April 2026 arXiv papers — page 145

Tianyu Li, Jiong Li, Chong Li, Peiyuan Huang

Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire.

H. P. Zhang, Z. Song

A quadratic well plays a central role in a wide variety of modern physical theories and applications. In this work, we investigate many-body dynamics in a quadratic well, using an Ising chain as a paradigmatic example. In contrast to a uniform Ising chain, where the quantum phase transition is driven by the field strength, the present system exhibits spatial

Xinjie Zhang, Qiang Li, Xiaowen Ma, Axi Niu

Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over multimodal inputs and multi-dimensional assessments. Existing MLLM-based approaches often rely on human heuristic prompting, leading to two key limitations: rigid metric prompting and

Jaime Banks

In discussions of human relations with conversational agents (CAs; e.g., voice assistants, AI companions, some social robots), they are increasingly referred to as parasocial. This is a misapplication of the term, heuristically taken up to mean "unreal." In this provocation, I briefly account for the theoretical trajectory of parasociality and detail

G. Zullo, C. Pallanca, F. R. Ferraro, B. Lanzoni

The James Webb Space Telescope provides an exciting opportunity to investigate stellar systems located in heavily obscured regions like the Galactic bulge. Possibly, the most enigmatic among them is Terzan 5: long classified as a globular cluster, it is now known to host distinct stellar populations with different iron abundances (ranging approximately from

Marcel R. R. Hughes, Masaki Shigemori

This paper is a follow-up to the short paper arXiv:2509.19425, greatly expanding the discussion with examples and providing derivations and justifications of results presented there. We introduce a new supersymmetry index for the D1-D5 CFT on $T^4$, which we call the resolved elliptic genus (REG). It is a one-parameter generalisation of the standard supersym

Uma Meleti, Jeffrey J. Nirschl

Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often overconfident and poorly calibrated in out-of-distribution (OOD) settings, which limit trust and clinical adoption. Safety-critical medical imaging workflows benefit from intrinsic uncertainty-aware properties tha

Pavan Vynatheya, Luc Dessart, Taeho Ryu, Rüdiger Pakmor

In a dense star cluster core, a tidal disruption event (TDE) of a white dwarf (WD) can occur if the WD passes within the tidal radius of an intermediate-mass black hole (IMBH). Very close encounters cause extreme tidal compression in the WD, raising temperatures enough to induce runaway fusion and produce a thermonuclear supernova (SN). Using the hydrodynami

J. Adams, J. Alfaro, D. Allard, P. Alldredge

A review of the current status of the field of Ultra-High-Energy Cosmic Ray (UHECR) including a summary of remaining open questions was presented in the white paper "Ultra-High Energy Cosmic Rays: at the Intersection of the Cosmic and Energy Frontiers" (Astropart. Phys. 147 (2023) 102794; arXiv:2205.05845). The authors concluded that two types of nex

Songela W. Chen, David T. Limmer

Energy costs of information processing are growing exponentially. Bit erasure is a key problem in this energy-information nexus, and a number of seminal relationships have been deduced regarding the relationship between thermodynamic costs and memory storage. To continue making progress in the modern era, however, requires confronting thermodynamic costs in

Pavan Dharanipragada, Gurmeet Singh Punia, Amitabh Virmani

We construct singly and doubly spinning non-supersymmetric F1--P black ring solutions in five-dimensional supergravity. These black rings have regular horizons and non-zero temperature. The singly spinning configuration lies in the duality orbit of the black ring constructed by Elvang, Emparan, and Figueras, while the doubly spinning configuration is a charg

Sho Ebihara, Michiko S. Fujii, Takayuki R. Saitoh, Yutaka Hirai

Spectroscopic observations by the James Webb Space Telescope (JWST) have revealed young, compact, high-redshift ($z$) galaxies with high nitrogen-to-oxygen (N/O) ratios. GN-z11 at z=10.6 is one of these galaxies. One possible scenario for such a high N/O ratio is pollution from supermassive stars (SMSs), from which stellar winds are expected to be nitrogen-r

Dušan Oberta

In this paper we address the question of solvability of dynamic equations on time scales in Banach spaces. In particular, our main theorem extends the result for classical differential equations in Banach spaces of Banaś and Goebel (1980), to an arbitrary time scale. Central role is played by the axiomatic theory of measures of noncompactness and the newly i

Zdzisław Burda, Maciej Wołoszyn, Krzysztof Malarz, Krzysztof Kułakowski

We show that the Heider model with an external field is equivalent, in the limit of structural balance, to the Ising model with nearest-neighbor interactions without an external field. More precisely, we claim that the signs of the Heider relations that maintain structural equilibrium in the system can be represented as nearest neighbor Ising spin products.

