April 2026 arXiv papers — page 97

Swapnil Kumar Singh, Saleh O. Allehabi, Azzah A. Alshehri, Mahmoud Nasar

A phase-space approach to quantum-deformed gravity is developed. Following its reduction to an effective four-dimensional spacetime structure, we utilize it in reanalyzing the cosmic inflationary dynamics and quantum gravity. The construction starts on cotangent bundle, where the gravitational Hamiltonian is deformed by a zero-homogeneous scalar determined b

Nigel Lee En Hew, Luke Allen Myers, Shun-Li Shang, Zi-Kui Liu

While the recursive property of entropy is well known in information theory, it is rarely utilized in thermodynamics, despite entropy originating in this field. Moreover, computational tools to implement this concept within first-principles thermodynamics remain lacking. In this work, we introduce an open-source Python package, pyzentropy, to implement this

Ilja Gogić, Matija Kazalicki, Mateo Tomašević

Let $\mathbb{K}$ be a field of characteristic different from $2$, and let $M_n(\mathbb{K})$ be the algebra of all $n\times n$ matrices over $\mathbb{K}$. We consider the corresponding special Jordan algebra $\mathcal{A}:=M_n(\mathbb{K})^+$ with symmetrized product $A\circ B:=(AB+BA)/2$, and write $\mathcal{A}_{\mathrm v}:=M_n(\mathbb{K})$ for the underlying

Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi, Jeffrey Molendijk

Constitution-conditioned post-training can be analysed as a structured perturbation of a model's learned representational geometry. We introduce ATLAS, a geometry-first program that traces constitution-induced hidden-state structure across charts, models, and substrates. Instead of treating the relevant unit as a single behaviour, neuron, vector, or patch, A

Nathan Benedetto Proença, Marcel K. de Carli Silva, Cristiane M. Sato, Levent Tunçel

We present experimental work on a primal-dual framework simultaneously approximating maximum cut and weighted fractional cut-covering instances. In this primal-dual framework, we solve a semidefinite programming (SDP) relaxation to either the maximum cut problem or to the weighted fractional cut-covering problem, and then independently sample a collection of

Miguel Moyses, Rafael C. Nunes

Identifying new observational probes to constrain cosmological parameters has become an important goal in modern cosmology. In this work, we explore the potential of the cosmic star formation rate density (SFRD), compiled over the redshift range $z \in [0, 15]$, as a complementary probe of fundamental parameters, including $\Omega_{\rm m}$, $H_0$, and the da

Amr Ahmed

We introduce the Semantic Density Effect (SDE): the empirical finding that prompts carrying higher semantic information per token consistently produce more accurate, focused, and less hallucinated outputs across all major LLM families. SDE is defined as the ratio of semantically loaded tokens to total prompt tokens, adjusted for redundancy and concreteness.

Jiazheng Li, Emine Yilmaz, Bei Chen, Dieu-Thu Le

Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterized by long interaction traces, inter-agent dependencies, and delayed error manifestation. Existing diagnostic approaches often rely on expensive expert annotation or ''LLM-as-a-judge'' paradigms, which struggle

Renato Campanini

The multiplicity-dependent suppression of $\Upsilon(n\mathrm{S})$ states measured by CMS in $pp$ at $\sqrt s=7\,$TeV \cite{CMS2020}, and of $\psi(2S)\big/J/\psi$ measured by LHCb at $\sqrt s=13\,$TeV \cite{LHCb2024}, is subjected to four multi-differential tests: \emph{cone isolation}, \emph{azimuthal sectors}, \emph{transverse sphericity}, and \emph{prompt

Ibrahim Halil Tanboga

Random-effects meta-analysis summarizes heterogeneous trials by estimating an average effect over the observed evidence base, which may not represent the clinically relevant target population. In cardiovascular medicine, treatment effects vary systematically across era, endpoint definitions, background therapy, and case-mix, making the historical average oft

Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj, Gouthaman KV

Video-to-music (V2M) is the fundamental task of creating background music for an input video. Recent V2M models achieve audiovisual alignment by typically relying on visual conditioning alone and provide limited semantic and stylistic controllability to the end user. In this paper, we present Video-Robin, a novel text-conditioned video-to-music generation mo

