March 2026 arXiv papers — page 3

Nicola Tarasca

Here we construct spaces of coinvariants for Heisenberg vertex algebras on abelian varieties and show that these globalize to twisted $\mathscr{D}$-modules on the moduli space of abelian varieties. Remarkably, we recover the standard construction of these sheaves over the Torelli locus. As an example, in the case of commutative Heisenberg vertex algebras we

Thuong Dang

We present a geometric framework for filtered approximate nearest neighbor (ANN) search. Filtering a proximity graph by a metadata predicate produces a subgraph, a fiber, whose connectivity and geometry can differ sharply from the full graph. Using local signals, we propose a two-phase search algorithm that combines full-graph exploration with filtered-neigh

Ajjath A H, Ekta Chaubey, Hua-Sheng Shao

Light-by-light (LbL) scattering is one of the earliest predictions of quantum electrodynamics (QED). Interest in this process has been renewed following its experimental observation at the LHC and the prospects of future measurements at free-electron laser facilities. In this paper, we refine theoretical predictions for LbL scattering by improving the full f

Arka Banerjee

Halo bias is typically treated as a set of coefficients in a perturbative expansion. We show instead that every point in a Gaussian density field has a well-defined scale-independent Lagrangian bias, thereby defining a bias field. This property can be extended to any linear operator acting on the Lagrangian density field, generating secondary bias fields. Ha

Jack Holden

We analyse the 1/16-BPS superconformal index for BPS black holes at equal charge in $AdS_5 \times S_5$, uncovering evidence for a new instability in the microcanonical ensemble along the small black hole saddle. This is indicated by instanton condensation in the matrix model description of the index. This instability occurs for black holes of radius close to

Selin Bayramoğlu, George L Nemhauser, Nikolaos V Sahinidis

Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datasets and substantial computational resources for both training and deployment, typically with GPU parallelization. In this work, we take a dif

Dongyoung Kim, Junyong Lee, Abhijith Punnappurath, Mahmoud Afifi

Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more faithful for low-level vision tasks, collecting large-scale raw datasets remains a major bottleneck, as existing datasets are limited and tied to specific camera hardware. Generati

Niyusha Hosseini, Maximilian P. E. Lock

The time-of-arrival problem asks for the probability distribution for when a quantum particle reaches a specified location. It has been the subject of decades of debate, exemplifying the lack of a self-adjoint time observable in quantum theory. In the Page-Wootters framework, time is a relational quantity, emerging from correlations between a system and a cl

Maximilian Detering, Shyam Balaji

Dark matter (DM) particles decaying in the Galactic halo can inject energetic $e^\pm$ that inverse-Compton scatter (ICS) solar photons into $\gamma$-rays, producing a diffuse and extended halo of emission around the Sun. We present the first quantitative study of this signal as an indirect probe of decaying DM. The intense solar photon field in the inner hel

Valentin Thoss, Laura Lopez-Honorez, Florian Kühnel, Marco Hufnagel

Non-cold dark matter particles can arise from the evaporation of primordial black holes (PBHs). In this paper, we further investigate how the memory-burden effect, which delays the full evaporation of black holes, affects the Lyman-$\alpha$ bound on such non-cold dark matter (NCDM) particles. We mainly focus on scenarios in which PBHs have fully evaporated b

Anushko Chattopadhyay, Ambuj, Rakesh Das, Smritikana Patra

We introduce conclusive identification task for classical channels: a receiver identifies transmitted inputs without error when possible, and responds inconclusively when outputs are ambiguous. For a symmetric not-fully-corrupted channel $N : X \to X$, the single-shot conclusive identification index $\mathrm{ci}_\circ(N)$ counts the maximum number of conclus

Yeyang Sun, Shu Yang, Yi Yang

In ordered, translationally invariant non-Hermitian systems, the skin effect is understood as a boundary phenomenon: nonreciprocal hopping drives an extensive accumulation of eigenstates towards the edges, whereas the periodic-boundary spectrum remains Bloch extended. Here we experimentally reveal the opposite limit -- a disorder-enabled, boundary-independen

