March 2026 arXiv papers — page 41

Shuai Fu, Ye Yang, Guoquan Gao, Shuangjie Zhao

Two-dimensional polymers (2DPs) and their layer-stacked covalent organic frameworks (2D COFs) offer modular, atomically precise platforms for organic optoelectronics, yet their photoconductive responses remain fundamentally constrained by strong excitonic effects and localized charge transport. Here, we demonstrate that a diyne-linked 2DP crystal with axial

Jun Yang, Yuechun Sun, Yi Wu, Rodrigo Caridad

Large Language Models (LLMs) have shown promising results in automating formal verification. However, existing approaches treat proof generation as a static, end-to-end prediction over source code, relying on limited verifier feedback and lacking access to concrete program behaviors. We present EXVERUS, a counterexample-guided framework that enables LLMs to

Swapnil Shankar, Stephan Rosswog, Peter Diener

The dynamics and observable signatures of neutron star mergers are governed by physics under the most extreme conditions. They are particularly impacted by the high-density equation of state, which for the most sophisticated models is usually available in the form of tables. Numerical relativity codes usually evolve particularly well-behaved numerical ("cons

Ruoxi Zhang, Benjamin A. Foutty, Owen Sheekey, Trevor Arp

We report the observation of the Meissner effect in a rhombohedral graphene superconductor, realized via direct imaging of the static fringe magnetic field. In our few-micron sample, the onset of superconductivity manifests as a diamagnetic response that screens only $\sim 100$ ppm of the applied magnetic field. Tracking the evolution of the resulting nanote

Thiago Paulichen, Victor Freguglia

Variable-length Markov chains (VLMCs) are a flexible class of higher-order Markov models that admit a natural representation as context trees. Existing Bayesian methods for specifying prior distributions on tree structures rely on branching processes, but these suffer from a fundamental limitation. The connection between branching probabilities at individual

Marc Kegel, Isacco Nonino

Harvey-Kawamuro-Plamenevskaya demonstrated the existence of (transversely) non-isotopic transverse knots such that for every $n>1$ their $n$-fold cyclic branched covers are contactomorphic. In this short note, we construct other examples of non-isotopic transverse knots that have contactomorphic cyclic branched covers. Conversely, we prove that the transvers

Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo

Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains. However, their ability to replicate complex, multi-panel visualizations from real-world data remains largely unassessed. To address this gap, we introduce \textbf{\texttt{RealChart2Code}}, a new large-scale benchmark with over 2,800 instances gr

Spiros Baxevanakis, Platon Karageorgis, Ioannis Dravilas, Konrad Szewczyk

Training Vision Transformers (ViTs) presents significant challenges, one of which is the emergence of artifacts in attention maps, hindering their interpretability. Darcet et al. (2024) investigated this phenomenon and attributed it to the need of ViTs to store global information beyond the [CLS] token. They proposed a novel solution involving the addition o

Loïc Chadoutaud, Alice Blondel, Hana Feki, Jacqueline Fontugne

Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation learning at the single-cell level remains comparatively underexplored, despite its importance for characterizing cell types

Geun Ho Ahn, Ziyu Wang, Devin J. Dean, Hubert S. Stokowski

Laser intensity noise limits performance in quantum sensing, metrology, and computing. Existing stabilization methods face a trade-off between bandwidth and complexity: electronic feedback loops are speed-limited, while optical resonators are constrained by narrow linewidths and locking requirements. Here, we demonstrate an all-optical "noise eater" that pas

Maddie Juarez, Abha Rai, Kristen E. Ravi, Margaret C. Delaney

Low-income individuals can face multiple challenges in their ability to seek employment. Barriers to employment often include limited access to digital literacy resources, training, interview preparation and resume feedback. Prior work has largely focused on targeted social service or healthcare applications that address needs individually, with little empha

Abidemi Orimogunje, Hyunwoo Park, Kyeong-Ju Cha, Igbafe Orikumhi

We propose an occlusion-aware multimodal learning framework that is inspired by simultaneous localization and mapping (SLAM) concepts for trajectory interpretation and pose prediction. Targeting mmWave vehicle-to-infrastructure (V2I) beam management under dynamic blockage, our Transformer-based fusion network ingests synchronized RGB images, LiDAR point clou