Han Huang

Charge-density-wave (CDW) order in correlated metals originates in a peaked electronic susceptibility at a finite wavevector $\mathbf Q_0$, set either by Fermi-surface features (nesting or saddle-point singularities) or by momentum-resolved electron-phonon coupling, or by a combination of the two. CDW precursor fluctuations can attenuate heat-carrying acoust

Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam, Shirley Wu

Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and scale performance with test-time compute. In this paper, we present a framework for post-training language models (LMs) that explicitly encourages optimistic exploration and promotes a synergy between exploration an

Yuan Tian, Tianyi Zhang

Text-to-SQL systems often struggle with deep contextual understanding, particularly for complex queries with subtle requirements. We present PV-SQL, an agentic framework that addresses these failures through two complementary components: Probe and Verify. The Probe component iteratively generates probing queries to retrieve concrete records from the database

Hyam Omar Ali, Antoine Crosnier, Romain Abraham, Baptiste Combelles

Sentinel-5P (S5P) plays a critical role in atmospheric monitoring; however, its spatial resolution limits fine-scale analysis. Existing super-resolution (SR) approaches rely on supervised learning with synthetic low-resolution (LR) data, since true high-resolution (HR) data do not exist, limiting their applicability to real observations. We propose a self-su

Siyuan Meng, Chengbo Ai

World models, generative AI systems that simulate how environments evolve, are transforming autonomous driving, yet all existing approaches adopt an ego-vehicle perspective, leaving the infrastructure viewpoint unexplored. We argue that infrastructure-centric world models offer a fundamentally complementary capability: the bird's-eye, multi-sensor, persisten

Parker Seegmiller, Sarah Masud Preum

LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, prompts, and users are introduced to a deployed model. Such natural prompt distribution shift poses a major challenge to LLM reliability, particularly for specialized models designed for narrow domains or user

Koji Hashimoto, Norihiro Tanahashi

Optimal transport and Wasserstein distance are prominent tools to quantify the space of probability distributions. From a novel viewpoint of manifold hypothesis in machine learning being a possible guide for the holographic principle, we study how holographic spacetime can emerge from quantum systems in general as a Wasserstein space through optimal transpor

Olubusayo Olabisi, Ekata Mitra, Ameeta Agrawal

Summarizing deeply nested discussion threads requires handling interleaved replies, quotes, and overlapping topics, which standard LLM summarizers struggle to capture reliably. We introduce ThreadSumm, a multi-stage LLM framework that treats thread summarization as a hierarchical reasoning problem over explicit aspect and content unit representations. Our me

Girish, Mohd Mujtaba Akhtar, Muskaan Singh

In this work, we introduce a paralinguistic supervision paradigm for low-resource multilingual speech emotion recognition (LRM-SER) that leverages non-verbal vocalizations to exploit prosody-centric emotion cues. Unlike conventional SER systems that rely heavily on labeled verbal speech and suffer from poor cross-lingual transfer, our approach reformulates L

Marco Schreck, Rogeres A. da Silva Magalhães

The motivation behind the present work is to adopt methodology from field theory and high-energy physics to crystallography. In particular, we establish a relationship between the electromagnetic sector of the Standard-Model Extension (SME) for Lorentz invariance violation and optical media. At an effective level, electromagnetic properties associated with d

I. M. Ross

A number of optimization algorithms have been inspired by the physics of Newtonian motion. Here, we ask the question: do algorithms themselves obey some ``natural laws of motion,'' and can they be derived by an application of these laws? We explore this question by positing the theory that optimization algorithms may be considered as some manifestation of hi

Valerio Bertacchi

The Belle and Belle II experiments have collected a 1.3 ab$^{-1}$ sample of $e^+e^-\to B\bar B$ collisions at $\Upsilon(4S)$ centre-of-mass energy. This is ideal environment to search for rare electroweak penguin $B$ decays and notably those involving $B$ decays to final states with missing energy. Results on these datasets of $b\to s \ell^+\ell^-$ $(\ell=e,

Vinicius M. Netto, Caio Cacholas, Camila Carvalho, Edgardo Brigatti

The footprints of residential segregation have long been documented, yet the role of urban form as both medium and manifestation of segregation remains under-specified. We investigate whether the configuration of the built fabric may encode residential segregation in its spatial structure, hypothesising that built-form entropy (BFE) regimes are associated wi