A. A. Valverde, S. Cupp, A. Gross, B. Liu

Nuclear isomers can have significant impacts on astrophysical nucleosynthesis processes, with recent efforts demonstrating that the population of isomeric states with different half-lives may require separate treatment in reaction networks to accurately capture the differences in heating or in identifiable electromagnetic signals. Several potential so-called

Weichao Bu, Yang Lei

The Kerr/CFT correspondence establishes a relationship between extremal black holes in higher dimensions and a chiral conformal field theory (CFT) in their near-horizon limit. A generalization of this framework, known as the EVH/CFT correspondence, has been developed for four- and five-dimensional AdS black holes. It was further proposed in arXiv:1910.14293

Jun Ji, Joseph G. Thomas, Zichen Xi, Liyang Jin

Mechanical systems are pivotal in quantum technologies because of their long coherent time and versatile coupling to qubit systems. So far, the coherent and dynamic control of gigahertz-frequency mechanical modes mostly relies on optomechanical coupling and piezoelectric coupling to superconducting qubits. Here, we demonstrate on-chip cavity electro-acoustic

Rui Yang, Zheng Shi, Zhirong Lin, Waltraut Wustmann

Josephson parametric devices are widely used in superconducting quantum computing research but suffer from an inherent gain-bandwidth trade-off. This limitation is partly overcome by coupling the device to its input/output transmission line via a bandpass filter, leading to wider bandwidth at undiminished gain. Here we perform a non-perturbative circuit anal

Daiki Sekizawa, Sosuke Ito, Masafumi Oizumi

Nonlinear oscillations are commonly observed in complex systems far from equilibrium, such as living organisms. These oscillations are essential for sustaining vital processes, like neuronal firing, circadian rhythms, and heartbeats. In such systems, thermodynamic dissipation is necessary to maintain oscillations against noise. However, due to their nonlinea

Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou

Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, under what circumstances SAEs derive most fine-grained latent features for safety, a low-frequency concept domain, remains unexplored. Two key challenges exist: identifying SAEs with the greatest potential for generating

Hong-Er Gong, Junlin Qin, Yusen Wang, Bofeng Wu

This paper primarily investigates the optical characteristics of two minimal Schwarzschild black hole deformations, the Kazakov-Solodukhin and Ghosh-Kumar black holes, under different accretion models. The event horizon, photon sphere, and critical impact parameter of the former increase compared with the Schwarzschild black hole, but those of the latter dec

I. S. Popov

Investigation of the approximation properties, convergence, and stability of the ADER-DG method for solving an ODE system is carried out. The ADER-DG method is $A$- and $AN$-stable, $L$-stable, $B$- and $BN$-stable, and algebraically stable. Several other relations useful for an application and implementation of the ADER-DG method are proved. Applications of

Samuel Teuber, Mattias Ulbrich, André Platzer, Bernhard Beckert

Formally specifying, let alone verifying, properties of systems involving multiple programming languages is inherently challenging. We introduce Heterogeneous Dynamic Logic (HDL), a framework for combining reasoning principles from distinct (dynamic) program logics in a modular and compositional way. HDL mirrors the architecture of satisfiability modulo theo

Ralf Meyer

A groupoid correspondence on an etale, locally compact groupoid induces a C*-correspondence on its groupoid C*-algebra. We show that the Cuntz-Pimsner algebra for this C*-correspondence relative to an ideal associated to an open invariant subset of the groupoid is again a groupoid C*-algebra for a certain groupoid. We describe this groupoid explicitly and ch

Serena Dipierro, Edoardo Proietti Lippi, Caterina Sportelli, Enrico Valdinoci

We consider a "superposition operator" obtained through the continuous superposition of operators of mixed fractional order, modulated by a signed Borel finite measure defined over the set $[0, 1]$. The relevance of this operator is rooted in the fact that it incorporates special and significant cases of interest, like the mixed operator $-Δ+ (-Δ)^s$