Parniyan Farvardin, David Chapman

We present Feature-Align CNN (FA-CNN), a prototype CNN architecture with intrinsic class attribution through end-to-end feature alignment. Our intuition is that the use of unordered operations such as Linear and Conv2D layers cause unnecessary shuffling and mixing of semantic concepts, thereby making raw feature maps difficult to understand. We introduce two

David Andriot

Dark energy, the main constituent in our expanding universe, responsible for its acceleration, is currently being observed with unprecedented precision through various experiments. While several cosmological models can fit this latest data, deriving some of them from string theory would provide a valuable theoretical prior, with information on the nature of

Inbeom Lee, Tongtong Jin, Bryon Aragam

We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation for the general representation learning problem by bridging causal models with latent factor models in order to learn interpretable representations with cau

Md Riajul Haque, Fabio Iocco, Luca Visinelli

The LIGO-Virgo-KAGRA (LVK) candidate event S251112cm suggests the presence of at least one compact object with sub-solar masses. Since such objects cannot be produced through standard stellar evolution, this observation provides a potential indication of non-standard formation channels. Primordial black holes (PBHs), formed from the collapse of primordial de

Runze Li, Benjamin Nachman, Dennis Noll

High-dimensional feature spaces in particle physics events pose a fundamental challenge to density-estimation-based weakly supervised anomaly detection, whose fidelity degrades rapidly with an increasing number of dimensions. We propose a signal-aware latent space construction using supervised contrastive learning trained on simulated Standard Model backgrou

Jonathan Renusch

The identification and reconstruction of charged particles, such as muons, is a main challenge for the physics program of the ATLAS experiment at the Large Hadron Collider. This task will become increasingly difficult with the start of the High-Luminosity LHC era after 2030, when the number of proton-proton collisions per bunch crossing will increase from 60

Marc H. Pinsonneault, Jennifer L. van Saders, Lyra Cao, Jamie Tayar

In this paper we present the public release of the Yale Rotating Evolution Code (YREC). YREC is a stellar evolution code that covers brown dwarfs and stars across a wide range of masses, and evolutionary states from the pre-MS through helium burning. We summarize the key ingredients of the code, document the code performance, and discuss its strengths and li

Zikai Wang, Zhilu Zhang, Yiqing Wang, Hui Li

Existing hand-object interactions (HOI) methods are largely limited to rigid objects, while 4D reconstruction methods of articulated objects generally require pre-scanning the object or even multi-view videos. It remains an unexplored but significant challenge to reconstruct 4D human-articulated-object interactions from a single monocular RGB video. Fortunat

Ari J. Cukierman, Shuang-Shuang Chen, Jae Hwan Kang, Mary H. Minasyan

We present map-making methodologies and preliminary spectral data cubes for SPHEREx, a NASA Explorer mission that launched in March 2025 and has been performing an all-sky near-infrared spectral survey. The SPHEREx instrument observes from 0.75 to 5.0 microns with a spectral resolution ranging from 35 to 130 and a pixel size of 6.15". We define a nominal

Yale Yauk, Lucas Hackl, Alexander Hahn

We study bipartite entanglement statistics in one-dimensional anyon chains, whose Hilbert spaces are constrained by fusion rules of unitary pre-modular categories. Our setup generalizes previous frameworks on symmetry-resolved entanglement entropy for non-abelian Lie group symmetries to the setting of quantum groups. We derive analytical expressions for the

Christopher Cain, Aloha Das, Anson D'Aloisio, Simon Foreman

Small-scale physics in the intergalactic medium (IGM) plays a crucial role in shaping the progress of cosmic reionization and several high-redshift observables that probe this period. Several recent studies have characterized the complex, dynamical response of the IGM to reionization down to kilo-parsec scales, including its effect on observables such as the

Chiara Altavista, Edoardo Anastasi, Salvatore Raucci, Angel M. Uranga

We investigate dualities between ${\mathbf{Z}}_2$ quotients of recently proposed compactifications of M-theory on `quantum geometries' of the form ${\mathbf{S}}^1\vee{\mathbf{S}}^1$ and 10d orientifolds of type 0A and 0B string theories. In particular, we relate the Ho\v{r}ava-Witten theory on ${\mathbf{S}}^1\vee{\mathbf{S}}^1$ to a 0B orientifold with gauge