Mac M. McMullan, Sownak Bose, Azadeh Fattahi, Isabel Santos-Santos

Dwarf galaxies serve as key models for understanding galaxy assembly in the early universe, with their final properties influenced by environmental factors. Using the dark matter-only simulation "Copernicus Complexio" (COCO) and the semi-analytic model GALFORM, we examine the stellar mass assembly of dwarf galaxies across different cosmic web regions, define

Yuxing Lu, Yushuhong Lin, Jason Zhang

Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discar

Jonah Stalknecht

We define and study a positive geometry $\Delta^{(L)}$ which serves as a natural generalization of loop amplituhedra to two-dimensional Minkowski space $\mathbb{R}^{1,1}$. The geometry is formulated in the framework of lightcone geometries in dual momentum space, and can equivalently be obtained as a specific boundary of the $L$-loop amplituhedron for $\math

Yuheng Liu, Xin Lin, Xinke Li, Baihan Yang

Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues of completeness and global consistency. We propose OmniRoam, a controllable panoramic video generation framework that e

Emanuel Gafton, Gavin B. Dalton, Don Carlos Abrams, Jure Skvarč

We present the architecture, implementation, and on-sky validation of the fully automated acquisition and guiding system (AG) developed for the WEAVE instrument on the William Herschel Telescope. The AG operates in two distinct modes, corresponding to the observing modes of WEAVE. For the large integral field unit (LIFU), an off-axis imaging guider is used,

Kaleb Newman, Tyler Zhu, Olga Russakovsky

Video diffusion models exhibit emergent reasoning capabilities like solving mazes and puzzles, yet little is understood about how they reason during generation. We take a first step towards understanding this and study the internal planning dynamics of video models using 2D maze solving as a controlled testbed. Our investigations reveal two findings. Our fir

Xiangshan Tan, Jingtian Ji, Tianchong Jiang, Pedro Lopes

The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive directional cues within wearable teleoperation interfaces remains a bottleneck. Existing solutions, such as non-directional vibrations from handheld controllers, provide limited info

Ivan Beschastnyi, Francesco Cattafi, João Nuno Mestre

In this paper we discuss how to associate a suitable non-transitive version of a Cartan connection to sub-Riemannian manifolds of corank 1 (including contact and quasi-contact sub-Riemannian manifolds) with non-necessarily constant sub-Riemannian symbols. In particular, we recast the variation of the sub-Riemannian symbols into a suitable "type" map, which i

Izavan dos S. Correia, Henrique C. T. Santos, Tiago A. E. Ferreira

Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In

Chris Jones, Giulio Malavolta

The Grothendieck constant $K_{G}$ is a fundamental quantity in functional analysis, with important connections to quantum information, combinatorial optimization, and the geometry of Banach spaces. Despite decades of study, the value of $K_{G}$ is unknown. The best known lower bound on $K_{G}$ was obtained independently by Davie and Reeds in the 1980s. In th

Wenyi Li, Renkai Luo, Yue Yu, Huan-ang Gao

AI-assisted coding has rapidly reshaped software practice and research workflows, yet today's models still struggle to produce correct code for complex 3D geometric vision. If models could reliably write such code, the research of our community would change substantially. To measure progress toward that goal, we introduce GeoCodeBench, a PhD-level benchmark

Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah

Chain-of-Thought (CoT) monitoring, in which automated systems monitor the CoT of an LLM, is a promising approach for effectively overseeing AI systems. However, the extent to which a model's CoT helps us oversee the model - the monitorability of the CoT - can be affected by training, for instance by the model learning to hide important features of its reason

Ming-Hua Tsai, Phat Tran

This study investigates the use of NeuralUCB for cost-aware large language model (LLM) routing. Existing routing approaches can be broadly grouped into supervised routing methods and partial-feedback methods, each with different tradeoffs in efficiency and adaptivity. We implement a NeuralUCB-based routing policy and evaluate it on RouterBench under a simula

Yanting Wang, Jinyuan Jia

Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building robustly aligned LLM against jailbreaking attacks. However, the explanation of random subspace method lacks sufficient exploration. Existing state-of-the-art feature attribution methods, such as Shapley value and L