Mohd Mujtaba Akhtar, Girish, Muskaan Singh

In this study, we present Healthcare Codec-Fake Detection (HCFD), a new task for detecting codec-fakes under pathological speech conditions. We intentionally focus on codec based synthetic speech in this work, since neural codec decoding forms a core building block in modern speech generation pipelines. First, we release Healthcare CodecFake, the first patho

Kai Vylet, Xingkai Huang, Leon Balents

We study an effective low-energy Kondo model on the triangular lattice in which itinerant electrons occupy a valence pocket at $\Gamma$ and three conduction pockets at the $M$ points of the Brillouin zone. This construction has a Fermi-surface nesting structure that favors triple-$Q$ magnetic order while only assuming the low-energy band-structure. Treating

Zhong Zheng, Michael E. Papka, Zhiling Lan

Modern multi GPU HPC systems expose substantial computational capacity, yet inefficient GPU allocation often leads to wasted energy and underutilization. In practice, GPU applications exhibit heterogeneous and nonlinear scaling, making it inefficient to always use all available GPUs. We present EcoSched, an online scheduler that jointly optimizes GPU count s

Felix Höfer

We consider discounted infinite-horizon potential mean-field games (MFGs) on the $d$-dimensional torus. Without imposing monotonicity assumptions, we prove that every weak limit point of a time-dependent equilibrium, as time tends to infinity, is a stationary equilibrium. As a consequence, equilibria converge whenever the stationary solution is unique. The s

Arka Majhi, Aparajita Mondal, Satish B. Agnihotri

In India, community healthcare workers are the primary touchpoints between the state and the beneficiaries, such as pregnant mothers and children. Their healthcare knowledge directly impacts the quality of care they provide through home visits and community activities. Classroom in-person or traditional ways of training are found ineffective in imparting kno

Allen W. Shafter, Kamil Hornoch

The positions of more than 1300 nova eruptions in M31 catalogued through the end of calendar year 2025 have been compared in order to identify recurrent nova candidates. The work extends the study of Shafter et al. (2015) who identified a total of 12 recurrent novae with high confidence (plus four possible recurrent novae) from an analysis of 964 M31 novae o

Lina Nikolaidou, Ali R Khojasteh, Angeliki Laskari, Tom van Terwisga

Air lubrication regimes were studied using simultaneous drag force measurements and multi-plane imaging to characterize the regimes and identify the governing mechanisms of drag reduction. A bubbly, transitional, and air layer regime are identified over a large range of freestream velocities ($U_{\infty}$), air flow rates ($Q_{air}$), and Froude-depth number

Zhong Zheng, Michael E. Papka, Zhiling Lan

Power-constrained HPC systems increasingly run heterogeneous CPU--GPU applications under strict cluster-wide power limits. Existing cluster-wide power management policies rely on fair-share or utilization heuristics and do not capture application-specific sensitivity to CPU and GPU power caps, leading to inefficient use of reclaimed power. We present EcoShif

Alejandro de la Fuente, Fernando Galindo, Uriel García-Bárbulo, Sandra-Noemy Arana-Alegre

This paper explores the integration of reconfigurable intelligent surfaces (RISs) into cell-free massive multiple-input-multiple-output (CF-mMIMO) networks operating in FR1 and FR3 frequency bands. We present a comprehensive framework for analyzing RIS-assisted CF-mMIMO systems under realistic propagation conditions, accounting for frequency-dependent charac

Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, Heli Qi

Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monolingual contexts. To bridge this critical disconnect, we present a holistic study dedicated to code-switching IR. We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), const

Alejandro de la Fuente, Adrián Espinosa, Jan García-Morales, Guillem Femenias

This paper studies scalable conjugate beamforming (CB) variants for physical-layer multicasting in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Focusing on fully distributed precoding, we analyze classical CB, normalized CB (NCB), and enhanced CB (ECB) within a subgroup-centric multicast framework. Multicast users are partitioned into

Gengzhi Yang, Di Wu, Haizhao Yang, Xiaodi Wu

We propose a versatile and efficient algorithmic framework for optimizing fermion-to-qubit mappings by generalizing the idea of randomized block coordinate descent. Our greedy approach, termed Randomized Subsystem Descent, iteratively samples a tractable subsystem from the full Hamiltonian, performs optimization within the subsystem under a given metric, and