Philipp Horak, Robert Kowalewski, Tommy Martinov

The need to map parton-level processes to color-neutral hadrons in a way that respects quark-hadron duality arises in several areas of physics, including in the semileptonic decays of $B$ mesons. Integrated over large regions of phase space, the quark-level and hadron-level quantities are expected to be equal. However, the breakdown of duality is manifest at

Xiuyuan Zhang, Andreas Thoyas, Lina Necib, Andrew Wetzel

The majority of terrestrial direct detection experiments for Dark Matter (DM) rely on the Standard Halo Model (SHM), which assumes the local DM velocity distribution follows a Maxwell-Boltzmann distribution. However, galaxy mergers can deposit DM that remains kinematically clustered today, inducing deviations from the smooth SHM prediction. Previous studies

John Preskill, Mykhaylo Usatyuk, Shreya Vardhan

One central question in quantum gravity is to understand how and why predictions from semiclassical gravity can break down in regimes with low spacetime curvature. One diagnostic of such a breakdown is that states which are orthonormal at the semiclassical level can receive large corrections to their inner products from quantum fluctuations. We study this ef

Yawen Luo, Xiaoyu Shi, Junhao Zhuang, Yutian Chen

Multi-shot video generation is crucial for long narrative storytelling, yet current bidirectional architectures suffer from limited interactivity and high latency. We propose ShotStream, a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. By reformulating the task as next-shot generation con

Yixing Lao, Xuyang Bai, Xiaoyang Wu, Nuoyuan Yan

Existing feed-forward 3D Gaussian Splatting methods predict pixel-aligned primitives, leading to a quadratic growth in primitive count as resolution increases. This fundamentally limits their scalability, making high-resolution synthesis such as 4K intractable. We introduce LGTM (Less Gaussians, Texture More), a feed-forward framework that overcomes this res

Bocheng Zou, Mu Cai, Mark Stanley, Dingfu Lu

Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow these models to handle varying input sizes during training, inference typically remains restricted to a single, fixed scale. This prevalent single-scale paradigm overlooks a fundament

Jing-Yu Zhao, Ya-Hui Zhang

Motivated by recent experimental progress on high-temperature superconductivity in bilayer nickelates, we investigate the phase diagram of the normal state in a bilayer Kondo lattice model using single-site dynamical mean-field theory (DMFT). When the interlayer tunneling $t_\perp$ is absent, we identify a non-Fermi-liquid (NFL) critical point tuned by the i

Sicheng Zuo, Yuxuan Li, Wenzhao Zheng, Zheng Zhu

Vision-language-action models have reshaped autonomous driving to incorporate languages into the decision-making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large-sca

Zehao Wang, Huaide Jiang, Shuaiwu Dong, Yuping Wang

Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end-to-end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacki

Xincheng Shuai, Song Tang, Yutong Huang, Henghui Ding

Graphic design is a creative and innovative process that plays a crucial role in applications such as e-commerce and advertising. However, developing an automated design system that can faithfully translate user intentions into editable design files remains an open challenge. Although recent studies have leveraged powerful text-to-image models and MLLMs to a

Yuxing Lu, Xukai Zhao, Wei Wu, Jinzhuo Wang

The knowledge base in a retrieval-augmented generation (RAG) system is typically assembled once and never revised, even though the facts a query requires are often fragmented across documents and buried in irrelevant content. We argue that the knowledge base should be treated as a trainable component and propose WriteBack-RAG, a framework that uses labeled e

Akihiro Kubota, Tomoya Hasegawa, Ryo Kawahara, Ko Nishino

Gauging an individual's skill level is crucial, as it inherently shapes their behavior. Quantifying skill, however, is challenging because it is latent to the observed actions. To explore skill understanding in human behavior, we focus on dyadic sports -- specifically table tennis -- where skill manifests not just in complex movements, but in the subtle nuan

Gabriele Montefalcone, Richard Stiskalek

We introduce a minimal two-parameter formulation of the dark energy (DE) density evolution normalized to its present-day value, $f_{\rm DE}(z) \equiv \rho_{\rm DE}(z)/\rho_{\rm DE,0}$, in terms of $f_p\equiv f_{\rm DE}(z_p)$ and the DE equation of state $w_p\equiv w(z_p)$, at a pivot redshift $z_p$. This provides an alternative framework for assessing the ev