Milan Donvil, Stefaan Vaes

A discrete group $G$ is called W*-superrigid if the group $G$ can be entirely recovered from the ambient group von Neumann algebra $L(G)$. We introduce an analogous notion for discrete quantum groups. We prove that this strengthened quantum W*-superrigidity property holds for a natural family of co-induced discrete quantum groups. We also prove that, remarka

Anuj Abhishek, Thilo Strauss

In this work, we consider the non-invasive medical imaging modality of Electrical Impedance Tomography (EIT), where the goal is to recover the conductivity in a medium from boundary current-to-voltage measurements, i.e., the Neumann-to-Dirichlet (N--t--D) operator. We formulate this inverse problem as an operator-learning task, where the aim is to approximat

Igor V. Nikolaev

We use $K$-theory of the $C^*$-algebras to study the Arakelov geometry, i.e. a compactification of the arithmetic schemes $V\to Spec ~\mathbf{Z}$. In particular, it is proved that the Picard group of $V$ is isomorphic to the $K_0$-group of a Cuntz-Pimsner algebra associated to $V$. We apply the result to the finiteness problem for the algebraic varieties ove

Martin Olsson

We extend the notions of Hochschild and cyclic homology to morphisms from algebraic spaces to algebraic stacks. Using this, we obtain generalizations to log schemes in the sense of Fontaine and Illusie of these homology theories.

O. Goktug Poyrazoglu, Yukang Cao, Rahul Moorthy, Volkan Isler

Trajectory optimization depends heavily on initialization. In particular, sampling-based approaches are highly sensitive to initial solutions, and limited exploration frequently leads them to converge to local minima in complex environments. We present Uncertainty Guided Exploratory Trajectory Optimization (UGE-TO), a trajectory optimization algorithm that g

Rohit Gupta, Jayakrishnan Unnikrishnan, Fan Fei, Sheng Liu

Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperform specialized embedding-based models in Retrieval tasks, such as Text-toVideo Retrieval and Moment Retrieval. We introduce ViLL-E (Video-LLM-Embed), a unified VideoLLM architectu

Felipe Estrada

This work addresses the existence of transitive extensions of certain infinite permutation groups which arise as the automorphism groups of model-theoretic structures which are generic in the Fra\"iss\'e sense. The study of transitive extensions has hitherto largely concerned itself with finite permutation groups. Moving beyond the finite realm, we develop c

Xiangyu Zhang, Benjamin John Southwell, Siqi Pan, Xinlei Niu

Audio tokenization has emerged as a critical component in end-to-end audio language models, enabling efficient discrete representation learning for both audio understanding and generation tasks. However, existing audio tokenizers face fundamental limitations in understanding tasks due to single-modality constraints, particularly when audio signals contain am

Liam Wheen

The 100,000-year problem concerns the dominant period of glacial-interglacial cycles over the past 800,000 years and their correlation with Earth's orbital eccentricity, despite eccentricity's weak influence on solar radiation. Two theories compete: the astronomical theory, in which orbital forcing drives the cycles with amplification from Earth system feedb

Nam Trinh

Motivated behaviour relies on the brain's capacity to evaluate effort and reward. Dysregulation within these processes contributes to a spectrum of conditions, from hyperactivity in attention-deficit/hyperactivity disorder (ADHD) to diminished goal-directed behaviour in apathy. This thesis investigates the neural mechanisms underlying ADHD using electroencep

Rishabh Bhardwaj, Alexander Reed Muñoz, Travis E. Jones, John Golden

Strongly correlated materials are a natural target for fault-tolerant quantum computers, but they require tools beyond those developed for molecules. Electronic wavefunctions vary rapidly near nuclei yet remain delocalized across many unit cells, and bulk properties must be converged systematically with respect to finite-size errors. To resolve such issues,

Mario Kieburg

We review the ideas of how random matrix theory has to be properly applied to quantum physics; particularly we focus on how the spectrum has to be properly prepared and the random matrix correctly identified before the random matrix and the physical eigenvalue spectrum can be compared. We explain the ideas of the symmetry classification of symmetric matrix s