Mainak Singha, Tanisha Gupta, Ankit Jha, Muhammad Haris Khan

Pretrained biomedical vision-language models (VLMs) such as BioMedCLIP perform well on average but often degrade on challenging modalities where inter-class margins are small and acquisition-specific variations are pronounced, especially under few-shot supervision and when modality priors differ from pretraining corpora substantially. We propose BioVLM, a pr

Cai Parry-Jones

Wales' political landscape has been marked by growing accusations of bias in Welsh media. This paper takes the first computational step toward testing those claims by examining Nation.Cymru, a prominent Welsh political news outlet. I use a two-stage natural language processing (NLP) pipeline: (1) a robustly optimized BERT approach (RoBERTa) bias detector for

Christian Lysenstøen

Serving large language models under latency service-level objectives (SLOs) is a configuration-heavy systems problem with an unusually failure-prone search space: many plausible configurations crash outright or miss user-visible latency targets, and standard black-box optimizers treat these failures as wasted trials. We present SLO-Guard, a crash-aware autot

Marko Djukanović, Nikola Balaban, Christian Blum, Aleksandar Kartelj

This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible gap constraints between consecutive solutions' characters. The problem arises in molecular sequence comparison, where structural distance constraints between residues must be respected, and in time-series analy

Peter Bajcsy, Walid Keyrouz

This work addresses the challenge of disseminating reusable artificial intelligence (AI) models accompanied by AI documentation (a.k.a., AI model cards). The work is motivated by the large number of trained AI models that are not reusable due to the lack of (a) AI documentation and (b) the temporal lag between rapidly changing requirements on AI model reusab

Rahul K. Dass, Shubham Puri, Arpit Khandelwal, Xiao Jin

Scalable AI tutoring for procedural skill learning requires structured knowledge representations, yet constructing these representations remains a labor-intensive bottleneck. This paper introduces a new LLM-assisted text-to-model (TTM) methodology that transforms instructional materials into schema-complete Task-Method-Knowledge (TMK) models through ontology

Pronob Kumar Barman, Pronoy Kumar Barman, Plaban Kumar Barman, Rohan Mandar Salvi

Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data bias. We present FASE, a Fairness Aware Spatiotemporal Event Graph framework, which integrates spatiotemporal crime prediction with fairness constrained patrol allocation and a closed loop

Swattik Maiti, Ritik Pratap Singh, Fardina Fathmiul Alam

Credit risk default prediction remains a cornerstone of risk management in the financial industry. The task involves estimating the likelihood that a borrower will fail to meet debt obligations, an objective critical for lending decisions, portfolio optimization, and regulatory compliance. Traditional machine learning models such as logistic regression and t

Arka Majhi, Satish B. Agnihotri, Aparajita Mondal

High and persistent child malnutrition levels with tardy reduction, seen in successive health surveys, continue to be a matter of concern in India, drawing attention to the need to revamp the four-decade-old Government program, Integrated Child Development Scheme (ICDS). ICDS field functionaries or Anganwadi Workers' (AWWs) capacity deficit was identified as

Ingrid Torres, Alex Krasnok

Flat optics is now judged by more than a strong simulation or a single laboratory demonstration. To reach release, a device must survive a chain of handoffs: requirements, model selection, verification, layout release, fabrication, calibrated validation, packaging, and qualification. Diffractive optics brings mature routes for beam shaping and compact wavefr

Pablo Jara, Alexey Yamilov

Advances in computational methods have made full-wave simulations in large disordered media increasingly feasible, but the resulting field data, scaling with the cube of the ratio of system size to wavelength, creates a severe storage and post-processing bottleneck. Generic compression methods are sample-specific and preclude operations on compressed data. W

Yuxuan Li, Kyzyl Monteiro, Hirokazu Shirado, Sauvik Das

Policymakers in domains such as emergency management, public health, and urban planning must make decisions under deep uncertainty, where outcomes depend on how large populations interpret information, coordinate, and adopt over time. Existing tools only partially support this process: tabletop exercises enable collaborative discussion but lack dynamic feedb