Ziyin Wang, Sirui Xu, Chuan Guo, Bing Zhou

Generating realistic human-object interaction (HOI) animations remains challenging because it requires jointly modeling dynamic human actions and diverse object geometries. Prior diffusion-based approaches often rely on hand-crafted contact priors or human-imposed kinematic constraints to improve contact quality. We propose LIGHT, a data-driven alternative i

Jiwook Han, Geo Ahn, Youngrae Kim, Jinwoo Choi

Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-grained temporal understanding, making task-specific fine-tuning indispensable. This fine-tuning causes models to memorize dataset-specific shortcuts rather than faithfully groundin

Yan Li, Zezi Zeng, Ziwei Zhou, Xin Gao

Recent advances in image generation models have expanded their applications beyond aesthetic imagery toward practical visual content creation. However, existing benchmarks mainly focus on natural image synthesis and fail to systematically evaluate models under the structured and multi-constraint requirements of real-world commercial design tasks. In this wor

Maria C. Straight, Tanvi Karwal, José Luis Bernal, Kimberly K. Boddy

We present profile-likelihood constraints on velocity-independent dark matter-proton scattering, including cases in which only a fraction of dark matter has such non-gravitational interactions. Frequentist profile-likelihood techniques provide prior-independent constraints, circumventing prior-volume effects that we show arise in Bayesian constraints on this

Xiaofeng Mao, Shaohao Rui, Kaining Ying, Bo Zheng

Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel thre

Csaba Csáki, Eric Kuflik, Wei Xue, Taewook Youn

We present a bottom-up holographic description of the QCD $θ$-vacuum and the $U(1)_A$ anomaly in five dimensions. The multi-branched $θ$-vacuum structure emerges geometrically from a higher-dimensional gauge field, while the axial anomaly is realized through a Stückelberg coupling that is dual to a Chern-Simons term. In this framework, the $η'$ meson app

Jiabin Hua, Hengyuan Xu, Aojie Li, Wei Cheng

Fine-grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE-Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade-off between expression editing and identity p

Geeyang Tay, Wentao Ma, Jaewon Lee, Yuzhi Tang

Automatic speech recognition (ASR) systems have achieved near-human accuracy on curated benchmarks, yet still fail in real-world voice agents under conditions that current evaluations do not systematically cover. Without diagnostic tools that isolate specific failure factors, practitioners cannot anticipate which conditions, in which languages, will cause wh

Masoud Moghani, Mahdi Azizian, Animesh Garg, Yuke Zhu

Large-scale robot datasets have facilitated the learning of a wide range of robot manipulation skills, but these datasets remain difficult to collect and scale further, owing to the intractable amount of human time, effort, and cost required. Simulation and synthetic data generation have proven to be an effective alternative to fuel this need for data, espec

Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao

Robust perception and reasoning require consistency across sensory modalities. Yet current multimodal models often violate this principle, yielding contradictory predictions for visual and textual representations of the same concept. Rather than masking these failures with standard voting mechanisms, which can amplify systematic biases, we show that cross-mo

Abhishek Bhandwaldar, Mihir Choudhury, Ruchir Puri, Akash Srivastava

We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs from high-level algorithmic specifications. We introduce an agent factory, a two-stage pipeline that constructs and coordinates multiple autonomous optimization agents. In Stage~1, the pipeline decomposes a design into

Vladimir Bruevich, Dmitry Maslennikov, Beier Hu, Artem A. Bakulin

We demonstrate an all solid state semiconductor device, based on epitaxial single crystalline metal halide perovskites, enabling reversible control of a perovskite photoluminescence with a gate voltage. Fundamentally distinct from electroluminescent diodes, such a photoluminescence field effect transistor uses the gate electric field to electrostatically mod

Minoru Hirose, Nobuo Sato

We introduce iterated beta integrals, a new class of iterated integrals on the universal abelian covering of the punctured projective line that unifies hyperlogarithms and classical beta integrals while preserving their fundamental properties. We establish various analytic properties of these integrals with respect to both the exponent parameters and the mai

Kaijin Chen, Dingkang Liang, Xin Zhou, Yikang Ding

Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subjects hide out of sight and later re-emerge, current methods often struggle, leading to frozen, distorted, or vanishing subjects. To address this, we introduce Hybrid Memory, a novel