Vitor F. Grizzi, Luke N. Pretzie, Jiayi Xu, Cong Liu

We present XANE(3), a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly from atomic structures. The model combines tensor-product message passing with spherical harmonic edge features, absorber-query attention pooling, custom equivariant layer normalization, adaptive gated residua

Maximilian Schaller, Stephen Boyd

The relationship between demand and prices of a set of products can be modeled as a linear mapping from logarithmic price changes to logarithmic changes in demand. We consider the problem of estimating the coefficient matrix of this mapping, the elasticity matrix, based on observed data consisting of real-valued prices and integer-valued demands. We regulari

Vasiliki Stoumpou, Dimitris Bertsimas, Samuel Singer, Georgios Antonios Margonis

Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational survival data. First, we infer a latent prognostic factor (U) from res

Eunghyun Lee

We introduce a class of multispecies exclusion processes with long-range swap interactions, incorporating species-dependent interpolation between TASEP-type and drop--push-type dynamics: each species $i$ is assigned a parameter $\mu_i$ governing same-species interactions, resulting in a heterogeneous system in which different species follow distinct microsco

F. L. Carneiro, S. C. Ulhoa, J. W. Maluf

The article deals with photon propagation in pp-wave spacetimes in the strong gravitational-wave regime and its consequences for redshift measurements. We show that null geodesics crossing a localized pp-wave pulse exhibit an energy memory effect, producing a finite asymptotic shift in the photon frequency measured by static observers. This path-dependent co

Jörg Hennig

An intriguing open problem in general relativity is whether a stationary equilibrium configuration of multiple, physically relevant black holes can exist. In such a hypothetical setup, the gravitational attraction would need to be balanced by the repulsive spin-spin and electromagnetic interactions. This contribution reports on a method to address this probl

Willy Carlos Tchuitcheu, Tan Lu, Ann Dooms

Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout permutations. This paper introduces the Platonic Representa

Jolie LHeureux, Genaro Suárez, Johanna M. Vos, Stanimir Metchev

We examine the positions of substellar objects in mid-infrared color-magnitude and color-color diagrams to distinguish between cloudy and cloud-free atmospheres. Using Spitzer mid-infrared spectra of 113 M5-T9 ultracool dwarfs, we derive synthetic photometry for the JWST MIRI F560W, E'770W, F1000W, and F 1280W filters, which cover key absorption features inc

François Le Gall, Suguru Tamaki

The short-path quantum algorithm introduced by Hastings (Quantum 2018, 2019) is a variant of adiabatic quantum algorithms that enables an easier worst-case analysis by avoiding the need to control the spectral gap along a long adiabatic path. Dalzell, Pancotti, Campbell, and Brand\~{a}o (STOC 2023) recently revisited this framework and obtained a clear analy

Fatih Kızılaslan

Stress-strength models are widely used to assess the reliability of systems under uncertain conditions. While most studies assume independence between stress and strength variables, such an assumption may be unrealistic in many practical situations where these components are inherently dependent. In this study, we investigate stress-strength reliability unde

Swanand Rao, Kiran Kashalkar, Parvathi Somashekar, Priya Krishnan

The transition from stateless model inference to stateful agentic execution is reshaping the systems assumptions underlying modern AI infrastructure. While large language models have made persistent, tool-using, and collaborative agents technically viable, existing runtime architectures remain constrained by materialization-heavy instantiation models that im

Ji Ho Bae

We investigate how self-referential inputs alter the internal matrix dynamics of large language models. Measuring 106 scalar metrics across up to 7 analysis passes on four models from three architecture families -- Qwen3-VL-8B, Llama-3.2-11B, Llama-3.3-70B, and Gemma-2-9B -- over 300 prompts in a 14-level hierarchy at three temperatures ($T \in \{0.0, 0.3, 0

Anas Abognah, Otman Basir

The management of radio frequency spectrum is undergoing a paradigm shift from static, centralized command-and-control models to dynamic, market-driven approaches. However, the realization of Dynamic Spectrum Management has been hindered by the lack of an automated, trustworthy, and intelligent coordination infrastructure that can operate without a central a

Rongzhe Wei, Ge Shi, Min Cheng, Na Zhang

Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions. However, executing multi-step tasks within massive tool libraries remains challenging due to two critical bottlenecks: (1) the absence of rigorous, plan-level evaluation frameworks and (2) the computational demand of exploring v