Feiyang Kang, Mahavir Dabas, Myeongseob Ko, Ruoxi Jia

Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what matters that need not align with the model's internal representations. We argue that when the g

Damek Davis

Erd\H{o}s asked for the largest size $f(n)$ of a subset of $\{1,\dots,n\}$ with no element dividing two others. We show that $f(n)=c_2\,n+o(n)$ for an effectively computable constant $c_2$, and moreover that the number $q(n)$ of such subsets satisfies $q(n)=\beta_2^{n+o(n)}$ for a computable constant $\beta_2$. To prove this, we recast the divisibility const

Md Mezbahul Islam, John Michael Templeton, Christian Poellabauer, Ananda Mohan Mondal

Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essential for clinical monitoring and treatment planning. However, many computational studies emphasize binary PD detection and do not fully use repeated follow-up clinical assessments for stage-aware prediction. This

Erik Floden, Alex Granados, Vuk Mandic

We consider a search for the anisotropic stochastic gravitational-wave background (SGWB) that decomposes the sky map into its spherical harmonics components in order to obtain estimators of the angular power spectrum. Such a search often requires the inversion of a Fisher information matrix which contains small singular values. Rather than dealing with biase

Leon Engländer, Sophia Althammer, Ahmet Üstün, Matthias Gallé

LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should naturally lead to a model exploiting its own discoveries. We show that this assumption is false for current LLM-based agents, which struggle to reflect or react to unexpected information. Across three benchm

Abdoulaye Thiam

This Part establishes the geometric theory of uniformly hyperbolic sets with explicit quantitative bounds throughout, and contains five main theorems. The Stable Manifold Theorem is proved via the backward graph transform, with a complete fiber-contraction argument yielding $C^r$ regularity and H\"{o}lder dependence of the local stable and unstable manifolds

Bilal Ahmad Rather, Mustapha Aouchiche, Victor A. Bovdi

The characteristic polynomials of the Laplacian and the distance Laplacian matrices of power graphs of groups of order $ pqr $, where $ p,q $ and $ r $ are { primes,} are obtained. Further, the characteristic polynomials of these matrices for proper power graphs of cyclic and dicyclic groups are given. The important inequalities for the zeros of the distance

Nurcan Gücüyenen Kaymak, Fatma Zürnacı-Yetiş, Muaz Seydaoğlu

Operator splitting is an effective technique for the numerical solution of nonlinear partial differential equations by decomposing a complex problem into simpler subproblems. In this study, we present and analyze a fully discrete scheme for the fifth-order Korteweg-de Vries-Burgers-Fisher equation (KBF) by combining Strang splitting for time discretization w

C. A. Lindstrøm, E. Adli, J. B. B. Chen, P. Drobniak

Plasma acceleration promises to deliver high-energy particle beams by combining, or staging, several low- or medium-energy accelerator stages. However, chromatic aberrations from the combination of high divergence and energy spread make it nontrivial to transport beams between plasma-accelerator stages. This paper describes a compact and achromatic lattice o

Arka Majhi, Aparajita Mondal, Satish B. Agnihotri

India's recent health surveys have highlighted a worrying trend of incomplete child immunization rates across several district clusters in India. Conventional training methods for community healthcare workers (CHWs) in India are inadequate for improving their skills and knowledge. Smartphone games could be a viable and cost-effective method of refresher trai

Shigeng Wang, Sijia Geng

Future inverter-dominated power systems feature higher variability and more stressed operating conditions, which motivates the consideration of stability in operational settings. Existing approaches to stability-constrained OPF often rely on eigenvalue calculation, global model information, or dynamic evaluation inside optimization formulation, which are com

Luca Gallo, Riccardo Di Clemente, Balázs Lengyel

The AI race amplifies security risks and international tensions. While the US restricts mobility and knowledge flows, challenges regulatory efforts to protect its advantage, China leads initiatives of global governance. Both strategies depend on cross-country relationships in AI innovation; yet, how this system evolves is unclear. Here, we measure the proces

Giovanni Russo, Ezequiel E. Ferrero, Alejandro B. Kolton, Alberto Rosso

We investigate the creep dynamics of a driven elastic line at finite temperature, well below the depinning threshold. We show that creep is governed by two distinct length scales. The first, $\ell_{\mathrm{opt}}$, corresponds to the optimal activated rearrangements that control the dynamics' bottleneck and remains essentially temperature-independent. The sec