Carlos I. Pérez Sánchez

For a family of two-matrix models \[ \frac{1}{2} \operatorname{Tr}(A^2+B^2) - \frac{g}{4} \operatorname{Tr}(A^4+B^4) - \begin{cases} \frac{h}{2} \operatorname{Tr}( A BA B) \\ \frac{h}{4} \operatorname{Tr}( A BA B+ ABBA ) \\ \frac{h}{2} \operatorname{Tr}( A B BA ) \end{cases} \] with hermitian $A$ and $B$, we provide, in each case, a Monte Carlo estimate of t

Selim Ghazouani, Florestan Martin-Baillon

Consider a topological surface $\Sigma$. We introduce the spectrum of a representation from the fundamental group of $\Sigma$ to SL(2,R), which is a subset of projective measured lamination on the surface, which captures the directions along which the representation fails to be Fuchsian, and which characterizes the action of the mapping class group on this r

Konrad P. Kording, Anton Arkhipov, Davy Deng, Sean Escola

High-resolution brain imaging can now capture not just synapse locations but their molecular composition, with the cost of such mapping falling exponentially. Yet such ultrastructural data has so far told us little about local neuronal physiology - specifically, the parameters (e.g., synaptic efficacies, local conductances) that govern neural dynamics. We pr

Vishal Narnaware, Animesh Gupta, Kevin Zhai, Zhenyi Wang

Multimodal Diffusion Large Language Models (MDLLMs) achieve high-concurrency generation through parallel masked decoding, yet the architectures remain prone to multimodal hallucinations. This structural vulnerability stems from an algorithmic flaw: the decoder ranks candidate tokens based on textual likelihood without verifying localized visual support. We e

Gianluca Calcagni, Fabio Briscese

Motivated by quantum gravity on spacetimes with multi-scale geometry, we analyze quantum field theories with a self-adjoint fractional power $(\Box^2)^{\gamma/2}$ of the d'Alem\-bert\-ian in the kinetic term, for any real $\gamma>0$. Selecting a particularly simple version of the kinetic term which we call hermitian polynomial, we study the spectral decompos

Zhen Huang, Zhiyan Ding, Ke Wang, Jason Kaye

Gaussian baths are widely used to model non-Markovian environments, yet the cost of accurate simulation at long times remains poorly understood, especially when spectral densities exhibit nonanalytic behavior as in a range of realistic models. We rigorously bound the complexity of representing bath correlation functions on a time interval $[0,T]$ by sums of

Quynh Phung, Long Mai, Cusuh Ham, Feng Liu

We study object motion path editing in videos, where the goal is to alter a target object's trajectory while preserving the original scene content. Unlike prior video editing methods that primarily manipulate appearance or rely on point-track-based trajectory control, which is often challenging for users to provide during inference, especially in videos with

Jinbo Xing, Zeyinzi Jiang, Yuxiang Tuo, Chaojie Mao

Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only single-modality outputs. This challenge of producing interleaved content is mainly due to training data scarcity and the difficulty of modeling long-range cross-modal context. To addre

A. Daria Dumitriu-I., Feng Liu, Alexander E. Kazantsev, Alessandro Principi

We study the anomalous thermoelectric Hall response of two-dimensional massive Dirac fermions to first order in the electron-electron interaction. We compute both the Nernst response to a Luttinger-type gravitational potential and the particle magnetization, the latter being required to remove spurious non-transport contributions. We show that, for arbitrary

Haohao Zhang

Standard Model predictions for Majorana neutrino transition magnetic moments (TMMs) are subject to severe chiral and GIM-like suppressions, rendering them vanishingly small. To dynamically generate a macroscopic TMM, we propose a dark sector framework featuring a $U(1)_D$ gauge symmetry, a vector-like lepton doublet, and two complex dark scalars. We demonstr

Andrew McLeod, Andrzej Pokraka, Lecheng Ren

We show that the wavefunction of the universe in theories of conformally coupled scalars in power-law Friedmann-Robertson-Walker (FRW) cosmologies satisfies a graphical coaction, by means of which we can understand its complete analytic structure in terms of the acyclic minors of Feynman graphs. Our construction extends to all particle multiplicities and any