Leandro Lyra Braga Dognini

This paper bridges social security design and general equilibrium theory to conceive optimally balanced pay-as-you-go systems. The design is based on the backward calculation algorithm from Dognini (2025), which is used to find optimal monetary equilibria of prone-to-savings non-stationary overlapping generations economies with heterogeneous households. In p

Conor Kresin, Duncan A. Clark, Louis Davis, Martin Hazelton

We develop a framework for causal inference with continuous spatiotemporal point-process outcomes under cell-level interventions and outcome spillover. Potential outcomes are indexed by full treatment allocations, and the observed post-treatment process is represented as an unlabelled superposition of latent control and treatment components. On the observed

Bachan Ghimire, Nitin Gupta

Understanding how developers combine programming languages in practice reveals the hidden structure of the software ecosystem: which languages are used as complements, which define coherent technology stacks, and which bridge disparate communities. We present a three-phase empirical pipeline that mines Stack Overflow posts by hundreds of thousands of develop

Lawrence Feldman, Matthew Bomparola

This memo compares two methods, Powerwise (PWR) and the NCAA Power Index (NPI), that aim to rank NCAA Division I, II, and III teams on the basis of deservedness of an invite to end-of-season championship tournaments. It find that while the NPI might be a fit for sports like hockey, it falls short of the PWR method for use in ranking team sports that regularl

Vinicius Oliveira, D. Cogollo, A. Doff, C. A. de S. Pires

We show that the $\mathrm{SU}(3)_C \times \mathrm{SU}(3)_L \times \mathrm{U}(1)_N$ model with right-handed neutrinos naturally accommodates a viable sub-MeV dark matter (DM) candidate realized as pseudo-Goldstone boson that acquires tiny mass through gravitational effects. The observed relic abundance is obtained via freeze-in in a low-reheating temperature

Drazen Adamovic, Xingjun Lin, Jinwei Yang

In this paper, we prove the category of finite length modules for the $\mathbb{Z}_2$-orbifold $M(1)^+$ of the Heisenberg vertex operator algebra whose simple composition factors are $M(1)^\pm$ or $M(1,\lambda)$ for $\lambda \in \mathbb{C}^\times$ is a vertex and braided tensor category. Our strategy is to show these simple composition factors are $C_1$-cofin

Md Tanvirul Alam

Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from rule-mapping failures. We study this behavior as semantic fixation: preserving a default interpretation even when the prompt specifies an alternative, equally valid mapping. To isolate this effect, we introduce

Christoph Kehle, Georgios Moschidis

In the presence of confinement, small-data solutions to nonlinear dispersive equations can exhibit a gradual energy transfer from low to high frequencies, a mechanism driving the emergence of weakly turbulent dynamics. We show that such a forward energy transfer, manifested as arbitrary inflation of higher order Sobolev norms, occurs for small-data solutions

M. Miguel Ocampo, Marcelo M. Miller Bertolami

Turbulent mixing remains one of the primary uncertainties in the modeling of stellar interiors. In stellar evolution simulations, regions where mixing occurs are typically identified using instability criteria. A particularly interesting situation arises when nuclear reactions produce inversions in the mean molecular weight within stellar interiors. Under th

Xinyun Liu

Large vision-language models (LVLMs) achieve strong multimodal performance, but still suffer from hallucinations caused by unstable visual grounding and over-reliance on language priors. Existing training-free decoding methods typically apply calibration at every decoding step, introducing unnecessary computation and potentially disrupting stable predictions

Onésime Hounkpe, Dena Firoozi, Shuang Gao

We propose a decomposition method for solving a general class of linear-quadratic (LQ) McKean-Vlasov control problems involving conditional expectations and random coefficients, where the system dynamics are driven by two independent Wiener processes. Unlike existing approaches in the literature for these problems, such as the extended stochastic maximum pri

Minjae Lee, Sungwoo Hur, Soojin Hwang, Won Hwa Kim

Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and its variants necessitate substantial manual effort for prompt generation and additional training for specific applications. Recent approaches address these limitations by integrating SAM into in-context (one/f

David Benatia, Rémy Molinié, Pierre-Olivier Pineau

What drives cross-state differences in U.S. energy consumption? We combine LMDI decomposition, stochastic frontier analysis, and variable-importance methods on a panel of 50 states plus DC over the 2006--2022 period. The observed 12.8% decline in per capita energy use is driven almost entirely by intensity improvements. A variance decomposition attributes 63