Christina Sormani

In 1979, Schoen and Yau proved their famous Positive Mass Theorem which is a combination of a comparison theorem: {\em a three dimensional asymptotically flat Riemannian manifold with nonnegative scalar curvature has nonnegative ADM mass}, and a rigidity theorem: {\em if such a manifold has zero ADM mass then it is isometric to Euclidean space}. Here we revi

Marcin Pietroń

Deep learning models are the most efficient models in many machine learning tasks. The main disadvantage when using them in IoT, mobile devices, independent autonomous or real-time systems is their complexity and memory size. Therefore, much research has concentrated on compression techniques of deep learning architectures. One of the most popular technique

Aaron McLean, Makena Coffman, Andy Yu, Scott Nicolas

Climate hazards in Hawai'i are increasing in both frequency and severity, with varying impacts over vulnerable communities. This paper presents the Community Census and Spatial Visualization Index (CCSVI), a web-based geospatial visualization platform that integrates climate hazard data with socioeconomic and infrastructural datasets. This system enables use

Ivan Bercovich, Ivgeni Segal, Kexun Zhang, Shashwat Saxena

We release Terminal Wrench, a subset of 331 terminal-agent benchmark environments, copied from the popular open benchmarks that are demonstrably reward-hackable. The data set includes 3,632 hack trajectories and 2,352 legitimate baseline trajectories across three frontier models (Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.4). Each entry preserves the original ta

Bartłomiej Kielak, Daniel Král', Ander Lamaison, Xichao Shu

We show that the average length of a fundamental cycle with respect to any fixed spanning tree of the $n\times n$ square grid is at least $Ω(\log n)$; the bound is asymptotically tight. This result answers in the affirmative a question posed by McCarty in relation to sparse representations of binary matroids.

Abhishek Chilampankunnel Prasannan, Baris Pekerten, Nowar Alashkar, Alex Matos-Abiague

We theoretically investigate the magnetic and crystalline anisotropies of the superconducting diode effect (SDE) in proximitized planar Josephson junctions (JJs) with coexisting Rashba and Dresselhaus spin-orbit couplings (SOCs) under an in-plane magnetic field. A symmetry analysis identifies geometric constraints on magnetic-field and crystallographic orien

Yoshimasa Uematsu, Shinya Tanaka

We propose post-screening portfolio selection (PS$^2$), a two-step framework for high-dimensional mean--variance investing. First, assets are screened by Lasso-type regression of a constant on excess returns without an intercept. Second, portfolio weights are estimated on the selected set using standard low-dimensional methods. Because strong factors can des

Kirill Kapralov, Vladislav Atlasov, Alina Khisameeva, Viacheslav Muravev

Many contemporary photodetectors operate beyond the readout of light intensity and enable the reconstruction of spectrum and polarization at the single-pixel level. However, the determination of light incidence direction with reconstructive detectors has not been realized so far. We show that photodetectors based on symmetric junctions of metals and 2d elect

Yunfeng Zhang

We present a basic pointwise bound for the irreducible characters of $\mathrm{SU}(3)$ and, as an application, derive new $L^p$ bounds for these characters. Our approach is based on the descent of characters to singular sets and the cancellation in this formula.

Roberto De Leo

We study the qualitative dynamics of general Iterated Function Systems (IFSs) through chain recurrence and a decomposition into recurrent and gradient-like behavior. At the same time, we move the focus from the topological property of the phase space to the dynamical properties of the system and we say that an IFS has compact dynamics if it has an invariant

William M. Parris

Practitioners have reported a directional pattern in AI-assisted code generation: AI-generated code tends to fail quietly, preserving the appearance of functionality while degrading or concealing guarantees. This paper introduces the Reward-Shaped Failure Hypothesis - the proposal that this pattern may reflect an artifact of optimization through human feedba

Masanari Asano, Andrei Khrennikov

This paper starts with surveying the evolution of quantum-like models of cognition and decision making, transitioning from static kinematic representations to a robust dynamical framework based on open quantum systems. We provide a comprehensive analysis of the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation's application in cognitive psychology