Timon Klein, Jonas Kusch, Sebastian Sager, Stefan Schnake

The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head latent attention (MLA). The methods leverage specialized low-rank factorizations across embedding dimensions or attention heads. From the point of view of class

Paige Tuttösí, Angelica Lim, H. Henny Yeung, Yue Wang

Human talkers often address listeners with language-comprehension challenges, such as hard-of-hearing or non-native adults, by globally slowing down their speech. However, it remains unclear whether this strategy actually makes speech more intelligible. Here, we take advantage of recent advancements in machine-generated speech allowing more precise control o

Davide Di Gioia

Autonomous tool-using agents in networked environments must decide which information source to query and when to stop querying and act. Without principled bounds on information-acquisition costs, unconstrained agents exhibit systematic failure modes: excessive tool use under congestion, prolonged deliberation under time decay, and brittle behavior under ambi

Kelsey Lieberman, Laura Slivinski, Matt Bender, Chris Miller

Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This stu

Dimitris Gkoulis

Modular software deployed on mini compute units in controlled distributed environments often needs two messaging paths: low-overhead in-process coordination and selective cross-node distribution. In practice, event identity, serialization, and transport bridging are frequently implemented as ad hoc glue, which complicates inter-process communication (IPC), s

Jinjin Xie, Gary A. Fuller, Di Li, Rowan Smith

There is increasing evidence for global collapse of clumps over parsec-scales in massive star formation regions. Such collapse may result in characteristic molecular line emission profiles but the spatial variation of such lines has rarely been quantitatively examined. Here we explore the infall properties using the spatially-resolved HCO$^+$ J=1--0 and H$^{

Iulian Lucău, Adelin-George Voicu

This paper evaluates whether commercial large language models (LLMs) can function as reliable political advisory tools by comparing their outputs against official legislative reasoning. Using a dataset of 15 Romanian Senate law proposals paired with their official explanatory memoranda (expuneri de motive), we test six LLMs spanning three provider families a

Richard J. Mitchell

The accelerating displacement of human labor by artificial intelligence (AI) and robotic systems represents a structural transformation whose societal consequences extend far beyond conventional labor market analysis. This paper presents a systematic multi-domain examination of the likely effects on economic structure, psychological well-being, political sta

Mohammed Barkatou

We introduce a unified geometric framework for domains satisfying a geometric normal property (C-GNP) relative to a strictly convex set \(C\). Under the fundamental assumption that the source \(f\) is supported within the core \(C\), we establish the stability of superlevel sets for elliptic equations and prove a rigid symmetry result for a classical Serrin-

Yufeng Li, Rrubaa Panchendrarajan, Arkaitz Zubiaga

Automated fact-checking pipelines typically begin with a filtering stage that decides which claims are worth verifying, given that the later evidence retrieval and verification components are expensive to apply at scale. A central task in this stage is verifiable claim detection, which asks whether a statement is in principle checkable against external evide

Konstantin V. Grigorishin

It is demonstrated the fundamental role of the equivalence principle in gravity for the Yukawa coupling between scalar and fermion fields. The Kibble-Zurek mechanism for formation of topological defects as vortexes and monopoles breaks down in system with a global gauge symmetry only. At the same time, the different vacuums can occur, which are separated be

Vladislav Katkov, Nikola Zlatanov

We present a theoretical framework for recovering the amplitude and carrier phase of a single received RF field with a Rydberg-atom receiver, without injecting an RF local oscillator (LO) into the atoms. The key enabling mechanism is a static DC bias applied to the vapor cell: by Stark-mixing a near-degenerate Rydberg pair, the bias activates an otherwise ab

Md Saad, Sajjad Hussain, Mohd Suhaib

This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low-level control and LLMs for high level task planning and understanding of natural language, the proposed framework effectively connects low-level execution with high-lev

Yury Makarychev

We study approximation algorithms for satisfiable and nearly satisfiable instances of ordering constraint satisfaction problems (ordering CSPs). Ordering CSPs arise naturally in ranking and scheduling, yet their approximability remains poorly understood beyond a few isolated cases. We introduce a general framework for designing approximation algorithms for o