Biswajit Mitra, Sourav Koner

In this paper, we introduce an algebraic-topological invariant for commutative pm-rings, termed the spectral fundamental group, which is denoted by $\pi_{k}^{alg}(A)$. This group is defined via homotopy classes of loops within the space of induced spectral maps, which are generated by the $k$-algebra endomorphism monoid of the ring. We establish foundational

Man-Ling Sung, Jan Silovsky, Man-Hung Siu, Herbert Gish

Transfer learning and knowledge distillation has recently gained a lot of attention in the deep learning community. One transfer approach, the student-teacher learning, has been shown to successfully create ``small'' student neural networks that mimic the performance of a much bigger and more complex ``teacher'' networks. In this paper, we investigate an ext

Yuhan Fu, Jiahao Liu

We study the coaction of cosmological wavefunction coefficients of conformally coupled scalars in FRW background of a two-site example, which turns out to have an elegant diagrammatic interpretation. We show how the coaction acts on the twisted integrals for wavefunction coefficients, decomposing them into contributions associated with subtopologies and cuts

Yannick Roy

Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User x 1000', where an LLM agent exercises that surface as a syn

Vaishnavi, Lokesha, H., Vedamurthy

Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input

Wouter Lueks, Stephan Dreyer, Hannes Federrath, Judith Simon

In this paper, we provide an overview and evaluation of different types of age assurance technologies (AAT). We describe and analyse 1) different approaches to age assurance online (age verification, age estimation, age inference, and parental control and consent), as well as 2) different age assurance architectures (online, offline device-based, offline cre

Aiden S. Zelakiewicz, Elijah Mullens, Lisa Kaltenegger, Dmitry Savransky

Characterizing the surface and atmosphere of Earth-like planets in reflected light is a key goal for upcoming direct imaging surveys. NASA's next flagship-class astrophysics mission concept, the Habitable Worlds Observatory (HWO), is a space-based Ultraviolet/Optical/Near-Infrared observatory with a mission design requirement to reach the $10^{-10}$ contrast

Yuting Wang, Gong-Bo Zhao, Kazuya Koyama, Ruiyang Zhao

We present joint measurements of the pre- and post-reconstruction power spectra, $P_{\rm pre}$ and $P_{\rm post}$, together with their cross-power spectrum, $P_{\rm cross}$, for the Luminous Red Galaxies (LRGs) in the DESI Data Release 1 (DR1). We jointly analyse these observables with an emulator-based full-shape modeling framework, thereby, for the first t

Xueji Zhao, Likai Pei, Jianbo Liu, Kai Ni

Trustworthy artificial intelligence increasingly relies on probabilistic computation to achieve robustness, interpretability, security and privacy. In practical systems, such workloads interleave deterministic data access with repeated stochastic sampling across models, data paths and system functions, shifting performance bottlenecks from arithmetic units t

Johannes J. Brust, Tamara G. Kolda

Tensor decompositions are a fundamental tool in scientific computing and data analysis. In many applications -- such as simulation data on irregular grids, surrogate modeling for parameterized PDEs, or spectroscopic measurements -- the data has both discrete and continuous structure, and may only be observed at scattered sample points. The CP-HIFI (hybrid in

Ishaan Gakhar, Laven Srivastava, Sankarshanaa Sagaram, Aditya Kasliwal

Semantic segmentation in marine environments is crucial for the autonomous navigation of unmanned surface vessels (USVs) and coastal Earth Observation events such as oil spills. However, existing methods, often relying on deep CNNs and transformer-based architectures, face challenges in deployment due to their high computational costs and resource-intensive

Yifei Li, Ruizhe Fu, Huihang Liu, Guha Manogharan

As the demand for mass customization increases, manufacturing systems must become more flexible and adaptable to produce personalized products efficiently. Additive manufacturing (AM) enhances production adaptability by enabling on-demand fabrication of customized components directly from digital models, but its flexibility remains constrained by fixed equip

Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov, Amir Gholami

Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building foundation models in Natural Language Processing and Computer Vision. We study neural scaling in Scientific Machine Learning, focusing on models for weather forecasting. To analyze sc