Erich Trieschman, Saurabh Amin

Financial Transmission Rights (FTRs) enable electricity market participants to hedge congestion risk in Day Ahead Market (DAM) operations, but for the market to be solvent, Independent System Operators (ISOs) must ensure that FTR payouts do not exceed the collected DAM merchandising surplus that funds them. We show that FTR underfunding (or conversely, hedgi

Suklav Ghosh, Arijit Sur, Pinaki Mitra

Salient object detection (SOD) requires modeling both long-range contextual dependencies and fine-grained structural details, which remains challenging for convolutional, transformer-based, and Mamba-based state space models. While recent Mamba-based state space approaches enable efficient global reasoning, they often struggle to recover precise object bound

Jiachen Zhang, Chengtai Li, Jianfeng Ren, Linlin Shen

Abstract visual reasoning remains challenging as existing methods often prioritize either global context or local row-wise relations, failing to integrate both, and lack intermediate feature constraints, leading to incomplete rule capture and entangled representations. To address these issues, we propose the Dual-Inference Rule-Contrastive Reasoning (DIRCR)

Jiayuan Chen, Ruoqi Liu, Zishan Gu, Ping Zhang

Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scarce. Existing multimodal approaches either use images to support other modalities or naively align representations by sample identity, ignoring cell-type and dose variations in weakly paired data-limiting genera

Nadim Ghaddar, Kareem M. Attiah, Wei Yu

This paper develops an active sensing framework for designing the transmit and receive beamformers of a multiple-input multiple-output (MIMO) radar system. In the proposed technique, the beamformers are adaptively designed in each sensing stage based on the measurements made in the previous sensing stages. The beamformers are determined by minimizing the Bay

Paul M. Thompson

How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress increasingly depends on predicting when additional data will substantially improve performance. In practice, model development often relies on empirical scaling curves measured across architectures, modalities,

Heng Xie

We calculate the Witt ring of the real sphere.

Anastasiia Zbandut, Carolina Goldstein

We derive five tractable credit risk metrics for DeFi lending vault depositors, grounded in a formal three level decomposition of vault risk into mechanical loss channels (Level 1), governance quality (Level 2) and smart contract code integrity (Level 3). For Level 1, we show that six structural features of onchain execution (oracle execution divergence, end

Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu, Eric Eaton

Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years, the theoretical development of CL remains in its infancy. At the heart of developing CL theory lies the challenge that the data distribution varies across tasks, and we argue tha

Christopher D. Long

We formulate and prove an exact finite-horizon quantile theorem for repeated identical multi-outcome Kelly wagering in wealth-profile / Arrow--Debreu coordinates. For a fixed $m$-outcome event repeated independently over a horizon $n$, the terminal wealth induced by a one-period wealth profile $W$ is a monomial $W^N$ in the multinomial count vector $N$. We s

Magdalena Toda, Erhan Güler, Madusha Dilhani Atampalage

We develop a Weierstrass-Kenmotsu type representation for conformal immersions of constant mean curvature $0\le H<1$ in hyperbolic $3$-space $\HH$. The construction is based on the Hermitian model of $\HH$, a balanced spectral deformation, and Iwasawa splitting of $\SL$. We show that such immersions arise locally from a rank-one $(1,0)$-form $\eta$ and a con

Philipp Denter

Motivated by Germany's April 2026 fuel price regulation, in this note I study a two-period pricing problem with demand uncertainty and a rule that prohibits more than one price increase during the day. Under flexible pricing, the firm chooses the static monopoly price in each period. Under the regulation, by contrast, it may price strategically high in perio

Ganesh Sahadeo Meshram, Suman Chakraborty, Nishant Sinha, Partha Pratim Chakrabarti

In the complex domain of microfluidics systems, analysing fluid flow patterns through random-shaped circular microchannels is significantly challenging task. Conventional approach of solving such problems using computational fluid dynamics often incapable due to their intensive computational requirements and high simulation times. In this study, addressing t

Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang

Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts. The task focuses on generating plausible yet incorrect options, known as distractors, for multiple-choice questions. A reliable distractor must be contextually relevant to the question and able to mislead examinees through implicit reasoning when ide

Jazmia Henry

We argue that current evaluation frameworks for large language models (LLMs) suffer from four systematic failures that make them structurally inadequate for deployed, agentic systems: distributional, temporal, scope, and process invalidity. These failures compound in RLHF, making reward hacking a predictable consequence of evaluation design rather than an un

