy in temperature measurements of the same galaxy clusters made by the two most sensitive X-ray observatories, Chandra and XMM-Newton. The NuSTAR X-

Rhitabrat Pokharel, Yufei Tao, Ameeta Agrawal

Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet e

Soham Hans, Volkan Ustun, Benjamin Nye, James Sterrett

Achieving expert-level performance in simulation-based training relies on the creation of complex, adaptable scenarios, a traditionally laborious and resource intensive process. Although prior research explored scenario generation for military training, pre-LLM AI tools struggled to generate sufficiently complex or adaptable scenarios. This paper introduces

Zain Muhammad Mujahid, Dustin Wright, Isabelle Augenstein

Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, where conventional metrics struggle with input length limitations and long-range dependencies. In this work, we systematically evaluate the reliability of six widely used reference-free factuality metrics, originally proposed

Braden Meyers, Joshua G. Mangelson

Testing marine robotics systems in controlled environments before field tests is challenging, especially when acoustic-based sensors and control surfaces only function properly underwater. Deploying robots in indoor tanks and pools often faces space constraints that complicate testing of control, navigation, and perception algorithms at scale. Recent develop

J. W. Moffat, E. J. Thompson

In a complex manifold, one can bridge anti-de Sitter and de Sitter spacetimes via analytic continuation, preserving geometric invariants and regularity, avoiding singularities during the AdS-dS transition. It unifies gravitational and gauge interactions under a complexified symmetry group, maintaining bulk unitarity for both AdS and dS. Boundary unitarity is

Manasi Sharma, Chen Bo Calvin Zhang, Chaithanya Bandi, Clinton Wang

Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Evaluating DR remains challenging because responses are lengthy

Jingye Li, Alex Bespalov, Jinglai Li

We introduce a Nemytskii neural operator framework for nonlinear model reduction of parametrized steady-state partial differential equations. The method generalizes reduced basis approaches by replacing linear combinations of basis functions with a structured nonlinear mapping realized through a pointwise Nemytskii operator acting on fixed feature functions.

Mahipal Gurram

In this paper, we derive a unified generalization of Ramanujan's transformation identities for the theta function $f(a,b)$, originally appearing in Ramanujan's Notebooks, Parts~III and IV. Using an approach based on residue-class dissections and modular substitutions, we obtain a closed transformation formula for $f(\zeta a, \zeta b)$, where $\zeta$ is a pri

Michael Hoffmann, Jophin John, Jan Fillies, Adrian Paschke

This study introduces 'Malinowski's Lens', the first AI-native educational game for anthropology that transforms Bronislaw Malinowski's 'Argonauts of the Western Pacific' (1922) into an interactive learning experience. The system combines Retrieval-Augmented Generation with DALL-E 3 text-to-image generation, creating consistent VGA-style visuals as players e

Vítor A. Barbosa, Sunil Tiwari, Rafael A. Melo

We introduce the Pickup and Delivery Problem with Time Windows and Scheduling on the Edges (PDPTW-SE), a generalization of the PDPTW that integrates vehicle routing and machine scheduling. The problem involves defining routes for transportation requests with specific pickup and delivery locations using a heterogeneous vehicle fleet, while machines must be sc

Sajad Salami

Let $k$ be a number field. We investigate the Mordell-Weil ranks of Jacobian varieties $J_C$ associated with algebraic curves $C$ of genus $g \geq 1$ defined by affine equations of the form $y^s=x(ax^r+b)$, where $a, b \in k$ ($ab \neq 0$), and $r \geq 1, s \geq 2$ are fixed integers. Assuming the strong version of Lang's conjecture concerning rational point

Rohan Alur, Bradly C. Stadie, Daniel Kang, Ryan Chen

This technical report describes the AIA Forecaster, a Large Language Model (LLM)-based system for judgmental forecasting using unstructured data. The AIA Forecaster approach combines three core elements: agentic search over high-quality news sources, a supervisor agent that reconciles disparate forecasts for the same event, and a set of statistical calibrati

Feyisayo Olalere, Kiki van der Heijden, H. Christiaan Stronks, Jeroen Briaire

Classroom environments are particularly challenging for children with hearing impairments, where background noise, multiple talkers, and reverberation degrade speech perception. These difficulties are greater for children than adults, yet most deep learning speech separation models for assistive devices are developed using adult voices in simplified, low-rev

Ziqing Guo, Jan Balewski, Wenshuo Hu, Alex Khan

The encoding of classical to quantum data mapping through trigonometric functions within arithmetic-based quantum computation algorithms leads to the exploitation of multivariate distributions. The studied variational quantum gate learning mechanism, which relies on agnostic gradient optimization, does not offer algorithmic guarantees for the correlation of

Jinesh Patel, Arpit Malhotra, Ajay Pande, Prateek Caire

Retrieval-Augmented Generation (RAG) based chatbots are not only useful for information retrieval through questionanswering but also for making complex decisions based on injected private data.we present a survey on how much search time can be saved when retrieving complex information within an organization called "X Systems"(a stealth mode company) by using

Frank Gilson

We study the topos $\mathcal{E}=\mathsf{Sh}(H\ltimes 2^{\mathbb{N}})$ arising from a nontrivial finite group $H$ acting freely on Cantor space. Using a local embedding property for the relevant epimorphisms together with effective descent for monomorphisms, we show that the \emph{internal} set universe $V$ obtained from algebraic set theory (AST) inside $\ma