Dionisios Margetis

By an idealized quantum mechanical model, we formally describe the dispersion of nonretarded electromagnetic waves that express charge density oscillations near a fixed plane in three spatial dimensions (3D) at zero temperature. Our goal is to capture the interplay of microscopic scales that include a confinement length in the emergence of the surface plasmo

Celal Ziftci, Ray Liu, Spencer Greene, Livio Dalloro

Integration testing is critical for the quality and reliability of complex software systems. However, diagnosing their failures presents significant challenges due to the massive volume, unstructured nature, and heterogeneity of logs they generate. These result in a high cognitive load, low signal-to-noise ratio, and make diagnosis difficult and time-consumi

William C. J. Buchholtz, Daniel L. Blair, Jeffrey S. Urbach, H. A. Vinutha

We use a minimal model for a dense suspension undergoing thickening and thinning to investigate microstructural changes in 2d simulations. Our simulations show that in steady flow the contact network contains distinct building blocks which are clearly signaled by sharp peaks in the radial distribution function, similar to what is observed in granular jamming

Saumya Goyal, Rohith Rongali, Ritabrata Ray, Barnabás Póczos

We study learning to learn through the lens of hyperparameter tuning. We propose the Langevin Gradient Descent Algorithm (LGD), which approximates the mean of the posterior distribution defined by the loss function and regularizer of a regression task with convex objective. For classification tasks, the LGD algorithm estimates the posterior probabilities of

Ion Matei, Maksym Zhenirovskyy, Praveen Kumar Menaka Sekar, Hon Yung Wong

Automatically reconstructing BPMN models from unstructured natural-language descriptions remains challenging due to heterogeneous modeling conventions, multilingual sources, and the lack of reliable ground truth. We present a scalable, multi-stage LLM-driven pipeline that automates both ground-truth construction and model reconstruction. Multilingual BPMN XM

Caroline Haslebacher, Emeline Bolmont, Marco Cilibrasi, Jonathan Grone

The outgassing signatures of Io, Europa, Enceladus, Triton, and Io-like exomoons are the focus of this review chapter. The rocky volcanic world of Io is unique in our Solar System, with plumes reaching to hundreds of kilometres in altitude. Io-like exomoons could leave signatures strong enough to be detected with ground-based telescopes. The icy moons Europa

Ananda Chakrabarti, Haitham H. Saleh, Indranil Nayak, Balasubramaniam Shanker

We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine structure directly into the DMD regression step. Unlike existing parametric DMD methods which interpolate modes, eigenvalues, or reduced operators and can be fragile with sparse training data or multi-dimensio

Arun Sharma

We introduce compute-grounded reasoning (CGR), a design paradigm for spatial-aware research agents in which every answerable sub-problem is resolved by deterministic computation before a language model is asked to generate. Spatial Atlas instantiates CGR as a single Agent-to-Agent (A2A) server that handles two challenging benchmarks: FieldWorkArena, a multim

Zhanchun Li, Renwu Zhang

Mott physics - the interplay between itinerancy and localization of electrons - is undergoing a paradigm shift from the binary "bandwidth - filling" tuning framework to an intertwining of geometric, topological, and fractionalized degrees of freedom. Based on a series of breakthroughs in 2024 - 2025, this paper proposes five pioneering discoveries: (1) Predi

Syed Fahim Ahmed, Gnanesh Rasineni, Florian Koehler, Abu Zahid Bin Aziz

Whole-slide image (WSI) classification in computational pathology is commonly formulated as slide-level Multiple Instance Learning (MIL) with a single global bag representation. However, slide-level MIL is fundamentally underconstrained: optimizing only global labels encourages models to aggregate features without learning anatomically meaningful localizatio

Sandesh S Rangreji, Mian Zhong, Anjalie Field

Analyses of document collections often require selecting what data to analyze, as not all documents are relevant to a particular research question and computational constraints preclude analyzing all documents, yet little work has examined effects of selection strategy choices. We systematically evaluate seven selection methods (from random selection to hybr