Yunus Talha Erzurumlu, Jiyong Kwag, Alper Yilmaz

Cross-view geo-localization (CVGL) estimates a camera's location by matching a street-view image to geo-referenced overhead imagery, enabling GPS-denied localization and navigation. Existing methods almost universally formulate CVGL as an image-retrieval problem in a contrastively trained embedding space. This ties performance to large batches and hard negat

Jai Bardhan, Patrik Drozdik, Josef Sivic, Vladimir Petrik

Action-conditioned robot world models generate future video frames of the manipulated scene given a robot action sequence, offering a promising alternative for simulating tasks that are difficult to model with traditional physics engines. However, these models are optimized for short-term prediction and break down when deployed autoregressively: each predict

Sheena Shaji, Suraj Goel, Julian Wiercinski, Frederik Brooke Barnes

Quantum interference of indistinguishable photons is the foundation of photonic quantum technologies, yet scaling from a few to many identical quantum light sources remains a major challenge. In solid-state platforms, spatial and spectral inhomogeneity and resource-intensive architectures impede scaling. As a result, interference between remote, independent

Yoshinori Matsuo

In Einstein gravity, matter with an arbitrarily small density can be a black hole. Pressure in the star diverges if size of the star is smaller than 9/8 of the Schwarzschild radius, implying the gravitational collapse into a black hole. By taking quantum effects of matter, however, pressure is bounded from above, and a core with negative energy appears inste

Kubra Aksoy, Adnan Rashid, Osman Hasan, Sofiene Tahar

Network topology matrices are algebraic representations of graphs that are widely used in modeling and analysis of various applications including electrical circuits, communication networks and transportation systems. In this paper, we propose to use Higher-Order-Logic (HOL) based interactive theorem proving to formalize network topology matrices. In particu

Haoyan Yang, Mario Xerri, Solha Park, Huajian Zhang

As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach human-level capabilities in certain domains, human feedback may no longer provide sufficiently informative signals for further improvement. At the same time, the growing ability of

CHIME Collaboration, Mandana Amiri, Kevin Bandura, Arnab Chakraborty

Observations with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) have been used to measure the 21 cm intensity mapping auto power spectrum, at $z\sim 1$, over a frequency range from 608.2 MHz to 707.8 MHz at wavenumbers $0.4~h~{\rm Mpc}^{-1} \lesssim k \lesssim 1.5~h~{\rm Mpc}^{-1}$. In this paper, we present the results of two different approach

Rafael Parra

We investigate finiteness conditions on modules of bounded projective dimension and their connection with generalized notions of coherence. For a ring $R$, we consider the class $\mathsf{FP}_n^{\le d}(R)$ of finitely $n$-presented modules of projective dimension at most $d$ and develop the corresponding relative homological theory. We establish several chara

Mohamed Bouka, Moulaye Abdel Kader Ould Moulaye Ismail

Small, trade-dependent economies often exhibit limited maritime connectivity, yet empirical evidence on the structural configuration of their container systems remains limited. This study analyzes route concentration and node distributions in Mauritania's maritime container system during 2019-2022 using shipment-level data measured in forty-foot equivalent u

Ivon Dorado, Gonzalo Medina

We define a unified categorical framework for studying six subproblems arising from the classical Four Subspace Problem. For each subproblem, we construct a functor from its associated category to the category of representations of the quiver corresponding to the Four Subspace Problem. This approach gives a common structural setting for the six cases conside

Christian Amend, Marcello Carioni, Konstantinos Zemas

One of the most popular approaches for solving total variation-regularized optimization problems in the space of measures are Particle Gradient Flows (PGFs). These restrict the problem to linear combinations of Dirac deltas and then perform a Euclidean gradient flow in the weights and positions, significantly reducing the computational cost while still decre

Cole Walsh, Rodica Ivan

Automated systems have been widely adopted across the educational testing industry for open-response assessment and essay scoring. These systems commonly achieve performance levels comparable to or superior than trained human raters, but have frequently been demonstrated to be vulnerable to the influence of construct-irrelevant factors (i.e., features of res

Diego Jimenez-Oviedo, Ruben Vera-Rodriguez, Ruben Tolosana, Juan Carlos Ruiz-Garcia

Early detection of atypical cognitive-motor development is critical for timely intervention, yet traditional assessments rely heavily on subjective, static evaluations. The integration of digital devices offers an opportunity for continuous, objective monitoring through digital biomarkers. In this work, we propose an AI-driven longitudinal framework to model