Sebastian Reich

This note outlines a mean-field approach to dynamic optimal transport problems based on the recently proposed McKean-Pontryagin maximum principle. Key aspects of the proposed methodology include i) avoidance of sampling over stochastic paths, ii) a fully variational approach leading to constrained Hamiltonian equations of motion, and iii) a unified treatment

Drew Flieder

This paper develops a formal theory of musical scales and their harmonic coverings and introduces orbit covers: coverings obtained by translating a fixed subset across a scale via a group action. Orbit covers generalize familiar constructions, such as the covering of the diatonic scale by tertian triads, and are motivated by the search for a generalized harm

Yichen Yang, Olivier Pierron, Josh Kacher, David Rowenhorst

4D-STEM-based orientation and phase mapping has enabled rapid microstructure quantification that can be directly combined with standard TEM- and STEM-based imaging modes. Typically, orientation mapping is coupled with beam precession (i.e. precession electron diffraction) to achieve high indexing rates, adding to the cost and often decreasing the spatial res

Tor Lattimore

We propose a simple detection mechanism for the Gumbel watermarking scheme proposed by Aaronson (2022). The new mechanism is proven to be near-optimal in a problem-dependent sense among all model-agnostic watermarking schemes under the assumption that the next-token distribution is sampled i.i.d.

Chong Xiang, Drew Zagieboylo, Shaona Ghosh, Sanjay Kariyappa

AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt injection attacks. We articulate three positions: (1) dynamic

Irena Ristova, Vincenzo Stoico

Microservice architectures form the backbone of modern software systems for their scalability, resilience, and maintainability, but their rise in cloud-native environments raises energy efficiency concerns. While prior research addresses microservice decomposition and placement, the impact of topology, the structural arrangement and interaction pattern among

Damek Davis

We give a precise asymptotic formula for the number of $n\times 4t$ partial Hadamard matrices in the regimes $t/n^3\to\infty$ and $t/n^3\to\Theta$ for sufficiently large fixed $\Theta$. This strengthens earlier results of de~Launey and Levin, who obtained the asymptotic for $t/n^{12}\to\infty$, and of Canfield, who extended this to $t/n^4\to\infty$.

Saurav Goyal, Sven-Olaf Moch, Vaibhav Pathak, V. Ravindran

We present results for unpolarized and polarized semi-inclusive deep-inelastic scattering mediated by electroweak gauge bosons at next-to-next-to-leading order (NNLO) in perturbative quantum chromodynamics. The results include all relevant structure functions arising from both neutral current (NC) and charged current (CC) interactions, incorporating contribu

Olga Podvigina

We investigate stability of a new class of heteroclinic cycles that we call heteroclinic cycles of type Y. The cycles can be regarded as a generalisation of heteroclinic cycles of type Z introduced in [Podvigina, Nonlinearity 25, 2012]. The type Y cycles differ from the cycles of type Z in the following: The trajectories comprising a cycle of type Y belong t

Mohammed Barkatou, Mohamed El Morsalani

We study the geometric and dynamical structure induced by the return map associated with domains in the class \(\mathcal{O}_{C}\). This map, defined through a geometric round-trip between the convex core and the outer boundary, generates a discrete dynamical system on the boundary \(\partial C\). Building on previous results establishing global convergence o

Mukti Acharya

In this paper, we construct additively graceful signed graphs S from a given graph G that may be additively graceful or not be additively graceful. We also show the construction of additively graceful signed graphs from additively graceful signed graphs. We find the values of m, n in non-divisible sum graph, denoted as G(m, n), that admit additively graceful

QIfan Zhang, Hao Wang, Xiangrong Qin, Ruijie Li

Camouflaged object detection (COD) aims to identify targets that are highly blended with their backgrounds. Recent works have shown that the optical characteristics of polarization cues play a significant role in improving camouflaged object detection. However, most existing polarization-based approaches depend on complex visual encoders and fusion mechanism