Zhanwei Cao, YeoJin Go, Yifan Hu, Shanu Sushmita

Large language models (LLMs) are increasingly used in daily applications, from content generation to code writing, where each interaction treats the model as stateless, generating responses independently without memory. Yet human writing is inherently longitudinal: authors' styles and cognitive states evolve across months and years. This raises a central que

Luyi Ma, Wanjia Sherry Zhang, Zezhong Fan, Shubham Thakur

On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LLM-HYPER, a novel framework that treats large language models (LLMs) as hypernetworks to directly generate the parameters of the click-through rate (CTR) estimator in a training-fre

Faruk Muritala, Austin Brown, Dhrubajyoti Ghosh, Sherry Ni

Monitoring binomial proportions across multiple independent streams is a critical challenge in Statistical Process Control (SPC), with applications from manufacturing to cybersecurity. While EWMA charts offer sensitivity to small shifts, existing implementations rely on asymptotic variance approximations that fail during early-phase monitoring. We introduce

Emily Adlam

I argue that there is a straightforward way to understand the occurrence of wavefunction collapses or 'quantum events' in relational approaches to quantum mechanics: we necessarily encounter a discontinuity in our description when a system interacts with the reference relative to which we are describing it, since the reference system cannot be described rela

Shogo Kusano, Masayuki Uchida

Structural equation modeling (SEM) is a statistical method for analyzing relationships among latent variables. Since SEM is a confirmatory method, the model needs to be specified in advance. In practice, however, statisticians have several candidate models and aim to select the most appropriate one among them. In this paper, we consider model selection in SE

Ryan Matheu, John S. Baras, Calin Belta

Behavior Trees (BTs) provide designers an intuitive graphical interface to construct long-horizon plans for autonomous systems. To ensure their correctness and safety, rigorous formal models and verification techniques are essential. Temporal BTs (TBTs) offer a promising approach by leveraging existing temporal logic formalisms to specify and verify the exec

Matteo Giardi, László Székelyhidi

For any $0\leq \gamma < 1/5$, we construct weak solutions $(v, B, p )$ of the Ideal MHD Equations which do not conserve the total kinetic energy, the cross-helicity and lie in $C^\gamma(\mathbb{T}^3\times\mathbb{R})$. In the spirit of Arnold's formulation of ideal hydrodynamics, a solution is thought of as a path of volume-preserving diffeomorphisms; the pro

Jonas Svedas, Nathan Laubeuf, Ryan Harvey, Arjun Singh

Predicting the performance of large-scale distributed machine learning (ML) workloads across multiple accelerator architectures remains a central challenge in ML system design. Existing GPU and TPU focused simulators are typically architecture-specific, while distributed training simulators rely on workload-specific analytical models or costly post-execution

Francesca Bonaiti, Gaute Hagen, Thomas Papenbrock

We study the spatiotemporal patterns of density fluctuations in $^{16,24}$O and $^{48}$Ca using nuclear interactions from chiral effective field theory and the time-dependent coupled-cluster method. We find that two-particle-two-hole excitations generate small-amplitude fluctuations that are fast, short-ranged and of stochastic character.

Kazi Amit Hasan, Yuan Tian, Safwat Hassan, Steven H. H. Ding

Continuous Integration/Continuous Delivery (CI/CD) caching is widely used to reduce repeated computation and improve CI/CD efficiency, yet maintaining effective caching requires ongoing maintenance effort. In this paper, we present the first empirical study on how developers configure and evolve caching in CI/CD workflows on GitHub Actions. We analyze 952 Gi

Isaac Remy, Caleb Chang, Karen Leung

Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates from their desired policy to accommodate others, can inform the design of socially compliant and trustworthy autonomous systems. In this wo

Yan Zhang, Stanislav Volgushev

We study the nonparametric maximum likelihood estimator (NPMLE) for Gaussian and Poisson mixture models, assuming the support of the true mixing distribution lies in a fixed bounded set. In this setting, we establish exact parametric rates for both, marginal density estimation and the posterior mean when the true mixing distribution is finitely discrete. Mor

Zixuan Liu, Xiaolin Sun, Zizhan Zheng

Designing robust reinforcement learning (RL) agents in the presence of imperfect reward signals remains a core challenge. In practice, agents are often trained with proxy rewards that only approximate the true objective, leaving them vulnerable to reward hacking, where high proxy returns arise from unintended or exploitative behaviors. Recent work formalizes