Gabriele Oliva, Bianca Mazzà, Roberto Setola

Modern maritime navigation and control systems rely on digital sensing, estimation, and communication pipelines that fuse GNSS, radar, inertial, and AIS data through approaches such as Kalman-filter-based estimators. While these technologies are essential for safety and efficiency, their growing interconnection also exposes vessels to faults and cyber-physic

Oscar Senlis, John N. Le, Anthony Grbic, Mauro Ettorre

This paper introduces two methods for analyzing periodic leaky-wave antennas (LWAs) within a new framework denoted as multi-modal network theory (MNT) with open periodic boundaries (OPBs). The approach is hybrid, combining analytical techniques with a commercial full-wave solver. The first method computes the dispersion diagram of periodic LWAs. It is iterat

Yuanlong Wang, Weichi Chen, Adrian Rajab, Wenfang Liu

Peripheral Blood Smear (PBS) is a critical microscopic examination in hematopathology that yields whole-slide imaging (WSI). Unlike solid tissue pathology, PBS interpretation focuses on individual cell morphologies rather than tissue architecture, making it distinct in both visual characteristics and diagnostic reasoning. However, current multimodal large la

Salam Albatarni, May Bashendy, Sohaila Eltanbouly, Tamer Elsayed

Automated Essay Scoring (AES) faces significant challenges in cross-prompt settings, where models must generalize to unseen writing prompts. To address this limitation, we propose MAPLE, a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts. Across three diverse datasets (ELLIPSE

Yujia Zheng, Zijian Li, Shunxing Fan, Andrew Gordon Wilson

Given only observational data $X = g(Z)$, where both the latent variables $Z$ and the generating process $g$ are unknown, recovering $Z$ is ill-posed without additional assumptions. Existing methods often assume linearity or rely on auxiliary supervision and functional constraints. However, such assumptions are rarely verifiable in practice, and most theoret

Fan Yang, Changsoo Jung, Ryosuke Kawamura, Hon Yung Wong

Multi-camera systems are widely employed in sports to capture the 3D motion of athletes and equipment, yet calibrating their extrinsic parameters remains costly and labor-intensive. We introduce an efficient, tool-free method for multi-camera extrinsic calibration tailored to sports involving stick-like implements (e.g., golf clubs, bats, hockey sticks). Our

Achraf El Messaoudi, Noureddine Khaous, Karim Cherifi

Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identification and forecasting approaches become markedly less reliable in turbulent-flow regimes, where the dynamics are high-dimensional, strongly nonlinear, and highly sensitive to comp

CMS Collaboration

A search is performed for the single production of a heavy vector-like quark (VLQ), decaying into a W boson and a b quark. The analysis uses proton-proton collision data collected by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb$^{-1}$. The search targets events with leptonic W

Llorenç Balada Gaggioli, Didier Henrion, Milan Korda

We study polynomial optimization problems whose objective has a composition or tensor train structure. These polynomials can be evaluated as a sequence of maps, giving rise to intermediate variables (``states'') of dimension lower than the ambient dimension. Structures like these arise naturally in dynamical systems, Markov chains, and neural networks. We de

Hailin Liu, Eugene Ilyushin, Jie Ni, Min Zhu

Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and persistent context, making input-output filtering alone insufficient for reliable protection. This paper presents SafeAgent, a runtime security architecture that treats agent safety as a stateful decision problem ov

Pouria Fatemi, Hoomaan Maskan, Alp Yurtsever, Suvrit Sra

We present the Multi-Block DC (BDC) class, a rich class of structured nonconvex functions that admit a DC ("difference-of-convex") decomposition across parameter blocks. This multi-block class not only subsumes the usual DC programming, but also turns out to be provably more powerful. Specifically, we demonstrate how standard models (e.g., polynomials and te

Ruiliang Li

We study finite-window black-hole spectroscopy in the loud-event regime and ask when a multimode ringdown fit supports a stable common-remnant Kerr interpretation. Starting from whitened, tapered detector-frame data, we prove a deterministic frequency-extraction theorem for a projected sampled Prony--matrix-pencil pipeline with explicit statistical, algorith