Yucheng Zhang, Chayanon Wichitrnithed, Shukai Cai, Sourav Dutta

Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics. Exact Riemann solvers faithfully solve the underlying equations, but can be computationally expensive due to the iterative root-finding procedures they often r

Igor Vaiman

Context. Visualization of 2D distributions is an essential task, commonly done with a 2D histogram. The histogram is built by subdividing the sample space into regions and color-coding the number of samples in each region. Aims. We aim to solve long-standing problems with common 2D histogram methods: lack of thematic, visual, and conceptual unity with underl

Hamid Abdollahi, Robert Doot, Raul Porto, Anthony Young

Background: Renal blood flow (RBF) is an important marker of kidney health, but noninvasive assessment is not routinely used in clinical imaging. We evaluated the feasibility and physiologic validity of quantifying renal transport of Rubidium-82 (K1) during standard myocardial perfusion imaging (MPI) PET. Methods: We studied 126 patients (age 60 +/- 12 years

Adi Wijaya, Budi Hermawan, Wiga Maulana Baihaqi, Catur Supriyanto

This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanc

G. Alencar, T. M. Crispim

In General Relativity, supporting known traversable wormhole geometries with electromagnetic fields typically requires complex Non-Linear Electrodynamics (NED). We demonstrate that Unimodular Gravity (UG) elegantly resolves this limitation. By relaxing energy-momentum conservation, UG introduces a dynamical cosmological term, $Λ(x)$, enabling a semi-classica

Alan Sun, Mariya Toneva

Mechanistic interpretability (MI) is an emerging framework for interpreting neural networks. Given a task and model, MI aims to discover a succinct algorithmic process, an interpretation, that explains the model's decision process on that task. However, MI is difficult to scale and generalize. This stems in part from two key challenges: there is no precise n

M. I. Belishev, S. A. Simonov

We prove that minimal Dirac operators on the half-line are self-modeling, which means that such an operator is determined by its arbitrary unitary copy uniquely up to a transformation (shape equivalence) which changes its potential by a constant factor of modulus one. This result is obtained using the wave functional model of the minimal matrix Schr\"odinger

Giovanni Antonio Chirilli

Relating the high-energy dipole description of deep-inelastic scattering to the standard light-ray operator formulation at finite Bjorken $x_B$ is essential for connecting the small-$x$ framework to the usual partonic description. I demonstrate that this connection already emerges at the first sub-eikonal order. At the differential level, the first sub-eikon

Gianluca Aguzzi, Davide Domini, Nicolas Farabegoli, Mirko Viroli

Aggregate programming is a field-based coordination paradigm with over a decade of exploration and successful applications across domains including sensor networks, robotics, and IoT, with implementations in various programming languages, such as Protelis, ScaFi (Scala), and FCPP (C++). A recent research direction integrates machine learning with aggregate c

Mohammadhossein Khojasteh, Yifan Jiang, Stefano De Giorgis, Frank van Harmelen

Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of s

Francesco Iacovelli

Unlike electromagnetic telescopes, gravitational-wave (GW) detectors cannot produce pretty pictures, but we can convert GW signals into sound. I compute what the Universe actually sounds like by averaging over $\sim10^6$ synthetic compact binary coalescence events occurring throughout 2026. The result: a soothing, low-frequency rumble, perfect for sleeping,

Vrunda N. Sukhadia, Shammur Absar Chowdhury

Large self-supervised speech (SSL) models achieve strong downstream performance, but their size limits deployment in resource-constrained settings. We present HArnESS, an Arabic-centric self-supervised speech model family trained from scratch with iterative self-distillation, together with lightweight student variants that offer strong accuracy-efficiency tr

Haowen Xu, Xin Chen

With the increasing penetration of inverter-based resources (IBRs) in power grids, system-level coordinated optimization of IBR controllers has become increasingly important for maintaining overall system stability. Unlike most existing methods that rely on simplified or linearized dynamic models and focus on small-signal stability or isolated tuning of indi