Mariko Kimura, Osaki Yoji

Previous theoretical studies indicate that the inner disk in dwarf novae evaporates into a high-temperature, optically thin, and geometrically thick accretion flow during quiescence, with the inner edge moving toward the white dwarf at the onset of an outburst. We incorporate this process into the numerical model developed by Kimura & Osaki (2023) and test t

Bonian Han, Cong Qi, Przemyslaw Musialski, Zhi Wei

Spatial transcriptomics (ST) measures mRNA expression while preserving spatial organization, but multi-slice analysis faces two coupled difficulties: large non-rigid deformations across slices and inter-slice batch effects when alignment and integration are treated independently. We present INST-Align, an unsupervised pairwise framework that couples a coordi

Ruixiang Huang, Weifan Liu

Simulating large-scale microswimmer dynamics in viscous fluid poses significant challenges due to the coupled high spatial and temporal complexity. Conventional high-performance computing (HPC) methods often address these two dimensions in isolation, leaving a critical gap for synergistic acceleration. This paper introduces a heterogeneous CPU--GPU computing

Alessandro Falezza

Battery energy storage systems (BESS) participating in multi-market electricity trading require price forecasts to optimize dispatch decisions. A widely held assumption is that forecast accuracy, measured by standard metrics such as mean absolute error (MAE), drives trading performance. We challenge this assumption using a hierarchical three-layer optimizati

Hangyeol Kang, Slava Voloshynovskiy, Nadia Magnenat Thalmann

Memory is fundamental to social interaction, enabling humans to recall meaningful past experiences and adapt their behavior accordingly based on the context. However, most current social robots and embodied agents rely on non-selective, text-based memory, limiting their ability to support personalized, context-aware interactions. Drawing inspiration from cog

Yunlong Zhang, Markus Millinger, Fredrik Hedenus, Karin Pettersson

Renewable liquid fuels are essential for achieving emissions targets for hard-to-electrify sectors such as aviation and shipping. While biofuels and synthetic e-fuels have been well-studied, e-biofuels, produced by adding renewable hydrogen to biomass conversion to better utilise the biogenic carbon, remain understudied and lack a clear role in EU fuel regul

Rupchand Sutradhar, Anuj Mishra, Malay Banerjee, Subhra Sankar Dhar

Infectious diseases continue to pose significant public health challenges worldwide, requiring effective prevention and control strategies to mitigate their negative impact. Infectious diseases can be broadly classified into two groups: vaccine-preventable diseases (e.g., measles, polio, influenza, hepatitis B, pneumonia) and vaccine-non-preventable diseases

William Youngwoo Chung, Hamza Errahmouni Barkam, Tamoghno Das, Mohsen Imani

Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability in aggressively scaled semiconductor devices. Compute-in-memory (CIM) architectures alleviate data-movement bottlenecks and improve energy efficiency yet introduce nonlinear distortions and reliability concern

Gökhan Elmas, Janis Nötzel

We study deterministic multi-user identification over bosonic channels using coherent-state signatures. Each user is assigned a coherent product state under an average energy constraint, and identification is performed by a user-specific binary quantum test. In contrast to classical multi-user identification models based on shared codebooks, this formulation

Marek Belda, Lorenzo Schena, Romain Poletti, Martin Isoz

Multiscale Proper Orthogonal Decomposition (mPOD) decomposes fluid flows into energy-optimal modes within prescribed frequency bands by combining Proper Orthogonal Decomposition with a multiresolution analysis (MRA). In its classical formulation, mPOD relies on a filter bank of finite impulse response (FIR) filters, enabling lossless reconstruction while mit

Syed Rifat Raiyan

The Identifiable Victim Effect (IVE) $-$ the tendency to allocate greater resources to a specific, narratively described victim than to a statistically characterized group facing equivalent hardship $-$ is one of the most robust findings in moral psychology and behavioural economics. As large language models (LLMs) assume consequential roles in humanitarian

Maaike Galama, Nina Kozar-Gillan, Christina Embacher, Todd Dembo

The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characterization from routine hematoxylin and eosin (H&E)-stained histopathology remains scarce. We introduce OpenTME, an open-access dataset of pre-computed TME profiles derived from 3,634 H