Yuqian Shao, Xiaosong Jia, Langechuan Liu, Junchi Yan

End-to-end autonomous driving (E2E-AD) has achieved remarkable progress. However, one practical and useful function has been long overlooked: users may wish to customize the desired speed of the policy or specify whether to allow the autonomous vehicle to overtake. To bridge this gap, we present Bench2Drive-Speed, a benchmark with metrics, dataset, and basel

Navnil Choudhury, Yizhuo Tan, Jiaqi Yu, Jakub Szefer

As superconducting processors scale, understanding how physical layout shapes qubit interactions is essential for architectural reliability. Existing methods offer limited insight into how electromagnetic design choices translate into execution-level behavior. We present EPAR, an electromagnetic-to-architecture framework that predicts robustness early direct

Agnieszka Wierzchucka, Pablo J. Bilbao, Alexander G. R. Thomas, Dmitri A. Uzdensky

The adiabatic equation of state $P \propto n^{\Gamma}$ describes the pressure evolution of highly collisional, isotropic plasmas in terms of their density, providing a possible closure of the fluid moment hierarchy in the absence of heat fluxes and dissipation. An analogous closure exists for collisionless, magnetised plasmas, whose pressure tensor is anisot

Yuhui Wang, Andrew M. Thomas, Michael Jauch

We introduce a generalized Bayesian method for multiple changepoint analysis with a loss function inspired by multinomial logistic regression. The method does not require a specification of the data-generating process and avoids restrictive assumptions on the nature of changepoints. From the joint posterior distribution, we can make simultaneous inference on

Benjamin Werner, Ondřej Rokoš, Jan Zeman

Lattice systems are effective for modeling heterogeneous materials, but their computational cost is often prohibitive. The QuasiContinuum (QC) method reduces this cost by interpolating the lattice response over a coarse finite-element mesh, yet material interfaces in heterogeneous systems still require fine discretizations. Enrichment strategies from the eXt

Stephen W. Yan, Yimu Bao, Sagar Vijay

The surface code is a promising platform for a quantum memory, but its threshold under coherent errors remains incompletely understood. We study maximum-likelihood decoding of the square-lattice surface code in the presence of single-qubit unitary rotations that create electric anyon excitations. We microscopically derive a non-linear sigma model with target

Tobias Barthel, Logan Hyslop, Maxime Ramzi

In this paper, we study geometric points in tensor triangular geometry. In doing so, we construct a counter-example to Balmer's Nerves of Steel conjecture using free constructions in higher Zariski geometry. We then go on to introduce and discuss constructible spectra in the context of tensor triangular geometry. For tensor triangulated categories satisfying

Ricardo Fernandes, Alberto Alamia, Sina Kalweit, Marta Victoria

The electrification of energy demand across sectors, powered by solar and wind generation, is the best strategy for achieving carbon neutrality. Carbon dioxide removal (CDR) strategies are also expected to play a crucial role by providing net-negative emissions that can offset residual CO2 emissions, including those from cement manufacturing. While previous

Zhongyuan Che, Niko Tratnik, Petra Žigert Pleteršek

We prove that if $A$ and $B$ are daisy cubes whose $\tau$-graphs are forests, then $A$ and $B$ are isomorphic if and only if their $\tau$-graphs are isomorphic. The result is applied to show that a daisy cube with at least one edge is the resonance graph of a plane bipartite graph $G$ if and only if its $\tau$-graph is a forest which is isomorphic to the inn

Wenxuan Song, Jiayi Chen, Shuai Chen, Jingbo Wang

This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they ty

Ka Ching Chan

SHAPR (Solo Human-Centred and AI-Assisted Practice) is a framework for research software development that integrates human-centred decision-making with AI-assisted capabilities. While prior work introduced SHAPR as a conceptual framework, this paper focuses on its operationalisation as a structured, traceable, and knowledge-generating approach to AI-assisted

Nan Xiao, Yunxin Fan, Farong Wang, Fei Liu

Affordance reasoning provides a principled link between perception and action, yet remains underexplored in surgical robotics, where tissues are highly deformable, compliant, and dynamically coupled with tool motion. We present arg-VU, a physics-aware affordance reasoning framework that integrates temporally consistent geometry tracking with constraint-induc