Jules Arzel, Noureddine Lehdili

This paper studies the problem of hedging and pricing a European call option under proportional transaction costs, from two complementary perspectives. We first derive the optimal hedging strategy under CARA utility, following the stochastic control framework of Davis et al. (1993), characterising the no-transaction band via the Hamilton-Jacobi-Bellman Quasi

Nathan Heath

Myopic Optimization with Non-myopic Approval (MONA) mitigates multi-step reward hacking by restricting the agent's planning horizon while supplying far-sighted approval as a training signal~\cite{farquhar2025mona}. The original paper identifies a critical open question: how the method of constructing approval -- particularly the degree to which approval depe

Quanyu Tang

Let $p$ be a prime and, for $A\subseteq \mathbb F_p$, define $A^\ast=(A+A)\cup(AA)$. S\'ark\"ozy conjectured that there exist constants $c>0$ and $p_0$ such that, for every prime $p>p_0$, every set $A\subseteq \mathbb F_p$ with $|A|>\left(\frac12-c\right)p$ satisfies $\mathbb F_p^\times\subseteq A^\ast$. We disprove this conjecture: for every odd prime $p\ge

Abdullah Thabit, Mohamed Benmahdjoub, Rafiuddin Jinabade, Hizirwan S. Salim

Augmented reality (AR) devices with head mounted displays (HMDs) facilitate the direct superimposition of 3D preoperative imaging data onto the patient during surgery. To use an HMD-AR device as a stand-alone surgical navigation system, the device should be able to locate the patient and surgical instruments, align preoperative imaging data with the patient,

Fedor B. Lyudogovskiy

For the partition graph $G_n$ on the set of partitions of $n$, we study the stratification induced by the local simplex dimension $\dim_{\mathrm{loc}}(\lambda)$, defined as the maximal dimension of a simplex of the clique complex $K_n=\mathrm{Cl}(G_n)$ containing $\lambda$. Using the previously established description of maximal cliques through a vertex in t

Liuhang Ye, Bjarne Bergh, Nilanjana Datta

A strong converse bound for the classical identification capacity of a quantum channel is an upper bound on the asymptotic identification rate of classical messages sent through the channel, such that, above this rate, the probability of an identification error necessarily converges to one. Converse bounds for identification are notoriously difficult to obta

Jeffrey D. Varner

Mathematical models of natural and man-made systems often have many adjustable parameters that must be estimated from multiple, potentially conflicting datasets. Rather than reporting a single best-fit parameter vector, it is often more informative to generate an ensemble of parameter sets that collectively map out the trade-offs among competing objectives.

Gregory Faurot, Giovanni Ferrer

We compute the homotopy groups at each unital abelian C*-algebra $C(T)$ in the Morita $3$-category of abelian C*-algebras, C*-algebras with central maps, C*-correspondences, and adjointable bimodule maps. We describe these groups in terms of the topological data of the underlying compact Hausdorff space $T$. We also compute the actions of the first homotopy

Mutian Shen, Zohar Nussinov, Yang-Yu Liu

We introduce a systematic method for expanding general spin-glass Hamiltonians in terms of Mattis interactions, providing a novel perspective for understanding the fundamental differences between short-range Edwards-Anderson (EA) and mean-field Sherrington-Kirkpatrick (SK) spin glasses. By iteratively extracting patterns from the coupling matrix, we expand t

Mathias Zambach, Miriam Varón, Thomas Veile, Matti Knaapila

Nanocomposites comprised of insulated magnetic single-domain particles are promising candidates for high-frequency, eddy current free, soft magnetic materials, but tend to suffer from low magnetic susceptibility ($<20$). Particle alignment has been proposed to increase nanocomposite susceptibility and reduce magnetic losses but experimental verification has

Quanyan Zhu, Zhengye Han

This paper introduces a performative scenario optimization framework for decision-dependent chance-constrained problems. Unlike classical stochastic optimization, we account for the feedback loop where decisions actively shape the underlying data-generating process. We define performative solutions as self-consistent equilibria and establish their existence

Sean Howe

We formulate a conjecture about intersections between the Banach-Colmez space $\mathrm{BC}(1/2)$ and germs of smooth rigid analytic curves at the origin in $\mathbb{A}^2_{\mathbb{C}_p}$