Francisco J. Gozzi, Manuela A. Cerdeiro, Pablo E. Riera

A $1$-Lipschitz map between compact metric spaces $f\colon X\to Y$ induces a homomorphism of persistence modules on degree-$d$ Vietoris--Rips persistent homology. We define the persistent cost of $f$ from this induced homomorphism by quantifying the persistence carried by its kernel and cokernel modules. We prove that the persistent cost controls the interle

K. Zhao

The search for neutrinoless double beta decay (0$\nu\beta\beta$) is fundamental for investigating lepton-number violation, probing new physics beyond the Standard Model, and determining whether neutrinos are Majorana particles. CUORE (Cryogenic Underground Observatory of Rare Events), a cryogenic bolometric experiment at LNGS, studies 0$\nu\beta\beta$ in $^{

Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski

Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain from selecting a certain design. However, accurate Bayesian inference relies on the assumption that one's statistical model of the data-generating process is correctly specified. I

Mohammad Umar, Sarvesh Bansal, Paramasivam Senthilkumaran

In a manner commensurate to the SU(2) gadget for the Poincar\'{e} sphere, which involves a combination of two quarter-wave plates and one half-wave plate regardless of their sequential order, an analogous construct for the higher-order Poincar\'e sphere had long remained elusive. To address this, we recently demonstrated, by modifying Euler-angle parameteriz

Alejandro R. Jadad

Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustai

Ran Azouri

In this survey, we explain how to compute both the quadratic Euler characteristic of nearby cycles, and the motivic monodromy, at a quasi-homogeneous singularity. This gives, for such singularity, a quadratic refinement to the Deligne--Milnor formula in characteristic zero, and an enhancement of the Picard--Lefschetz formula to Voevodsky motives with rationa

R. Fitzpatrick

The safety-factor profile of a magnetically diverted tokamak plasma diverges logarithmically as the magnetic separatrix (a.k.a. the last closed magnetic flux-surface) is approached. At first sight, this suggests that, when determining the response of such a plasma to a static, externally generated, resonant magnetic perturbation (RMP), it is necessary to inc

Yuzhe Fu, Changchun Zhou, Hancheng Ye, Bowen Duan

Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousan

Paweł Liskowski, Benjamin Han, Paritosh Aggarwal, Bowei Chen

Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at pro

Owen Lailey, Dusan Sarenac, Charles W. Clark, David G. Cory

The experimental realization of neutron orbital angular momentum (OAM) states and neutron Airy beams has opened new avenues for structured neutron science in both materials characterization and fundamental physics. These additional degrees of freedom in scattering experiments enable the exploration of selection rules for neutrons, the analysis of scattering

Khalid M. Saqr

Age-related arterial remodeling is dominated by progressive loss of elastic-fiber function and concomitant stiffening, and in many vascular beds it is also accompanied by measurable geometric remodeling (e.g., elongation and tortuosity). These changes are clinically relevant because they modify pulsatile phase relationships, near-wall shear, and axial transp

Ayano Moritaka, Shin-ichi Nakano, Kento Tanaka, Noriaki Yoshida

Many approximation algorithms and heuristic algorithms to find a fair clustering have emerged. In this paper we define a new and natural variant of fair clustering problem and design a polynomial time algorithm to compute an optimal fair clustering. Let P be a set of n points on a plane, and each point has a color in C, corresponding to a group. For each col

Sai Shridhar Balamurali, Lu Cheng

Evaluating answers from state-of-the-art large language models (LLMs) is challenging: lexical metrics miss semantic nuances, whereas "LLM-as-Judge" scoring is computationally expensive. We re-evaluate a lightweight alternative -- off-the-shelf Natural Language Inference (NLI) scoring augmented by a simple lexical-match flag and find that this decades-old tec

Xiaomeng Yang, Jian Gao, Yanzhi Wang, Xuan Zhang

Although recent advancements in learning-based analog circuit design automation have tackled tasks such as topology generation, device sizing, and layout synthesis, efficient performance evaluation remains a major bottleneck. Traditional SPICE simulations are time-consuming, while existing machine learning methods often require topology-specific retraining o

Veera V S Bhargav Nunna, Shinae Kang, Zheyuan Zhou, Virginia Wang

Enterprise relational databases increasingly contain vast amounts of non-semantic data - IP addresses, product identifiers, encoded keys, and timestamps - that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in non-semanti

Leonardos Gkouvelis

Analytical models are essential for building physical intuition and guiding the interpretation of exoplanet observations by clarifying the dependencies that shape atmospheric signatures. We present a generalization of the classical isothermal, isobaric transmission model by allowing the opacity to vary with pressure as a power law, $\kappa \propto P^{n}$, an

Leonardo Pedroso, Andrea Agazzi, W. P. M. H. Heemels, Mauro Salazar

We consider a class of continuous-time dynamic games involving a large number of players. Each player selects actions from a finite set and evolves through a finite set of states. State transitions occur stochastically and depend on the player's chosen action. A player's single-stage reward depends on their state, action, and the population-wide distribution

Yinsen Jia, Boyuan Chen

Temporal awareness plays a central role in intelligent behavior by shaping how actions are paced, coordinated, and adapted to changing goals and environments. In contrast, most robot learning algorithms treat time only as a fixed episode horizon or scheduling constraint. Here we introduce time-aware policy learning, a reinforcement learning framework that tr