Christoph Brauer, Arne Hindersmann, Timo de Wolff

In this article, we investigate vacuum leakage detection problems in composite manufacturing. Our approach uses Voronoi diagrams, a well-known structure in discrete geometry. The Voronoi diagram of the vacuum connection positions partitions the component surface. We use this partition to narrow down potential leak locations to a small area, making an efficie

Junwei Yu, Mufeng Yang, Yepeng Ding, Hiroyuki Sato

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structur

Naveena Ragunathan, Adam Van Tuyl

The van der Waerden simplicial complex, denoted ${\tt vdw}(n,k)$, is the simpicial complex whose facets correspond to the arithmetic progressions of length $k$ in the set $\{1,\ldots,n\}$. We study the Lefschetz properties of the Artinian ring $A({\tt vdw}(n,k)) = K[x_1,\ldots,x_n]/(I_{{\tt vdw}(n,k)} + \langle x_1^2,\ldots,x_n^2\rangle)$ where $I_{{\tt vdw}

Iain Swift, JingHua Ye, Ruairi O'Reilly

Multimodal deep learning for cancer prognosis is commonly assumed to benefit from synergistic cross-modal interactions, yet this assumption has not been directly tested in survival prediction settings. This work adapts InterSHAP, a Shapley interaction index-based metric, from classification to Cox proportional hazards models and applies it to quantify cross-

Hang Liu, Junjie Li, Yinzhi Wang, Niraj K. Nepal

This study explores the use of INT8-based emulation for accelerating traditional FP64-based HPC workloads on modern GPU architectures. Through SCILIB-Accel automatic BLAS offload tool for cache-coherent Unified Memory Architecture, we emulate FP64 matrix multiplications in the LSMS CPU application in the MuST suite without code changes. We find that accuracy

Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Accurate forecasting of air pollution is important for environmental monitoring and policy support, yet data-driven models often suffer from limited generalization in regions with sparse observations. This paper presents Meteorology-Driven GPT for Air Pollution (GPT4AP), a parameter-efficient multi-task forecasting framework based on a pre-trained GPT-2 back

Zhenglin Lai, Sirui Huang, Yuteng Li, Changxin Huang

Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physical risk and severe consequences is rarely evaluated.We find that these models often downplay or omit key danger cues and severe outcomes for hazardous actions, which can induce unsafe preferences during planni

Zhi-Wei Sun

In this paper we collect over 150 new series identities (involving binomial coefficients) conjectured by the author in 2026. The values involved are related to $\pi$ or Riemann's zeta function or Dirichlet's $L$-function. For example, we conjecture that $$\sum_{k=0}^\infty\frac{16k+3}{(-202^2)^k}\binom{2k}kT_k(19,-20)T_{2k}(9,-5)=\frac{43\sqrt{101}}{75\pi},$

Malwina A. Marczak, Spencer J. Johnson, Mark R. Hogg, Timon L. Baltisberger

Entangled photon pairs are a ubiquitous resource in quantum technologies, used in quantum key distribution and quantum networking as well as fundamental tests of non-locality. For scalable quantum networks, pairs that are indistinguishable in all unentangled degrees of freedom are essential, as they enable high-fidelity entanglement swapping across network n

David Kurniadi Angdinata, Evan Chen, Chris Cummins, Ben Eltschig

Lehmer conjectured that Ramanujan's tau-function never vanishes. In a related direction, a folklore conjecture asserts that infinitely many primes arise as absolute values of Ramanujan's tau-function. Recently, Xiong showed that these prime values form a subset of the primes with density at most $2/11$. Assuming the $abc$ Conjecture, we prove the stronger up

Iain Swift, JingHua Ye

Multimodal deep learning has improved prognostic accuracy for brain tumours by integrating histopathology and genomic data, yet the contribution of volumetric MRI within unified survival frameworks remains unexplored. This pilot study extends a bimodal framework by incorporating Fluid Attenuated Inversion Recovery (FLAIR) MRI from BraTS2021 as a third modali

Badhan Mazumder, Sir-Lord Wiafe, Aline Kotoski, Vince D. Calhoun

Understanding how brain structure and function interact is key to explaining intelligence yet modeling them jointly is challenging as the structural and functional connectome capture complementary aspects of organization. We introduced Multi-scale Adaptive Graph Network (MAGNet), a Transformer-style graph neural network framework that adaptively learns struc

Sicheng Lu, Zikai Xiao, Jianhui Wei, Danyu Sun

Surgical video understanding is essential for computer-assisted interventions, yet existing surgical foundation models remain constrained by limited data scale, procedural diversity, and inconsistent evaluation, often lacking a reproducible training pipeline. We propose SurgRec, a scalable and reproducible pretraining recipe for surgical video understanding,

Pierre Clare, Tyrone Crisp

We calculate the $K$-theory of the reduced $C^*$-algebra $C^*_r(G)$ of a reductive $p$-adic group $G$. To do so, we show that each direct summand in Plymen&#39;s Plancherel decomposition of $C^*_r(G)$ is Morita equivalent to a twisted crossed product for an action of a finite group on the blow-up of a compact torus along the zero-locus of a certain Planchere

Benne Holwerda

We estimate the Hollyfeld Gambit for the Powerball lottery and its return on investment compared to present and extrapolated federal funding for astrophysical grants. Using a Monte Carlo estimation of rate of return for the Powerball, we conclude a Hollyfeld Gambit is a better bet than a federal grant by the end of the decade if current trends hold.

Mayura Balakrishnan, Robert Frazier, Joseph Michail

The nature of Sagittarius A* (Sgr A*) has been the subject of intense study and debate for over half a century. Herein, we present the first successful interview with an astrophysical object, exploring the perspective of this supermassive black hole and, in doing so, challenging the traditional observational paradigm of astrophysics. Rather than treating ast

Shi Li, Vinkle Srivastav, Nicolas Chanel, Saurav Sharma

Surgical procedures are inherently complex and risky, requiring extensive expertise and constant focus to navigate evolving intraoperative scenes. Computer-assisted systems such as surgical visual question answering (VQA) offer promises for education and intraoperative support. Current surgical VQA research largely focuses on static frame analysis, overlooki

Boris Alexeev, Moe Putterman, Mehtaab Sawhney, Mark Sellke

We give a triplet of short proofs, each of which answers a question raised by Erd\H{o}s. The first concerns the small prime factors of $\binom{n}{k}$, the second concerns whether an additive basis $A$ can always be split into pieces $A_1$ and $A_2$ such that each of $A_i + A_i$ has bounded gaps, and the final concerns whether $\{\alpha p\}$ is "well-distribu

Badhan Mazumder, Sir-Lord Wiafe, Vince D. Calhoun, Dong Hye Ye

Early identification of adolescents at risk for substance use initiation (SUI) is vital yet difficult, as most predictors treat connectivity as static or cross-sectional and miss how brain networks change over time and with behavior. We proposed NeuroBRIDGE (Behavior conditioned RIemannian Koopman Dynamics on lonGitudinal connEctomes), a novel graph neural n

Ingyu Jang, Leila J. Bridgeman

Despite longstanding interest, controller synthesis remains challenging for networks of heterogeneous, nonlinear agents. Moreover, the requirements for computational scalability and information privacy have become increasingly critical. This paper introduces a dissipativity-based distributed controller synthesis framework for networks with heterogeneous agen

Evgenios T. A. Kakariadis, Malte Leimbach, Ivan G. Todorov, Walter D. van Suijlekom

The extension problem asks whether positive semi-definite functions on a symmetric unital subset of a discrete group can be extended to positive semi-definite functions on the whole group. It has been known at least since the work of Rudin in the 1960s that this is closely related to the problem of finding sums of squares factorisations of positive elements

Xue Jiang, Tianyu Zhang, Ge Li, Mengyang Liu

Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from critical limitations in code generation, where upfront thinking is often insufficient as problems' full complexity only reveals itself during code implementation. Moreover, it can

Marios Impraimakis, Feiyu Zhou, Andrew Plummer

The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert sys